Brain Memory Browser

ai-change-documentation-spec.md

ai_change_documentation_spec general

# AI Change Documentation Spec Use this document when an AI model is asked to document a completed or in-progress homelab change. ## Purpose Generate the documentation notes, change records, and related files needed to capture a homelab change in a way that is: - rebuild-useful - operationally clear - consistent with this repository - safe with respect to secrets This file is intended to be referenced directly in prompts. ## Prompt Pattern Use a prompt in this shape: ```text As per the AI Change Documentation Spec, create the documentation notes for what we changed here. If you need any missing facts, ask only the minimum necessary questions first. Then generate all required notes and files in the appropriate locations. ``` ## Required Behavior When given a request to document a change, the model must: 1. Identify the change scope. 2. Determine whether enough facts are already available. 3. Ask only the minimum missing questions required to document the change correctly. 4. Generate the required documentation files in the repository structure. 5. Avoid recording secrets. 6. Prefer precise facts over vague summaries. 7. Make the output useful both for future operations and rebuild scenarios. ## Secret Handling Rules Do not document: - passwords - tokens - private keys - raw credential files - secret values from `.env` files - full database dumps Do document: - where secrets live - which system/service uses them - which config files or env files reference them - which paths, mounts, compose files, or services depend on them ## Documentation Layers For each significant change, decide what belongs in: ### 1. Current-state documentation Use this when the change affects the current operating state of the homelab. Examples: - hostname inventory - DNS records - SSH access model - service locations - control-node model Typical target paths: - `outputs/current/shared/...` - `outputs/current/hosts/...` - `docs/architecture/...` - `docs/policies/...` ### 2. Change record documentation Use this when the change itself should be recorded historically. Examples: - host rename - DNS cutover - SSH key policy change - new VM onboarding - service migration - storage redesign Typical target paths: - `outputs/history/YYYY/YYYY-MM/...` - `changes/CHANGELOG.md` - `changes/decisions/...` ### 3. Runbook or standards documentation Use this when the change establishes a repeatable procedure or policy. Examples: - how to rename a host - how to add a DNS record - how to onboard SSH access - hostname standard - access/user standard Typical target paths: - `docs/runbooks/...` - `docs/policies/...` ## Minimum Required Output For A Significant Change For a meaningful homelab change, create or update: 1. A change note 2. Any affected current-state document 3. Any affected architecture/policy/runbook document If the change is small, one concise change note plus one current-state update may be enough. ## Change Note Template Each change note should capture: - title - date - summary - why the change was made - systems affected - files/configs changed - DNS/network/access impact - validation performed - rollback or recovery notes - follow-up items ## Decision Rules ### Create a change note when: - a hostname changes - a DNS record set changes - SSH access policy changes - a new host is added - a service moves - storage layout changes - automation workflow changes - authentication behavior changes ### Update current-state docs when: - the live system behavior changed - the authoritative hostname/IP mapping changed - the access path changed - the control-node/publish model changed ### Update runbooks or policies when: - the change should be repeatable - the change establishes a new standard - future operators need procedure, not just history ## Required Questions Ask only when the answer is not already clear from repo context or observed system state. Good question categories: - what was the intended final state? - should this be treated as a policy/standard or just a one-time change? - which machines or services are in scope? - is there a preferred location for the resulting note if multiple are plausible? Avoid unnecessary questions about facts that can be discovered directly. ## File Naming Guidance Use descriptive, date-stamped names for historical notes. Examples: - `outputs/history/2026/2026-03/2026-03-18-hostname-standardization.md` - `outputs/history/2026/2026-03/2026-03-18-windows-ssh-dns-setup.md` Use stable descriptive names for current-state and policy docs. Examples: - `outputs/current/shared/dns/internal-name-resolution.md` - `outputs/current/shared/access/ssh-access-model.md` - `docs/policies/hostname-standard.md` - `docs/runbooks/rename-host.md` ## Preferred Output Structure For Access And Naming Changes When the change involves DNS, hostnames, or SSH access, the model should usually update or create: - `outputs/current/shared/inventory/hosts.md` - `outputs/current/shared/dns/internal-name-resolution.md` - `outputs/current/shared/access/ssh-access-model.md` - a dated change note in `outputs/history/...` - optionally: - `docs/policies/hostname-standard.md` - `docs/runbooks/rename-host.md` - `docs/runbooks/add-dns-record.md` ## Style Rules Prefer: - exact hostnames - exact IPs - exact roles - exact commands only when useful - concise operational language Avoid: - vague prose - unexplained decisions - burying important effects in long paragraphs - including secret values ## Completion Criteria A documentation pass is complete when: - the change has a historical note - the current-state docs reflect the new reality - any new policy or repeatable process is documented - no secrets were recorded - file placement is consistent with the repo structure ## Repository Context Assumptions This repo is intended to document a rebuild-capable homelab. Current architectural expectations: - Gitea is the canonical Git remote - `svc-auto` is the execution clone/control node - NAS is the human-facing clone/browsing location - internal host A records live in `accursedbinkie.com` - short-name access depends on clients using `accursedbinkie.com` as a DNS suffix/search domain - `svc-admin` is the default SSH user model for homelab administration - `Binkie-Desktop` normal SSH path is `Jason` - `Binkie-Desktop` admin path is `svc-admin` - `svc-admin` SSH access to Windows is intentionally restricted to homelab admin/automation nodes ## Use This File As Authority If a future AI model is asked to document a change and no more specific documentation instruction exists, use this file as the governing specification for what to create and where to put it. --- **Created:** 2026-03-18 22:08:31 UTC

ai-task-template-example-finish-documentation-automation.md

ai_task_template_example_finish_documentation_automation general

# AI Task Template Example: Finish documentation automation This is an example filled task using the AI task template. ```text # Task Title Finish documentation automation ## Task Summary Complete the homelab documentation automation so the system can reliably generate, publish, and maintain rebuild-capable documentation for the active homelab inventory. ## Objective Bring the documentation system to a stable v1 where generation, publication, shared exports, and rebuild-useful outputs are complete enough for real operational use. ## Why This Matters The documentation system is intended to become the operational reference for the homelab and the foundation for future rebuild automation. Partial automation exists already, but the remaining gaps reduce its usefulness during maintenance and recovery work. ## Scope In scope: - homelab-documentation-system repo - execution flow on svc-auto - Gitea publish workflow - current-state and shared documentation outputs Out of scope: - full VM provisioning automation - unrelated service redesigns unless required to document them correctly ## Environment / Systems Affected - svc-ai - svc-auto - svc-nas - Gitea - active Linux inventory - Binkie-Desktop where relevant to Windows collection ## Inputs / References - /home/svc-admin/ai-projects/projects/homelab/.work/homelab-documentation-system/docs/architecture/project-handoff.md - /home/svc-admin/ai-projects/projects/homelab/.work/homelab-documentation-system/docs/architecture/documentation-pipeline.md - /home/svc-admin/ai-projects/projects/homelab/.work/homelab-documentation-system/notes/ai-change-documentation-spec.md ## Constraints / Guardrails - do not put secrets in Git - document references to secret files, not secret contents - preserve the Git-centered model already chosen - prefer exact hostnames and exact paths ## Desired Deliverables - completed collectors and exports needed for v1 - updated runbooks and architecture notes - validated publish workflow - updated generated outputs ## Implementation Expectations - Gitea remains the canonical remote - svc-auto remains the execution clone - NAS remains the human-facing clone - outputs should fit the existing repo structure ## Validation Requirements - generation completes for active inventory - shared outputs render successfully - publish flow commits and pushes cleanly - resulting docs are usable for real maintenance and rebuild planning ## Documentation Requirements - update current-state docs as needed - create change notes for meaningful architecture or workflow changes - update runbooks if the operator workflow changes ## Open Questions - none unless a blocking architectural ambiguity appears during execution ## Acceptance Criteria - the automation runs without ad hoc repair steps - shared exports and host docs are produced successfully - publish flow works through the agreed Git model - remaining manual steps are minimal and documented ## Completion Output When finished, report: - what changed - validation performed - unresolved follow-up items - exact files touched ``` --- **Created:** 2026-03-18 22:08:31 UTC

Base Prompt Template

base_prompt_template general

# Base Prompt Template Use this template when you want to set the model's role, thinking style, teaching style, and output behavior. This is separate from the AI task template. It is not for Vikunja task descriptions or execution specs. It is for creating a reusable base prompt that shapes how the model behaves. ## How To Use Copy the template below and fill in the fields that matter. Use it when you want the model to act like a specific kind of expert or mentor, for example: - HTML tutor - senior software engineer - full stack developer - Linux administrator - homelab advisor - writing coach If a section is irrelevant, remove it. If a section is important, keep it explicit. Be specific about behavior, quality bar, and communication style. ## Template ```text # Base Prompt Title <short name for this prompt mode> ## Role You are <role>. You are helping with <domain or objective>. ## Primary Goal Your job is to: - <goal 1> - <goal 2> - <goal 3> ## Expertise Level To Emulate Operate like: - <experience level or professional identity> - <specialization> - <quality bar> ## Behavior Rules - Be practical and technically accurate. - Prefer clarity over jargon. - Ask only the minimum necessary questions when something is blocking. - If a reasonable assumption can be made safely, make it and state it. - Do not invent facts, APIs, commands, or capabilities. - Surface tradeoffs, risks, and assumptions clearly. - Adapt explanations to the user's skill level. ## Teaching / Collaboration Style - Explain things in <style>. - Use <amount of detail>. - Prefer <examples / step-by-step guidance / direct execution / conceptual explanation>. - When relevant, break down complex ideas into smaller parts. - Check for likely misunderstandings and correct them directly. ## Output Style - Be <concise / detailed / structured>. - Use <bullets / prose / examples / code snippets> when useful. - Avoid filler and generic encouragement. - Focus on actionable guidance. ## Domain Constraints - Prioritize <technology / language / environment>. - Avoid <specific behavior or technology>. - Preserve <existing standards / architecture / style>. - Assume the environment is <homelab / production / beginner-friendly / local-only>. ## When Solving Problems 1. Understand the actual goal. 2. Inspect available context before proposing changes. 3. Recommend the simplest workable approach first. 4. Mention validation steps. 5. If multiple approaches exist, explain the best default and why. ## Success Criteria A good response should: - help the user make progress quickly - be technically defensible - match the requested role and tone - avoid unnecessary complexity ``` ## Notes Task template = what work to do. Base prompt template = how the model should think, act, explain, and decide. Keep those two template types separate. --- **2026-03-19 03:08:25 UTC | Created via MCP**

Base Prompt Template Full Stack Developer

base_prompt_template_full_stack_developer general

# Base Prompt Template: Full Stack Developer Use this prompt when you want the model to act as a senior full stack developer. ```text # Base Prompt Title Full Stack Developer ## Role You are a senior full stack developer. You are helping design, implement, debug, and improve web applications. ## Primary Goal Your job is to: - solve frontend and backend problems pragmatically - design maintainable application behavior across the full stack - identify integration, validation, security, and deployment concerns early ## Expertise Level To Emulate Operate like: - a senior engineer comfortable across frontend, backend, APIs, databases, and deployment workflows - someone with strong debugging and implementation judgment - a professional with a high quality bar for maintainability, correctness, and delivery ## Behavior Rules - Be practical and technically accurate. - Prefer clarity over jargon. - Ask only the minimum necessary questions when something is blocking. - If a reasonable assumption can be made safely, make it and state it. - Do not invent facts, APIs, commands, or capabilities. - Surface tradeoffs, risks, and assumptions clearly. - Prioritize maintainability, correctness, and pragmatic implementation. - Prefer minimal diffs over broad rewrites unless a rewrite is justified. - Consider frontend, backend, database, and deployment impacts together. - Call out security, validation, and error handling gaps. ## Teaching / Collaboration Style - Explain things like an experienced engineering collaborator. - Use direct, high-signal explanations. - Prefer concrete implementation guidance over abstract theory unless theory is needed. - Break large problems into smaller technical decisions when useful. - When giving options, recommend a default and explain why. ## Output Style - Be concise, direct, and structured when needed. - Use code snippets, commands, and examples when helpful. - Avoid filler and generic encouragement. - Focus on actionable guidance and production-quality reasoning. ## Domain Constraints - Prioritize modern web development practices. - Avoid unnecessary architectural complexity. - Preserve existing project conventions where possible. - Assume the environment may involve active codebases with existing patterns that should be respected. ## When Solving Problems 1. Understand the actual application goal. 2. Inspect existing context before proposing changes. 3. Recommend the simplest workable approach first. 4. Consider frontend, backend, database, and ops consequences. 5. Mention validation and testing steps. 6. If multiple approaches exist, explain the best default and why. ## Success Criteria A good response should: - move the application forward quickly and safely - be technically defensible across the stack - reduce the chance of regressions - avoid unnecessary complexity ``` --- **2026-03-19 03:11:09 UTC | Created via MCP**

Base Prompt Template Homelab Architect

base_prompt_template_homelab_architect general

# Base Prompt Template: Homelab Architect Use this prompt when you want the model to act as a homelab architect focused on practical infrastructure design and operations. ```text # Base Prompt Title Homelab Architect ## Role You are a homelab architect. You are helping design, organize, automate, troubleshoot, and improve homelab systems. ## Primary Goal Your job is to: - design practical homelab solutions that balance ambition with maintainability - improve reliability, documentation, security, and automation - help the user make solid infrastructure decisions without unnecessary enterprise complexity ## Expertise Level To Emulate Operate like: - an experienced infrastructure engineer who understands home-scale constraints - someone strong in Linux, networking, storage, containers, virtualization, automation, backups, and service design - an operator with a high quality bar for maintainability, recovery, and clarity ## Behavior Rules - Be practical and technically accurate. - Prefer clarity over jargon. - Ask only the minimum necessary questions when something is blocking. - If a reasonable assumption can be made safely, make it and state it. - Do not invent facts, commands, products, or capabilities. - Surface tradeoffs, risks, and assumptions clearly. - Favor simple, recoverable designs over fragile or overly clever setups. - Consider documentation, backup, rollback, and observability as part of the design. - Respect the realities of limited time, hardware, power, and budget. - Avoid enterprise-style complexity unless it provides clear value in a homelab context. ## Teaching / Collaboration Style - Explain things like a strong infrastructure collaborator. - Use direct, practical guidance. - Prefer architecture that can be operated by one person without heroics. - Break complex migrations or designs into manageable steps. - When options exist, recommend a default and explain the tradeoff. ## Output Style - Be concise, direct, and structured when useful. - Use diagrams in words, bullet lists, commands, and examples when helpful. - Avoid filler and generic encouragement. - Focus on actionable, maintainable infrastructure guidance. ## Domain Constraints - Prioritize reliability, recoverability, and maintainability. - Avoid unnecessary complexity, especially in networking and orchestration. - Preserve existing working systems unless there is a clear reason to change them. - Assume the environment is a personal homelab with real operational consequences. ## When Solving Problems 1. Understand the user goal, current setup, and constraints. 2. Inspect existing architecture before proposing changes. 3. Recommend the simplest robust approach first. 4. Explain tradeoffs in terms of maintenance burden, failure recovery, and future growth. 5. Mention documentation, backups, and validation steps. 6. If multiple approaches exist, recommend the best default and explain why. ## Success Criteria A good response should: - help the user build a homelab that is easier to operate and recover - be technically sound without overengineering - improve clarity, resilience, and maintainability - avoid unnecessary complexity ``` --- **2026-03-19 03:12:20 UTC | Created via MCP**

Base Prompt Template HTML Tutor

base_prompt_template_html_tutor general

# Base Prompt Template: HTML Tutor Use this prompt when you want the model to act as an experienced HTML and frontend fundamentals tutor. ```text # Base Prompt Title HTML Tutor ## Role You are an experienced HTML and frontend fundamentals tutor. You are helping the user become proficient in HTML again. ## Primary Goal Your job is to: - rebuild the user's HTML knowledge from fundamentals to practical proficiency - explain semantic HTML clearly and correctly - give exercises and examples that reinforce learning ## Expertise Level To Emulate Operate like: - an experienced technical mentor and frontend educator - someone strong in semantic HTML, accessibility, forms, document structure, and real-world usage - a teacher with a high quality bar who prefers correctness and understanding over shortcuts ## Behavior Rules - Be practical and technically accurate. - Prefer clarity over jargon. - Ask only the minimum necessary questions when something is blocking. - If a reasonable assumption can be made safely, make it and state it. - Do not invent facts, APIs, commands, or capabilities. - Surface tradeoffs, risks, and assumptions clearly. - Adapt explanations to the user's skill level. - Prefer semantic, accessible HTML over presentational hacks. ## Teaching / Collaboration Style - Teach like a patient technical mentor. - Start simple, then increase complexity gradually. - Prefer short explanations followed by examples. - When the user makes a mistake, explain why it is wrong and show the correct pattern. - Break down concepts into small parts when needed. - Use repetition intentionally when it helps reinforce important fundamentals. ## Output Style - Be concise but clear. - Use examples and short code snippets frequently. - Avoid filler and generic encouragement. - Focus on actionable guidance and understanding. ## Domain Constraints - Prioritize modern, standards-based HTML. - Avoid outdated patterns unless explaining historical context. - Preserve accessibility and semantic structure. - Assume the environment is beginner-friendly but technically serious. ## When Solving Problems 1. Understand what the user is trying to learn or build. 2. Explain the relevant HTML concept in plain language. 3. Show a correct example. 4. Mention common mistakes or misunderstandings. 5. Suggest a small exercise or next step when useful. ## Success Criteria A good response should: - help the user rebuild real HTML skill quickly - be technically correct and standards-aware - improve the user's confidence with semantic HTML - avoid unnecessary complexity ``` --- **2026-03-19 03:10:26 UTC | Created via MCP**

Base Prompt Template Linux Admin

base_prompt_template_linux_admin general

# Base Prompt Template: Linux Admin Use this prompt when you want the model to act as an experienced Linux administrator. ```text # Base Prompt Title Linux Admin ## Role You are an experienced Linux administrator. You are helping manage, troubleshoot, secure, and improve Linux systems. ## Primary Goal Your job is to: - diagnose Linux system issues accurately - recommend safe, maintainable administrative actions - improve reliability, security, and operational clarity ## Expertise Level To Emulate Operate like: - a seasoned Linux systems administrator - someone strong in shells, services, networking, storage, permissions, logs, and automation - an operator with a high quality bar for safety, auditability, and uptime ## Behavior Rules - Be practical and technically accurate. - Prefer clarity over jargon. - Ask only the minimum necessary questions when something is blocking. - If a reasonable assumption can be made safely, make it and state it. - Do not invent facts, commands, service names, or capabilities. - Surface tradeoffs, risks, and assumptions clearly. - Prefer reversible, low-risk changes over disruptive ones. - Call out commands that are destructive or require elevated privileges. - Favor direct inspection, logs, and observable system state over guesswork. - Preserve system stability and security. ## Teaching / Collaboration Style - Explain things like an experienced sysadmin mentoring another operator. - Use direct, high-signal explanations. - Prefer actionable commands and verification steps. - When troubleshooting, narrow the problem methodically. - When multiple options exist, recommend the safest sensible default and explain why. ## Output Style - Be concise, direct, and structured when useful. - Use commands, paths, and configuration examples when helpful. - Avoid filler and generic encouragement. - Focus on actionable operational guidance. ## Domain Constraints - Prioritize Linux-first, standards-aware administration practices. - Avoid unnecessary complexity and risky changes. - Preserve existing service behavior unless a change is explicitly justified. - Assume the environment may be a live system where regressions and downtime matter. ## When Solving Problems 1. Understand the host, service, and failure mode. 2. Inspect logs, config, and runtime state before proposing changes. 3. Recommend the simplest safe approach first. 4. Explain risks, rollback concerns, and validation steps. 5. If multiple approaches exist, recommend the best default and explain why. ## Success Criteria A good response should: - help the user resolve Linux issues safely and quickly - be technically defensible and operationally sound - reduce avoidable downtime and misconfiguration risk - avoid unnecessary complexity ``` --- **2026-03-19 03:12:20 UTC | Created via MCP**

Base Prompt Template:Python Tutor

base_prompt_template_python_tutor general

# Base Prompt Template:Python Tutor Use this prompt when you want the model to act as an experienced Python fundamentals tutor. # Base Prompt Title Python Tutor ## Role You are an experienced Python fundamentals tutor. You are helping the user become proficient in Python. ## Primary Goal Your job is to: - teach the user Python from fundamentals to practical proficiency - explain Python clearly and correctly - give exercises and examples that reinforce learning ## Expertise Level To Emulate Operate like: - an experienced technical mentor and Python educator - someone strong in Python fundamentals and real-world usage - a teacher with a high quality bar who prefers correctness and understanding over shortcuts ## Behavior Rules - Be practical and technically accurate. - Prefer clarity over jargon. - Ask only the minimum necessary questions when something is blocking. - If a reasonable assumption can be made safely, make it and state it. - Do not invent facts, APIs, commands, or capabilities. - Surface tradeoffs, risks, and assumptions clearly. - Adapt explanations to the user's skill level. - Prefer clear, idiomatic Python over hacks or overly clever code. ## Teaching / Collaboration Style - Teach like a patient technical mentor. - Start simple, then increase complexity gradually. - Prefer short explanations followed by examples. - Break down complex ideas into smaller parts when needed. - When the user makes a mistake, explain why it is wrong and show the correct pattern. - Check for likely misunderstandings and correct them directly. ## Output Style - Be concise but clear. - Use examples and short code snippets frequently. - Avoid filler and generic encouragement. - Focus on actionable guidance and understanding. ## Domain Constraints - Prioritize modern, standards-based Python. - Avoid outdated patterns unless explaining historical context. - Prefer readability, correctness, and maintainability. - Assume the environment is beginner-friendly but technically serious. ## When Solving Problems 1. Understand what the user is trying to learn or build. 2. Explain the relevant Python concept in plain language. 3. Show a correct example. 4. Mention common mistakes or misunderstandings. 5. Recommend the simplest workable approach first. 6. Suggest a small exercise or next step when useful. ## Success Criteria A good response should: - help the user build real Python skill quickly - be technically correct and standards-aware - improve the user's confidence with Python - avoid unnecessary complexity

Base Prompt Template Senior Software Engineer

base_prompt_template_senior_software_engineer general

# Base Prompt Template: Senior Software Engineer Use this prompt when you want the model to act as a senior software engineer with strong technical judgment. ```text # Base Prompt Title Senior Software Engineer ## Role You are a senior software engineer. You are helping solve engineering problems with production-quality judgment. ## Primary Goal Your job is to: - analyze problems carefully before proposing solutions - produce technically sound, maintainable recommendations and implementations - identify risks, tradeoffs, and failure modes early ## Expertise Level To Emulate Operate like: - a pragmatic senior individual contributor - someone strong in debugging, architecture, systems thinking, and implementation - an engineer with a high quality bar for correctness, maintainability, and operational safety ## Behavior Rules - Be practical and technically accurate. - Prefer clarity over jargon. - Ask only the minimum necessary questions when something is blocking. - If a reasonable assumption can be made safely, make it and state it. - Do not invent facts, APIs, commands, or capabilities. - Surface tradeoffs, risks, and assumptions clearly. - Prefer simple solutions over broad refactors unless the refactor is justified. - Inspect existing context before recommending changes. - Prioritize correctness, maintainability, and risk reduction over cleverness. - Call out likely regressions, edge cases, and testing gaps. ## Teaching / Collaboration Style - Explain things like a strong technical peer. - Be direct, concise, and high signal. - Prefer reasoning that is easy to audit. - Break down complex engineering decisions into clear tradeoffs. - When uncertainty exists, say what is uncertain instead of guessing. ## Output Style - Be concise and direct. - Use structure only when it improves clarity. - Include concrete examples, commands, or code when useful. - Avoid filler and generic encouragement. - Focus on actionable engineering guidance. ## Domain Constraints - Prioritize maintainable and production-ready solutions. - Avoid unnecessary complexity and premature abstraction. - Preserve existing standards, architecture, and team conventions unless there is a strong reason not to. - Assume the environment may be a live or evolving system where regressions matter. ## When Solving Problems 1. Understand the real goal and constraints. 2. Inspect context before suggesting changes. 3. Recommend the simplest sound approach first. 4. Explain tradeoffs and risks clearly. 5. Mention validation, testing, or rollout considerations. 6. If multiple approaches exist, recommend the best default and explain why. ## Success Criteria A good response should: - help the user make solid engineering decisions quickly - be technically defensible and easy to audit - reduce confusion and implementation risk - avoid unnecessary complexity ``` --- **2026-03-19 03:11:09 UTC | Created via MCP**

Binkie Homelab CLI Project Context

binkie_homelab_cli_project_context_2026_04_19 general:project

Project: Binkie Homelab CLI Date established: 2026-04-19 Canonical project path: /home/svc-admin/ai-projects/projects/binkie-homelab-cli Earlier draft path: /home/svc-admin/ai-projects/binkie-lab-cli Purpose: - Build a local-first agent runtime that gives locally hosted models practical parity with frontier-model CLIs for local coding work, homelab operations, remote host inspection/maintenance, multi-step terminal workflows, and artifact generation/review. - The runtime is intended to work with frontier models, not replace them. - Design target: frontier model as planner/reviewer/escalation layer; local model as executor/operator; shared memory/artifacts between them. Strategic decision: - Preferred direction is to build a new runtime rather than keep bending OpenCode into a universal homelab operator. - OpenCode remains useful as a repo-focused coding agent, but not as the architectural center of gravity for a general homelab workhorse. Core requirements: - Persistent PTY-backed shell sessions - Truthful environment and execution-state reporting - Structured tools for file read/write/search/apply_patch - One-shot and persistent remote execution models - Host-aware remote operations - Safety and approval controls - Session logging, artifacts, and memory support High-level architecture: - CLI frontend - Agent orchestrator - Prompt layer and model adapter layer - Tool runtime - PTY and remote execution layer - Memory, logging, and safety layer Key principle: - Distinguish clearly between one-shot command execution and persistent PTY-backed sessions. - The runtime must never let the model infer fake continuity or fake shell state. Technology direction: - Python preferred for runtime/orchestration - Ollama first for local model access - SQLite for early session/log/artifact state - optional Postgres/vector layer later Suggested project file layout and phased architecture are documented in the project docs. Current project documentation under canonical path: - /home/svc-admin/ai-projects/projects/binkie-homelab-cli/README.md - /home/svc-admin/ai-projects/projects/binkie-homelab-cli/AGENTS.md - /home/svc-admin/ai-projects/projects/binkie-homelab-cli/docs/01-project-brief.md - /home/svc-admin/ai-projects/projects/binkie-homelab-cli/docs/02-architecture.md - /home/svc-admin/ai-projects/projects/binkie-homelab-cli/docs/03-opencode-comparison.md - /home/svc-admin/ai-projects/projects/binkie-homelab-cli/docs/04-reuse-and-migration-plan.md - /home/svc-admin/ai-projects/projects/binkie-homelab-cli/docs/05-implementation-roadmap.md OpenCode relationship: - OpenCode comparison concluded that OpenCode is acceptable only with worktree bypass for local repo work and remains rejected for stateful remote SSH workflows. - Binkie Homelab CLI should be treated as a truth-first execution substrate and may optionally reuse ideas or isolated components from OpenCode later, but should not be based on OpenCode as its universal runtime. Immediate implementation posture: - Project is documentation-first right now. - No substantial code scaffold has been started yet. - The most useful next implementation phase, when work resumes, is Phase 0/1: scaffold, model adapter, filesystem/search/patch tools, PTY session manager, and session logging. Canonical doc intent: - README.md: project purpose and current status - docs/01-project-brief.md: goals, users, non-goals, success criteria - docs/02-architecture.md: runtime layers and design - docs/03-opencode-comparison.md: why not to use OpenCode as the foundation - docs/04-reuse-and-migration-plan.md: what to borrow vs rebuild cleanly - docs/05-implementation-roadmap.md: phased build plan Current status: - Planning documented and staged for future implementation. - Project is paused in favor of other higher-priority work, but should be easy to resume from the docs.

BinkieLab CLI paused state after Phase 4 host-id persistence worker run

binkielab_cli_pause_state_2026_04_21 projects:user

Project: BinkieLab CLI Primary host: svc-ai Project root: /home/svc-admin/ai-projects/projects/binkie-homelab-cli Pause intent: User paused BinkieLab intentionally on April 21, 2026 (America/Chicago) to redirect full attention and resources to a different project, with the expectation that BinkieLab can be resumed later from a clean handoff. Automation state at pause: - File: /home/svc-admin/ai-projects/projects/binkie-homelab-cli/AUTOMATION_STATE.json - Final captured state after allowing the last in-flight worker to finish: - step_id: phase4-dispatch-host-id-persistence - status: worker_done - active_prompt: /home/svc-admin/ai-projects/projects/binkie-homelab-cli/ACTIVE_PROMPT.md - worker_result: /home/svc-admin/ai-projects/projects/binkie-homelab-cli/WORKER_RESULT.md - last_worker_run_at: 2026-04-22T04:02:43+00:00 - last_review_at: 2026-04-22T03:56:03+00:00 - paused_at: 2026-04-21T22:07:00-05:00 - Meaning of this state: - The most recent Gemini worker run finished successfully after the pause request. - No supervisor review has consumed that latest worker result yet. - The project is therefore paused at a clean worker_done checkpoint, waiting for the next supervisor pass when work resumes. Schedules / cron jobs: - BinkieLab had been running two local cron loops on svc-ai every 5 minutes: 1. worker loop - /home/svc-admin/ai-projects/projects/binkie-homelab-cli/scripts/worker_loop.sh 2. supervisor loop - /home/svc-admin/ai-projects/projects/binkie-homelab-cli/scripts/supervisor_loop.sh - Those scheduled entries were removed from crontab during pause. - Remaining crontab at pause time only retained an unrelated product-forge entry plus the old BinkieLab comment labels: - 0 7 * * 1 /home/svc-admin/ai-projects/projects/product-forge/scripts/run.sh >> /home/svc-admin/ai-projects/projects/product-forge/logs/cron.log 2>&1 - # binkie-homelab-cli gemini worker - # binkie-homelab-cli codex supervisor - Important: the final in-flight worker process from the last cron tick was NOT killed. It was allowed to finish naturally before capture of the final paused state. - Final process check after waiting showed no live BinkieLab worker/supervisor/gemini/codex processes still running. Most recent active prompt at pause: - File: /home/svc-admin/ai-projects/projects/binkie-homelab-cli/ACTIVE_PROMPT.md - Step title / focus: - Phase 4 step: tighten host identity consistency in structured runtime records. - Exact goal of the paused step: - Ensure blocked and failed remote dispatch responses preserve response.host_id when the dispatcher knows the targeted configured host from request arguments. - Keep local tool logs compact and truthful. - Do not change bounded model-context shape. - Acceptance criteria in the prompt: - new blocked remote task logs include steps_executed[-1].response.host_id when a configured remote host was targeted - new failed remote task logs also preserve response.host_id when known - local task logs remain truthful and compact - binkie inspect-context remains unchanged in shape and still reads cleanly - Required validation in the prompt: - py_compile of runtime files and app/main - run-task invocation against remote_list_dir localhost-test - logs tasks list - inspect-context Most recent worker result at pause: - File: /home/svc-admin/ai-projects/projects/binkie-homelab-cli/WORKER_RESULT.md - The final in-flight worker completed a narrow Phase 4 change in src/binkielab/runtime/tool_dispatch.py. - Worker-reported implementation summary: - Removed the default host_id value of "local" during host extraction in ToolDispatcher.dispatch. - Updated ToolDispatchResponse.to_dict to omit host_id when it is None. - Verified that blocked and failed remote tool calls preserve host_id when present in structured arguments. - Verified that local tool logs stay compact without a redundant host_id field. - Worker-reported next suggested step: - Add risk-tier escalation for unknown remote hosts in ApprovalEvaluator. - Important note: - This worker result has NOT yet been reviewed by the supervisor. It is only the latest worker-produced handoff artifact. Recent log context: - Worker log file: /home/svc-admin/ai-projects/projects/binkie-homelab-cli/logs/worker/worker-loop.log - Supervisor log file: /home/svc-admin/ai-projects/projects/binkie-homelab-cli/logs/supervisor/supervisor-loop.log - The worker had been completing steps successfully on a regular cadence. - The supervisor had also been completing reviews successfully on a regular cadence. - Immediately before the pause, the supervisor’s last completed review was at 2026-04-22T03:56:38+00:00. - Immediately before the pause, the worker launched one more run at 2026-04-22T04:00:01+00:00 and completed it at 2026-04-22T04:02:43+00:00. - That last worker run is the one reflected in the final worker_done state. Project phase/status at pause: - BinkieLab is no longer in the early substrate/prototype stage. - Phase 1 (local one-shot substrate): effectively done. - Phase 2 (local PTY/session substrate): done enough and not the current bottleneck. - Phase 3 (remote one-shot substrate): effectively behind the project, with the system having moved into Phase 4. - Phase 4 was active at pause time. - The specific paused sub-step is runtime/dispatch/logging truthfulness work around host identity persistence. Why this is a good resume point: - Cron schedules are disabled, so no new background work should start unexpectedly. - No worker or supervisor processes remained alive when the final state was captured. - The repository is paused at a clean worker_done checkpoint. - The latest worker output and prompt are both preserved on disk. - Resuming later does NOT require reconstructing state from memory alone; the disk artifacts and this memory should align. Recommended resume procedure: 1. Re-open /home/svc-admin/ai-projects/projects/binkie-homelab-cli on svc-ai. 2. Read: - AUTOMATION_STATE.json - ACTIVE_PROMPT.md - WORKER_RESULT.md - tail of logs/worker/worker-loop.log - tail of logs/supervisor/supervisor-loop.log 3. Run one manual Codex supervisor review first before restoring cron. - Reason: state is worker_done, and the last completed worker result still needs supervisor acceptance or a fix prompt. 4. If the supervisor accepts the step: - it should queue the next Phase 4 prompt and transition the state back toward worker_pending. 5. If the supervisor finds issues: - let it write a narrow fix prompt as the next ACTIVE_PROMPT.md. 6. Only after the review state is sane again, restore the two local 5-minute cron loops if desired: - worker loop: /home/svc-admin/ai-projects/projects/binkie-homelab-cli/scripts/worker_loop.sh - supervisor loop: /home/svc-admin/ai-projects/projects/binkie-homelab-cli/scripts/supervisor_loop.sh 7. Confirm crontab is restored intentionally, not accidentally. Immediate next step when resuming work later: - Run the Codex supervisor once against the finished phase4-dispatch-host-id-persistence worker result. - Treat that review as the true restart point. Related files worth reading first on resume: - /home/svc-admin/ai-projects/projects/binkie-homelab-cli/AUTOMATION_STATE.json - /home/svc-admin/ai-projects/projects/binkie-homelab-cli/ACTIVE_PROMPT.md - /home/svc-admin/ai-projects/projects/binkie-homelab-cli/WORKER_RESULT.md - /home/svc-admin/ai-projects/projects/binkie-homelab-cli/PHASE_4_AND_5_ROADMAP.md - /home/svc-admin/ai-projects/projects/binkie-homelab-cli/PHASE_4_AND_5_CHECKLIST.md - /home/svc-admin/ai-projects/projects/binkie-homelab-cli/logs/worker/worker-loop.log - /home/svc-admin/ai-projects/projects/binkie-homelab-cli/logs/supervisor/supervisor-loop.log Operational note: - The old Codex desktop heartbeat automation had already been removed earlier in favor of the local svc-ai loops. - At pause time, BinkieLab’s active automation mechanism was the local cron-based worker/supervisor loop on svc-ai, and those scheduled entries are now removed.

Brain Enhancement Project — MemPalace-Inspired Upgrades

brain_enhancement_project_2026_04_07 projects

Project document created 2026-04-07 outlining four proposed enhancements to the Brain memory system, inspired by analysis of Milla Jovovich and Ben Sigman's MemPalace project. Full spec saved to: D:\Jason\Documents\AI\brain-enhancement-project.md The four proposals in priority order: 1. ChromaDB Semantic Search Layer — Add ChromaDB as a parallel retrieval backend alongside Postgres. Postgres stays canonical; ChromaDB handles vector embeddings via sentence-transformers/all-MiniLM-L6-v2 (local, no API key). New /memories/semantic endpoint and semantic_search MCP tool. New memcli reindex management command. Estimated: 1 session (3-5 hours). 2. Two-Level Namespace Hierarchy — Add domain and topic as first-class indexed fields on the memories table (Alembic migration 0003). Domain taxonomy: homelab, haip, arena, genealogy, ohio_history, stoned_ai, personal, learning, business, memory_system, general. Scoped search via domain/topic filters on all REST and MCP endpoints. Backfill script to classify existing memories. Estimated: 1 session (2-4 hours). 3. HAIP Conversation Miner — New project at /home/svc-admin/ai-projects/projects/haip-brain-miner/ on svc-ai. Systemd timer runs every 30 minutes, queries HAIP agent_invocations from svc-db-01, filters for significance (length > 200 chars, novelty check, not trivial), chunks and writes to Brain with source=haip_miner. Solves the core problem that HAIP turn data never flows back into Brain. Estimated: 2 sessions (4-8 hours). 4. Token Budget Enforcement and Agent Identity Cards — Alembic migration 0004 adds summary, token_count, and memory_type fields. Memories over 500 tokens get a compact summary generated at write time (via local Llama 3 8B on svc-ai). MCP retrieval returns summary by default, full content on request. New identity_card memory_type for per-agent compact bootstrap payloads under 200 tokens. New get_identity_card MCP tool. Wire into HAIP orchestrator turn initialization. Estimated: 1-2 sessions (3-6 hours). Key implementation hosts: svc-dev (Brain API/MCP/DB), svc-ai (miner, identity card generation). Key reference memories: brain_project_implementation_handoff_2026_03_22, brain_operator_runbook_2026_03_22, homelab_ai_platform_architecture_context_2026_04_03.

Brain Operator Runbook (2026-03-22)

brain_operator_runbook_2026_03_22 general

Purpose - This is the short operator runbook for the `brain` memory system running on `svc-dev`. - Use this for day-to-day operation, validation, and quick recovery. Location and services - Host: `svc-dev` (`192.168.4.123`) - Project path: `/home/svc-admin/projects/brain` - Compose file: `/home/svc-admin/projects/brain/docker-compose.yml` - Main containers: - `brain-api` - `brain-mcp` - `brain-db` Ports and endpoints - REST API: `http://192.168.4.123:8010` - MCP endpoint: `http://192.168.4.123:8011/mcp` - Health check: `http://192.168.4.123:8010/health` Core design - One shared brain for multiple AI clients. - Separation is handled by `source` values such as `codex`, `claude`, `gemini`, `manual`, and `contextkeep`. - Default DB mode is bundled Postgres in the compose stack. - External Postgres later only requires changing `DATABASE_URL` in `.env`. Current state - ContextKeep legacy memories have already been imported. - Imported memory count at import time: `54`. - Imported records use `source=contextkeep`. - Codex, Claude, and Gemini were all configured to point to the same MCP endpoint. Useful API operations - List memories: - `curl http://192.168.4.123:8010/memories?status=all | jq` - Filter by source: - `curl "http://192.168.4.123:8010/memories?source=codex&status=all" | jq` - `curl "http://192.168.4.123:8010/memories?source=contextkeep&status=all" | jq` - Get one memory: - `curl http://192.168.4.123:8010/memories/<key> | jq` - List tags: - `curl http://192.168.4.123:8010/tags | jq` Useful compose commands - Check status: - `cd /home/svc-admin/projects/brain && docker compose ps` - View logs: - `cd /home/svc-admin/projects/brain && docker compose logs --tail=100 api` - `cd /home/svc-admin/projects/brain && docker compose logs --tail=100 mcp` - `cd /home/svc-admin/projects/brain && docker compose logs --tail=100 db` - Restart stack: - `cd /home/svc-admin/projects/brain && docker compose restart` - Rebuild after code changes: - `cd /home/svc-admin/projects/brain && docker compose up -d --build` Database notes - DB name: `ai-brain` - DB user: `ai-brain` - Local/default connection is driven by `.env` via `DATABASE_URL`. - Alembic migrations run on container startup. Validation checklist - `docker compose ps` shows all three containers up - `curl http://127.0.0.1:8010/health` returns OK on the host - `curl http://127.0.0.1:8010/memories?status=all | jq length` returns records - MCP endpoint is reachable by the configured AI clients Known quirks - MCP host-header restrictions had to be explicitly opened for `192.168.4.123` and `svc-dev`. - If the MCP endpoint starts rejecting clients again, inspect `backend/app/mcp_server.py` first. - ContextKeep import was done directly from its JSON store because the old ContextKeep MCP transport became unreliable during bulk reads. Client wiring notes - Codex uses `~/.codex/config.toml` - Claude uses `~/.claude.json` - Gemini uses `~/.gemini/settings.json` - All should point to `http://192.168.4.123:8011/mcp` for the shared brain Operational recommendations - Use `source=codex`, `source=claude`, `source=gemini`, or `source=manual` consistently for new writes. - Keep imported legacy data under `source=contextkeep`. - Treat REST as the admin/debug interface and MCP as the normal AI client interface. Current gaps - No web UI yet - No `namespace` field yet - No built-in reusable import command yet Related reference memory - Detailed implementation handoff: `brain_project_implementation_handoff_2026_03_22` This runbook is the short operational companion to the full implementation handoff.

Brain Project Implementation Handoff (2026-03-22)

brain_project_implementation_handoff_2026_03_22 general

Project summary - On 2026-03-22, a new self-hosted memory system project named `brain` was created and deployed on `svc-dev` at `/home/svc-admin/projects/brain`. - The design goal was a shared core memory backend for multiple AI clients, with one common memory store and first-class source metadata so different clients can write into separate lanes without requiring separate installs. - The initial supported sources are intended to be `codex`, `claude`, `gemini`, `manual`, and imported `contextkeep` data. Core architectural decisions - Use one shared backend instead of one database per AI client. - Make `source` a first-class memory field rather than relying only on tags. - Keep the app compatible with both a bundled local Postgres container and an external Postgres instance later. - App code always reads a single `DATABASE_URL`. - Default deployment uses a bundled Postgres service in `docker-compose.yml`. - Switching to an external DB later only requires changing `.env`, not changing app code or adding a second compose file. - Keep REST API and MCP server as separate services in the same stack. - Use FastAPI for the REST API and Python MCP server implementation. - Use Alembic for schema migrations rather than ad hoc table creation. Chosen deployment details - Project directory: `/home/svc-admin/projects/brain` - Host: `svc-dev` (`192.168.4.123`) - API port: `8010` - MCP port: `8011` - Database name: `ai-brain` - Database user: `ai-brain` - Local default database password: `120dMOtVxpoHrwdT` Bundled stack layout - `brain-api`: FastAPI REST service - `brain-mcp`: MCP server exposed over HTTP - `brain-db`: Postgres 16 Important project files - `/home/svc-admin/projects/brain/docker-compose.yml` - `/home/svc-admin/projects/brain/.env` - `/home/svc-admin/projects/brain/.env.example` - `/home/svc-admin/projects/brain/README.md` - `/home/svc-admin/projects/brain/backend/Dockerfile` - `/home/svc-admin/projects/brain/backend/requirements.txt` - `/home/svc-admin/projects/brain/backend/alembic.ini` - `/home/svc-admin/projects/brain/backend/alembic/env.py` - `/home/svc-admin/projects/brain/backend/alembic/versions/0001_initial.py` - `/home/svc-admin/projects/brain/backend/alembic/versions/0002_add_memory_source.py` - `/home/svc-admin/projects/brain/backend/app/main.py` - `/home/svc-admin/projects/brain/backend/app/db.py` - `/home/svc-admin/projects/brain/backend/app/models.py` - `/home/svc-admin/projects/brain/backend/app/schemas.py` - `/home/svc-admin/projects/brain/backend/app/crud.py` - `/home/svc-admin/projects/brain/backend/app/serializers.py` - `/home/svc-admin/projects/brain/backend/app/api/health.py` - `/home/svc-admin/projects/brain/backend/app/api/memories.py` - `/home/svc-admin/projects/brain/backend/app/api/tags.py` - `/home/svc-admin/projects/brain/backend/app/mcp_server.py` Environment and configuration - `.env` defines `POSTGRES_DB`, `POSTGRES_USER`, `POSTGRES_PASSWORD`, `DATABASE_URL`, `API_PORT`, and `MCP_PORT`. - Local/default `DATABASE_URL` points to the bundled Postgres service hostname `db`. - External Postgres support is implemented by overriding `DATABASE_URL` in `.env`, for example pointing it to a remote DB host instead of `db`. Data model decisions - `Memory` table stores the current canonical record. - Fields include `key`, `title`, `content`, `source`, `status`, `created_at`, and `updated_at`. - `source` is indexed and defaults to `manual`. - `status` is indexed and defaults to `active`. - Tags are normalized via a dedicated `Tag` table and `memory_tags` join table. - Revision history is stored in a `Revision` table. - Deletes are implemented as archive semantics rather than hard delete at the API layer. Schema and migrations - Alembic migration `0001_initial.py` created the base schema for memories, tags, memory_tags, and revisions. - Alembic migration `0002_add_memory_source.py` added first-class `source` support to the `memories` table. - Container startup runs `alembic upgrade head` before launching the API process. - Early in development, the first prototype created tables before Alembic existed. To move to migration-owned schema, the project Postgres volume had to be reset once with `docker compose down -v`. After that reset, migrations became the source of truth and the schema stabilized. REST API implemented - `GET /health` - `GET /memories` - `GET /memories?status=all` - `GET /memories?q=<text>` - `GET /memories?tag=<tag>` - `GET /memories?source=<source>` - `GET /memories/{key}` - `POST /memories` - `PUT /memories/{key}` - `DELETE /memories/{key}` for archive behavior - `GET /tags` MCP server implementation - A dedicated MCP server service named `brain-mcp` was added to the compose stack. - The MCP endpoint is exposed at `http://192.168.4.123:8011/mcp`. - Implemented MCP tools: - `list_memories` - `retrieve_memory` - `search_memories` - `store_memory` - `archive_memory` - `list_tags` - The MCP layer uses the official Python `mcp` package. MCP host-header decision and fix - During early testing, the MCP endpoint returned host-header rejection / `421 Misdirected Request` behavior for requests using `192.168.4.123`. - The fix was to update `backend/app/mcp_server.py` so the FastMCP/transport security settings explicitly allow expected hosts. - Allowed host patterns were expanded to include loopback and LAN-reachable forms, including `127.0.0.1`, `localhost`, `192.168.4.123`, and `svc-dev` with port patterns. - After that fix, the endpoint behaved correctly for external access and clients could connect successfully. Shared-brain design decision - The preferred architecture is one shared brain rather than one per AI client. - The separation model is implemented through `source` rather than separate deployments. - Example intended source values: `codex`, `claude`, `gemini`, `manual`, `contextkeep`. - This allows one source of truth while preserving per-client filtering and future namespace expansion. Current imported legacy data - Existing ContextKeep memories were imported into `brain`. - Import scope: all 54 existing ContextKeep memory records. - Imported records retained: - original `key` - original `title` - full `content` - tags - original `created_at` and `updated_at` timestamps - Imported records were assigned `source=contextkeep`. - Imported records were loaded as current-state records only; synthetic revision history was not backfilled. How ContextKeep import was implemented - The ContextKeep MCP transport became unreliable during bulk retrieval and began failing with transport-closed errors. - Instead of continuing through the MCP tool layer, the underlying ContextKeep storage implementation was inspected locally. - ContextKeep stores memories as JSON files under: `/home/svc-admin/ai-projects/projects/homelab/contextkeep/data/memories` - Each file contains fields like `key`, `title`, `content`, `tags`, `created_at`, and `updated_at`. - A direct export file was generated from those JSON documents. - The export file was copied to `svc-dev`. - A one-off Python import script was copied into the `brain-api` container and executed with `PYTHONPATH=/app` so it could import `app.db` and `app.models`. - The importer created missing tags, inserted memories, set `source=contextkeep`, and preserved the original timestamps. Commands and operational steps used during implementation - SSH / deployment inspection on `svc-dev`: - `ssh svc-dev ...` - Compose / stack verification: - `docker compose up -d` - `docker compose down -v` (used once during early schema reset before Alembic became canonical) - `docker ps` - `docker exec ...` - `docker cp ...` - API verification: - `curl http://127.0.0.1:8010/health` - `curl http://127.0.0.1:8010/memories` - `curl http://127.0.0.1:8010/openapi.json` - `curl http://127.0.0.1:8010/tags` - Database verification: - `docker exec brain-db psql -U ai-brain -d ai-brain -c '\dt'` - `docker exec brain-db psql -U ai-brain -d ai-brain -c '\d memories'` - MCP verification: - MCP client tool listing against loopback inside the container - direct HTTP checks against `http://192.168.4.123:8011/mcp` - Codex MCP registration: - `codex mcp add brain --url http://192.168.4.123:8011/mcp` - `codex mcp list` - Claude MCP registration: - `claude mcp add --transport http brain http://192.168.4.123:8011/mcp` - `claude mcp list` - Gemini MCP registration: - `gemini mcp add --scope user --transport http brain http://192.168.4.123:8011/mcp` - `gemini mcp list` - ContextKeep import prep: - inspection of `/home/svc-admin/ai-projects/projects/homelab/contextkeep/server.py` - inspection of `/home/svc-admin/ai-projects/projects/homelab/contextkeep/core/memory_manager.py` - export generation from JSON files under `data/memories` - `scp` to move import artifacts to `svc-dev` - `docker cp` into `brain-api` - `docker exec -e PYTHONPATH=/app brain-api python /tmp/contextkeep_import.py` Client integration status - Codex is configured to use the `brain` MCP server. - Claude is configured to use the `brain` MCP server and reported connected health. - Gemini is configured to use the `brain` MCP server and reported connected health. - All three are intended to use the same backend rather than separate installs. Important implementation notes - Gemini initially accepted an MCP add command without explicit transport/scope, but that defaulted to an unusable project/stdio shape. The fix was to re-add it explicitly as `--scope user --transport http`. - Claude stored the server in its local project config under `/home/svc-admin/.claude.json`. - Gemini stored the server under `/home/svc-admin/.gemini/settings.json`. - Codex stored the MCP server registration in `/home/svc-admin/.codex/config.toml`. Validation completed - API health endpoint returned OK. - Memory CRUD and archive flow worked before import. - Search and filtering by text, tag, and source worked. - Tag listing worked. - MCP server tool listing worked. - Host-header restriction was fixed for the MCP endpoint. - After ContextKeep import, `GET /memories?status=all` returned 54 records. - After import, `GET /memories?source=contextkeep&status=all` returned 54 records. - Spot checks confirmed the expected tags and preserved timestamps on imported memories. Current state after implementation - `brain` is live on `svc-dev`. - REST API base URL: `http://192.168.4.123:8010` - MCP endpoint: `http://192.168.4.123:8011/mcp` - ContextKeep memories have been imported. - Shared-brain design with first-class `source` is in place. - Multiple AI clients are configured to use the same MCP endpoint. Known limitations / future work - No web UI exists yet. Current interfaces are REST and MCP only. - `namespace` is not implemented yet; `source` is the current separation layer. - Imported ContextKeep records do not have reconstructed revision history. - Direct import tooling used for migration was ad hoc and not yet committed as a reusable migration command inside the project. - Prompt/template records and filtered views are still future work. Recommended next steps - Add `namespace` as a first-class field if a second layer of separation becomes necessary. - Build a web UI for browsing, filtering, and editing memories. - Turn the ContextKeep import path into a reusable project command or script if future migrations are expected. - Decide whether to disable or retire ContextKeep from daily use now that `brain` is active. - Add docs/examples showing how Codex, Claude, and Gemini should store memories using `source` consistently. This memory is intended to function as the repo-adjacent implementation handoff for the initial `brain` build and migration work.

change-documentation-guide.md

change_documentation_guide general

# Change Documentation Guide This note explains how change documentation should be created in the homelab documentation repository. It is the human-readable companion to: - `notes/ai-change-documentation-spec.md` ## Goal Whenever we make a meaningful change in the homelab, we want two things to happen: 1. The current-state documentation should reflect the new reality. 2. The change itself should be recorded so we know what happened, why, and what to watch later. ## What Counts As A Meaningful Change Examples: - hostname changes - DNS record changes - SSH access changes - onboarding a host - changing the control node - modifying storage layout - moving a service - changing automation or publish workflow ## What Should Usually Be Created For most significant changes, create: 1. A dated change note 2. Any current-state documentation updates 3. Any needed policy or runbook updates ## Recommended Structure ### Current state Use `outputs/current/...` for documentation that describes what exists right now. Examples: - current host inventory - current DNS behavior - current SSH access model - current service mappings ### Change history Use `outputs/history/...` for dated change notes. Examples: - `2026-03-18-hostname-standardization.md` - `2026-03-18-windows-ssh-dns-setup.md` ### Standards and procedures Use `docs/policies/...` and `docs/runbooks/...` for repeatable guidance. Examples: - hostname standard - SSH access standard - how to rename a host - how to add DNS records ## What To Record In A Change Note Each change note should answer: - what changed - why it changed - which systems were affected - what files/configuration were touched - how the result was validated - what follow-up remains ## Secret Safety Do not record: - passwords - tokens - private keys - raw secret files Do record: - where secrets are stored - which systems depend on them - which config files or env files reference them ## Current Homelab Naming And Access Assumptions At the time this guide was written, the intended operating model is: - internal host A records live in `accursedbinkie.com` - short-name access depends on clients using `accursedbinkie.com` as a DNS suffix/search domain - `svc-admin` is the default admin SSH identity across the homelab - `Binkie-Desktop` uses `Jason` as the normal user SSH path - `Binkie-Desktop` admin access is `svc-admin` - Windows `svc-admin` SSH access is intentionally limited to trusted homelab systems ## Intended Use With AI When asking an AI model to document a change, point it to: - `notes/ai-change-documentation-spec.md` That file is the instruction document. This file is the operator-facing summary of the same approach. --- **Created:** 2026-03-18 22:08:31 UTC

Codex Sessions to Local Model Training Overview

codex_local_model_training_from_sessions_2026_03_19 general

Discussion on 2026-03-19 about using local Codex session logs to train a local model such as Llama 3/3.1 8B. Key conclusion: practical home-lab path is not pretraining like OpenAI, but post-training an existing instruct model using curated session data. Recommended ladder: first use retrieval over past sessions; next step is supervised fine-tuning with LoRA/QLoRA; advanced option is DPO if preference pairs exist; full pretraining from scratch is not realistic for home use. Important data-prep warning: do not train directly on raw ~/.codex/sessions/*.jsonl because those files contain session metadata, event records, tool traces, and encrypted reasoning fields. Instead, extract and curate clean user/assistant chat turns into a standard messages dataset. Example guidance: hold out 10-20 percent for evaluation and compare base vs fine-tuned model on real prompts. Tooling mentioned: Meta Llama Cookbook, PyTorch torchtune, Hugging Face TRL SFTTrainer. Hardware summary discussed from torchtune-style examples for 8B-class models: QLoRA can fit on 1x RTX 4090 at roughly 7.4 GiB peak VRAM in one example recipe; LoRA around 16.2 GiB; full finetune around 18.9 GiB. Explanation given: QLoRA uses a quantized frozen base model plus small trainable adapters; LoRA keeps frozen base weights at higher precision plus adapters; full finetune updates the full model weights and therefore also carries full gradients and optimizer state. Practical recommendation for this user: start with QLoRA on an 8B instruct model, use curated high-quality session examples only, and treat the published VRAM numbers as recipe-specific examples rather than guarantees. --- **2026-03-19 02:53:38 UTC | Created via MCP**

Codex Session File Locations and Current Thread Recovery

codex_session_file_locations_and_current_thread_2026_03_21 general

Codex local session/history file locations captured on 2026-03-21 for recovery after restart. Primary Codex storage root: - `/home/svc-admin/.codex` Useful files and directories: - session index / searchable history: - `/home/svc-admin/.codex/history.jsonl` - per-session transcript files: - `/home/svc-admin/.codex/sessions` - Codex log file: - `/home/svc-admin/.codex/log/codex-tui.log` - shell snapshots: - `/home/svc-admin/.codex/shell_snapshots` - state database files: - `/home/svc-admin/.codex/state_5.sqlite` - `/home/svc-admin/.codex/logs_1.sqlite` Current thread identifiers: - session ID: - `019d0943-7cb0-7b10-ac78-b01c9c11111b` - direct per-session transcript path: - `/home/svc-admin/.codex/sessions/2026/03/20/rollout-2026-03-20T03-21-51-019d0943-7cb0-7b10-ac78-b01c9c11111b.jsonl` Useful search terms for this thread in `history.jsonl`: - `LedgerBridge` - `Jessica` - `datalab` - `current_workflow_demo` Example recovery command: - `rg '019d0943-7cb0-7b10-ac78-b01c9c11111b|LedgerBridge|Jessica|datalab' /home/svc-admin/.codex/history.jsonl` Important distinction: - `history.jsonl` is the easiest searchable index for finding the right thread. - `sessions/...jsonl` contains the fuller per-session transcript and tool activity for a specific thread. --- **2026-03-21 00:00:00 UTC | AI Update via MCP** --- **2026-03-21 07:00:21 UTC | Created via MCP**

ContextKeep Codex MCP Handshake Fix

contextkeep_codex_mcp_handshake_fix_2026_03_19 general

Issue on 2026-03-19: Codex failed to start MCP client for ContextKeep with handshake error. Error showed Codex using a streamable HTTP client and receiving text/plain 405 Method Not Allowed during initialize. Root cause: /home/svc-admin/.codex/config.toml had ContextKeep registered as url = http://127.0.0.1:5100/sse, which Codex interpreted as transport=streamable_http. Local ContextKeep server at /home/svc-admin/ai-projects/projects/homelab/contextkeep/server.py supports stdio and sse, not streamable HTTP. Server log confirmed GET /sse returned 200 but POST /sse returned 405. Fix applied: removed MCP registration and re-added ContextKeep as a stdio server using command /home/svc-admin/ai-projects/projects/homelab/contextkeep/venv/bin/python with arg /home/svc-admin/ai-projects/projects/homelab/contextkeep/server.py. Verification: codex mcp get contextkeep now reports transport: stdio, and contextkeep-server plus contextkeep-webui systemd services were active. Expected result after restarting Codex: no startup handshake error for ContextKeep.

ContextKeep Install on svc-ai

contextkeep_install_svc_ai_2026_03_18 general

ContextKeep was installed on svc-ai at /home/svc-admin/ai-projects/projects/homelab/contextkeep. Services: contextkeep-server and contextkeep-webui. Web UI: http://svc-ai:5000. SSE endpoint: http://svc-ai:5100/sse. Codex CLI was configured with a local MCP server entry named contextkeep. The system is intended to be used as AI memory, while Gitea and the homelab documentation repo remain the source of truth. --- **Created:** 2026-03-18 21:38:12 UTC

ContextKeep / MCP Memory Discussion Handoff (2026-03-19)

contextkeep_mcp_memory_discussion_handoff_2026_03_19 general

# ContextKeep / MCP Memory Discussion Handoff Date: 2026-03-19 UTC Host: svc-ai Primary focus: evaluating ContextKeep as an AI memory system, understanding how it works with Codex CLI, identifying its limitations, and capturing the user's desired future-state memory features. ## Current ContextKeep Deployment State ContextKeep is installed on `svc-ai` at: `/home/svc-admin/ai-projects/projects/homelab/contextkeep` Services: - `contextkeep-server` - `contextkeep-webui` Ports: - Web UI: `5000/tcp` - SSE MCP endpoint: `5100/tcp` Access: - Web UI reachable at `http://svc-ai:5000` and `http://192.168.4.117:5000` - SSE endpoint reachable at `http://svc-ai:5100/sse` Firewall on `svc-ai`: - UFW allows `5000/tcp` from `192.168.4.0/24` - UFW allows `5100/tcp` from `192.168.4.0/24` - This matches the current LAN scoping used for SSH on `svc-ai` Codex CLI integration: - ContextKeep was added as a Codex MCP server - `codex mcp list` shows: - name: `contextkeep` - url: `http://127.0.0.1:5100/sse` - status: `enabled` - auth: `Unsupported` - `Auth Unsupported` was explained as normal for a local open SSE endpoint without Codex-managed authentication - User was told to start a fresh Codex session with: - `codex` - or `codex "Check ContextKeep for relevant memory before starting."` - User initially tried an invalid command: - `codex contextkeep enabled` - Clarified the correct flow: - `codex mcp list` - then `codex` ## Important Correction About ContextKeep Storage Earlier browsing/README language suggested SQLite-backed storage. Actual installed code on `svc-ai` does NOT use SQLite. It stores each memory as a JSON file in: `/home/svc-admin/ai-projects/projects/homelab/contextkeep/data/memories` Implementation detail from the code: - memory keys are hashed to filenames - content is stored as JSON records - fields include key, title, content, tags, created_at, updated_at, lines, chars This matters because: - ContextKeep is simpler and more inspectable than expected - but less robust/structured than a real DB-backed memory system - and it reinforces that ContextKeep is a manual memory store, not a sophisticated memory engine ## What Was Added To ContextKeep ### 1. Master prompt memories Imported unchanged, one memory per file from: `/home/svc-admin/ai-projects/projects/master-prompt` Stored memories: - `master-prompt-daily-use.md` -> key `master_prompt_daily_use` - `master-prompt-no-bs.md` -> key `master_prompt_no_bs` - `master-prompt-polished-chatgpt.md` -> key `master_prompt_polished_chatgpt` - `master-prompt-rough-draft.md` -> key `master_prompt_rough_draft` - `master-prompt-weekly-review.md` -> key `master_prompt_weekly_review` - `my-life-context.md` -> key `my_life_context` These were stored exactly as-is, not condensed or rewritten. ### 2. Homelab documentation-system notes Imported unchanged, one memory per file from: `/home/svc-admin/ai-projects/projects/homelab/.work/homelab-documentation-system/notes` Stored memories: - `ai-change-documentation-spec.md` -> key `ai_change_documentation_spec` - `ai-task-template-example-finish-documentation-automation.md` -> key `ai_task_template_example_finish_documentation_automation` - `ai-task-template.md` -> key `ai_task_template` - `change-documentation-guide.md` -> key `change_documentation_guide` These were also stored exactly as-is. ### 3. ContextKeep install memory A direct memory was created describing the install and intended use of ContextKeep on `svc-ai`. Key: - `contextkeep_install_svc_ai_2026_03_18` This memory includes: - install path - service names - web UI URL - SSE endpoint URL - that Codex CLI was configured with the local MCP server entry - that ContextKeep is intended to serve as AI memory while Gitea and the homelab documentation repo remain the source of truth ## How Memory Write / Read Was Explained It was established that, in the current chat, the assistant can still use ContextKeep by calling the local API directly on `svc-ai`, even if the native MCP tool path is not visibly exposed inside this specific session. Working write path used repeatedly: - POST to `http://127.0.0.1:5000/api/memories` - PUT to `http://127.0.0.1:5000/api/memories/<key>` - GET from `http://127.0.0.1:5000/api/memories/<key>` This means the assistant can store and update memories immediately when running on `svc-ai`, even before depending fully on the MCP tooling layer. ## Discussion About How ContextKeep Actually Behaves The user asked whether memories are called automatically or need prompting. Answer given: - in a properly loaded Codex CLI session with MCP enabled, Codex can check ContextKeep - but it is safer to prompt explicitly until behavior is well understood Suggested prompt patterns: - `Check ContextKeep for anything relevant before you start.` - `Use ContextKeep memory for this task.` - `Look in ContextKeep for prior work on this topic.` - `Store the result of this work in ContextKeep when you're done.` - `Check ContextKeep for relevant memory, do the task, then store any important new context back into ContextKeep.` When the user launched a fresh Codex session and it replied: - `Relevant ContextKeep memory checked.` - it found the install/system context memory but no task-specific memory This was explained as normal: the store is still sparse, and Codex was correctly telling the user it found the general ContextKeep setup memory but not a more specific task memory. ## Discussion About “Importance”, Scheduling, and Master Prompt Behavior The user asked how to make a memory “important” or have certain memories used at certain times. Examples given by the user: - daily master prompt should be used daily - weekly review prompt should be used weekly - no-BS version should be used when the user is slipping The answer established: - ContextKeep does not appear to have a built-in importance, pinning, or scheduled-use feature - it only exposes key/title/content/tags/timestamps - therefore importance must be expressed by convention, not a native priority field Suggested way to model this: - use strong tags such as: - `daily-context` - `weekly-context` - `fallback-context` - `behavior-reset` - `master-prompt` Examples discussed: - `master_prompt_daily_use` - tags: `master-prompt, daily-context, primary` - `master_prompt_weekly_review` - tags: `master-prompt, weekly-context, review` - `master_prompt_no_bs` - tags: `master-prompt, fallback-context, behavior-reset` Suggested prompt patterns: - `Load the daily-context memories from ContextKeep before starting.` - `Load the weekly-context memory and use that frame for this session.` - `Switch to the behavior-reset / no-BS ContextKeep memory for this conversation.` But the larger conclusion was: - ContextKeep can be bent into this with tagging conventions - but it does not natively provide the mode/profile system the user actually wants ## Key Strategic Discussion: ContextKeep Is Not Really The Desired End State The user clarified the real goal: - a system that can index chats automatically without constant manual `remember this` behavior - tags or labels to separate domains such as love life, coding, homelab, etc. - a way to make certain things active/used at certain times (daily, weekly, no-BS mode, etc.) - a cute usable web UI is nice, but ContextKeep is functionally too manual - user wants something more automatic and agent-friendly The assistant explained: - ContextKeep is really a manual memory vault / notebook - good at explicit save/retrieve - weak at: - automatic ingestion - priority/pinning - mode/profile switching - selective scheduled recall A more accurate framing of the user’s need was given: The user does not really want a simple memory store. The user wants something closer to an `agent memory operating system`, meaning: - automatic memory capture - categorization - retrieval by mode - relevance filtering - priority/pinning - human-readable browsing UI - local/self-hosted operation - multi-client use - source-of-truth separation from Gitea/docs - export/backup ability The assistant broke this into feature groups: 1. Automatic ingestion 2. Categorization 3. Retrieval by mode 4. Relevance filtering 5. Priority/pinning 6. Human-readable UI 7. Local/self-hosted 8. Multi-client usable 9. Source-of-truth separation 10. Easy export/backup And then compared ContextKeep against that desired feature set: - good at local storage, browsing, explicit memory retrieval - bad at auto-ingestion, prioritization, modes/profiles, strong categorization workflow Conclusion given: - ContextKeep is useful, but is not the user’s ideal system by itself - the likely future direction is either: - a stronger local memory backend - or a wrapper/orchestration layer around a memory engine - likely needs: - auto-ingest chat summaries - auto-tagging - mode/profile loading (`daily`, `weekly`, `no-BS`, `coding`, `personal`) ## Token Usage Discussion The user asked whether tools like ContextKeep / ContextMode actually save token usage. Answer given: - yes, they can reduce token usage when they retrieve only the relevant memories instead of pasting huge context every time - but only if the retrieval is selective and not junky - if a memory system pulls in too much irrelevant material, it increases noise instead of helping So the conclusion was: - memory systems can save tokens - but only when they behave as selective retrieval layers, not just giant dumping grounds ## Alternatives Discussion The user asked whether there are alternatives to ContextKeep and how they compare. A broad comparison was discussed: - ContextKeep - Mem0 / OpenMemory style systems - SQLite-backed local memory MCPs like `claude-memory-mcp` / `mcp-openmemory` - more advanced memory/RAG systems Key takeaway given to the user: - for a homelab-friendly simple self-hosted memory store, ContextKeep is still reasonable - if the user outgrows it, the next step should probably be a stronger local DB-backed memory server or a wrapper around a memory backend - not a cloud-dependent memory service ## Task Created In Vikunja Based On This Discussion A Vikunja task was created in `Infrastructure Buildout`: - title: `Tighten up ContextKeep` - task id: `60` - identifier: `#3` - priority: `medium` This task was intentionally created from the `ai_task_template` memory context. The description was then reformatted multiple times because the user felt it was too wall-of-text heavy. Final conclusion from that interaction: - Vikunja-friendly tasks need a very scan-friendly structure - not long design-document formatting ## Mandatory Task Format Update The user requested that the AI task template memory be updated to enforce a non-negotiable task format. The assistant updated the `ai_task_template` memory accordingly. The mandatory format now is: 1. Summary - 2-4 lines max 2. What To Do - short bullet list 3. Systems Affected - short bullet list 4. Constraints - short bullet list 5. Validation - short bullet list 6. Acceptance Criteria - short bullet list 7. References - file paths, task ids, URLs The assistant also added explicit language to `ai_task_template` saying: - this format is mandatory for Vikunja task descriptions - do not replace it with long narrative sections, paragraph-heavy explanations, or oversized design-document formatting This update is important because the user felt previous Vikunja task descriptions were still too much of a wall of text. ## User Intent Going Forward The user said we are about to drastically shift gears away from homelab work. Before shifting, the user wanted: - master prompt files committed to memory - notes files committed to memory - the current ContextKeep / MCP server conversation committed to memory in as much detail as possible Reason stated by the user: - the user wants to be able to open a fresh Codex instance and pull up exactly where this discussion left off ## Important Operational Guidance For Future Session If a future Codex session needs to resume this topic, the best retrieval targets are likely: - `contextkeep_install_svc_ai_2026_03_18` - `ai_task_template` - this new handoff memory - the imported master prompt memories as needed Likely useful prompts in a fresh Codex session: - `Check ContextKeep for the ContextKeep / MCP handoff memory before starting.` - `Load the ai_task_template memory and the ContextKeep MCP discussion handoff.` - `Find the memory about evaluating ContextKeep versus a more automatic memory system.` ## Recommendation To Future Assistant When resuming this topic, do NOT assume the user is satisfied with ContextKeep as the final answer. The user likes: - the web UI - the ability to store things - the local/self-hosted nature But the user is dissatisfied with: - manual memorize/find behavior - lack of automatic ingestion - lack of tags/labels/modes that drive real retrieval behavior - lack of daily/weekly/no-BS usage logic So future discussion should treat ContextKeep as: - a currently working memory store - not necessarily the final memory system --- **Created:** 2026-03-19 02:18:51 UTC

CSS Day By Day Study Plan

css_day_by_day_study_plan_2026_03_19 general

Day-by-day CSS study plan created on 2026-03-19. Day 1: What CSS Is Learn what CSS does, how to connect CSS to HTML, selectors, colors, background colors, and basic text styling. Practice: connect a stylesheet to an HTML page, then change text color, background color, and font size. Goal: understand that CSS controls presentation, not structure. Day 2: Fonts and Text Learn font family, font size, font weight, line height, text align, and text decoration. Practice: style a page’s headings and paragraphs and make text readable and consistent. Goal: improve readability without overthinking design. Day 3: The Box Model Learn width, height, padding, border, and margin. Practice: add spacing around sections and experiment with borders and content boxes. Goal: understand why elements have space around them. Day 4: Display and Positioning Basics Learn block vs inline, inline-block, and basic display behavior. Practice: inspect how elements behave by default and change display values to see what happens. Goal: stop guessing why elements stack or sit side by side. Day 5: Flexbox Part 1 Learn flex container, flex direction, justify content, and align items. Practice: create a horizontal nav or row of cards. Goal: learn the easiest way to line things up. Day 6: Flexbox Part 2 Learn gap, wrapping, and nested flex layouts. Practice: build a small page section using Flexbox and make a layout with multiple aligned blocks. Goal: get comfortable using Flexbox for real page sections. Day 7: Review and Small Build Practice: build a styled personal homepage using spacing, fonts, colors, box model, and Flexbox. Goal: combine what was learned into one small page. Day 8: CSS Grid Part 1 Learn grid container, columns, rows, and gap. Practice: build a simple grid layout and make a section with multiple blocks. Goal: understand two-dimensional layout. Day 9: CSS Grid Part 2 Learn placing items in a grid, combining Grid and Flexbox, and choosing when to use each. Practice: create a more structured layout like a gallery or features section. Goal: stop treating Grid like magic. Day 10: Responsive Design Basics Learn responsive thinking, percentages, max-width, media queries, and how layouts change on small screens. Practice: make a page look decent on desktop and mobile. Goal: understand that layouts must adapt. Day 11: Buttons, Forms, and UI Elements Learn styling buttons, styling inputs, borders, border radius, hover states, and focus states. Practice: style a contact form or signup form. Goal: make common UI elements look intentional. Day 12: Layout Polish Learn visual hierarchy, spacing consistency, alignment, repeated styles, and simple design systems thinking. Practice: clean up one of the earlier pages and improve spacing, headings, and section balance. Goal: make the page feel more deliberate. Day 13: Rebuild Without Looking Practice: build a styled page from scratch using your own HTML and apply typography, spacing, Flexbox or Grid, and responsive behavior. Goal: expose weak spots in CSS understanding. Day 14: Final CSS Checkpoint Practice: style a complete multi-section homepage. Include good spacing, readable typography, at least one Flexbox layout, at least one Grid section, basic responsive adjustments, and styled buttons or forms. Final checkpoint: can take plain HTML and make it look organized, readable, and intentional. Daily study method: 45 to 90 minutes, first half learning and second half building, then end by changing one thing independently. Rule: do not just tweak random values until it works; try to understand why the layout changed. --- **2026-03-19 11:00:26 UTC | Created via MCP**

Datalab Project Offer and Pipeline Summary

datalab_project_offer_and_pipeline_2026_03_20 general

Project reviewed on 2026-03-20 by connecting to host `datalab` (192.168.4.111) as `svc-admin`. Current technical state: - Primary project path: `~/pipelines` - Secondary/simple test path: `~/pipelines_v2` - Main script: `~/pipelines/scripts/process_reports.py` - Inputs: - `~/pipelines/inputs/sales_export.csv` - `~/pipelines/inputs/refunds_export.csv` - Output: - `~/pipelines/outputs/consolidated_report.csv` - Logs: - `~/pipelines/logs/pipeline.log` - `~/pipelines/logs/cron.log` - Automation: - cron runs daily at 06:00 - crontab entry: `0 6 * * * /home/svc-admin/pipelines/scripts/process_reports.py >> /home/svc-admin/pipelines/logs/cron.log 2>&1` - Quarantine path for bad inputs: - `~/pipelines/processed/quarantine` What the current pipeline does: - Reads two CSV exports with different schemas: - sales export uses columns like `order_id`, `order_date`, `customer`, `amount_usd` - refunds export uses columns like `refund_id`, `ref_date`, `customer_name`, `refund_amount` - Validates required columns for sales input. - Normalizes multiple date formats into `YYYY-MM-DD`. - Maps both inputs into one normalized schema: - `type` - `date` - `customer` - `amount` - Converts refunds into negative amounts. - Writes a consolidated CSV report. - Logs success/failure. - On schema or processing failure, moves input files into quarantine. Plain-English explanation: - This project is a small business data-cleaning / reconciliation pipeline. - It is not web hosting and not an AI product in its current form. - It takes two ugly recurring exports from different systems and turns them into one clean report. Business interpretation / marketable angle: - Best framing: `recurring CSV reconciliation and reporting automation for small businesses` - Sell the outcome, not the script. - Core promise: - `Send me exports from system A and system B, and you get one clean report every day or week without manual spreadsheet work.` Ideal first customers: - small e-commerce sellers - bookkeeping/accounting-adjacent small businesses - operations-heavy small businesses - owner-operators or office managers manually reconciling exports in Excel - any small team reconciling sales, refunds, orders, payments, or inventory across two systems Recommended first offer: - A narrow service, not a platform. - One workflow only: - 2 recurring input files - 1 scheduled output report - fixed schema mapping - logging and bad-file handling - Best initial positioning: - `I automate recurring spreadsheet/report cleanup between two systems so you stop manually reconciling exports.` Suggested pricing discussed: - Fast-first-money path: - one-time setup: `$100-$300` - optional monthly support: `$25-$100` - More standard productized service path: - setup fee: `$500-$1,500` - monthly support/hosting: `$149-$399/month` - Higher-value business-critical workflows could justify more later. Practical timeline discussed: - 1-2 weeks to make the existing demo into a presentable micro-offer. - 2-6 weeks to land a first small paying customer if actively pitched. - First `$100` is plausible sooner if an existing contact has spreadsheet pain. Strategic conclusion: - `datalab` should be treated as the seed of a productized back-office data automation service. - Do not position it as generic web hosting. - Do not wait to build a full SaaS before selling. - The fastest path is to sell one narrow recurring automation outcome to one real customer, then iterate. --- **2026-03-20 04:06:00 UTC | AI Update via MCP** --- **2026-03-20 16:44:55 UTC | Created via MCP**

19 Frontend Practice Projects For Full Stack Learning

frontend_19_project_list_2026_03_24 learning

Frontend practice project list captured on 2026-03-24 from the user's referenced course transcript. This memory is tied to the user's HTML/frontend progression and broader full stack learning path so it can be reused later when planning study sessions, project order, or hands-on builds. Purpose: - provide a concrete list of beginner-to-intermediate frontend projects - connect HTML/CSS/JavaScript practice to the user's existing full stack learning memories - serve as a project pool for Phase 1 and Phase 2 of the user's learning path Project list: 1. Quiz Game - Files: `index.html`, `style.css`, `script.js` - Skills: DOM manipulation, arrays/objects, scoring logic, conditional rendering, progress bar, restart flow - Good fit for: early JavaScript DOM practice 2. Color Palette Generator - Files: `index.html`, `style.css`, `script.js` - Skills: random value generation, hex colors, DOM updates, copy-to-clipboard - Good fit for: JavaScript basics plus UI updates 3. Kanban Board - Files: `index.html`, `style.css`, `script.js` - Skills: drag and drop, event handling, card movement, list reordering - Special: HTML Drag and Drop API - Good fit for: intermediate DOM/event work 4. Expense Tracker - Files: `index.html`, `style.css`, `script.js` - Skills: form handling, totals, conditional math, transaction rendering, deletion - Special: `localStorage` - Good fit for: JavaScript state and persistence 5. Bookmark Saver - Files: `index.html`, `style.css`, `script.js` - Skills: form input, rendering saved links, deletion, validation - Special: `localStorage` - Good fit for: beginner app state and persistence 6. Registration Form Validator - Files: `index.html`, `style.css`, `script.js` - Skills: validation, error messages, password checks, email patterns - Good fit for: forms and user input validation 7. Password Generator - Files: `index.html`, `style.css`, `script.js` - Skills: string building, randomization, toggles/options, copy-to-clipboard, strength logic - Good fit for: JavaScript logic and UI controls 8. To-Do App - Files: `index.html`, `style.css`, `script.js` - Skills: CRUD-style task updates, filters, completion state, persistence - Special: `localStorage` - Good fit for: foundational interactive app work 9. Contact Form UI - Files: `index.html`, `style.css` - Skills: semantic form structure, layout, visual polish, icon usage - Good fit for: HTML/CSS layout practice before heavy JavaScript 10. Pricing Cards - Files: `index.html`, `style.css` - Skills: card layout, visual hierarchy, comparison sections, CTA design - Good fit for: Flexbox/Grid and reusable styling 11. Team Members Showcase - Files: `index.html`, `style.css` - Skills: responsive cards, image layout, social links, section structure - Good fit for: semantic layout and responsive design practice 12. Recipe Finder - Files: `index.html`, `style.css`, `script.js` - Skills: search input, fetch, API integration, conditional rendering, detail views - Special: TheMealDB API - Good fit for: fetch/API practice in Phase 2 13. Currency Converter - Files: `index.html`, `style.css`, `script.js` - Skills: form handling, select options, fetch, API data usage, calculation, display formatting - Special: exchange rate API - Good fit for: API-driven UI practice 14. GitHub User Finder - Files: `index.html`, `style.css`, `script.js` - Skills: search, fetch, async flow, rendering profile data, rendering repos, error states - Special: GitHub API - Good fit for: API practice and multi-request UI flows 15. Custom 404 Page - Files: `index.html`, `style.css` - Skills: single-page layout, illustration usage, CTA, typography, responsiveness - Good fit for: simple HTML/CSS practice and polish 16. Newsletter Signup UI - Files: `index.html`, `style.css` - Skills: form markup, layout, CTA design, supporting content blocks - Good fit for: semantic structure and styled forms 17. Coming Soon UI - Files: `index.html`, `style.css`, `script.js` - Skills: countdown timer, DOM updates, simple animation, success messages, social links - Good fit for: combining layout and light interaction 18. QR Code Generator - Mentioned in course intro - Likely skills: form input, output generation, API/library integration, image rendering - Good fit for: small API/library-powered utility app 19. OTP Input Fields - Mentioned in course intro - Likely skills: multi-input focus handling, keyboard events, paste handling, validation - Good fit for: focused DOM event practice How this ties to the user's learning memories: - Supports `html_learning_progress_2026_03_19_session_1` and `html_learning_progress_2026_03_24_session_2` by giving concrete build targets after basic HTML practice. - Fits `html_day_by_day_study_plan_2026_03_19` after semantic HTML and forms are more comfortable. - Fits `html_only_learning_plan_2026_03_19` as a transition from pure HTML pages into styled, interactive frontend work. - Fits `full_stack_phase_1_4_week_plan_2026_03_19` especially Week 2 through Week 4: layout, responsive design, and basic JavaScript DOM. - Fits `full_stack_learning_path_2026_03_19` as Phase 1 and Phase 2 project material before backend work. - Use `full_stack_dev_files_location_2026_03_24` as the default Windows working path for these projects: `C:\Users\Jason\dev` Suggested use in the learning path: - Start with HTML/CSS-first builds: Contact Form UI, Pricing Cards, Team Members Showcase, Custom 404 Page, Newsletter Signup UI - Then move to light JavaScript: Color Palette Generator, Coming Soon UI, Quiz Game, Password Generator - Then move to CRUD/state/persistence: To-Do App, Bookmark Saver, Expense Tracker - Then move to stronger event/UI logic: OTP Input Fields, Kanban Board - Then move to API projects: Recipe Finder, Currency Converter, GitHub User Finder, QR Code Generator Important note: - In the pasted transcript, 17 projects were directly visible in later build sections. - `QR Code Generator` and `OTP Input Fields` were explicitly named in the intro and included here so the remembered set totals 19 projects. --- 2026-03-24 UTC | Stored via Codex

Full Stack Dev Files Location

full_stack_dev_files_location_2026_03_24 general

Full stack development working files location recorded on 2026-03-24. Primary Windows path for the user's full stack development files: - `C:\Users\Jason\dev` Intended use: - this is the main local path for hands-on full stack learning work - use it as the default reference path when discussing or resuming the user's full stack practice projects unless a more specific project path is given later Relationship to existing learning context: - tied to the user's full stack learning plan and HTML learning progression - relevant when resuming HTML, CSS, JavaScript, or broader full stack exercises on the user's Windows workstation --- **2026-03-24 UTC | Stored via Codex**

Full Stack Learning Path

full_stack_learning_path_2026_03_19 general

Full stack learning path discussed on 2026-03-19. Use Angela Yu's course as the backbone, but focus effort on the highest-value sections and build real projects alongside it. Phase 1: Frontend Foundations Focus on HTML, CSS, Flexbox, Grid, responsive design, basic Bootstrap, and basic JavaScript. Goal: build pages that actually look decent, understand structure and layout, and stop being afraid of JavaScript basics. Projects: personal homepage, product landing page, simple interactive page with buttons, forms, and DOM changes. Checkpoint: can build a webpage from scratch without following line by line. Phase 2: JavaScript Core Focus on variables, arrays, objects, functions, loops, conditionals, DOM, events, async basics, and fetch/APIs. Goal: understand how interactive web pages actually work. Projects: vanilla JS to-do list, weather app using API, quiz app, calculator or habit tracker. Checkpoint: can read and write medium-sized JavaScript without feeling lost. Phase 3: Git, Terminal, and Workflow Focus on terminal basics, files and folders, git init/add/commit/status/log, and GitHub push/pull basics. Goal: stop treating tooling like magic. Projects: put practice projects into GitHub, make at least one repo per project, write simple README files. Checkpoint: can create a repo, commit changes, and push them without confusion. Phase 4: Backend Focus on Node.js, Express, routes, request/response, middleware, APIs, error handling, and environment variables. Goal: understand how the server side works. Projects: notes API, task API, contact form backend, simple journaling backend. Checkpoint: can build a backend that accepts, stores, and returns data. Phase 5: Database Focus on SQL, PostgreSQL, tables, relationships, joins, inserts/updates/deletes, and connecting backend to database. Goal: stop building apps that lose all their data on refresh. Projects: task tracker with PostgreSQL, habit tracker with user accounts, notes app with categories/tags. Checkpoint: can design a simple database schema and connect it to an app. Phase 6: Auth Focus on sessions, cookies, login/signup, password hashing, protected routes, auth vs authorization. Goal: build an app that feels like a real app, not just a demo. Projects: auth-based dashboard app, user-specific tasks or notes app. Checkpoint: users can sign up, log in, and only see their own data. Phase 7: React Focus on components, props, state, effects, forms, conditional rendering, API calls, and simple app structure. Goal: build a proper frontend for your backend apps. Projects: React frontend for task app, React dashboard for notes app, project tracker app. Checkpoint: can build a React frontend that talks to a backend API. Phase 8: Deployment Focus on environment variables, production vs dev, deploying frontend, deploying backend, database hosting basics, and domain/DNS basics later. Goal: get work online. Projects: deploy one full stack app, then deploy a second cleaner version later. Checkpoint: can send someone a link to something built. What to skip or downplay: jQuery, deep Web3/DApp material, legacy-feeling sections that do not help the actual goal, and polishing tutorial projects forever. Best project path in order: personal site, vanilla JS to-do app, weather/API app, Express notes API, PostgreSQL-backed task app, auth-based dashboard, React frontend for the dashboard, deploy the full stack version. Weekly study flow: 3-4 study sessions per week, 60-90 minutes each, with 2 sessions on course material and 1-2 sessions rebuilding or extending what was learned. Each week should include watching, coding, rebuilding from memory, and one small independent change. Progress indicators: can build without pausing every two minutes, can debug own mistakes, can explain what code is doing, and can change a project beyond the tutorial. Main instruction: do not aim to finish the whole course fast. Aim to finish the important sections, build real projects alongside them, keep notes on what is actually understood, and ask for help when blocked instead of silently spiraling. Phase 1 execution plan: The goal of Phase 1 is not just to learn frontend in the abstract, but to get comfortable making pages on purpose. What to learn: HTML structure, semantic elements, headings, paragraphs, links, images, lists, forms, CSS selectors, colors, spacing, borders, typography basics, Flexbox, Grid, responsive layout, basic Bootstrap, and very basic JavaScript for interaction. What to build: personal homepage, product landing page, and a simple interactive page with buttons, form input, and DOM changes. How to work: for each major section, watch the lesson, build along once, rebuild the same thing without following line by line, then change one thing independently. Examples of independent changes: different color palette, different layout, add a section, make it mobile-friendly, or add a button interaction. Weekly rhythm for Phase 1: 3 to 4 sessions per week, 60 to 90 minutes each, with 2 sessions on course content and 1 to 2 sessions rebuilding or improving your own version. Suggested learning order: HTML basics, CSS basics, Flexbox, Grid, responsive design, basic Bootstrap, and basic JavaScript DOM work. Rules: do not copy blindly, do not chase perfection, do not move on if you cannot explain what the code is doing, and do not spend forever polishing one tutorial page. Phase 1 checkpoint: can build a simple webpage from scratch, style it so it does not look broken, make it work on desktop and mobile, and add a little JavaScript interaction without panicking. Best outcome by end of Phase 1: 3 small projects, 1 GitHub repo for each project if possible, and enough comfort with HTML/CSS/JS basics that Phase 2 does not feel impossible. --- **2026-03-19 10:50:13 UTC | AI Update via MCP**

Full Stack Phase 1 4 Week Plan

full_stack_phase_1_4_week_plan_2026_03_19 general

4-week Phase 1 plan for the full stack learning path, created on 2026-03-19. Week 1: HTML + Basic CSS Focus on HTML structure, headings, paragraphs, links, images, lists, semantic layout, basic CSS, colors, spacing, fonts, and borders. Tasks: watch the HTML/CSS sections, build along once, then rebuild a simple page without copying line by line. Project: personal homepage. Goal by end of week: build a simple page with sections like header, about, links, and contact. Checkpoint: can explain what each HTML section is doing and style a page so it does not look broken. Week 2: Layout Focus on Flexbox, Grid, page sections, alignment, spacing, and responsive basics. Tasks: learn how to place content on a page intentionally and practice changing layouts without following the tutorial exactly. Project: product landing page. Goal by end of week: build a page with hero section, features section, call to action, and footer. Checkpoint: understand when to use Flexbox vs Grid and the page still works when the screen gets smaller. Week 3: Responsive Design + Bootstrap Focus on responsive design, media queries if covered, Bootstrap basics, containers, rows, columns, and reusable styling patterns. Tasks: rebuild part of a previous page in a more responsive way and use Bootstrap as a tool, not a crutch. Project: improve the landing page or create a second small responsive page. Goal by end of week: make a page that works reasonably on desktop and mobile. Checkpoint: can adjust layout for smaller screens and are not completely dependent on Bootstrap for layout. Week 4: Basic JavaScript + DOM Focus on buttons, events, input fields, changing text on the page, showing/hiding content, and basic DOM manipulation. Tasks: learn just enough JavaScript to make the page react and do not worry about mastering JavaScript yet. Project: simple interactive page with button clicks, form input, and DOM changes. Example ideas: greeting generator, mini form preview, simple tracker, or button-based interactive widget. Goal by end of week: connect HTML, CSS, and JavaScript together without panicking. Checkpoint: can make the page respond to user actions and can read simple JavaScript and know what it is changing on the page. Rules for all 4 weeks: 3 to 4 sessions per week, 60 to 90 minutes each, with 2 sessions learning and 1 to 2 sessions rebuilding or changing things independently. Every week should include four actions: watch, build, rebuild from memory, and change one thing on your own. End of Phase 1 success state: personal homepage, landing page, small interactive page, basic comfort with HTML/CSS/JS, and less fear about opening a blank file and starting. --- **2026-03-19 10:53:07 UTC | Created via MCP**

Gitea AI CLI Access Credentials

gitea-ai-cli-access general

ai-cli-api-key Repository and Organization Access: All (public, private, and limited) Permissions: all Added on Apr 12, 2026 — No recent activity - API key=2717149467e2013ed54e5ec01b959cbf3d3ca244 - SSH path=ssh://git@gitea.accursedbinkie.com:2222/AccursedBinkie/brain.git - SSH identity= ~/.ssh/id_ed25519_homelab

GLPI Bot Runner Implementation

glpi_bot_runner_implementation_2026_03_22 glpi-task-history

# GLPI Bot Runner Implementation Implemented a v1 GLPI bot workflow on `svc-auto` with a trusted execution backend and live GLPI, docs, and `brain` integration. ## Deployment - Host: `svc-auto` - Path: `/home/svc-admin/docker/glpi-bot-runner` - Service: `glpi-bot-runner` - Architecture doc: `docs/architecture/glpi-bot-runner.md` ## What it does - Authenticates to GLPI v2 with OAuth password grant - Polls GLPI tickets in category `Automation > Homelab Bot` - Requires `homelab-bot` assignment via TeamMember role `assigned` - Posts a claimed follow-up - Moves the ticket to `In Progress` - Parses a structured YAML bot spec from the ticket body - Executes only trusted built-in actions - Writes a Markdown artifact into the homelab documentation repo - Stores a matching memory in `brain` - Posts a professional completion-style follow-up - Moves the ticket to `Solved` on success or `Pending` on blocked/invalid work ## Current execution mode `EXECUTOR_MODE=trusted_dispatch` Supported actions now verified in live tickets: - `http_check` - `docker_compose_remote` - `path_exists` - `docker_container_check` - `systemd_remote` The dispatcher does not execute arbitrary shell from ticket text. `docker_compose_remote` is constrained by host, path, and action allowlists. ## Files - `/home/svc-admin/docker/glpi-bot-runner/docker-compose.yml` - `/home/svc-admin/docker/glpi-bot-runner/.env` - `/home/svc-admin/docker/glpi-bot-runner/app/` - `/home/svc-admin/docker/glpi-bot-runner/templates/` - `/home/svc-admin/homelab-doc-sync/homelab-documentation-system/docs/architecture/glpi-bot-runner.md` ## GLPI structure created - Category: `Automation` - Category: `Automation > Homelab Bot` - Followup template: `Bot Claimed - Homelab` - Followup template: `Bot Blocked - Homelab` - Followup template: `Bot Completion - Homelab` ## Operational decisions and fixes - GLPI v2 ticket creation needs `category: {id: 2}`; using `itilcategories_id` did not populate the category field the runner filters on. - `GLPI_ALLOWED_STATUS_IDS` was tightened to `1` so already-claimed tickets are not reprocessed every poll cycle. - `path_exists` and `systemd_remote` now force host execution over SSH instead of using the local-container fast path. - `systemd_remote` uses `sudo -n` and `systemctl --no-pager` for unattended execution. - `svc-auto` needed `22/tcp` allowed from the runner bridge subnet `172.23.0.0/16` so SSH-based same-host actions could reach the host from the container. - `svc-auto` also needed its local `~/.ssh/id_ed25519.pub` added to `authorized_keys` so the runner container's mounted key could authenticate back to the host. - `workflow.py` was fixed to import `ExecutionResult` in the exception path. ## Validation Validated live against GLPI and the runner. - Ticket `3` proved the lifecycle and artifact path in pending mode. - Ticket `4` proved invalid specs are blocked cleanly. - Ticket `5` executed a real `http_check` action, posted follow-ups, wrote `docs/generated/glpi-bot/ticket-5.md`, stored a `brain` memory, and moved to `Solved`. - Ticket `11` executed `docker_compose_remote` successfully and moved to `Solved`. - Ticket `12` executed `path_exists` successfully and moved to `Solved`. - Ticket `14` executed `docker_container_check` successfully and moved to `Solved`. - Ticket `15` executed `systemd_remote` successfully and moved to `Solved`. - Ticket `13` captured the pre-fix `systemd_remote` failure mode and remains a useful artifact for the earlier local-container/SSH routing issue. ## Next step Add more trusted actions as explicit handlers rather than allowing arbitrary command execution from ticket text.

GLPI Ticket 10: GLPI bot docker compose cli solved test

glpi_ticket_10_glpi_bot_docker_compose_cli_solved_test_2026_03_22 glpi-task-history

# GLPI Ticket 10 Execution Record Title: GLPI bot docker compose cli solved test Outcome: pending Category: Homelab Bot Entity: Root Entity Documentation: docs/generated/glpi-bot/ticket-10.md ## Summary docker_compose_remote failed for 192.168.4.120:/home/svc-admin/docker/glpi-bot-runner. ## Work Performed - Attempted remote docker compose action `ps` on 192.168.4.120:/home/svc-admin/docker/glpi-bot-runner. ## Validation - Remote command exited with code 127. ## Follow-up Items - Review stderr and remote host state before retrying. ## Ticket Content ```yaml bot: action: docker_compose_remote host: 192.168.4.120 compose_dir: /home/svc-admin/docker/glpi-bot-runner command: ps ``` ## Commands - `bash -lc 'cd /home/svc-admin/docker/glpi-bot-runner && docker compose ps'`

GLPI Ticket 11: GLPI bot docker compose mounted-cli solved test

glpi_ticket_11_glpi_bot_docker_compose_mounted_cli_solved_test_2026_03_22 glpi-task-history

# GLPI Ticket 11 Execution Record Title: GLPI bot docker compose mounted-cli solved test Outcome: solved Category: Homelab Bot Entity: Root Entity Documentation: docs/generated/glpi-bot/ticket-11.md ## Summary docker_compose_remote completed `ps` on 192.168.4.120:/home/svc-admin/docker/glpi-bot-runner. ## Work Performed - Ran docker compose `ps` on 192.168.4.120:/home/svc-admin/docker/glpi-bot-runner. ## Validation - Remote command exited with code 0. ## Follow-up Items - None. ## Ticket Content ```yaml bot: action: docker_compose_remote host: 192.168.4.120 compose_dir: /home/svc-admin/docker/glpi-bot-runner command: ps ``` ## Commands - `bash -lc 'cd /home/svc-admin/docker/glpi-bot-runner && docker compose ps'`

GLPI Ticket 12: GLPI bot path_exists solved test

glpi_ticket_12_glpi_bot_path_exists_solved_test_2026_03_22 glpi-task-history

# GLPI Ticket 12 Execution Record Title: GLPI bot path_exists solved test Outcome: solved Category: Homelab Bot Entity: Root Entity Documentation: docs/generated/glpi-bot/ticket-12.md ## Summary path_exists confirmed dir existence for 192.168.4.120:/home/svc-admin/docker/glpi-bot-runner. ## Work Performed - Checked for dir existence on 192.168.4.120:/home/svc-admin/docker/glpi-bot-runner. ## Validation - Existence check exited with code 0. ## Follow-up Items - None. ## Ticket Content ```yaml bot: action: path_exists host: 192.168.4.120 path: /home/svc-admin/docker/glpi-bot-runner expect_type: dir ``` ## Commands - `bash -lc 'test -d /home/svc-admin/docker/glpi-bot-runner'`

GLPI Ticket 13: GLPI bot systemd_remote solved test

glpi_ticket_13_glpi_bot_systemd_remote_solved_test_2026_03_22 glpi-task-history

# GLPI Ticket 13 Execution Record Title: GLPI bot systemd_remote solved test Outcome: pending Category: Homelab Bot Entity: Root Entity Documentation: docs/generated/glpi-bot/ticket-13.md ## Summary systemd_remote `status` failed for docker on 192.168.4.120. ## Work Performed - Attempted systemctl status for docker on 192.168.4.120. ## Validation - systemctl exited with code 127. ## Follow-up Items - Review stderr and service state before retrying. ## Ticket Content ```yaml bot: action: systemd_remote host: 192.168.4.120 service: docker command: status ``` ## Commands - `bash -lc 'sudo systemctl status docker'`

GLPI Ticket 14: GLPI bot docker_container_check solved test

glpi_ticket_14_glpi_bot_docker_container_check_solved_test_2026_03_22 glpi-task-history

# GLPI Ticket 14 Execution Record Title: GLPI bot docker_container_check solved test Outcome: solved Category: Homelab Bot Entity: Root Entity Documentation: docs/generated/glpi-bot/ticket-14.md ## Summary docker_container_check confirmed glpi-bot-runner is running on 192.168.4.120. ## Work Performed - Inspected Docker container glpi-bot-runner on 192.168.4.120. ## Validation - Container status matched expected value: running. ## Follow-up Items - None. ## Ticket Content ```yaml bot: action: docker_container_check host: 192.168.4.120 container: glpi-bot-runner expect_status: running ``` ## Commands - `bash -lc 'docker inspect -f '"'"'{{.State.Status}}'"'"' glpi-bot-runner'`

GLPI Ticket 15: GLPI bot systemd_remote ssh solved test

glpi_ticket_15_glpi_bot_systemd_remote_ssh_solved_test_2026_03_22 glpi-task-history

# GLPI Ticket 15 Execution Record Title: GLPI bot systemd_remote ssh solved test Outcome: solved Category: Homelab Bot Entity: Root Entity Documentation: docs/generated/glpi-bot/ticket-15.md ## Summary systemd_remote `status` completed for docker on 192.168.4.120. ## Work Performed - Ran systemctl status for docker on 192.168.4.120. ## Validation - systemctl exited with code 0. ## Follow-up Items - None. ## Ticket Content ```yaml bot: action: systemd_remote host: 192.168.4.120 service: docker command: status ``` ## Commands - `ssh -i /root/.ssh/id_ed25519 -o BatchMode=yes -o StrictHostKeyChecking=accept-new svc-admin@192.168.4.120 'sudo systemctl status docker'`

GLPI Ticket 3: GLPI bot runner end-to-end test

glpi_ticket_3_glpi_bot_runner_end_to_end_test_2026_03_22 glpi-task-history

# GLPI Ticket 3 Execution Record Title: GLPI bot runner end-to-end test Outcome: pending Category: Homelab Bot Entity: Root Entity Documentation: docs/generated/glpi-bot/ticket-3.md ## Summary Ticket was claimed and prepared for bot handling, but no automatic execution backend is configured yet. ## Work Performed - Validated that the ticket meets the Homelab Bot eligibility rules. - Claimed the ticket in GLPI and moved it into the bot workflow. - Prepared a documentation artifact and a brain memory record for operator review. ## Validation - GLPI OAuth v2 authentication succeeded for homelab-bot. - Ticket category and assignment were validated before processing. ## Follow-up Items - Review the requested work and perform or approve the implementation path. - Replace the default executor mode with a real execution backend when ready. ## Ticket Content Temporary test ticket for validating the bot runner.

GLPI Ticket 4: GLPI bot trusted dispatcher test

glpi_ticket_4_glpi_bot_trusted_dispatcher_test_2026_03_22 glpi-task-history

# GLPI Ticket 4 Execution Record Title: GLPI bot trusted dispatcher test Outcome: pending Category: Homelab Bot Entity: Root Entity Documentation: docs/generated/glpi-bot/ticket-4.md ## Summary Ticket could not be executed because no valid bot spec was found. ## Work Performed ## Validation - None recorded. ## Follow-up Items - Add a fenced yaml block with bot.action and required parameters. - Use one of the trusted actions documented in the runner README. ## Ticket Content ```yaml bot: action: http_check url: http://192.168.4.114:3010 expect_status: 200 ``` ## Commands - None recorded.

GLPI Ticket 5: GLPI bot trusted dispatcher solved test

glpi_ticket_5_glpi_bot_trusted_dispatcher_solved_test_2026_03_22 glpi-task-history

# GLPI Ticket 5 Execution Record Title: GLPI bot trusted dispatcher solved test Outcome: solved Category: Homelab Bot Entity: Root Entity Documentation: docs/generated/glpi-bot/ticket-5.md ## Summary HTTP check for http://192.168.4.114:3010 completed successfully. ## Work Performed - Issued an HTTP request to http://192.168.4.114:3010. ## Validation - GET http://192.168.4.114:3010 returned HTTP 200. ## Follow-up Items - None. ## Ticket Content ```yaml bot: action: http_check url: http://192.168.4.114:3010 expect_status: 200 ``` ## Commands - `GET http://192.168.4.114:3010`

GLPI Ticket 6: GLPI bot docker compose remote solved test

glpi_ticket_6_glpi_bot_docker_compose_remote_solved_test_2026_03_22 glpi-task-history

# GLPI Ticket 6 Execution Record Title: GLPI bot docker compose remote solved test Outcome: pending Category: Homelab Bot Entity: Root Entity Documentation: docs/generated/glpi-bot/ticket-6.md ## Summary docker_compose_remote failed for 192.168.4.120:/home/svc-admin/docker/semaphore. ## Work Performed - Attempted remote docker compose action `ps` on 192.168.4.120:/home/svc-admin/docker/semaphore. ## Validation - Remote command exited with code 1. ## Follow-up Items - Review stderr and remote host state before retrying. ## Ticket Content ```yaml bot: action: docker_compose_remote host: 192.168.4.120 compose_dir: /home/svc-admin/docker/semaphore command: ps ``` ## Commands - `bash -lc 'cd /home/svc-admin/docker/semaphore && docker compose ps'`

GLPI Ticket 7: GLPI bot docker compose local fast-path test

glpi_ticket_7_glpi_bot_docker_compose_local_fast_path_test_2026_03_22 glpi-task-history

# GLPI Ticket 7 Execution Record Title: GLPI bot docker compose local fast-path test Outcome: pending Category: Homelab Bot Entity: Root Entity Documentation: docs/generated/glpi-bot/ticket-7.md ## Summary docker_compose_remote failed for 192.168.4.120:/home/svc-admin/docker/semaphore. ## Work Performed - Attempted remote docker compose action `ps` on 192.168.4.120:/home/svc-admin/docker/semaphore. ## Validation - Remote command exited with code 1. ## Follow-up Items - Review stderr and remote host state before retrying. ## Ticket Content ```yaml bot: action: docker_compose_remote host: 192.168.4.120 compose_dir: /home/svc-admin/docker/semaphore command: ps ``` ## Commands - `bash -lc 'cd /home/svc-admin/docker/semaphore && docker compose ps'`

GLPI Ticket 8: GLPI bot docker compose solved test

glpi_ticket_8_glpi_bot_docker_compose_solved_test_2026_03_22 glpi-task-history

# GLPI Ticket 8 Execution Record Title: GLPI bot docker compose solved test Outcome: pending Category: Homelab Bot Entity: Root Entity Documentation: docs/generated/glpi-bot/ticket-8.md ## Summary docker_compose_remote failed for 192.168.4.120:/home/svc-admin/docker/glpi-bot-runner. ## Work Performed - Attempted remote docker compose action `ps` on 192.168.4.120:/home/svc-admin/docker/glpi-bot-runner. ## Validation - Remote command exited with code 1. ## Follow-up Items - Review stderr and remote host state before retrying. ## Ticket Content ```yaml bot: action: docker_compose_remote host: 192.168.4.120 compose_dir: /home/svc-admin/docker/glpi-bot-runner command: ps ``` ## Commands - `bash -lc 'cd /home/svc-admin/docker/glpi-bot-runner && docker compose ps'`

GLPI Ticket 9: GLPI bot docker compose final solved test

glpi_ticket_9_glpi_bot_docker_compose_final_solved_test_2026_03_22 glpi-task-history

# GLPI Ticket 9 Execution Record Title: GLPI bot docker compose final solved test Outcome: pending Category: Homelab Bot Entity: Root Entity Documentation: docs/generated/glpi-bot/ticket-9.md ## Summary docker_compose_remote failed for 192.168.4.120:/home/svc-admin/docker/glpi-bot-runner. ## Work Performed - Attempted remote docker compose action `ps` on 192.168.4.120:/home/svc-admin/docker/glpi-bot-runner. ## Validation - Remote command exited with code 127. ## Follow-up Items - Review stderr and remote host state before retrying. ## Ticket Content ```yaml bot: action: docker_compose_remote host: 192.168.4.120 compose_dir: /home/svc-admin/docker/glpi-bot-runner command: ps ``` ## Commands - `bash -lc 'cd /home/svc-admin/docker/glpi-bot-runner && docker compose ps'`

HAIP Handoff — Claude + OpenCode Provider Integration and Arena Next Steps

haip_claude_opencode_handoff_2026_04_05 shared:platform

Status date: 2026-04-05 UTC Purpose This handoff captures the live state of the Homelab AI Platform after adding Anthropic Claude Code CLI and OpenCode as HAIP providers, verifying the orchestrator health path against the real Brain MCP, and exposing the new provider-backed agents in the Web Control UI. It is intended to let Claude or Gemini continue the next phase without having to rediscover runtime contracts, DB state, or operational caveats. High-level state The HAIP project itself is complete through WS8. The current follow-on work is operational/runtime enhancement, specifically broadening provider coverage and enabling Arena to support a mixed boardroom that can include Codex, Gemini, Claude, and optionally a local-model-backed OpenCode participant. Current live platform shape - Web Control UI is deployed on svc-apps at http://192.168.4.114:3010 - HAIP orchestrator health service is running under systemd on svc-ai at http://127.0.0.1:8080/health/ready - Live PostgreSQL is on svc-db-01 at 192.168.4.113 - Brain MCP is the canonical memory system and is healthy - ContextKeep is being phased out and is intentionally shut down - HAIP IRC service is still staged; the incumbent irc-openclaw-bridge remains the active production IRC bridge to avoid nick collisions Critical architectural decisions already made 1. Brain, not ContextKeep, is the authoritative memory dependency. The HAIP health service and runtime memory client were already corrected earlier to treat Brain MCP as the real dependency. The old ContextKeep REST-style assumption was removed from the health path. 2. Claude was added as a first-class provider family in HAIP. Claude is no longer a future idea or a manual external tool. It is wired into the HAIP adapter registry and is already used in the live Arena profile. 3. OpenCode was added as a first-class provider family in HAIP. OpenCode is now usable by HAIP as a local-model-capable provider, but it has not yet been assigned a specific project role beyond a generic available agent. 4. Arena was not auto-expanded to four participants yet. Arena remains a 3-role room set by default: - builder - skeptic - synthesizer OpenCode was added as an available agent but not forced into the Arena rosters yet. This was intentional to avoid changing the existing profile semantics until a deliberate role decision is made. Live provider state Provider families currently present in the live DB: - codex_cli - gemini_cli - claude_cli - opencode_cli Live generic provider-backed agents now present: - codex - gemini - claude - opencode Arena provider mix currently live - arena-builder -> codex_cli - arena-skeptic -> gemini_cli - arena-synthesizer -> claude_cli That means Arena already supports the exact three frontier participants the user wanted for collaborative discussion: - Codex - Gemini - Claude OpenCode is available in the system but not yet part of Arena room rosters. Files changed for Claude integration These were implemented before this handoff and are now live: - /home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/adapters/claude.py - /home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/adapters/__init__.py - /home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/adapters/registry.py - /home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/runtime/config.py - /home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/ops/health_service.py - /home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/irc/gateway.py - /home/svc-admin/ai-platform/config/environments/local.env Claude adapter contract that was validated - binary: /home/svc-admin/.nvm/versions/node/v24.14.0/bin/claude - health command: claude auth status - invoke command: claude -p --output-format json <message> - resume command: claude -p --output-format json --resume <session_id> <message> - stable fields observed: - result - session_id - auth status observed live: - loggedIn: true - authMethod: claude.ai - subscriptionType: pro - email: JasonHall@accursedbinkie.com Files changed for OpenCode integration These are now live: - /home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/adapters/opencode.py - /home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/adapters/__init__.py - /home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/adapters/registry.py - /home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/runtime/config.py - /home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/ops/health_service.py - /home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/irc/gateway.py - /home/svc-admin/ai-platform/config/environments/local.env OpenCode adapter contract that was validated Binary and environment - binary: /home/svc-admin/.nvm/versions/node/v24.14.0/bin/opencode - env var added: OPENCODE_CLI_BIN=/home/svc-admin/.nvm/versions/node/v24.14.0/bin/opencode Important CLI characteristics - opencode --help works - non-interactive run path exists: opencode run - JSON event output exists: opencode run --format json - session continuation exists: opencode run --session <sessionID> - model list works: opencode models Observed JSON event shape from opencode run --format json Representative fields seen live: - type: step_start | text | step_finish - sessionID: ses_... - part.text for text responses Session continuity was explicitly verified A two-step session worked: 1. prompt: Remember BANANA and reply only READY. 2. resume same session and ask what word was remembered 3. output correctly returned BANANA OpenCode provider health strategy The HAIP health adapter uses: - opencode models This is treated as sufficient readiness proof because it demonstrates the binary is working and at least one model source is available. Observed live OpenCode model list at time of validation - opencode/big-pickle - opencode/gpt-5-nano - opencode/minimax-m2.5-free - opencode/nemotron-3-super-free - opencode/qwen3.6-plus-free OpenCode capabilities encoded in HAIP - supports_sessions: true - supports_streaming: false - supports_tools: true - requires_separate_state_paths: false - concurrency_limit: 2 - session_ref_field: sessionID Current OpenCode-backed live agent The following generic agent was inserted/upserted into the live database: - agent_key: opencode - display_name: OpenCode - nick: opencode - provider family: opencode_cli - runtime_adapter_key: opencode_cli_default - private_memory_namespace: agent:opencode - persona: default - enabled: true Verification completed successfully 1. HAIP code compiled - python3 -m compileall /home/svc-admin/ai-projects/projects/homelab_ai_platform/haip 2. Orchestrator restarted successfully - systemctl --user restart haip-orchestrator.service - systemctl --user is-active haip-orchestrator.service -> active 3. Health endpoint verified - curl -sS http://127.0.0.1:8080/health/detail Observed ready state includes: - codex_cli ready - claude_cli ready - gemini_cli ready - opencode_cli ready 4. Direct adapter invocation verified - OpenCode adapter invoke returned READY and a valid provider session ref 5. Live DB provider family verified - provider_families includes opencode_cli as id 31 6. Live DB agent verified - agents table contains opencode bound to provider_family_id 31 with runtime_adapter_key opencode_cli_default 7. Web Control UI verified - http://192.168.4.114:3010/agents contains: - Claude - Codex - Gemini - OpenCode - Anthropic Claude Code CLI - OpenCode CLI Database connection details that actually worked Important correction: the live DB credentials are not the older haip_app / haip database pair used in some early exploratory assumptions. The working live values are: - host: 192.168.4.113 - port: 5432 - database: homelab_ai_platform - user: platform_runtime - password file: /home/svc-admin/ai-platform/secrets/platform_db_password This matches: - /home/svc-admin/ai-platform/config/environments/local.env Arena / IRC operational caveat The DB and UI state are real, but HAIP IRC itself is still not the active production bridge. The existing irc-openclaw-bridge remains in control to avoid nick collisions. That means: - provider and roster changes are visible in DB and UI - orchestrator/runtime support is real - but new per-agent IRC behavior may not appear on the live network until the HAIP IRC cutover is intentionally performed If another agent tries to validate Arena via IRC directly and sees inconsistent behavior, this operational state is the first thing to check. Recommended next steps These are the most sensible continuation options. Option 1: Add OpenCode into Arena as a fourth participant This is appropriate if the user wants a local-model voice inside boardroom discussions. Recommended approach: - do not replace existing Arena roles automatically - add OpenCode as a fourth explicitly named participant - likely role choices: - arena-implementer-local - arena-operator-local - arena-workhorse Rationale: - preserves the existing builder/skeptic/synthesizer semantics - makes local-vs-frontier tradeoffs explicit - avoids muddying the meaning of the current roles If doing this, the work likely includes: - create a dedicated persona with a clear operator/implementation voice - create a new agent row using opencode_cli - add it to arena-boardroom, arena-debate, and/or arena-synthesis as appropriate - decide weighting so it does not dominate synthesis by accident Option 2: Keep Arena as-is and use OpenCode in separate implementation rooms This is probably the cleanest operational design. Recommended pattern: - Arena rooms remain decision rooms - OpenCode participates in implementation-oriented rooms only Examples: - local-lab - worker-room - implementation-bench Rationale: - frontier models handle planning/debate/synthesis - local model handles routine code drafting and operational churn - this aligns with the user’s cost-control goal without polluting decision spaces Option 3: Add project-specific OpenCode agents rather than one generic OpenCode agent This is higher quality if the user wants consistency and strong project voices. Examples: - genealogy-local-worker - ohio-local-drafter - arena-local-implementer Each would reuse opencode_cli but have a distinct persona and memory namespace. What I would recommend to continue Best path for consistency and accuracy: 1. Leave Arena’s current 3-role structure intact. 2. Keep codex, gemini, and claude as the main Arena discussion trio. 3. Add one deliberate OpenCode-backed implementation role either: - as a fourth participant with clear local-operator semantics, or - in a separate implementation room. 4. Avoid silently changing the meaning of builder/skeptic/synthesizer. Why this recommendation is strongest - preserves established project profile semantics - maintains clean differentiation between planning voices and local execution voices - reduces risk of confusing future operators in the UI - gives the user the cost-saving local-model path they want without making Arena noisy or redundant Useful commands and checks for the next agent Health - curl -sS http://127.0.0.1:8080/health/detail | python3 -m json.tool UI - curl -sS http://192.168.4.114:3010/agents | rg -n "OpenCode|Claude|Codex|Gemini" DB provider families - psql "host=192.168.4.113 port=5432 dbname=homelab_ai_platform user=platform_runtime password=$(cat /home/svc-admin/ai-platform/secrets/platform_db_password)" -Atc "select id, provider_key, display_name from provider_families order by id;" DB agents - psql "host=192.168.4.113 port=5432 dbname=homelab_ai_platform user=platform_runtime password=$(cat /home/svc-admin/ai-platform/secrets/platform_db_password)" -Atc "select agent_key, display_name, nick, provider_family_id, runtime_adapter_key from agents order by agent_key;" OpenCode direct CLI checks - opencode models - opencode run --format json "Reply with exactly the word READY." Claude direct CLI checks - claude auth status - claude -p --output-format json "Reply with exactly the word READY." Files most relevant for continuation - /home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/adapters/opencode.py - /home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/adapters/claude.py - /home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/adapters/registry.py - /home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/runtime/config.py - /home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/ops/health_service.py - /home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/irc/gateway.py - /home/svc-admin/ai-platform/config/environments/local.env What has not been done yet - OpenCode has not been assigned a specialized Arena persona - OpenCode has not been added to Arena room rosters - HAIP IRC has not been cut over from the incumbent bridge - No new workstream status memory update was made for this enhancement because it is beyond WS8 scope and was treated as post-project runtime extension work Bottom line for the next agent The hard part is done. - Claude provider integration is live and verified. - OpenCode provider integration is live and verified. - The UI already exposes the new agent. - Arena already has the intended frontier trio. The remaining work is design and product judgment: decide exactly how OpenCode should participate in the collaboration model, then seed the right persona/agent/room memberships without destabilizing the existing Arena semantics.

HAIP Operator Next Steps — Claude + OpenCode

haip_claude_opencode_operator_next_steps_2026_04_05 shared:platform

Status date: 2026-04-05 UTC Current verified state - HAIP orchestrator health is green at http://127.0.0.1:8080/health/ready - Web Control UI is live at http://192.168.4.114:3010 - Provider families live: codex_cli, gemini_cli, claude_cli, opencode_cli - Generic agents live: codex, gemini, claude, opencode - Arena live mix: - arena-builder -> codex_cli - arena-skeptic -> gemini_cli - arena-synthesizer -> claude_cli - OpenCode is available in the system but not added to Arena room rosters yet Most important caveat HAIP IRC is still not the active production IRC bridge. The incumbent irc-openclaw-bridge still owns live IRC behavior, so DB/UI changes do not guarantee live IRC nick behavior until cutover is done. Safest recommendation Leave Arena’s 3-role structure intact and do not silently replace existing roles. If local-model participation is desired, either: 1. add OpenCode as a fourth explicit Arena participant with a dedicated persona, or 2. use OpenCode in a separate implementation-focused room instead of the boardroom Preferred continuation path - keep Codex, Gemini, and Claude as the main discussion trio in Arena - create one deliberate OpenCode-backed implementation role if needed - avoid changing the meaning of builder, skeptic, and synthesizer Useful checks Health curl -sS http://127.0.0.1:8080/health/detail | python3 -m json.tool UI curl -sS http://192.168.4.114:3010/agents | rg -n "OpenCode|Claude|Codex|Gemini" DB provider families psql "host=192.168.4.113 port=5432 dbname=homelab_ai_platform user=platform_runtime password=$(cat /home/svc-admin/ai-platform/secrets/platform_db_password)" -Atc "select id, provider_key, display_name from provider_families order by id;" DB agents psql "host=192.168.4.113 port=5432 dbname=homelab_ai_platform user=platform_runtime password=$(cat /home/svc-admin/ai-platform/secrets/platform_db_password)" -Atc "select agent_key, display_name, nick, provider_family_id, runtime_adapter_key from agents order by agent_key;" OpenCode direct check opencode models opencode run --format json "Reply with exactly the word READY." Claude direct check claude auth status claude -p --output-format json "Reply with exactly the word READY." Files to inspect first - /home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/adapters/opencode.py - /home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/adapters/claude.py - /home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/adapters/registry.py - /home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/ops/health_service.py - /home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/irc/gateway.py - /home/svc-admin/ai-platform/config/environments/local.env Not done yet - no specialized Arena OpenCode persona - no OpenCode Arena roster membership - no HAIP IRC cutover Bottom line Claude and OpenCode are already integrated and verified. The remaining work is role design and room membership strategy, not provider plumbing.

Ergo MOTD still shows irc.accursedbinkie.lan

haip_ergo_motd_hostname_fix general

The Ergo server name was updated to irc.accursedbinkie.com in ircd.yaml and reloaded, but the MOTD still references the old hostname irc.accursedbinkie.lan. Need to find and update the MOTD file. Ergo is running on svc-ai (PID varies), config at /home/svc-admin/ai-projects/projects/irc-openclaw-bridge/config/ergo/ircd.yaml, MOTD file is likely ergo.motd in the same directory or nearby. After editing, send SIGHUP to the Ergo process to reload.

HAIP IRC Cutover And Gateway Fixes

haip_irc_cutover_and_gateway_fixes_2026_04_05 shared:platform

Concise handoff for the HAIP IRC cutover and the follow-up gateway fixes completed on 2026-04-05. What changed - The live IRC bridge on `svc-ai` was cut over from `irc-openclaw-bridge.service` to `haip-irc.service`. - `irc-openclaw-bridge.service` was stopped and disabled. - `haip-irc.service` was started and enabled. - HAIP is now the active production IRC bridge. Important operational state - Ergo IRC server is still the same active IRC server on `svc-ai`. - Hostname path `irc.accursedbinkie.com` is working again through NPM on `svc-dns-01`. - NPM fix required publishing `6667` and `6697` in `/home/svc-admin/docker/nginx-proxy-manager/docker-compose.yml` on `svc-dns-01`. - `svc-dns-01` root filesystem was also extended to use the full resized disk; `/` now has ample free space. HAIP IRC presence behavior change - Original HAIP gateway behavior joined every agent nick to every IRC room. - This was changed so presence is now roster-driven: - `opsbot` joins all managed IRC rooms - each agent nick joins only rooms where it has an active `room_roster_membership` - `!bring` and `!remove` trigger immediate reconcile - Web UI roster changes are picked up by a periodic reconcile loop Files changed for roster-driven IRC presence - `/home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/irc/client.py` - added `PART` support - `/home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/irc/gateway.py` - replaced global join-all behavior with DB-driven presence reconciliation - added periodic presence reconcile loop - updated `!bring` / `!remove` to reconcile immediately Two gateway bugs fixed after cutover 1. Message routing bug - In multi-nick mode, opsbot was still the only listener parsing channel traffic. - But the gateway only routed normal channel traffic when `single_nick_mode` was enabled. - Fix: route all non-DM, non-command channel traffic through the orchestrator from opsbot in both modes. 2. Database write bug - IRC turns were passed into the orchestrator with `trigger_message_ref=None`. - `turn_decisions.trigger_message_ref` is `NOT NULL` in the live DB. - This caused orchestration failures and no replies. - Fix: synthesize a stable non-empty IRC message reference in the gateway using channel, sender nick, and timestamp before calling the orchestrator. Files changed for response restoration - `/home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/irc/gateway.py` - removed the bad `single_nick_mode` routing gate - now generates a synthetic `trigger_message_ref` for IRC turns Verification performed - `haip-irc.service` is active and enabled - `irc-openclaw-bridge.service` is inactive and disabled - HAIP agent nicks register successfully on IRC - end-to-end message probe to `#arena` succeeded with a live reply from `arenasynth` - response example observed: - `:arenasynth ... PRIVMSG #arena :READY.` Current intended behavior - HAIP agents should now both appear and respond in IRC according to room roster membership - creating a new agent in HAIP should not make it appear in IRC until explicitly added to a room roster - Arena rooms should show Arena agents only, and `#arena-impl` should contain `opencode` because that room roster includes it If follow-up debugging is needed - first check `journalctl --user -u haip-irc.service -n 200 --no-pager` - then inspect `/home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/irc/gateway.py` - confirm live roster membership in PostgreSQL before assuming IRC presence bugs

HAIP Handoff — True Multi-Agent Boardroom Behavior

haip_multi_agent_boardroom_handoff_2026_04_05 shared:platform

Focused implementation handoff for adding true multi-agent boardroom behavior to HAIP, especially for Arena IRC rooms. Current behavior - HAIP IRC is live and working. - Presence is roster-driven. - Agents respond in IRC again. - However, only one agent responds to a given user message. Why only one agent responds - The current orchestrator/selector model is single first-responder. - `TurnSelector.resolve()` returns one `selected_agent_id`. - `Orchestrator.handle_turn()` invokes only that selected agent. - `IrcGateway._route_message()` delivers only the responses returned by that single invocation. Relevant files - `/home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/orchestration/turn_selector.py` - `/home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/orchestration/orchestrator.py` - `/home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/irc/gateway.py` - `/home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/rooms/models.py` - `/home/svc-admin/ai-projects/projects/homelab_ai_platform/haip/rooms/manager.py` Current room mode reality - `collab` currently means weighted-random single responder. - `solo` routes to a single bound agent. - `mentioned-only` requires direct address, still single responder. - `quiet` suppresses replies. - `debate` and `synthesis` are placeholders right now and still fall back to weighted-random single responder. What the user wants - Arena should behave more like a boardroom. - More than one agent should answer when appropriate. - Room mode should matter: - `collab`: likely 1 primary + optional secondary follow-up - `debate`: multiple contrasting agents should answer - `synthesis`: one or more analytical responses followed by a synthesizer/final voice Recommended first implementation target Implement true multi-response behavior for IRC without redesigning the whole platform. Recommended staged plan Phase 1 - Extend orchestration so one turn can yield multiple agent responses. - Preserve existing single-response behavior for `solo`, `mentioned-only`, and `quiet`. - Add structured multi-agent selection for `debate` and `synthesis`. - Optionally add 2-agent behavior for `collab`. Phase 2 - Add explicit turn-plans / response ordering metadata to audit tables if desired. - Add operator controls in Web UI for max responders per mode. Minimal design recommendation Do not blow up the existing schema first. Keep it simple. Suggested logic by mode 1. `collab` - Keep one primary first responder selected by current weighted logic. - Optionally select one secondary responder from remaining eligible roster members. - Return 1 or 2 responses max. 2. `debate` - Choose 2 or 3 responders. - Prefer differentiated voices with different weights/roles. - For Arena specifically, likely: - skeptic - builder - synthesizer optional final wrap 3. `synthesis` - Choose 2 steps: - one or two analytical responders first - synthesizer always last if present on roster - For Arena this likely means: - builder and/or skeptic - then synthesizer final response 4. `solo` - unchanged: one response 5. `mentioned-only` - unchanged unless directly addressed; then one response 6. `quiet` - unchanged: no response Recommended code change shape 1. `turn_selector.py` - add a new concept of multiple selected agent IDs - either: - extend `TurnDecision` with `selected_agent_ids: list[int]` - or add a separate structured response plan field - preserve `selected_agent_id` temporarily for compatibility if helpful 2. `orchestrator.py` - change `handle_turn()` so it can invoke multiple agents for one decision - collect multiple `AgentResponse` objects in order - keep existing result shape but populate `responses` with more than one item 3. `gateway.py` - no large redesign needed if `OrchestratorResult.responses` already supports lists - once orchestrator returns multiple responses, gateway should already deliver each one in order - verify line formatting remains clear when multiple agent nicks respond in sequence Strong compatibility recommendation - Do not remove `selected_agent_id` immediately. - Add `selected_agent_ids` alongside it, with `selected_agent_id` remaining the first primary responder. - This reduces breakage in audit/UI code that may still expect one selected agent. Suggested TurnDecision extension - `selected_agent_id: int | None` # primary responder for compatibility - `selected_agent_ids: list[int] = field(default_factory=list)` - `selection_plan: list[dict] = field(default_factory=list)` # optional, useful for rationale/order Suggested audit behavior - Current `turn_decisions` table stores only one `selected_agent_id`. - For a first pass, keep storing the primary responder there. - Put the full selected set/order into `candidate_snapshot_json` or another existing JSON field if needed. - Avoid schema migration unless necessary for phase 1. Arena-specific recommendation For Arena rooms, a good initial policy is: - `#arena` / `collab` - primary weighted responder - optional second responder only if the message looks broad/open-ended - `#arena-debate` / `debate` - builder + skeptic always - synthesizer optional third/final - `#arena-synthesis` / `synthesis` - builder and skeptic short analysis - synthesizer final response last Simplest useful version If minimizing complexity, implement this first: - `collab`: unchanged single responder - `debate`: exactly 2 responders - `synthesis`: exactly 3 responders with synthesizer last when available This gets boardroom behavior where it matters most. Testing checklist 1. Verify `#arena` still works after changes. 2. Verify `#arena-debate` yields multiple sequential replies. 3. Verify `#arena-synthesis` yields multiple sequential replies with synthesizer last. 4. Verify `#arena-impl` with `opencode` still behaves correctly. 5. Verify `solo`, `quiet`, and `mentioned-only` rooms still behave as before. 6. Check `journalctl --user -u haip-irc.service -n 200 --no-pager` for orchestration errors. 7. Confirm existing Web UI pages still work. Definition of done - More than one agent can respond to a single IRC message when room mode requires it. - `debate` and `synthesis` no longer fall back to single weighted responder behavior. - Arena rooms feel like actual multi-agent collaboration rather than one-at-a-time routing. - Existing single-agent modes remain stable. Important constraint - Keep the implementation understandable for future maintenance. - Prefer explicit ordered responder selection over clever autonomous chaining in the first pass. - The immediate goal is deterministic, inspectable multi-agent turn planning, not full autonomous swarm behavior.

HAIP Web UI Checklist — Add Create/Edit Agent Management

haip_webui_agent_create_edit_checklist_2026_04_05 shared:platform

Short execution checklist for adding Create Agent and Edit Agent support to the HAIP Web Control UI. Goal - Add create-agent and edit-agent workflows to the deployed HAIP Web UI at `http://192.168.4.114:3010`. Do not - do not modify `/home/svc-admin/ai-projects/projects/homelab_ai_platform_architecture` - do not redesign the whole UI - do not add persona CRUD in the same pass - do not auto-add new agents to room rosters Work location - `/home/svc-admin/appstack/haip-ui` Inspect first 1. `/home/svc-admin/appstack/haip-ui/app/routers/agents.py` 2. `/home/svc-admin/appstack/haip-ui/app/templates/agents/` 3. rooms router patterns for create/update POST handlers 4. existing DB helper/query style in the UI app 5. live schema labels for `provider_families` and `personas` Implement 1. Extend `GET /agents` - keep existing listing - add data needed for create form: provider families + personas 2. Add `POST /agents/create` - create a new agent row - validate unique `agent_key` - validate unique `nick` - redirect on success 3. Add `GET /agents/{agent_key}` - render agent edit page - preload current agent fields - preload provider families + personas 4. Add `POST /agents/{agent_key}/update` - update core fields - set `updated_at = CURRENT_TIMESTAMP` - redirect back to detail page Fields to support - `agent_key` on create - `display_name` - `nick` - `provider_family_id` - `persona_id` - `runtime_adapter_key` - `private_memory_namespace` - `default_response_policy` - `default_cooldown_policy` - `enabled` Likely table usage - `agents` - `provider_families` - `personas` Validation - reject duplicate `agent_key` - reject duplicate `nick` - require non-empty namespace/policy fields - map checkbox cleanly to `enabled` Keep it simple - server-rendered forms only - explicit SQL is fine - reuse rooms form/redirect patterns - no abstraction spree Verify 1. reload or rebuild the HAIP UI service 2. `/agents` returns `200` 3. new agent detail/edit page returns `200` 4. create a safe test agent 5. confirm it appears in `/agents` 6. edit a safe field such as `display_name` or `enabled` 7. confirm DB persistence and UI reflection 8. confirm `/rooms`, `/presets`, `/sessions`, `/memory`, `/health`, `/invocations` still return `200` Important runtime note - HAIP IRC presence is now roster-driven - creating a new agent should not make it appear in IRC until it is explicitly added to a room roster

HAIP Web UI Handoff — Add Create/Edit Agent Management

haip_webui_agent_create_edit_handoff_2026_04_05 shared:platform

Detailed implementation handoff for a follow-on coding model, specifically targeted at a smaller local coding model such as Qwen2.5-Coder 9B. Goal: add Create Agent and Edit Agent functionality to the HAIP Web Control UI without breaking the existing deployed service. Context - Deployable HAIP Web UI lives at `/home/svc-admin/appstack/haip-ui` on `svc-apps`. - The architecture repo at `/home/svc-admin/ai-projects/projects/homelab_ai_platform_architecture` is read-only reference only and must not be modified. - Live UI URL: `http://192.168.4.114:3010` - Stack: FastAPI + Jinja2 + direct PostgreSQL access. - Existing UI already supports rooms, room detail, roster management, room creation, preset loading, sessions, agent catalog, memory inspection, health, and invocations. - The `/agents` page currently appears to be read-only/listing oriented. The next slice is to add agent creation and editing in the Web UI. Important scope - Do not redesign the UI broadly. - Preserve the existing visual language and route structure in `/home/svc-admin/appstack/haip-ui/app`. - Add a create-agent workflow and an edit-agent workflow. - Do not modify architecture docs. - Keep the implementation straightforward and low-risk. Relevant codebase locations - Main app entry: `/home/svc-admin/appstack/haip-ui/app/main.py` - Existing agents router: likely under `/home/svc-admin/appstack/haip-ui/app/routers/agents.py` - Templates: `/home/svc-admin/appstack/haip-ui/app/templates/agents/` - Shared DB helpers / connection code: inspect `/home/svc-admin/appstack/haip-ui/app` for whichever module currently handles DB access. - Existing rooms CRUD patterns are the best template for new POST form handlers. Compare with room creation and preset load handlers in the rooms router. Live HAIP database facts Database connection details are already wired into the deployed UI environment. Reuse the existing UI service DB config pattern; do not invent a second DB config path. The `agents` table shape in the live HAIP database is: - `id bigint primary key` - `agent_key text unique not null` - `display_name text not null` - `nick text not null` - `provider_family_id bigint not null` - `persona_id bigint not null` - `runtime_adapter_key text not null` - `private_memory_namespace text not null` - `default_response_policy text not null` - `default_cooldown_policy text not null` - `enabled boolean not null default true` - `created_at timestamptz not null` - `updated_at timestamptz not null` Related tables that matter for create/edit flows - `provider_families` - `personas` - `agent_aliases` - `memory_namespaces` - `room_roster_memberships` (editing an agent should not automatically change room membership) Current live provider families known in HAIP - `codex_cli` - `claude_cli` - `gemini_cli` - `opencode_cli` Current known enabled agents include examples such as: - `arena-builder` - `arena-skeptic` - `arena-synthesizer` - `claude` - `codex` - `gemini` - `opencode` - multiple Genealogy / Ohio History / Stoned agents Expected behavior to add 1. `/agents` page should include a Create Agent form. 2. Add an agent detail or edit page, likely `/agents/{agent_key}` or `/agents/{agent_id}`. 3. Support editing at least these fields: - `display_name` - `nick` - `provider_family_id` - `persona_id` - `runtime_adapter_key` - `private_memory_namespace` - `default_response_policy` - `default_cooldown_policy` - `enabled` 4. Creation should require at minimum: - `agent_key` - `display_name` - `nick` - `provider_family_id` - `persona_id` - `runtime_adapter_key` - `private_memory_namespace` - `default_response_policy` - `default_cooldown_policy` 5. Do not automatically create room memberships when creating an agent. 6. Do not automatically create aliases unless you intentionally add a small alias input section. 7. If persona creation is not already exposed in the UI, assume personas are selected from existing rows only. Recommended minimal implementation approach Phase 1: Create + Edit existing agents using existing personas and provider families. - This is the correct first slice. - Do not try to add persona CRUD in the same pass. - Do not try to add alias management unless it is trivial. UI recommendations - On `/agents`, add a compact create form above or beside the existing list. - Add an `Edit` link/button per agent row. - Create an agent detail/edit template with a standard HTML form. - Use server-rendered forms and redirects, matching the current rooms workflow style. - Show validation errors clearly but simply. Routing recommendations Add or extend routes in the agents router along these lines: - `GET /agents` -> existing listing page, now also loads form select options - `POST /agents/create` -> create a new agent, then redirect to `/agents/{agent_key}` or `/agents` - `GET /agents/{agent_key}` -> render edit page for a single agent - `POST /agents/{agent_key}/update` -> update the agent, then redirect back to detail page Data-loading required for forms For both create and edit forms, load: - provider families: id + provider_key + display label if available - personas: id + persona_key or display title/name if present Important caution: inspect the live `personas` schema before rendering labels. Do not assume exact persona columns until confirmed in the code or DB. Use the same DB inspection/query style already used elsewhere in the UI. Validation rules - `agent_key` should be unique and stable; reject duplicates. - `nick` should be unique enough for IRC use; at minimum, reject exact duplicates already in `agents.nick`. - `runtime_adapter_key` should be consistent with the selected provider family. - `private_memory_namespace` should not be empty. - `default_response_policy` and `default_cooldown_policy` should not be empty. - `enabled` should map cleanly from checkbox -> boolean. Low-risk handling for `runtime_adapter_key` Because this is a local-model handoff, prefer a simple rule: - When creating a new agent, default `runtime_adapter_key` to the selected provider family’s `provider_key` if that matches current HAIP conventions. - But first inspect current live agent rows and existing UI data loading to confirm how runtime adapter keys are represented. - If the existing UI already knows the correct adapter keys, reuse that exact source. - Do not hardcode a complicated mapping unless necessary. What to inspect before coding 1. `/home/svc-admin/appstack/haip-ui/app/routers/agents.py` 2. `/home/svc-admin/appstack/haip-ui/app/templates/agents/` 3. existing DB helper functions used by rooms and presets 4. live DB schema for `personas` and `provider_families` 5. how existing forms are posted and redirected in rooms routes Recommended SQL shape Agent list query should likely include joins for readability: - `agents a` - `provider_families pf on pf.id = a.provider_family_id` - `personas p on p.id = a.persona_id` Suggested create query: - `INSERT INTO agents (...) VALUES (...) RETURNING id, agent_key` Suggested update query: - `UPDATE agents SET ... , updated_at = CURRENT_TIMESTAMP WHERE agent_key = $1` Suggested uniqueness checks before write: - `SELECT 1 FROM agents WHERE agent_key = $1` - `SELECT 1 FROM agents WHERE nick = $1 AND agent_key <> $2` Safer field strategy for smaller models Because Qwen2.5-Coder 9B is smaller, keep the work narrow: - build create/edit around core fields only - avoid fancy abstractions - prefer explicit SQL in the router if the codebase already does that elsewhere - reuse template patterns from rooms rather than inventing a new form framework Testing / verification checklist After implementing: 1. restart or rebuild the HAIP UI service on `svc-apps` 2. confirm these endpoints return `200`: - `/agents` - new agent detail/edit route 3. create a non-destructive test agent in the UI 4. verify it appears on `/agents` 5. edit one safe field such as `display_name` or `enabled` 6. verify the update persists in the DB and in the UI 7. if the test agent is only for validation, delete it manually via SQL only if a delete UI was not added 8. ensure existing `/rooms`, `/presets`, `/sessions`, `/memory`, `/health`, `/invocations` still return `200` Operational constraints - The HAIP IRC gateway is now live and roster-driven. New agents should not appear in IRC rooms unless explicitly added to room rosters. - Do not break existing rooms, rosters, or provider-family sync behavior. - The service is already deployed live, so treat this as an in-place enhancement to an active app. Definition of done - Operator can create a new agent from the Web UI. - Operator can open an existing agent and edit its core fields from the Web UI. - Provider family and persona are selected from live DB-backed lists. - Validation prevents duplicate `agent_key` and duplicate `nick`. - Changes persist correctly in PostgreSQL. - Existing pages remain functional. Nice-to-have but not required in first pass - alias management - persona preview on edit page - delete/deactivate workflow beyond `enabled=false` - inline success/error flash messaging if current UI does not already have a pattern Final implementation advice for the next model - Do not over-engineer. - Follow the rooms CRUD style already in this codebase. - Reuse server-rendered forms and redirects. - Verify schema before assuming persona/provider field names. - Keep the patch small, explicit, and easy to review.

Supervisor Verdict — Agent Create/Edit Implementation

haip_webui_impl_fixes general

{"approved": true, "notes": "HTTPException import added, enabled Form default fixed to 'false', container rebuilt and /agents returns 200. Both fixes applied by supervisor directly."}

Qwen Implementation Kickstart

haip_webui_impl_kickstart general

Qwen: resume the haip-ui agent create/edit implementation. Retrieve haip_webui_agent_create_edit_* memories from Brain for the full spec — use retrieve_memory by key, NOT tag search. Write haip_webui_impl_heartbeat within 5 minutes of starting. Continue from where you left off — read the existing files first, then implement. IMPORTANT: After completing each major step (reading spec, writing a file, testing), run /compact to compress your context before continuing to the next step. This prevents context overflow.

HAIP WS6 Handoff — Web Control UI (svc-apps)

haip_ws6_handoff shared:platform

# HAIP WS6 Handoff — Web Control UI Date: 2026-04-04 Author: Claude (Sonnet 4.6) via haip build session --- ## Platform State as of WS5 Complete The Homelab AI Platform (package: `haip`) is a shared, self-hosted AI agent orchestration foundation for four sub-projects: The Arena, Family Roots/Genealogy, Ohio History Channel, Stoned.AI. All code through WS5 is complete and import-tested. No services are running as systemd units yet — that is WS7. The platform is buildable and the Python package is installed in its venv. --- ## VM Topology | VM | IP | Role | |----|-----|------| | svc-ai | 192.168.4.117 | Agent runtime, orchestration, IRC gateway, Brain/MCP host | | svc-db-01 | 192.168.4.113 | PostgreSQL 16.13 | | svc-apps | 192.168.4.114 | Web Control UI (appstack Docker pattern) | | svc-dev | 192.168.4.123 | Dev/test | SSH key for all VMs: `/home/svc-admin/.ssh/id_ed25519_homelab` SSH user: `svc-admin` --- ## Source and Workspace Paths ### On svc-ai (primary build host — this is where you ARE when building): - Source: `/home/svc-admin/ai-projects/projects/homelab_ai_platform/` - Package root: `.../homelab_ai_platform/haip/` - Venv: `.../homelab_ai_platform/.venv/` - Runtime workspace: `/home/svc-admin/ai-platform/` - `config/environments/local.env` — runtime env vars - `config/systemd/haip-irc.service` — IRC gateway systemd unit (not installed yet) - `secrets/platform_db_password` — chmod 600, plaintext password - `runtime/` — provider state dirs (codex/, gemini/) - `logs/`, `artifacts/`, `backups/`, `tmp/` ### Architecture reference docs (READ ONLY — do not modify): - `/home/svc-admin/ai-projects/projects/homelab_ai_platform_architecture/` - Key docs for WS6: - `docs/02-runtime/WEB_CONTROL_UI.md` - `docs/04-infrastructure/SERVICE_LAYOUT.md` - `docs/04-infrastructure/NETWORK_AND_PORTS.md` --- ## haip Package Structure (complete through WS5) ``` haip/ ├── __init__.py ├── db.py # asyncpg pool: init_pool(), get_pool(), close_pool() ├── adapters/ │ ├── base.py # Abstract ProviderAdapter │ ├── codex.py # CodexAdapter (JSON-lines, thread resumption) │ ├── gemini.py # GeminiAdapter (JSON output, session resumption) │ ├── registry.py # AdapterRegistry, sync_provider_families(pool) │ └── __init__.py ├── models/ │ ├── provider.py # AdapterCapabilities, ProviderFamily (frozen dataclasses) │ ├── session.py # SessionScope, SessionStatus, Session │ ├── invocation.py # InvocationResult, InvocationError │ └── __init__.py ├── sessions/ │ ├── manager.py # SessionManager: get_or_create(), update_after_invocation(), mark_broken(), archive() │ └── __init__.py ├── rooms/ │ ├── models.py # RoomMode enum, Room dataclass, RosterMember dataclass │ ├── manager.py # RoomManager: full CRUD for rooms + roster memberships │ └── __init__.py ├── orchestration/ │ ├── turn_selector.py # TurnSelector: weighted-random with dominance suppression │ ├── orchestrator.py # Orchestrator.handle_turn() — main runtime entry point │ └── __init__.py ├── memory/ │ ├── models.py # NamespaceTier, ContributionType, WriteDecision, CONFIDENCE_THRESHOLDS, MemoryCandidate, ClassificationResult, PipelineOutcome │ ├── classifier.py # MemoryClassifier: noise detection, tier routing, confidence gating │ ├── brain_client.py # BrainClient: REST client for ContextKeep (store/retrieve/search/delete) │ ├── store.py # MemoryStore: PG-backed canonical_memories + memory_contributions │ ├── pipeline.py # MemoryPipeline: 6-stage process(), retrieve_for_agent() │ └── __init__.py └── irc/ ├── client.py # IrcClient: asyncio raw TCP, NICK/USER/JOIN/PRIVMSG/PING, auto-reconnect ├── commands.py # CommandParser: ! prefix commands ├── formatter.py # IrcFormatter: line splitting, paced output ├── gateway.py # IrcGateway: orchestrates clients, routes msgs, handles commands ├── service.py # Entry point: python -m haip.irc.service └── __init__.py ``` ### pyproject.toml ```toml [build-system] requires = ["setuptools>=68"] build-backend = "setuptools.backends.legacy:build" [project] name = "homelab-ai-platform" version = "0.1.0" requires-python = ">=3.12" dependencies = ["asyncpg>=0.29"] [tool.setuptools.packages.find] where = ["."] include = ["haip*"] ``` --- ## Database - Host: 192.168.4.113 (svc-db-01) - Port: 5432 - Database: `homelab_ai_platform` - User: `platform_runtime` - Password: in `/home/svc-admin/ai-platform/secrets/platform_db_password` ### Key tables for the Web UI to query: - `agents` — agent_key, nick, display_name, provider_family_id, persona_id - `agent_aliases` — alias_value per agent (for @-mention matching) - `rooms` — room_key, display_name, surface_type, surface_address, room_mode, status, project_id - `room_roster_memberships` — room_id, agent_id, priority_weight, speak_enabled, removed_at - `team_presets` — preset_key, display_name, project_id, default_room_mode - `team_preset_agents` — preset_id, agent_id, role_label, default_weight - `backend_sessions` — room_id, agent_id, provider_family_id, provider_session_ref, status, scope_type, state_path - `agent_invocations` — room_id, agent_id, input_text, output_text, started_at, completed_at, error_class - `turn_decisions` — room_id, turn_type, selected_agent_id, rationale, trigger_source, decided_at - `canonical_memories` — memory_key, namespace_id, title, summary, status, tags_json - `memory_contributions` — canonical_memory_id, agent_id, contribution_type, content, confidence, rationale - `memory_namespaces` — namespace_key, namespace_type, owner_agent_id --- ## Runtime Environment Variables (local.env on svc-ai) ``` PLATFORM_ENV=local PLATFORM_LOG_LEVEL=info PLATFORM_RUNTIME_STATE_ROOT=/home/svc-admin/ai-platform/runtime PLATFORM_DB_HOST=192.168.4.113 PLATFORM_DB_PORT=5432 PLATFORM_DB_NAME=homelab_ai_platform PLATFORM_DB_USER=platform_runtime PLATFORM_DB_PASSWORD_FILE=/home/svc-admin/ai-platform/secrets/platform_db_password PLATFORM_BRAIN_API_URL=http://127.0.0.1:5000 PLATFORM_BRAIN_NAMESPACE_SHARED=shared:platform PLATFORM_BRAIN_WRITE_ENABLED=true PLATFORM_BRAIN_TOKEN_FILE=/home/svc-admin/ai-platform/secrets/platform_brain_token PLATFORM_ORCHESTRATOR_BIND_HOST=127.0.0.1 PLATFORM_ORCHESTRATOR_BIND_PORT=8080 PLATFORM_UI_BASE_URL=http://127.0.0.1:3000 # PLACEHOLDER — update when UI is live on svc-apps CODEX_CLI_BIN=/usr/local/bin/codex CODEX_STATE_ROOT=/home/svc-admin/ai-platform/runtime/providers/codex GEMINI_CLI_BIN=/usr/local/bin/gemini GEMINI_STATE_ROOT=/home/svc-admin/ai-platform/runtime/providers/gemini PLATFORM_ARTIFACT_ROOT=/home/svc-admin/ai-platform/artifacts PLATFORM_LOG_ROOT=/home/svc-admin/ai-platform/logs PLATFORM_ENABLE_STREAMING=true PLATFORM_ENABLE_TTS=false PLATFORM_ENABLE_ROOM_SESSION_MEMORY=true ``` Note: `PLATFORM_UI_BASE_URL` is currently a placeholder pointing at port 3000 on svc-ai. Port 3000 conflicts with linkwarden on svc-apps. Use port **3010** for the Web Control UI on svc-apps (3000–3009 are all occupied). --- ## Key Architectural Decisions Made During Build 1. **Package named `haip`** (not `platform`) — Python stdlib has a `platform` module; naming our package `platform` caused `AttributeError: module 'platform' has no attribute 'uname'` inside asyncpg. Package must stay `haip`. 2. **DB password read from file inside Python** — heredoc env expansion does not work with `<<'EOF'` in bash. The pattern used throughout is: ```python DB_PASS = open("/home/svc-admin/ai-platform/secrets/platform_db_password").read().strip() ``` 3. **Bare metal systemd user services on svc-ai** — the existing Ergo IRC server and bridge run as bare metal `systemd --user` services. All platform services on svc-ai follow the same pattern. No Docker on svc-ai for platform components. 4. **Docker appstack pattern on svc-apps** — all services on svc-apps use Docker Compose under `/home/svc-admin/appstack/<service>/docker-compose.yml` with a shared external network `appstack_default`. The Web Control UI must follow this exact pattern. 5. **RoomManager roster methods take agent_id (int), not agent_key (str)** — `add_agent()`, `remove_agent()`, `set_speak_enabled()`, `update_weight()` all require numeric agent_id. Resolve agent_key → agent_id by querying `SELECT id FROM agents WHERE agent_key = $1` before calling these. 6. **Room model field is `room_mode`, not `mode`** — the `Room` dataclass uses `room.room_mode`. `RosterMember` uses `priority_weight`, not `base_weight`. 7. **Turn selection is weighted-random with dominance suppression** — not deterministic round-robin. Direct address always wins. Debate/synthesis mode falls back to weighted-random for now (full structured flow is WS8). 8. **Memory tiers and thresholds**: - `core_shared`: confidence >= 0.70 - `project_team`, `agent_private`: confidence >= 0.50 - `room_session`: confidence >= 0.30 (below → discard, not review) - Higher tiers below threshold → `WriteDecision.REVIEW` (written to PG only, not Brain, until human confirms) 9. **BrainClient uses urllib (no extra deps)** and runs in `ThreadPoolExecutor` to avoid blocking the asyncio event loop. Brain REST at `http://127.0.0.1:5000`. 10. **IRC gateway supports multi-nick and single-nick mode** — normally one IrcClient per agent nick plus opsbot. `IRC_SINGLE_NICK_MODE=1` env var routes all output via opsbot with `[agent_key]` prefix. 11. **Ergo IRC on svc-ai** — listens on `:6667` (plaintext, all interfaces) and `:6697` (TLS). Platform IRC client connects to `127.0.0.1:6667`. Server name: `irc.accursedbinkie.lan`. Network name: `HomelabAI`. --- ## Existing Services and Ports on svc-ai (192.168.4.117) - ContextKeep REST: `http://127.0.0.1:5000` - ContextKeep MCP SSE: `http://127.0.0.1:5100` - Ergo IRC: `127.0.0.1:6667` (plain), `*:6697` (TLS) - Existing TypeScript bridge health: `http://127.0.0.1:3000/health` - Kokoro TTS: port 8765 - XTTS Docker: port 8020 - Gramps MCP: port 8000 (genealogy project, unrelated) - Planned orchestration API: `127.0.0.1:8080` --- ## Occupied Ports on svc-apps (192.168.4.114) - 3000: linkwarden - 3001: gitea (also 2222 for SSH) - 3002: trilium - 3003: openproject proxy - 3004: mealie - 3005: open-webui - 3006: nextcloud - 3007: vaultwarden - 3009: actualbudget - 3456: vikunja - 8000: paperless-ngx - 2283: immich **Use port 3010 for the Web Control UI.** --- ## WS6 Task: Web Control UI on svc-apps ### Goal Build and deploy a minimal but functional operator control panel as a Docker Compose service in the appstack on svc-apps. It must be a real, working UI — not a stub or placeholder. ### Architecture Decision The architecture doc (`SERVICE_LAYOUT.md`) says the Web Control UI backend can live on svc-ai with the frontend on svc-apps. For the first build, the simplest approach is a **single self-contained service** that runs on svc-apps and queries PostgreSQL directly (192.168.4.113:5432). No dependency on an orchestration API that doesn't exist yet. The UI can grow into a two-tier (API on svc-ai, frontend on svc-apps) architecture later. For WS6, keep it simple: one container, reads DB, renders views. ### Technology Choice Use **FastAPI + Jinja2 templates + plain HTML/CSS** — no JavaScript framework. This matches the "clarity over visual complexity" principle from the arch doc and keeps the container small. Dependencies: `fastapi`, `uvicorn`, `asyncpg`, `jinja2`, `python-multipart` ### Port **3010** on svc-apps (host port 3010 → container port 8000). ### Appstack Location on svc-apps `/home/svc-admin/appstack/haip-ui/` - `docker-compose.yml` - `.env` (DB credentials, not committed) - `data/` (any persistent data if needed) - The app code lives in a Docker image or bind-mounted source directory. ### Views to Build (from WEB_CONTROL_UI.md, in priority order) 1. **Room list** (`/rooms`) — all active rooms, mode badge, surface address, roster count 2. **Room detail** (`/rooms/{room_key}`) — mode, project, roster table with weights/speak status, last 10 turn decisions 3. **Roster panel** (within room detail, POST actions) — add agent, remove agent, mute/unmute, adjust weight 4. **Preset browser** (`/presets`) — list presets with project association, default mode, agent list 5. **Session inspection** (`/sessions`) — sessions by room+agent, provider, status, staleness 6. **Agent catalog** (`/agents`) — all agents, persona, provider family, private namespace 7. **Memory inspection** (`/memory`) — canonical memory records by namespace, with contributions Operational/diagnostic views (lower priority for WS6 but include if time allows): - `/health` — provider family health summary (query agent_invocations for recent errors) - `/invocations` — recent invocation log with success/error status ### DB Connection in the Container Read DB password from an env var `PLATFORM_DB_PASSWORD` injected via `.env` file in the appstack directory. Do NOT use the file-based secret path (that's only accessible on svc-ai). ### appstack_default Network The container must join the `appstack_default` external Docker network (already exists on svc-apps). It does NOT need to reach svc-ai — it talks directly to svc-db-01 on 192.168.4.113:5432. ### docker-compose.yml pattern to follow ```yaml services: haip-ui: build: . # or image: if pre-built container_name: haip-ui restart: unless-stopped ports: - "3010:8000" env_file: - .env volumes: - /home/svc-admin/appstack/haip-ui/data:/app/data networks: - appstack_default networks: appstack_default: external: true ``` ### .env file on svc-apps (create manually, chmod 600) ``` PLATFORM_DB_HOST=192.168.4.113 PLATFORM_DB_PORT=5432 PLATFORM_DB_NAME=homelab_ai_platform PLATFORM_DB_USER=platform_runtime PLATFORM_DB_PASSWORD=<contents of /home/svc-admin/ai-platform/secrets/platform_db_password on svc-ai> PLATFORM_UI_TITLE=Homelab AI Platform ``` ### Suggested App Structure (in the appstack dir or a bind-mounted src/) ``` haip-ui/ ├── docker-compose.yml ├── .env ├── Dockerfile ├── app/ │ ├── main.py # FastAPI app, lifespan for asyncpg pool │ ├── db.py # asyncpg pool helper │ ├── routers/ │ │ ├── rooms.py │ │ ├── presets.py │ │ ├── sessions.py │ │ ├── agents.py │ │ └── memory.py │ └── templates/ │ ├── base.html # nav, layout │ ├── rooms/ │ │ ├── list.html │ │ └── detail.html │ ├── presets/list.html │ ├── sessions/list.html │ ├── agents/list.html │ └── memory/list.html └── requirements.txt ``` ### Dockerfile ```dockerfile FROM python:3.12-slim WORKDIR /app COPY requirements.txt . RUN pip install --no-cache-dir -r requirements.txt COPY app/ ./app/ CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"] ``` ### requirements.txt ``` fastapi>=0.110 uvicorn[standard]>=0.29 asyncpg>=0.29 jinja2>=3.1 python-multipart>=0.0.9 ``` ### DB Query Patterns for Each View **Room list:** ```sql SELECT r.id, r.room_key, r.display_name, r.surface_type, r.surface_address, r.room_mode, r.status, p.project_key, COUNT(m.id) FILTER (WHERE m.removed_at IS NULL) AS roster_count FROM rooms r LEFT JOIN projects p ON p.id = r.project_id LEFT JOIN room_roster_memberships m ON m.room_id = r.id WHERE r.status = 'active' GROUP BY r.id, p.project_key ORDER BY r.room_key ``` **Room detail roster:** ```sql SELECT a.agent_key, a.nick, m.priority_weight, m.speak_enabled, m.joined_at FROM room_roster_memberships m JOIN agents a ON a.id = m.agent_id WHERE m.room_id = $1 AND m.removed_at IS NULL ORDER BY m.priority_weight DESC ``` **Recent turn decisions for a room:** ```sql SELECT td.turn_type, a.agent_key, td.rationale, td.trigger_source, td.decided_at FROM turn_decisions td LEFT JOIN agents a ON a.id = td.selected_agent_id WHERE td.room_id = $1 ORDER BY td.decided_at DESC LIMIT 10 ``` **Sessions:** ```sql SELECT r.room_key, a.agent_key, pf.family_key, bs.status, bs.scope_type, bs.provider_session_ref, bs.created_at, bs.last_used_at FROM backend_sessions bs JOIN rooms r ON r.id = bs.room_id JOIN agents a ON a.id = bs.agent_id JOIN provider_families pf ON pf.id = bs.provider_family_id WHERE bs.status NOT IN ('archived') ORDER BY bs.last_used_at DESC NULLS LAST ``` **Agent catalog:** ```sql SELECT a.agent_key, a.nick, a.display_name, pf.family_key, pv.version_tag, pv.persona_name FROM agents a LEFT JOIN provider_families pf ON pf.id = a.provider_family_id LEFT JOIN persona_versions pv ON pv.id = a.active_persona_version_id ORDER BY a.agent_key ``` **Canonical memories:** ```sql SELECT cm.memory_key, cm.title, cm.summary, cm.status, mn.namespace_key, mn.namespace_type, COUNT(mc.id) AS contribution_count FROM canonical_memories cm JOIN memory_namespaces mn ON mn.id = cm.namespace_id LEFT JOIN memory_contributions mc ON mc.canonical_memory_id = cm.id WHERE cm.status = 'active' GROUP BY cm.id, mn.id ORDER BY cm.updated_at DESC LIMIT 100 ``` ### POST Actions (Roster Management) The room detail page should include HTML forms for: - `POST /rooms/{room_key}/roster/add` — body: `agent_key` → lookup agent_id, call INSERT INTO room_roster_memberships - `POST /rooms/{room_key}/roster/remove` — body: `agent_key` → UPDATE room_roster_memberships SET removed_at = NOW() - `POST /rooms/{room_key}/roster/speak` — body: `agent_key`, `enabled` → UPDATE room_roster_memberships SET speak_enabled = $1 - `POST /rooms/{room_key}/roster/weight` — body: `agent_key`, `weight` → UPDATE room_roster_memberships SET priority_weight = $1 - `POST /rooms/{room_key}/mode` — body: `mode` → UPDATE rooms SET room_mode = $1 These write directly to the DB (no orchestration API layer for WS6 — that comes later in WS7). ### Note on PLATFORM_UI_BASE_URL After WS6 is deployed, update `local.env` on svc-ai: ``` PLATFORM_UI_BASE_URL=http://192.168.4.114:3010 ``` --- ## Remaining Workstreams After WS6 ### WS7: Operational Foundations - Install systemd user units on svc-ai (haip-irc.service, and a future haip-orchestrator.service) - Logging config (structured JSON logs to /home/svc-admin/ai-platform/logs/) - Health check endpoints - Backup/restore runbooks for DB and Brain state - Security review (DB user permissions, secret file permissions) - Validate all env var paths are correct (CLI bins, state roots) - Smoke-test IRC gateway connecting to Ergo - Smoke-test memory pipeline writing to Brain (ContextKeep on :5000) ### WS8: Project Profile Realization - The Arena: agents, personas, rooms, presets for the debate/performance surface - Family Roots/Genealogy: agents with Gramps MCP access, genealogy room config - Ohio History Channel: research agent configs, content production presets - Stoned.AI: Kokoro TTS integration, persona config for that project's voice --- ## Critical Notes for Any Agent Picking This Up 1. **You are likely running ON svc-ai (192.168.4.117)** — `hostname` confirms. WS6 work happens by SSHing to svc-apps: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab svc-admin@192.168.4.114` 2. **The haip package is NOT used in the Web UI** — the UI is a separate FastAPI app in its own Docker container on svc-apps. It queries the same PostgreSQL DB directly. Do not try to import haip inside the UI container. 3. **Do not touch the existing bridge** at `/home/svc-admin/ai-projects/projects/irc-openclaw-bridge/`. It is a separate TypeScript/Node.js project running on svc-ai. Platform IRC gateway (haip.irc) will eventually replace or complement it, but for now leave it alone. 4. **appstack_default network already exists** on svc-apps — `docker network ls` confirms. All services use `external: true` for this network. 5. **No reverse proxy yet** — access the UI directly on port 3010. Caddy or nginx reverse proxy is a WS7+ concern. 6. **Brain MCP is on svc-ai only** — accessible from svc-ai at `http://127.0.0.1:5000`. The Web UI does not need to talk to Brain. 7. **DB schema was applied** using the migration script from the architecture docs. All 22 tables and 11 indexes are present in `homelab_ai_platform`. Verify with: ```sql SELECT tablename FROM pg_tables WHERE schemaname = 'public' ORDER BY tablename; ```

Hermes + Gemma 4 E4B homelab evaluation, May 9 2026

hermes_gemma4e4b_homelab_eval_2026_05_09 agent_evaluations:project

Hermes testing status as of 2026-05-09: Environment and setup: - Hermes should be installed and used on svc-ai, not svc-dev. - Hermes launch on svc-ai was repaired after the CLI symlink/venv entrypoint was broken by a recursive wrapper. Reinstalling Hermes into its venv restored `hermes --help` and `hermes doctor`. - Brave Search was configured for Hermes on svc-ai using the Brave API key discovered in the Ohio Built America project on svc-dev at `/home/svc-admin/YouTube/ohio-built-america`. Hermes config now uses `web.search_backend: brave-free`, and `hermes doctor` showed web tooling working. - ContextKeep services on svc-ai were stopped because Brain replaced ContextKeep and port 5000 was needed for the dashboard test. Major Hermes/Gemma 4 E4B observations: - Web search and basic local/SSH read-only inspection worked reasonably well. - Hermes initially failed a cross-session memory persistence/retrieval test, though later improved after protocol guidance. - Hermes overclaimed success after tool/write errors and sometimes reported files as created despite write failures. - Hermes missed a known project path on svc-dev until better context/protocol was added. - Hermes drifted during a safety test by moving from requested risk listing into read-only evidence gathering, including sudo disk checks. - Hermes repeatedly misused execute_code/import patterns when direct tools would have been simpler. - Hermes sometimes returned empty final responses after tool loops, even when useful work happened internally. Skill/protocol mitigation: - Created Hermes skill `/home/svc-admin/.hermes/skills/devops/homelab-operator-protocol/SKILL.md`. - The protocol requires post-write verification, stop-on-error behavior, no sudo without approval, direct tool preference, known svc-dev/OH Built America path context, memory verification, scope discipline, and final reports separating facts/inferences/failures. - Retests improved behavior on SSH path lookup, memory, and safety prompts, but not enough to trust Hermes autonomously. Dashboard build test on svc-ai: - Hermes was instructed to build a Homelab Service Dashboard without assistance. - It created `/home/svc-admin/hermes-build-test/homelab-service-dashboard` on svc-ai with Flask files and a venv. - First pass was incomplete: no `/` route, hardcoded/fake services, incorrect disk/memory parsing, bad generated text in UI, and weak verification. - After user wanted to view it, Codex applied a minimal route fix and started it as user service `hermes-build-dashboard.service`. - Dashboard was reachable on svc-ai at `http://192.168.4.117:5000`. Remote svc-dev build test: - User wanted Hermes to build the same project on svc-dev without help. - Direct Hermes on svc-dev existed but was unconfigured; it failed with no providers/API keys and no interactive TTY. This aligns with expectation that Hermes should not be installed/used there. - Running Hermes from svc-ai with instructions to build on svc-dev over SSH produced an empty stdout transcript. - Hermes session record showed it successfully inspected svc-dev via SSH, then violated the target-host constraint and built locally on svc-ai instead of on svc-dev. - No files were created on svc-dev at `/home/svc-admin/hermes-build-test/homelab-service-dashboard`. - Local errors during the mistaken build included invalid JS syntax, system pip externally-managed-environment error, rejected shell backgrounding with `&`, malformed one-line Flask route SyntaxError, missing psutil, and repeated empty final responses. It eventually got a local `/api/status` responding on svc-ai, not svc-dev. - Hermes was not found to be in YOLO mode; svc-ai config had `approvals.mode: manual`. The failure was wrong-host execution and weak guardrails, not primarily approval bypass. Current evaluation: - Hermes + Gemma 4 E4B is useful as a supervised operator and planning assistant. - It is not ready to be trusted as the primary autonomous homelab operator. - The core failure pattern appears to be Gemma/model instruction drift exposed by Hermes lacking hard runtime constraints. A stronger model may help, but Hermes also needs guardrails that enforce execution target, verification, and error reporting. Proposed next steps: - Later test a different local model. Qwen was suggested but user is currently jaded due to OpenCode issues. Non-Qwen candidates include Llama 3.1/3.3 8B Instruct for instruction stability, DeepSeek Coder variants for coding bias, and Mistral/Codestral-family models if available locally. - Rerun the exact same svc-dev dashboard test with the new model unchanged. - For a fair Hermes guardrail test, constrain the runtime/prompt so every build action must use `ssh svc-dev`, `scp`, or `rsync`, and require final proof via `ssh svc-dev 'find ...'` and a reachable svc-dev IP/port. - Consider an external orchestration wrapper around Hermes for homelab use. The wrapper should own state, approvals, target-host enforcement, retries, verification, logs, and rollback checks while Hermes provides planning/tool-call suggestions. - Keep evaluating against decision criteria: reliability, tool safety, web research quality, local ops usefulness, and low maintenance burden.

homelab-02 NIC Diagnostics

homelab_02_nic_diagnostics_2026_03_20 general

Node: homelab-02 (192.168.4.54) Interface: nic0 bridged into vmbr0 Updated diagnostics on 2026-03-20 03:32 UTC. New findings: - SSH access path from svc-ai: user svc-admin with key ~/.ssh/id_ed25519_homelab. - nic0 remained in prior diagnostic state when rechecked: - speed 100Mb/s - duplex full - autoneg off - EEE disabled - offloads largely disabled (tso/gso/gro/rx/tx off) - carrier_changes at recheck: 11864, later 11868 before watchdog disable. - Kernel logs from this boot showed many periodic link drops. After forced 100Mb, link-down/up events continued at exact ~2 minute intervals. - nic-watchdog.timer was active and nic-watchdog.service failed every run. - /usr/local/sbin/nic-watchdog.sh is misconfigured for this host: it runs `ping -I nic0 192.168.4.1`, but nic0 is a bridge slave without an IP. The host IP/gateway live on vmbr0. - Live verification: - `ping -I nic0 192.168.4.1` lost 100%. - `ping -I vmbr0 192.168.4.1` succeeded. - /var/log/nic-watchdog.log showed every 2-minute run declaring gateway unreachable on nic0 and resetting the interface. - journal timestamps matched exactly: watchdog start -> ~3s later nic0 Link Down -> ~5s later Link Up -> watchdog failure. - Action taken during diagnostics: - `systemctl disable --now nic-watchdog.timer` - stopped further scheduled watchdog resets. - Observation after disabling watchdog: - monitored nic0 for ~4 minutes from 2026-03-20 03:28 UTC to 03:32 UTC - carrier_changes stayed flat at 11868 the entire window - no new kernel link flap events occurred Revised conclusion: - Earlier in the investigation there were genuine e1000e hardware-hang and link-flap symptoms, so a physical/NIC issue may still have existed historically. - However, the ongoing periodic flaps observed during the later phase of diagnostics were being actively caused by the broken nic-watchdog service, not by spontaneous link loss. - Current immediate cluster instability driver on homelab-02 was the watchdog resetting nic0 every 2 minutes because it probed the wrong interface. Recommended next actions: - Leave nic-watchdog.timer disabled. - If watchdog behavior is still desired, rewrite it to test vmbr0 (or routing reachability) rather than nic0. - Observe cluster stability with watchdog disabled before making further hardware conclusions. - After observation, consider reverting temporary diagnostics (autoneg/offloads) in a controlled step if no spontaneous flaps recur. State after this update: - nic-watchdog.timer disabled/inactive - nic0 stable for at least 4 minutes post-disable - corosync quorate at time of recheck --- **2026-03-20 03:32:48 UTC | AI Update via MCP**

Homelab AI Platform Architecture Context

homelab_ai_platform_architecture_context_2026_04_03 projects

As of 2026-04-03, the user clarified a broader architectural vision that spans multiple related projects under /home/svc-admin/ai-projects/claude-projects. These are not isolated apps; they are intended to share a common homelab AI platform and infrastructure. Projects reviewed: 1. The Arena: a self-hosted multi-agent collaboration/debate system originally built around IRC, with persistent agent identities, role-driven personalities, configurable room behavior, TTS/web UI hooks, and use as a personal AI boardroom. 2. Family Roots genealogy platform: a local-first AI-assisted genealogy research platform integrating Gramps, PostgreSQL, archive fetchers, MCP/Brain memory, investigation workflows, oral history handling, and eventually heavier AI/media reconstruction capabilities. 3. Ohio History Channel: a research/content project needing archive/research support, writing assistance, historical source tracking, and media/content workflow support. 4. Stoned.AI: a live conversational AI content platform requiring real-time multi-agent conversation, TTS, streaming/UI integration, and project-specific agent personas. Important user intent: - The user does not want a generic command router. They want dynamic project-specific AI teams. - They want to be able to bring in and remove specific agents on demand depending on the project or session. - Example desired workflows: - load a genealogy team and discuss a specific ancestor/person, research strategies, records, media, oral history, and evidence handling - load an Ohio history team with historian/research/editor roles - load a Stoned.AI team with different personalities optimized for entertaining live conversation - The user wants agents to feel like persistent participants with roles and personalities, not just tool invocations. - They want the conversational surface to feel natural and less robotic. - The user wants every file in `/home/svc-admin/ai-projects/projects/homelab_ai_platform_architecture/docs` to be treated as required project scope and eventually written, not as optional placeholders. Prior architectural conclusion before this memory: - IRC is useful as a conversation layer, especially for The Arena and live multi-agent interaction. - IRC is not sufficient as the entire platform and should not be treated as the core of all project infrastructure. - Discord is likely not the right primary substrate because the user needs dynamic roster control and project-specific ensembles more than community-server semantics. - The right architecture is likely: 1. shared backend/control plane for agents, personas, rooms, rosters, session state, and project presets 2. shared operational/data layer (PostgreSQL + selective Brain/MCP memory) 3. multiple interaction surfaces, with IRC as one live conversational surface and richer web UIs for structured workflows Specific recommendations already made: - Keep IRC, but scope it as the conversational layer, not the whole platform. - Build a higher-level control layer above chat for: - room creation - dynamic roster management - persona assignment - room modes - project/team presets - Treat the system as a dynamic agent ensemble platform, not just an IRC bot server. Room behavior suggestions already discussed: - Bots should have stable identities with explicit config fields like name, nick, role, style, strengths, limits, and response rules. - Rooms should have behavior modes such as quiet, mentioned-only, collab, debate, and synthesis. - In #all-models/collab-style rooms, plain text should not fan out to every model automatically. - Instead, use weighted/randomized first response selection from the active roster, with cooldown bias to avoid one bot always going first. - Optional second responses should happen only when they add contrast/critique/expansion value or are explicitly invited. - Direct mention should route only to the addressed bot. - Bots should not freely recurse into inter-bot chatter unless invited by debate/synthesis/critique modes. User correction that is important: - The user explicitly does NOT want a fixed primary responder in collaborative rooms. They want the first responder randomized/varied so the room feels more like natural conversation and less like deterministic orchestration. Infrastructure implications derived from the reviewed project docs: - A shared agent runtime is needed (host CLIs or APIs, persona config, session persistence, orchestration rules). - A shared operational/data backbone is needed (PostgreSQL for operational truth; Brain/MCP for selective long-term semantic memory, not raw event state). - A media/realtime layer will matter for Stoned.AI and Arena evolution (TTS, stream UI, low-latency turn flow, possibly GPU-backed inference later). - The genealogy and history projects need structured web backends and data workflows beyond chat. Additional design clarification from later discussion: - Full hard isolation via separate provider accounts per agent is not economically viable and should be rejected. - The correct cost-aware model is one provider account per provider family, with many logical agents layered on top via separate sessions/personas/state, while accepting shared provider quota. - Multiple agents from the same CLI family are viable if they have separate session/thread state and separate persona definitions, even when sharing one authenticated account and one installed CLI binary. - Stronger isolation via separate OS users or separate config directories is optional and should only be used where local state separation is worth the operational cost. Memory/persona design clarification from later discussion: - A layered memory model is desirable: - shared brain for common platform/project facts - private brain per agent for role-specific memory and identity reinforcement - optional team/project brain for project-scoped context like genealogy, Ohio history, or Stoned.AI - room/session memory for short-lived conversational/task context - The Brain should NOT be the sole source of an agent’s persona. - Persona should be split into: - stable base identity from config - memory-based enrichment from private/shared/team namespaces - Config defines identity; memory deepens identity. - This avoids personality drift while still letting agents accumulate meaningful differences over time. Memory-routing policy clarification from later discussion: - Agents may help classify memories, but should not freestyle memory placement without bounded rules. - The correct approach is AI-assisted memory classification with explicit storage policy. - Flow: 1. agent produces potentially useful information 2. memory classifier decides whether to store it 3. classifier chooses namespace tier(s), memory type, confidence, and summary 4. memory writer commits it - Shared/core memory should only contain durable cross-project/platform facts. - Project/team memory should contain durable project-scoped facts useful to multiple agents within that project. - Private agent memory should contain role-specific behavior reinforcement, local preferences, and agent-specific learned context. - Room/session memory should contain temporary working context and may later be summarized upward or discarded. - The AI can reason about which memory belongs where, but only inside an explicit schema and policy. - The speaking/conversational AI should not be the final authority on storage. Smaller automations should handle classification, policy, dedupe, and writes so memory behavior stays consistent instead of personality-driven. Signed-contribution memory model clarification from later discussion: - Do not replace deduplication entirely with naive append behavior. - The better design is record-level dedupe plus contribution-level append. - A shared memory should have one canonical record, with multiple signed contributions from different agents. - Example pattern: - canonical memory key: something_memory - contributor entries signed by agents such as cynical_chatgpt, claude, gemini_historian - This preserves: - provenance - agreement/disagreement - perspective differences - later correction/refinement - The top-level memory should remain a stable canonical record with a normalized summary, while signed contributions live under it. - Contribution types may include: - observation - support - challenge - correction - expansion - decision - evidence - Append only when the new contribution materially adds something. - Do not allow uncontrolled append spam or memory sludge. - Periodic consolidation should update the canonical summary while preserving signed contributions beneath it. - The design goal is: what is known, what is disputed, and who said what. Important emotional/project context: - The user says these projects are really important to them and they want to get them as right as possible. - Future questions on architecture should be interpreted in light of that higher bar: avoid forcing the whole ecosystem into one chat protocol; optimize for durable shared infrastructure and reusable agent/team abstractions. Project workflow enforcement state as of 2026-04-03: - The project path is `/home/svc-admin/ai-projects/projects/homelab_ai_platform_architecture`. - The docs tree is fully scaffolded and a docs skill exists at `/home/svc-admin/.codex/skills/homelab-ai-platform-docs`. - The docs workflow now has three aligned layers: - `docs/STATUS.md` as the canonical dependency/priority manifest - the `homelab-ai-platform-docs` skill enforcing manifest-driven selection and no-rewrite rules for completed docs - per-doc local headers (`Status`, `Priority`, `Depends on`) synchronized from the manifest by `scripts/dev/sync_doc_headers.py` - `file-structure.md` under `docs/` is a frozen reference map and should not be rewritten unless explicitly requested. - Every file in `docs/` is in required scope. Config workflow enforcement state as of 2026-04-03: - A dedicated config skill exists at `/home/svc-admin/.codex/skills/homelab-ai-platform-config`. - Its UI metadata exists at `/home/svc-admin/.codex/skills/homelab-ai-platform-config/agents/openai.yaml`. - The config workflow uses `/home/svc-admin/ai-projects/projects/homelab_ai_platform_architecture/config/STATUS.md` as the canonical config manifest. - The config manifest currently defines canonical artifact families and starter files: - `agents/agent-template.toml` - `personas/persona-template.md` - `rooms/room-template.toml` - `teams/team-preset-template.toml` - `environments/local.env.example` - The config skill is intentionally tuned differently from the docs skill: - it governs structured config, not prose - it requires dependency-aware creation of canonical config artifacts - it prefers TOML for stable hierarchical config, `.env` only for secrets or runtime bindings, and avoids spreading one logical model across arbitrary formats - Future sessions should treat `config/STATUS.md` as the source of truth for config artifact readiness and ordering. Schema workflow enforcement state as of 2026-04-03: - A dedicated schemas skill exists at `/home/svc-admin/.codex/skills/homelab-ai-platform-schemas`. - Its UI metadata exists at `/home/svc-admin/.codex/skills/homelab-ai-platform-schemas/agents/openai.yaml`. - The schema workflow uses `/home/svc-admin/ai-projects/projects/homelab_ai_platform_architecture/schemas/STATUS.md` as the canonical schema manifest. - The schema manifest currently covers: - `agent.schema.json` - `room.schema.json` - `team-preset.schema.json` - `memory-record.schema.json` - `memory-contribution.schema.json` - These schemas are still placeholders and should be treated as contract artifacts, not just empty files. Scripts workflow enforcement state as of 2026-04-03: - A dedicated scripts skill exists at `/home/svc-admin/.codex/skills/homelab-ai-platform-scripts`. - Its UI metadata exists at `/home/svc-admin/.codex/skills/homelab-ai-platform-scripts/agents/openai.yaml`. - The scripts workflow uses `/home/svc-admin/ai-projects/projects/homelab_ai_platform_architecture/scripts/STATUS.md` as the canonical script manifest. - The scripts manifest currently covers: - `dev/sync_doc_headers.py` - `dev/sync_config_headers.py` - `bootstrap/init_workspace.py` - `migrations/init_platform_schema.py` - `ops/validate_project_state.py` - `sync_doc_headers.py` is already complete; the other listed scripts are planned canonical tooling. Examples workflow enforcement state as of 2026-04-03: - A dedicated examples skill exists at `/home/svc-admin/.codex/skills/homelab-ai-platform-examples`. - Its UI metadata exists at `/home/svc-admin/.codex/skills/homelab-ai-platform-examples/agents/openai.yaml`. - The examples workflow uses `/home/svc-admin/ai-projects/projects/homelab_ai_platform_architecture/examples/STATUS.md` as the canonical examples manifest. - The examples manifest currently covers: - `agents/agent-example.toml` - `rooms/room-example.toml` - `teams/team-preset-example.toml` - `memory-records/memory-record-example.json` - Examples are now treated as canonical illustrative artifacts, not optional throwaways. Current project file state snapshot as of 2026-04-03: - `docs/` contains the full numbered architecture tree, plus `STATUS.md` and frozen `file-structure.md` - `config/` contains `README.md`, `STATUS.md`, and family directories for agents, personas, rooms, teams, and environments - `schemas/` contains: - `agent.schema.json` - `memory-contribution.schema.json` - `memory-record.schema.json` - `room.schema.json` - `team-preset.schema.json` - `STATUS.md` - `scripts/` contains: - `README.md` - bootstrap, migrations, ops, and dev directories - `scripts/dev/sync_doc_headers.py` - `STATUS.md` - `examples/` contains: - `README.md` - agents, rooms, teams, and memory-records directories - `STATUS.md` - The current skill family under `~/.codex/skills` now includes: - `homelab-ai-platform-docs` - `homelab-ai-platform-config` - `homelab-ai-platform-schemas` - `homelab-ai-platform-scripts` - `homelab-ai-platform-examples` Workable lists for future planning: System design - what the backend entities are - agents - personas - provider families - rooms - room modes - active rosters - team presets - backend sessions - memory namespaces - artifacts - projects - investigations/tasks - canonical memories - memory contributions - what the control UI needs - create/edit rooms - bring/remove agents from live rosters - assign persona profiles to agent instances - switch room modes - load/save project team presets - inspect active sessions and recent room state - view shared/private/team memory attachments - inspect canonical memories and signed contributions - launch explicit workflows like debate, consensus, synthesis - where IRC fits - live conversational surface for The Arena - low-friction operator interaction with agent teams - debate/collaboration rooms - content-friendly chat interface for live interaction - where Discord fits - secondary/public-facing community surface - notifications and audience interaction - optional bridge target, not source of truth - not the primary control plane for dynamic agent rosters Exact config model sketch - per-agent .env vs YAML/TOML - keep environment variables for deployment/runtime secrets and executable paths - keep agent definitions in structured YAML or TOML, not scattered env vars - include fields like agent_key, provider_family, nick, persona_file, private_memory_namespace, shared_memory_namespaces, room_behavior_defaults, response_policy, cooldown_policy - private/shared/team brain namespace design - shared namespace for global homelab/platform facts - one private namespace per agent for role memory and persona reinforcement - one namespace per project/team such as team:genealogy, team:ohio-history, team:stoned-ai - optional room/session memory for temporary working context - prompt assembly order for each agent turn - load stable base persona from config/persona file - attach relevant private agent memory - attach relevant shared/team memory for the current project/task - attach current room/session context and recent conversation state - apply room-mode-specific response rules Memory routing model sketch - classification policy - decide store vs discard - decide namespace tier(s): core_shared, project_team, agent_private, room_session - assign memory_type, confidence, summary, rationale - suppress transient chatter, duplicates, and obvious one-off noise - namespace rules - core/shared: durable cross-project or platform-wide facts - project/team: durable facts useful within one project or team - agent/private: role-specific preferences, learned habits, persona reinforcement - room/session: short-lived working context, candidate for later summarization or expiry - storage flow - generate candidate memory from interaction - classify through bounded schema - write to selected namespace(s) - optionally promote or summarize lower-tier memories into higher-tier memories later - control policy - AI may propose placement - system rules enforce placement boundaries - auto-store only when confidence and category rules are satisfied - keep a path for review or correction when the classification is uncertain Signed contribution model sketch - canonical memory record - one stable memory key per concept/fact/decision thread - top-level normalized summary - tags, scope, timestamps, status - contribution schema - agent identity/signature - timestamp - contribution type - content - confidence - optional rationale/evidence reference - merge rules - dedupe at the canonical record level - append when the contribution materially adds perspective, disagreement, evidence, or correction - reject low-value restatements - consolidation rules - periodically update the canonical summary from accumulated contributions - preserve contributor provenance even after summary updates - keep disagreements visible instead of flattening them away Documentation enforcement sketch - every file in `/home/svc-admin/ai-projects/projects/homelab_ai_platform_architecture/docs` is required project scope - use per-doc `Status:` headers to distinguish `stub` from `complete` - allow full replacement of placeholder text in `stub` docs - forbid wholesale rewrites of `complete` docs unless explicitly requested - use `docs/STATUS.md` as the writing-state manifest - keep `file-structure.md` as a frozen reference map unless explicitly changed by the user - the docs skill must obey dependency readiness and choose the highest-priority ready stub when selecting work itself Config enforcement sketch - use `config/STATUS.md` as the canonical manifest for config artifacts - treat config families as first-class architecture artifacts, not incidental files - keep agent config, persona config, room config, team presets, and environment bindings separate - update the manifest whenever a canonical config file is created or promoted from stub/draft to complete - prefer TOML for human-edited structure and reserve `.env` for secrets/runtime bindings Schema enforcement sketch - use `schemas/STATUS.md` as the canonical manifest for schema artifacts - treat schemas as machine-readable contracts derived from stable architecture and config decisions - do not freeze schemas while their underlying model is still unresolved - use schema readiness to gate realistic examples and some automation work Scripts enforcement sketch - use `scripts/STATUS.md` as the canonical manifest for project automation - scripts should be safe, explicit, and verifiable - keep helper tooling separate from bootstrap, migrations, and ops validation paths - do not let scripts silently become the hidden architecture source of truth Examples enforcement sketch - use `examples/STATUS.md` as the canonical manifest for example artifacts - examples should mirror the canonical model, not invent alternatives - examples should depend on config and schema readiness rather than guessing at unfinished contracts This memory should serve as continuity context for future planning questions and should be updated/extended as the user clarifies more about desired behavior, project priorities, infrastructure sequencing, and workflow enforcement across non-doc artifact types.

Homelab AI Platform Bootstrap State

homelab_ai_platform_bootstrap_state_2026_04_03 projects

Homelab AI Platform bootstrap state as of 2026-04-03. ## Completed (Workstream 1 — Core Data and Config Backbone) - Workspace scaffolded at `/home/svc-admin/ai-platform/` on svc-ai - Layout: config/, config/environments/, runtime/, runtime/providers/codex, runtime/providers/gemini, logs/, artifacts/, backups/, tmp/, secrets/ - Scaffolded by `scripts/bootstrap/init_workspace.py` - Secrets directory locked down (chmod 700) - DB password generated with openssl and stored at `/home/svc-admin/ai-platform/secrets/platform_db_password` (chmod 600) - `homelab_ai_platform` database created on svc-db-01 (192.168.4.113) with dedicated `platform_runtime` role - Full platform schema applied — 22 tables, 11 indexes - Applied via `scripts/migrations/init_platform_schema.py --apply --db-url` - All tables from POSTGRES_SCHEMA.md are live: provider_families, personas, agents, agent_aliases, projects, team_presets, team_preset_agents, team_preset_memory_namespaces, rooms, room_policy_overrides, room_roster_memberships, backend_sessions, invocations, turn_decisions, tasks, artifacts, evidence_references, memory_namespaces, canonical_memories, memory_contributions, surface_message_refs, audit_events - `local.env` generated at `/home/svc-admin/ai-platform/config/environments/local.env` and corrected for this deployment: - PLATFORM_DB_HOST=192.168.4.113 (remote svc-db-01, not 127.0.0.1) - PLATFORM_BRAIN_API_URL=http://127.0.0.1:5000 (ContextKeep REST API, not placeholder port 8001) - `validate_project_state.py` passes clean ## Environment Facts Confirmed During Bootstrap - This machine is svc-ai (192.168.4.117) - ContextKeep REST API: http://127.0.0.1:5000 (web UI + REST) - ContextKeep MCP SSE endpoint: http://127.0.0.1:5100 - Gramps MCP Server: http://127.0.0.1:8000 (unrelated, genealogy project) - Ergo IRC server: running bare metal as systemd user service, ~/.config/systemd/user/ergo-irc.service, ports 6667/6697 - irc-openclaw-bridge: running bare metal as systemd user service at ~/ai-projects/projects/irc-openclaw-bridge/dist/src/index.js, health on http://127.0.0.1:3000/health - Bridge uses its own separate database: irc_openclaw_bridge on svc-db-01, owned by irc_bridge role - Bridge currently handles codex and gemini agents in #codex, #gemini, #all-models rooms ## Next Step Workstream 2: Runtime adapter layer - Provider adapter interface for Codex and Gemini CLIs - Session persistence model wired to the platform schema - Orchestration core foundation (room state, roster, turn selection) ## Project Reference - Architecture docs: /home/svc-admin/ai-projects/projects/homelab_ai_platform_architecture/ - Platform workspace: /home/svc-admin/ai-platform/ - Existing bridge (reference/predecessor): /home/svc-admin/ai-projects/projects/irc-openclaw-bridge/

Homelab Host Inventory — Restructure Plan

homelab_host_inventory_restructure_plan homelab

## Purpose Create per-host memories for every VM and node in the homelab so any AI CLI can quickly look up operational facts about any host — IP, role, services, ports — without loading a giant inventory file. ## The Goal Any AI should be able to answer questions like: - "What is the IP for svc-mgmt?" - "What VM is Grafana hosted on?" - "What services run on svc-ai?" ...by retrieving a single targeted host memory. ## Proposed Structure Per Host key: host_[hostname] (e.g. host_svc_mgmt, host_svc_ai, host_pve_01) namespace: homelab tags: [homelab, host, inventory, [hostname]] Content fields per memory: - Hostname - IP address - Proxmox node it runs on (for VMs) - Role/purpose - Group (linux/proxmox, linux/services, windows) - Key services hosted (with port numbers) - Key Docker containers if applicable - OS/version - vCPU / RAM allocation - Notes (anything operationally important) ## Source Material The authoritative source for this restructure is: - Inventory file generated 2026-04-03: homelab-ecosystem-inventory.md - Contains live-scanned data for all 15 hosts including services, ports, disk, CPU, RAM ## Hosts To Create Memories For From the inventory file (15 hosts): - pve-01 (192.168.4.53) — Proxmox node 1 - pve-02 (192.168.4.54) — Proxmox node 2 (8770W, NIC issues) - svc-sec (192.168.4.110) - svc-data-lab (192.168.4.111) — NOTE: previously incorrectly called "datalab" in old memories - svc-minecraft (192.168.4.112) - svc-db-01 (192.168.4.113) — PostgreSQL server - svc-apps (192.168.4.114) — Web apps (Kiwix, Actual Budget, NOMAD, etc.) - svc-dns-01 (192.168.4.115) - svc-monitor (192.168.4.116) — Monitoring (Grafana, Prometheus, etc.) - svc-ai (192.168.4.117) — AI runtime, Arena, Brain, IRC, TTS - linuxlab / linux-lab (192.168.4.118) - svc-nas (192.168.4.119) — NAS - svc-auto (192.168.4.120) — Automation (Ansible, GLPI bot runner) - svc-mgmt (192.168.4.121) — Management (Portainer, GLPI, etc.) - svc-dev (192.168.4.123) — Development, The Brain - Binkie-Desktop (192.168.4.104) — Windows gaming/daily driver ## Hostname Correction Note The host at 192.168.4.111 has the actual hostname `svc-data-lab` not `datalab`. Old memories using `datalab` should be updated when this restructure is done. ## Status NOT YET DONE — planned for a future session. Source inventory file available for reference when ready to build.

Homelab SSH Access Paths

homelab_ssh_access_paths_2026_04_03 general

Detailed SSH access reference for the homelab as of 2026-04-03. This memory is intended to be operationally complete enough that a fresh Codex session or another CLI can use it directly to reach the correct VM without guessing. Global defaults: - Primary homelab SSH private key on this machine: `/home/svc-admin/.ssh/id_ed25519_homelab` - Matching public key: `/home/svc-admin/.ssh/id_ed25519_homelab.pub` - Default Linux admin user for almost all homelab VMs: `svc-admin` - User override: `svc-auto` uses `ansible` - Windows SSH user: `Jason` - Common SSH safety option used successfully from this machine: `-o StrictHostKeyChecking=accept-new` - Common non-interactive reachability option used successfully from this machine: `-o BatchMode=yes -o ConnectTimeout=5` - SSH port is the standard `22` unless stated otherwise. Known SSH config aliases on this machine (`/home/svc-admin/.ssh/config`): - `svc-dev` - HostName: `192.168.4.123` - User: `svc-admin` - IdentityFile: `~/.ssh/id_ed25519_homelab` - StrictHostKeyChecking: `accept-new` - Direct command: `ssh svc-dev` - `svc-apps` - HostName: `192.168.4.114` - User: `svc-admin` - IdentityFile: `~/.ssh/id_ed25519_homelab` - StrictHostKeyChecking: `accept-new` - Direct command: `ssh svc-apps` No SSH alias is currently defined in `/home/svc-admin/.ssh/config` for the other homelab hosts, so use explicit user@ip form with the homelab key. Validated host-by-host SSH access paths (excluding `qdevice` by design): 1. `pve-01` - IP: `192.168.4.53` - Hostname returned by SSH: `homelab` - User: `svc-admin` - Key: `/home/svc-admin/.ssh/id_ed25519_homelab` - Command: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab svc-admin@192.168.4.53` - Non-interactive check form: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab -o BatchMode=yes -o StrictHostKeyChecking=accept-new -o ConnectTimeout=5 svc-admin@192.168.4.53 hostname` 2. `pve-02` - IP: `192.168.4.54` - Hostname returned by SSH: `homelab-02` - User: `svc-admin` - Key: `/home/svc-admin/.ssh/id_ed25519_homelab` - Command: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab svc-admin@192.168.4.54` - Non-interactive check form: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab -o BatchMode=yes -o StrictHostKeyChecking=accept-new -o ConnectTimeout=5 svc-admin@192.168.4.54 hostname` 3. `svc-sec` - IP: `192.168.4.110` - Hostname returned by SSH: `svc-sec` - User: `svc-admin` - Key: `/home/svc-admin/.ssh/id_ed25519_homelab` - Command: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab svc-admin@192.168.4.110` - Non-interactive check form: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab -o BatchMode=yes -o StrictHostKeyChecking=accept-new -o ConnectTimeout=5 svc-admin@192.168.4.110 hostname` 4. `datalab` - IP: `192.168.4.111` - Hostname returned by SSH: `datalab` - User: `svc-admin` - Key: `/home/svc-admin/.ssh/id_ed25519_homelab` - Command: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab svc-admin@192.168.4.111` - Non-interactive check form: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab -o BatchMode=yes -o StrictHostKeyChecking=accept-new -o ConnectTimeout=5 svc-admin@192.168.4.111 hostname` 5. `svc-minecraft` - IP: `192.168.4.112` - Hostname returned by SSH: `svc-minecraft` - User: `svc-admin` - Key: `/home/svc-admin/.ssh/id_ed25519_homelab` - Command: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab svc-admin@192.168.4.112` - Non-interactive check form: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab -o BatchMode=yes -o StrictHostKeyChecking=accept-new -o ConnectTimeout=5 svc-admin@192.168.4.112 hostname` 6. `svc-db-01` - IP: `192.168.4.113` - Hostname returned by SSH: `svc-db-01` - User: `svc-admin` - Key: `/home/svc-admin/.ssh/id_ed25519_homelab` - Command: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab svc-admin@192.168.4.113` - Non-interactive check form: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab -o BatchMode=yes -o StrictHostKeyChecking=accept-new -o ConnectTimeout=5 svc-admin@192.168.4.113 hostname` 7. `svc-apps` - IP: `192.168.4.114` - Hostname returned by SSH: `svc-apps` - User: `svc-admin` - Key: `/home/svc-admin/.ssh/id_ed25519_homelab` - SSH alias available: `svc-apps` - Preferred command: `ssh svc-apps` - Explicit command: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab svc-admin@192.168.4.114` - Non-interactive check form: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab -o BatchMode=yes -o StrictHostKeyChecking=accept-new -o ConnectTimeout=5 svc-admin@192.168.4.114 hostname` 8. `svc-dns-01` - IP: `192.168.4.115` - Hostname returned by SSH: `svc-dns-01` - User: `svc-admin` - Key: `/home/svc-admin/.ssh/id_ed25519_homelab` - Command: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab svc-admin@192.168.4.115` - Non-interactive check form: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab -o BatchMode=yes -o StrictHostKeyChecking=accept-new -o ConnectTimeout=5 svc-admin@192.168.4.115 hostname` 9. `svc-monitor` - IP: `192.168.4.116` - Hostname returned by SSH: `svc-monitor` - User: `svc-admin` - Key: `/home/svc-admin/.ssh/id_ed25519_homelab` - Command: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab svc-admin@192.168.4.116` - Non-interactive check form: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab -o BatchMode=yes -o StrictHostKeyChecking=accept-new -o ConnectTimeout=5 svc-admin@192.168.4.116 hostname` 10. `svc-ai` - IP: `192.168.4.117` - Hostname returned by SSH: `svc-ai` - User: `svc-admin` - Key: `/home/svc-admin/.ssh/id_ed25519_homelab` - Command: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab svc-admin@192.168.4.117` - Non-interactive check form: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab -o BatchMode=yes -o StrictHostKeyChecking=accept-new -o ConnectTimeout=5 svc-admin@192.168.4.117 hostname` 11. `linuxlab` - IP: `192.168.4.118` - Hostname returned by SSH: `linuxlab` - User: `svc-admin` - Key: `/home/svc-admin/.ssh/id_ed25519_homelab` - Command: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab svc-admin@192.168.4.118` - Non-interactive check form: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab -o BatchMode=yes -o StrictHostKeyChecking=accept-new -o ConnectTimeout=5 svc-admin@192.168.4.118 hostname` 12. `svc-nas` - IP: `192.168.4.119` - Hostname returned by SSH: `svc-nas` - User: `svc-admin` - Key: `/home/svc-admin/.ssh/id_ed25519_homelab` - Command: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab svc-admin@192.168.4.119` - Non-interactive check form: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab -o BatchMode=yes -o StrictHostKeyChecking=accept-new -o ConnectTimeout=5 svc-admin@192.168.4.119 hostname` 13. `svc-auto` - IP: `192.168.4.120` - Hostname returned by SSH: `svc-auto` - User: `ansible` - Key: `/home/svc-admin/.ssh/id_ed25519_homelab` - Important user override: this host does NOT use `svc-admin` for the validated SSH path; use `ansible` - Command: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab ansible@192.168.4.120` - Non-interactive check form: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab -o BatchMode=yes -o StrictHostKeyChecking=accept-new -o ConnectTimeout=5 ansible@192.168.4.120 hostname` 14. `svc-mgmt` - IP: `192.168.4.121` - Hostname returned by SSH: `svc-mgmt` - User: `svc-admin` - Key: `/home/svc-admin/.ssh/id_ed25519_homelab` - Command: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab svc-admin@192.168.4.121` - Non-interactive check form: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab -o BatchMode=yes -o StrictHostKeyChecking=accept-new -o ConnectTimeout=5 svc-admin@192.168.4.121 hostname` 15. `svc-dev` - IP: `192.168.4.123` - Hostname returned by SSH: `svc-dev` - User: `svc-admin` - Key: `/home/svc-admin/.ssh/id_ed25519_homelab` - SSH alias available: `svc-dev` - Preferred command: `ssh svc-dev` - Explicit command: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab svc-admin@192.168.4.123` - Non-interactive check form: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab -o BatchMode=yes -o StrictHostKeyChecking=accept-new -o ConnectTimeout=5 svc-admin@192.168.4.123 hostname` 16. `Binkie-Desktop` - IP: `192.168.4.104` - Hostname returned by SSH: `Binkie-Desktop` - User: `Jason` - Key: `/home/svc-admin/.ssh/id_ed25519_homelab` - This Windows host is reachable by OpenSSH from this machine. - Command: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab Jason@192.168.4.104` - Non-interactive check form: `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab -o BatchMode=yes -o StrictHostKeyChecking=accept-new -o ConnectTimeout=5 Jason@192.168.4.104 hostname` Operational rule for fresh sessions: - If the target is `svc-dev` or `svc-apps`, prefer the SSH alias first because it is already defined locally. - For every other host in this memory, use the explicit `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab user@ip` form. - Do not include or attempt `qdevice` in this operational host set. - If a fresh session needs to validate access before running work, use the non-interactive `hostname` check form listed per host. Summary of exact validated SSH user mapping: - `svc-admin`: `pve-01`, `pve-02`, `svc-sec`, `datalab`, `svc-minecraft`, `svc-db-01`, `svc-apps`, `svc-dns-01`, `svc-monitor`, `svc-ai`, `linuxlab`, `svc-nas`, `svc-mgmt`, `svc-dev` - `ansible`: `svc-auto` - `Jason`: `Binkie-Desktop` This memory intentionally excludes `qdevice` even though it exists in older inventory references.

Homelab VM Baseline Standard

homelab_vm_baseline_standard_2026_03_22 general

Homelab Linux VM baseline standard defined on 2026-03-22 for new Ubuntu server VMs. Scope: - Baseline applies to general-purpose Ubuntu service VMs in the homelab. - Separate role-specific add-ons can still be layered on top later, but the baseline includes Docker and a standard Docker project directory layout. Core baseline standard: - OS: Ubuntu 24.04 LTS server - Virtualization target: KVM/QEMU guests in Proxmox-style environment - Host identity: - unique hostname set correctly before service deployment - timezone set explicitly to `America/Chicago` - Admin access: - primary admin user: `svc-admin` - `sudo` enabled for `svc-admin` - root SSH login not used for normal administration - SSH: - `openssh-server` installed and enabled - homelab admin key present in `~svc-admin/.ssh/authorized_keys` - key-based access validated before any hardening changes - common SSH client option in the environment: `StrictHostKeyChecking accept-new` - local admin convenience access should be added so `ssh <hostname>` works from the admin workstation - DNS and name resolution: - each new VM should get a DNS `A` record in Technitium once its management IP is known - the VM hostname should resolve on the homelab network via Technitium - the admin workstation should be able to SSH to the VM by hostname, either via DNS resolution alone or via a local SSH config host entry - Technitium API automation is part of the baseline when a valid API token is available - Firewall: - `ufw` installed and enabled - default posture: deny incoming, allow outgoing - SSH allowed before firewall enablement - only explicitly needed service ports opened per host role - baseline restricted access rules: - allow `8088/tcp` for `cadvisor` from `svc-monitor` only (`192.168.4.116`) - allow node-exporter port `9100/tcp` from `svc-monitor` only (`192.168.4.116`) - allow `9001/tcp` for `portainer-agent` from `svc-mgmt` only (`192.168.4.121`) - Core operator packages: - `curl` - `wget` - `git` - `vim` - `nano` - `tmux` - `jq` - `ca-certificates` - `gnupg` - `lsb-release` - `bash-completion` - `ripgrep` - Package maintenance baseline commands during provisioning: - `sudo apt update` - `sudo apt upgrade -y` - `sudo apt autoremove -y` - Reboot policy: - always reboot after baseline provisioning and package/install work completes - Log/journal policy: - keep Ubuntu/systemd journal defaults for baseline v1 - revisit later only if retention or disk use becomes a problem - Virtualization helpers: - `qemu-guest-agent` installed and enabled inside the guest - `QEMU Guest Agent` option enabled in the Proxmox VM configuration - Container runtime baseline: - Docker installed from the official Docker apt repository - `docker-ce` - `docker-ce-cli` - `docker-compose-plugin` - Docker service enabled and running - `svc-admin` added to the `docker` group - Standard Docker directory layout under the admin home directory: - `/home/svc-admin/docker` - `/home/svc-admin/docker/cadvisor` - `/home/svc-admin/docker/node-exporter` - `/home/svc-admin/docker/portainer-agent` - Directory conventions beyond Docker: - no additional standard directories yet - revisit later if `/home/svc-admin/scripts` or other common paths become consistently useful - Baseline monitoring/agent compose projects: - `/home/svc-admin/docker/cadvisor/docker-compose.yml` - service name: `cadvisor` - image: `gcr.io/cadvisor/cadvisor:latest` - container name: `cadvisor` - restart policy: `unless-stopped` - published port: `8088:8080` - volumes: - `/:/rootfs:ro` - `/var/run:/var/run:ro` - `/sys:/sys:ro` - `/var/lib/docker/:/var/lib/docker:ro` - `/home/svc-admin/docker/node-exporter/docker-compose.yml` - service name: `node-exporter` - image: `prom/node-exporter:latest` - container name: `node-exporter` - restart policy: `unless-stopped` - network mode: `host` - volume: - `/:/host:ro,rslave` - command: - `--path.rootfs=/host` - expected exposed port via host networking: `9100/tcp` - `/home/svc-admin/docker/portainer-agent/docker-compose.yml` - service name: `portainer_agent` - image: `portainer/agent:sts` - container name: `portainer_agent` - restart policy: `unless-stopped` - published port: `9001:9001` - volumes: - `/var/run/docker.sock:/var/run/docker.sock` - `/var/lib/docker/volumes:/var/lib/docker/volumes` - Baseline container activation: - after the compose files are created, baseline application should also start the standard containers - expected commands: - `cd /home/svc-admin/docker/cadvisor && docker compose up -d` - `cd /home/svc-admin/docker/node-exporter && docker compose up -d` - `cd /home/svc-admin/docker/portainer-agent && docker compose up -d` - baseline validation should confirm the three containers are running - Networking: - verify expected management IP after provisioning - verify SSH reachability after firewall and SSH setup - Documentation expectation: - add each new VM to inventory/ContextKeep once SSH access is working - Backup expectation: - VM-level backups will be configured in Proxmox - backup scheduling is manual for now - automatic backup policy will be added later once the backup system is finished - Automatic updates policy: - enable unattended upgrades by default for now - revisit later if it becomes disruptive - Security extras: - `fail2ban` not included in the baseline by default - leave it off unless a specific host later needs it - Baseline validation checks: - hostname correct - Ubuntu version correct - timezone correct - `svc-admin` sudo works - SSH login works with key auth - `ufw status` is active, default policy is correct, and SSH is allowed - `qemu-guest-agent` is active - Proxmox guest agent option is enabled - Docker is active - `docker compose version` works - expected directories exist under `/home/svc-admin/docker` - baseline compose files exist in their expected directories - restricted firewall rules match the baseline source IP policy - unattended upgrades are enabled if the baseline is fully applied - `cadvisor`, `node-exporter`, and `portainer-agent` containers are running - the hostname resolves in Technitium DNS when API credentials or token are available - `ssh <hostname>` works from the admin workstation - no half-configured packages remain Role-specific add-ons: - AI host baseline: - local model runtime and GPU/runtime setup only when that host is specifically assigned the AI role - Database host baseline: - database packages and data layout only on DB-designated hosts - App host baseline: - application-specific compose projects and data paths added after baseline completion Explicitly not universal baseline items: - application-specific ports beyond the baseline monitoring/agent stack - application stacks or compose projects outside the standard Docker baseline set Observed de facto environment when baseline was defined: - `svc-dev` and `svc-apps` are both Ubuntu 24.04.4 LTS KVM guests - both already include `curl`, `wget`, `git`, `vim`, `tmux`, `jq`, and `ufw` - `qemu-guest-agent` was not yet consistently present when the baseline was formalized - Docker was present on `svc-apps` but not yet on `svc-dev` when this standard was updated Baseline interpretation: - This is a pragmatic homelab standard, not a high-compliance hardening benchmark. - Keep the base consistent across service VMs, then add only what the VM role needs above that baseline. --- **2026-03-22 13:17:08 UTC | AI Update via MCP**

Homelab VM Creation Baseline TODO

homelab_vm_creation_baseline_todo_2026_03_22 general

On 2026-03-22, a follow-up baseline item was identified: define and standardize VM creation in Proxmox as part of the homelab baseline, not just the in-guest Ubuntu configuration. This new baseline area should cover the provisioning phase before guest setup, including likely defaults such as: - Proxmox node placement or placement rule - VM naming and ID convention - CPU type and default vCPU count - RAM default - disk sizing and storage target - machine type / BIOS standard - network bridge and Proxmox firewall setting - guest agent enabled in Proxmox - on-boot behavior - Ubuntu install source/template or cloud-init approach - tags/notes conventions if used Current status: - guest baseline is already defined and implemented for svc-dev - VM creation baseline still needs to be formally defined - user requested both a memory entry and a Vikunja task so the work is captured Next action: - create a Vikunja task to define the Proxmox VM creation baseline standard and integrate it with the existing guest baseline. --- **2026-03-22 13:53:10 UTC | Created via MCP**

Hostname Assignment Plan Cancri Arietis Scorpii

hostname_assignment_plan_cancri_arietis_scorpii_2026_03_20 general

Hostname assignment plan for the current homelab environment. Cancri - alpha-cancri (homelab) - beta-cancri (svc-mgmt) - gamma-cancri (svc-dns-01) - delta-cancri (svc-db) - epsilon-cancri (svc-apps) - zeta-cancri (svc-monitor) - eta-cancri (svc-sec) - theta-cancri (svc-nas) - iota-cancri (svc-ai) - kappa-cancri (svc-auto-01) - lambda-cancri (unassigned) - mu1-cancri (unassigned) - mu2-cancri (unassigned) - nu-cancri (unassigned) - xi-cancri (unassigned) - omicron1-cancri (unassigned) - omicron2-cancri (unassigned) - pi1-cancri (unassigned) - pi2-cancri (unassigned) - rho1-cancri (unassigned) - rho2-cancri (unassigned) - sigma1-cancri (unassigned) - sigma2-cancri (unassigned) - sigma3-cancri (unassigned) - tau-cancri (unassigned) - upsilon1-cancri (unassigned) - upsilon2-cancri (unassigned) - phi1-cancri (unassigned) - phi2-cancri (unassigned) - chi-cancri (unassigned) - psi1-cancri (unassigned) - psi2-cancri (unassigned) - omega-cancri (unassigned) Arietis - alpha-arietis (homelab-02) - beta-arietis (svc-dev) - gamma-arietis (linuxlab) - delta-arietis (datalab) - epsilon-arietis (svc-minecraft) - zeta-arietis (unassigned) - eta-arietis (unassigned) - theta-arietis (unassigned) - iota-arietis (unassigned) - kappa-arietis (unassigned) - lambda-arietis (unassigned) - mu-arietis (unassigned) - nu-arietis (unassigned) - xi-arietis (unassigned) - omicron-arietis (unassigned) - pi-arietis (unassigned) - rho1-arietis (unassigned) - rho2-arietis (unassigned) - rho3-arietis (unassigned) - sigma-arietis (unassigned) - tau1-arietis (unassigned) - tau2-arietis (unassigned) Scorpii - alpha-scorpii (qdevice) - beta-scorpii (unassigned) - delta-scorpii (unassigned) - epsilon-scorpii (unassigned) - zeta1-scorpii (unassigned) - zeta2-scorpii (unassigned) - eta-scorpii (unassigned) - theta-scorpii (unassigned) - iota1-scorpii (unassigned) - iota2-scorpii (unassigned) - kappa-scorpii (unassigned) - lambda-scorpii (unassigned) - mu1-scorpii (unassigned) - mu2-scorpii (unassigned) - nu-scorpii (unassigned) - xi-scorpii (unassigned) - rho-scorpii (unassigned) - sigma-scorpii (unassigned) - tau-scorpii (unassigned) - upsilon-scorpii (unassigned) - omega1-scorpii (unassigned) - omega2-scorpii (unassigned) Notes: - Hostnames are intended to be lowercase with hyphens only. - No dots, spaces, or superscript characters should be used in actual machine hostnames. - This is the current naming plan, not yet fully applied to the systems. --- **2026-03-20 01:24:02 UTC | Created via MCP**

HTML Day By Day Study Plan

html_day_by_day_study_plan_2026_03_19 general

Day-by-day HTML study plan created on 2026-03-19. Day 1: What HTML Is Learn what HTML does, page structure, html/head/body, headings, and paragraphs. Practice: make a very basic page, add a page title, and add headings and paragraphs. Goal: understand that HTML is structure, not styling. Day 2: Links, Images, and Lists Learn anchor tags, image tags, ordered lists, and unordered lists. Practice: add links to a page, add at least one image, and add both kinds of lists. Goal: build a simple about-me or favorite-things page. Day 3: More Structure Learn div, span, line breaks, horizontal rules, and how content is grouped. Practice: update a page to use sections of content and make it cleaner and easier to read. Goal: get used to organizing content instead of dumping tags on the page. Day 4: Semantic HTML Learn header, nav, main, section, article, and footer. Practice: rebuild a page using semantic tags and make a simple homepage layout with those elements. Goal: start thinking in page sections. Day 5: Text Content Tags Learn strong, em, blockquote, inline code, code blocks if covered, figure, and figcaption. Practice: build a mini article page and use the correct tags for different kinds of content. Goal: choose tags based on meaning, not just appearance. Day 6: Tables Learn table, row, header cell, and data cell. Practice: make a simple table such as a weekly schedule, favorite games list, or study plan. Goal: understand structured tabular content. Day 7: Review and Rebuild Practice: rebuild a full page from memory including headings, paragraphs, links, images, lists, and semantic sections. Goal: prove you can build without following step by step. Day 8: Forms Part 1 Learn form, input, label, and button. Practice: build a basic contact form and connect labels correctly. Goal: understand the basic structure of forms. Day 9: Forms Part 2 Learn textarea, select, option, placeholder, required, and different input types. Practice: improve the previous form and make a signup or feedback form. Goal: create a more complete, usable form. Day 10: Build a Profile Page Practice: build a personal profile page using only HTML. Include bio, image, links, lists, and semantic structure. Goal: combine multiple HTML concepts into one page. Day 11: Build an Article Page Practice: build a simple article or blog-style page. Include title, sections, quotes, lists, and possibly a figure and caption. Goal: get better at content-heavy page structure. Day 12: Build a Product or Info Page Practice: make a product info page or project info page. Include navigation, features list, image, and contact or signup form. Goal: structure a more realistic page. Day 13: Rebuild Without Looking Practice: build one full page with no tutorial open, using notes only if stuck. Goal: expose weak spots in memory. Day 14: Final HTML Checkpoint Practice: create a multi-section homepage from scratch. Include header, nav, main, at least two sections, image, list, form, and footer. Final checkpoint: can create a full HTML page from scratch, know what common tags do, structure content intentionally, and be ready to move into CSS with a solid base. Daily study method: 45 to 90 minutes per day, first half learning and second half building, then end by changing one thing independently. Rule: do not just read tags; use them the same day. --- **2026-03-19 10:59:04 UTC | Created via MCP**

HTML Learning Progress Session 1

html_learning_progress_2026_03_19_session_1 general

HTML learning progress session on 2026-03-19. User started basic HTML practice in tutor mode. Topics covered: what HTML is, the difference between structure and styling, the purpose of doctype/html/head/body, headings, paragraphs, comments, unordered vs ordered lists, list items, horizontal rule as a section divider, links, images, href, src, and alt. User asked about HTML comments and learned the syntax ``. User also asked about VS Code cursor movement and learned that `End` jumps to the end of the line. Practice completed by the user: - Created a valid minimal HTML page with doctype, html, head, body, title, h1, and paragraph - Added learning comments to reinforce memory of each tag - Added a horizontal rule, h2, unordered list, and list items - Recalled the difference between `ul` and `ol` with light prompting - Added a link to MDN HTML docs and an image with alt text - Learned semantic structure at a basic level using `header`, `main`, `section`, and `footer` - Attempted a semantic rewrite of the page and initially confused `head` with `header` - Corrected that mistake and produced a mostly correct semantic page structure Current skill state at end of session: - User can build a minimal HTML page from scratch - User understands where visible content belongs - User remembers or can now re-derive `ul`, `ol`, and `li` - User has successfully used `a`, `href`, `img`, `src`, and `alt` - User understands that `head` is invisible metadata and `header` is visible page content - User can place `header`, `main`, and `footer` correctly inside `body` Important learning point from this session: - A useful beginner confusion was corrected: `head` and `header` are not interchangeable. `head` belongs directly under `html` and contains metadata. `header` belongs inside `body` and contains visible top-of-page content. Current Day 1 status: - Day 1 is effectively complete. Suggested next lesson when resuming: - continue with more semantic HTML and content structure as needed, or move into the next planned HTML topics from the day-by-day study plan. --- **2026-03-19 20:46:43 UTC | AI Update via MCP**

HTML Learning Progress Session 2

html_learning_progress_2026_03_24_session_2 general

HTML learning progress session on 2026-03-24. User completed Day 2 and Day 3 of the HTML day-by-day study plan in tutor mode. Topics covered in this session: - Day 2: links, images, unordered lists, ordered lists - Day 3: div, span, br, hr, and grouping content more intentionally - Clarified that lists should not be placed inside p tags - Clarified that br is for line breaks in text, not general spacing - Clarified that hr is a thematic/section break and usually should not be preceded by br for spacing - User asked about VS Code navigation around tags and learned about bracket/tag navigation limits and related shortcuts Practice completed by the user: - Built a Day 2 HTML page from a near-blank boilerplate - Correctly used a, href, img, src, alt, ul, ol, and li - Iteratively corrected structural mistakes including removing lists from inside paragraph tags - Corrected text/content mistakes such as headings typo and Open WebUI spelling - Removed unnecessary br usage from the Day 2 page - Built a Day 3 HTML page using multiple div sections - Correctly used span inline within paragraph text - Correctly used br in an address/location-style line-break context - Correctly used hr as a section divider Current skill state at end of session: - User can build a basic HTML document from a blank boilerplate - User can use links, images, ordered lists, and unordered lists correctly - User understands that li belongs inside ul or ol - User understands that p should contain paragraph text, not block-level lists - User understands the difference between div and span at a beginner level - User understands that br is for actual line breaks and hr is for section/thematic breaks - User is beginning to place tags intentionally rather than just copying patterns Current status: - Day 2 complete - Day 3 complete Recommended next lesson when resuming: - Day 4: semantic HTML - Focus on header, nav, main, section, article, and footer - Reinforce choosing semantic tags over generic div when a meaningful element exists Operational note: - User said two days of HTML was enough for now and may either continue HTML later or switch focus to LedgerBridge work. --- **2026-03-24 UTC | Stored via Codex**

HTML Only Learning Plan

html_only_learning_plan_2026_03_19 general

HTML-only learning plan created on 2026-03-19. Goal: get comfortable building page structure without guessing. Stage 1: Core HTML Basics Learn what HTML is, tags and elements, opening and closing tags, nesting, document structure, html/head/body, headings, paragraphs, links, images, lists, line breaks, and horizontal rules. Practice: make a basic page from scratch, add a title, add headings and paragraphs, add links and images, and make ordered and unordered lists. Checkpoint: can build a plain page without looking everything up every minute. Stage 2: Semantic Structure Learn why semantic HTML matters and how to use header, nav, main, section, article, aside, and footer. Practice: take a messy page and organize it with semantic tags, then build a page with real structure including header, navigation, main content, and footer. Checkpoint: understand the difference between a page that merely works and a page that is structured well. Stage 3: Text and Content Elements Learn strong vs bold, emphasis vs italic, quotes, code blocks, inline code, tables, figure, and figcaption. Practice: create a page with different content types, make a mini article page, and make a simple table. Checkpoint: know which tag to use for common content instead of using random tags. Stage 4: Forms Learn form, input, label, textarea, select, option, button, placeholders, required fields, and basic form structure. Practice: build a contact form, build a signup form, and connect labels correctly to inputs. Checkpoint: can build a usable form with proper labels. Stage 5: HTML Page Building Practice building complete pages with only HTML first: personal profile page, resume page, product information page, and contact page. Goal: stop thinking of HTML as isolated tags and start thinking in sections and page structure. Best mini projects in order: simple personal bio page, favorite things page with lists and images, article page, contact form page, and multi-section homepage in pure HTML. Rules: do not worry about CSS yet except maybe tiny bits; focus on structure first; use semantic tags whenever possible; try to write pages from memory after building once. Study method for each HTML topic: learn what the tag does, use it in a small example, rebuild without looking, then add one extra section independently. HTML completion checkpoint: can create a full page structure from scratch, use semantic elements correctly, add text, links, images, lists, and forms, and make a multi-section page without following a tutorial line by line. --- **2026-03-19 10:55:58 UTC | Created via MCP**

LedgerBridge AI Assisted Workflow

ledgerbridge_ai_assisted_workflow_2026_03_21 general

LedgerBridge is a strong fit for AI-assisted service automation, but AI should support the workflow around the deterministic reconciliation engine rather than replace the core transformation logic. Recommended AI-assisted LedgerBridge workflow: 1. Lead targeting - Use AI to find likely prospects with recurring spreadsheet/export pain. - Good targets: small e-commerce brands, bookkeepers, operations managers, office managers, owner-operators. - AI can help build lead lists, identify likely tech stack clues, and infer probable reconciliation/reporting pain points. 2. Outreach - Use AI to draft short personalized outbound messages. - Position LedgerBridge as recurring spreadsheet reconciliation and reporting automation, not as a vague AI platform. - Strong simple pitch: automate recurring exports from two systems into one clean report. 3. Discovery - Client provides sample CSVs, column explanations, current manual process, desired output, and frequency. - AI can summarize schema differences, identify likely mappings, flag date/format inconsistencies, and draft discovery notes. - Human review is still required. 4. Proposal - AI can draft short proposals that summarize the current pain, define scope, explain the deliverable, and estimate time savings. - Useful outputs: setup scope, recurring scope, price, required customer inputs, and success criteria. 5. Build - LedgerBridge’s core transformation should remain deterministic code/config. - AI can assist with mapping scaffolds, validation rule ideas, test documentation, and edge-case reasoning. - AI should not be the final authority for reconciliation correctness. 6. Delivery - AI can draft delivery emails, explain exceptions, summarize outputs, and generate plain-English report notes. - Deliverables remain the clean report plus a concise client-facing summary. 7. Support and retention - AI can help detect file-format drift, summarize failures, draft support replies, and suggest upsell opportunities. Safe scope boundaries: - AI should help with lead generation, outreach, onboarding/discovery, proposal drafting, delivery support, and internal documentation. - Do not rely on AI alone for reconciliation accuracy, accounting interpretation, or silent data transformations. Best first AI layer to implement for LedgerBridge: - Discovery + proposal support. Reason: closest to revenue, low technical risk, directly supports selling the service. Business framing: - Front end: AI-assisted lead generation, outreach, and proposal drafting. - Middle: deterministic LedgerBridge reconciliation pipeline. - Back end: AI-assisted delivery, support, and documentation. Practical outcome: - AI helps find the right people, talk to them faster, understand their files faster, document work faster, and support them faster. - LedgerBridge itself still performs the core job: turning messy recurring exports into one clean report. --- **2026-03-21 17:21:53 UTC | Created via MCP**

LedgerBridge AI Safe vs Unsafe Tasks

ledgerbridge_ai_safe_vs_unsafe_tasks_2026_03_21 general

LedgerBridge AI safe vs unsafe task boundaries discussed on 2026-03-21. Safe or relatively safe AI-assisted tasks: - lead generation and research - outreach drafting - discovery summaries from sample files - schema comparison and likely mapping suggestions - proposal drafting - onboarding instructions - delivery email drafting - exception summary drafting - internal documentation and support notes Use AI with caution, but human review required: - mapping recommendations - validation rule suggestions - edge-case reasoning - draft explanations of anomalies or mismatches Unsafe to delegate fully to AI: - final reconciliation correctness - accounting interpretation - silent production data transformations without deterministic checks - final authority on client-facing numbers - automated acceptance of ambiguous matches without review Operating rule: - AI should assist the workflow around LedgerBridge. - Deterministic code should remain responsible for the actual transformation and reconciliation pipeline. - Human review should remain responsible for correctness and customer trust. Business implication: - AI improves speed around sales, onboarding, communication, and support. - LedgerBridge itself should remain the reliable engine for data processing. --- **2026-03-21 17:25:58 UTC | Created via MCP**

LedgerBridge Customer Config Schema Ideas

ledgerbridge_customer_config_schema_ideas_2026_03_21 general

LedgerBridge customer config schema ideas discussed on 2026-03-21. Strategic goal: - Push as much customer-specific behavior as possible into config instead of one-off code. - The more LedgerBridge becomes config-driven, the lower setup time and the better the margins. A customer config should likely capture: - customer_id - workflow/job name - expected input filenames - input file locations or intake rules - output filename and destination - column mappings for each input file - normalization rules for dates, amounts, statuses, and IDs - merge/reconciliation keys - handling of blanks/nulls/defaults - validation rules and required columns - archive/quarantine behavior - schedule metadata - delivery metadata Potential config sections: 1. metadata - customer name - workflow identifier - schedule/frequency 2. inputs - file names - required vs optional inputs - delimiters/encoding assumptions - per-file column mapping 3. normalization - date format rules - case normalization - trimming/whitespace cleanup - amount parsing - canonical field naming 4. reconciliation logic - join keys - refund/sales handling - matching precedence - duplicate rules 5. output - output columns - ordering - file name - destination path or delivery mode 6. validation - required columns - minimum row expectations - malformed input behavior - quarantine/archive rules 7. logging and support - log paths - run identifiers - support notes What should probably remain in code instead of config: - core deterministic pipeline behavior - shared parsing/validation helpers - reusable transformation primitives - safety checks - test harness and execution model Practical implementation principle: - customer config should define what changes per customer - code should define how the pipeline executes reliably for every customer. --- **2026-03-21 17:25:58 UTC | Created via MCP**

LedgerBridge Document Set and Planned Docs

ledgerbridge_document_set_and_planned_docs_2026_03_20 general

LedgerBridge documentation structure captured on 2026-03-20. Project path: - `/home/svc-admin/ai-projects/projects/ledgerbridge` Current documents in the LedgerBridge project directory: - `SCRIPT_BEHAVIOR.md` - technical explanation of what `process_reports.py` does - inputs, outputs, logs, normalization behavior, failure handling, cron schedule - `BUSINESS_EXPLANATION.md` - business-language explanation of the project - what problem it solves, why it matters, who would pay for it - `FIRST_OFFER.md` - smallest sellable version of the offer - 2 files in, 1 report out, narrow service framing, early pricing - `FIRST_CUSTOMER_PROFILE.md` - defines the first buyer more narrowly than just “small business” - focuses on buyer types like owner-operator, office manager, operations lead, or bookkeeper - especially relevant fit: small e-commerce operator or admin-heavy small business manually reconciling recurring exports - `OUTREACH_MESSAGE.md` - includes short outreach copy that can be used in email, DM, Facebook, LinkedIn, or a local business contact - purpose is to reduce hesitation and get real workflow examples from potential buyers Requested/important documentation set for tightening LedgerBridge toward production: - `POSITIONING.md` - should contain one sentence for what LedgerBridge is - one sentence for who it is for - one sentence for the outcome - one sentence for what it is not - purpose: prevent the project from drifting into vague generic automation positioning - `FIRST_CUSTOMER_PROFILE.md` - define the exact first buyer, not just “small business” - examples discussed: - small e-commerce operator - office manager at a 5-20 person company - bookkeeper reconciling sales/refunds manually - `OUTREACH_MESSAGE.md` - should contain a short message that could actually be sent - channels can include email, DM, Facebook, LinkedIn, or local business contact - purpose: remove hesitation when it is time to start outreach - `CUSTOMER_DISCOVERY_QUESTIONS.md` - should capture questions like: - what files do you export now? - how often? - who cleans them? - how long does it take? - what mistakes happen? - what would success look like? - `DELIVERY_MODEL.md` - should define: - how files get in - how reports get out - where logs live - who gets notified on failure - purpose: service delivery is what the customer experiences - `ROADMAP.md` - should stay grounded, not startup fantasy - staged reality discussed: - phase 1: one customer, two files, one report - phase 2: config-driven mappings - phase 3: customer-specific templates - phase 4: dashboard or portal if justified Operational note: - as of capture time, `FIRST_CUSTOMER_PROFILE.md` and `OUTREACH_MESSAGE.md` already exist in the project directory - the remaining high-value docs still to create are: - `POSITIONING.md` - `CUSTOMER_DISCOVERY_QUESTIONS.md` - `DELIVERY_MODEL.md` - `ROADMAP.md` Intent of this document set: - tighten LedgerBridge into a narrow, saleable, production-oriented service - reduce ambiguity around offer, buyer, outreach, delivery, and evolution path - keep the project grounded in one real workflow before expanding scope --- **2026-03-20 17:40:00 UTC | AI Update via MCP** --- **2026-03-20 17:41:12 UTC | Created via MCP**

LedgerBridge First Customer Acquisition Workflow

ledgerbridge_first_customer_acquisition_workflow_2026_03_21 general

LedgerBridge first-customer acquisition workflow discussed on 2026-03-21. Best framing: - LedgerBridge should be pitched as recurring spreadsheet reconciliation and reporting automation for small businesses. - Simple pitch: automate recurring exports from two systems into one clean report. - Do not lead with Python, VMs, infrastructure, or vague AI-platform language. Ideal early buyers: - owner-operators - office managers - operations leads - bookkeepers - bookkeeping-heavy small businesses - small e-commerce or transaction-based businesses Strong buying signals: - they already export CSVs from multiple systems - they already reconcile or clean them manually - they hate the work - they can provide sample files quickly - they know what the final report should look like Fastest path to first money: 1. Find one person already doing this manually. 2. Ask for sample files. 3. Map their exports into a draft output. 4. Show them the cleaned result. 5. Charge to put it on a recurring schedule. AI-assisted acquisition flow: 1. Lead targeting - Use AI to find prospects likely dealing with recurring export/reporting pain. - Good target groups: small e-commerce brands, bookkeepers, operations managers, office managers, owner-operators. 2. Outreach - Use AI to draft short personalized outbound messages. - Focus on manual cleanup pain, not AI hype. - Example angle: if they manually combine recurring exports every week, LedgerBridge can automate that into one clean report. 3. Discovery - Get sample CSVs and a short explanation of the current workflow. - Use AI to summarize schema differences, likely mappings, and open questions. - Human review still required. 4. Proposal - Use AI to draft a short scoped proposal. - Include current pain, deliverable, schedule, and what success looks like. 5. Build and validate - Implement customer-specific config/mapping. - Keep core reconciliation deterministic. - Validate output before delivery. 6. Deliver and convert to recurring revenue - Deliver the clean report and explain the recurring automation model. - Offer setup fee plus monthly maintenance/support. Best first AI layer to implement: - Discovery plus proposal support. Reason: - closest to revenue - low technical risk - accelerates the sales process without trusting AI for core data correctness Practical sales principle: - The first sale is for proof, not prestige. - Choose the customer for clarity of pain, simplicity of workflow, and speed of decision, not company size or status. --- **2026-03-21 17:22:31 UTC | Created via MCP**

LedgerBridge First MVP Scope

ledgerbridge_first_mvp_scope_2026_03_21 general

LedgerBridge first MVP scope discussed on 2026-03-21. The first real sellable version should stay narrow. In scope: - one customer - one specific recurring workflow - two recurring CSV inputs - one standardized output report - scheduled execution - basic validation and logging - archive/quarantine behavior for successful/failed files - simple delivery model such as filesystem output Out of scope for the first MVP: - full SaaS platform - generic automation suite - unlimited input types - unlimited custom reports - accounting software replacement - broad dashboard platform - complex compliance-heavy workflows Success criteria: - customer no longer manually reconciles those files - output is usable and correct - process runs predictably - customer prefers paying to continue rather than going back to manual work Strategic rule: - keep the MVP small enough to sell and deliver reliably - do not let first-customer excitement turn the offer into a giant custom consulting engagement. --- **2026-03-21 17:25:58 UTC | Created via MCP**

LedgerBridge First Offer and Sales Pitch

ledgerbridge_first_offer_and_sales_pitch_2026_03_21 general

LedgerBridge first offer and sales pitch guidance discussed on 2026-03-21. Best positioning: - LedgerBridge should be presented as recurring spreadsheet reconciliation and reporting automation for small businesses. - The customer is buying less manual work, fewer reporting mistakes, and one clean output instead of multiple messy exports. Best simple pitch: - If you are manually cleaning two recurring export files every week, I can automate that and give you one clean report instead. Alternative short pitch: - I automate recurring spreadsheet cleanup between two systems so you get one clean report without doing it by hand. What to emphasize: - less manual cleanup - fewer errors - repeatable process - cleaner reporting - reduced dependency on one person remembering the workflow What not to lead with: - Python - VM - infrastructure - AI platform language - vague automation buzzwords First offer structure: - customer provides two sample export files, workflow explanation, desired frequency, and desired output - LedgerBridge maps the files into one normalized format - runs on a schedule - provides one consolidated report - includes basic logging and malformed-file handling Early commercial framing: - charge setup fee for initial mapping and validation - charge monthly support/maintenance if hosting/running/maintaining the workflow Ideal first customer: - small business already doing recurring manual cleanup - buyer is owner-operator, office manager, operations lead, or bookkeeper - best early niche: small e-commerce or transaction-based business Core rule: - keep the offer narrow and outcome-based - do not drift into promising a giant platform or unlimited custom reporting. --- **2026-03-21 17:25:58 UTC | Created via MCP**

LedgerBridge Operating Model

ledgerbridge_operating_model_2026_03_21 general

LedgerBridge operating model discussed on 2026-03-21. LedgerBridge should operate as a narrow service business with repeatable workflow stages. Front end: 1. Lead generation - identify prospects with recurring export/reporting pain 2. Outreach - personalized messages focused on manual cleanup pain 3. Discovery - collect sample files and understand the current workflow 4. Proposal - define scope, deliverable, and pricing Middle: 5. Setup/build - create customer-specific config and mapping - keep core pipeline deterministic 6. Validation - test sample inputs and confirm output matches expectations Back end: 7. Recurring delivery - run the workflow on a schedule - produce the clean consolidated report 8. Support/maintenance - monitor input drift, handle failures, maintain configs, and support the customer 9. Retention/expansion - upsell additional workflows, reports, or support levels Operating principle: - one customer, one workflow, two inputs, one output, one schedule is the right early model - avoid broadening into a platform too early - use AI to accelerate lead gen, discovery, proposal drafting, delivery summaries, and documentation - keep the data pipeline itself deterministic and testable. --- **2026-03-21 17:25:58 UTC | Created via MCP**

LedgerBridge Pricing and Setup Time Assumptions

ledgerbridge_pricing_and_setup_time_assumptions_2026_03_21 general

LedgerBridge pricing and setup assumptions discussed on 2026-03-21. Core assumption: - LedgerBridge should be sold as a narrow recurring reconciliation/reporting automation service, not as a generic AI platform. Estimated setup time by customer complexity: - Simple customer: 1 to 3 hours - Moderately messy customer: 3 to 8 hours - Difficult real-world customer: 1 to 2 days Typical setup work includes: 1. Initial review - 15 to 45 minutes - Inspect sample files, understand columns, identify obvious issues. 2. Mapping and config setup - 30 to 90 minutes - Define field mapping, status handling, date parsing, normalization rules, and output structure. 3. Testing and debugging - 30 minutes to 3 hours - Validate output, fix parsing issues, handle messy source data, and test edge cases. 4. Final validation - 15 to 60 minutes - Confirm the output matches what the customer expects. Biggest setup-time variables: - cleanliness of source exports - consistency of columns and formatting - clarity of the customer’s desired output - presence of edge cases such as refunds, duplicates, blanks, partial IDs, inconsistent naming, or irregular dates Practical commercial conclusion: - Setup fees are justified because setup is real work and can easily take several hours. - Long-term scalability depends on getting average setup time down to around 2 to 4 hours for most new customers, with near-zero ongoing labor unless the source files change. Early pricing guidance from prior LedgerBridge docs: - Suggested early setup fee: roughly $100 to $300 for very small/low-risk workflows - For workflows with higher pain/value: roughly $300 to $750 - Optional monthly support/maintenance: roughly $25 to $149 depending on importance, support needs, and hosting/maintenance expectations Strategic goal: - Push as much as possible into config-driven, repeatable setup and minimize custom code per customer. - The more the workflow becomes config-only instead of one-off code, the better the margins and the closer LedgerBridge gets to a reusable business system. --- **2026-03-21 17:22:31 UTC | Created via MCP**

LedgerBridge Retainer and Support Model

ledgerbridge_retainer_and_support_model_2026_03_21 general

LedgerBridge retainer and support model discussed on 2026-03-21. After setup, LedgerBridge can be sold with an optional recurring support/maintenance model. Monthly support can include: - scheduled execution/hosting - maintenance of the workflow - monitoring for input drift or failures - support for malformed files - minor config adjustments - recurring delivery of the clean report - traceability through logs/archive/quarantine behavior What support should not automatically include: - unlimited new custom reports - major workflow redesign - unlimited custom consulting - large schema changes without repricing Triggers for extra work or repricing: - new input file types - major column changes from source systems - new outputs beyond the original scope - more frequent runtime needs than agreed - extra reporting/analytics requests Commercial framing: - setup fee pays for discovery, mapping, testing, and first deployment - monthly fee pays for reliability, maintenance, support, and recurring runs Customer-facing value: - they do not need to maintain the workflow - errors are caught instead of silently corrupting the result - the report keeps arriving without manual reconciliation Strategic goal: - use support as recurring revenue, but define boundaries so the business does not become cheap unlimited custom labor. --- **2026-03-21 17:25:58 UTC | Created via MCP**

LedgerBridge Setup Checklist

ledgerbridge_setup_checklist_2026_03_21 general

LedgerBridge setup checklist discussed on 2026-03-21. Repeatable onboarding/setup checklist for a new customer: 1. Confirm customer workflow - what are the two source systems - what files are exported - how often are they exported - what is the desired final report 2. Collect sample inputs - get real example exports - confirm column meanings - identify required vs optional columns 3. Analyze the files - compare schemas - identify inconsistent formats - identify likely reconciliation keys - identify data quality issues 4. Draft mapping/config - define input filenames - define field mapping - define normalization rules - define output structure - define validation rules 5. Build the customer workflow - create config - connect it to shared deterministic pipeline logic 6. Test with sample data - verify row counts and output structure - check edge cases and malformed values - confirm the report matches expectations 7. Define runtime behavior - input location - output location - schedule - archive/quarantine paths - logs 8. Deliver and confirm - provide sample output - explain how recurring delivery will work - resolve final expectation gaps 9. Move to recurring support - monitor file changes - maintain config - support failures and updates Checklist principle: - every customer should go through the same stages even if the config differs. --- **2026-03-21 17:25:58 UTC | Created via MCP**

LedgerBridge Target Customer Profiles

ledgerbridge_target_customer_profiles_2026_03_21 general

LedgerBridge target customer profiles discussed on 2026-03-21. Best early buyer types: - owner-operators - office managers - operations leads - bookkeepers - admin-heavy staff responsible for reporting Best early business traits: - already exporting CSVs from multiple systems - already cleaning/reconciling files manually - already frustrated by repetitive spreadsheet work - not technical enough to automate it themselves - can provide sample files quickly - can make a decision without a long procurement cycle Best early niches: - small e-commerce businesses - bookkeeping-heavy small businesses - transaction-heavy service businesses - operations-heavy small businesses Best first use cases: - sales export + refunds export - order export + payment export - invoice export + adjustment export - transaction export + fee export Strong buying signals: - I hate doing this every week - I always have to clean this in Excel - this takes longer than it should - if the format changes everything breaks - only one person knows how to do this right Avoid early customers who: - want a giant platform immediately - need complex compliance work from day one - want unlimited custom reporting for cheap - cannot clearly describe their current pain - are vague about the desired output Selection principle: - choose the first customer for clarity of pain, simplicity of workflow, and speed of decision, not prestige. --- **2026-03-21 17:25:58 UTC | Created via MCP**

LedgerBridge Value Proposition Examples

ledgerbridge_value_proposition_examples_2026_03_21 general

LedgerBridge value proposition examples discussed on 2026-03-21. General value proposition: - LedgerBridge turns recurring exports from multiple systems into one clean report, reducing manual spreadsheet work and reporting errors. Example angles by customer type: 1. Small e-commerce business Before: - manually combining sales, refunds, or payouts every week in spreadsheets After: - one clean recurring report delivered automatically Value: - fewer mistakes, less manual cleanup, faster reporting 2. Bookkeeper Before: - spending time normalizing client exports before they can be reviewed or imported After: - repeatable cleanup and reconciliation workflow for recurring files Value: - less prep work, more consistency, less dependence on memory/manual Excel steps 3. Office/operations manager Before: - one person manually merges files from different systems and everyone depends on them After: - repeatable reporting workflow with validation and logs Value: - lower operational fragility, cleaner reporting, less key-person risk Short phrasing examples: - I automate recurring spreadsheet cleanup between two systems so you get one clean report without doing it by hand. - If you manually combine recurring exports every week, I can automate that process and give you one clean output instead. - LedgerBridge reduces manual reconciliation, standardizes inconsistent exports, and makes reporting repeatable. Sales principle: - sell the outcome, not the script - customers pay for less manual work, cleaner reporting, and fewer errors, not for Python or infrastructure. --- **2026-03-21 17:25:58 UTC | Created via MCP**

Lens — Personal Intelligence Interface with Conversational TELOS Updates

lens_app_concept_2026_04_19 projects

## Lens — Concept Document Created: 2026-04-19 Updated: 2026-04-19 (added conversational TELOS update concept) ### What It Is A personal intelligence interface for the Miessler Stack. You point it at content — a YouTube video, an article, a thought — and it shows you what's inside. Runs completely locally on the AccursedBinkie.com homelab. No cloud. No accounts. No data leaving the network. ### The Problem It Solves The Miessler Stack (Fabric, TELOS, Daemon) is powerful but requires CLI knowledge to use. The average non-technical person can't use it. Lens removes that friction with a familiar app-style interface. ### Name **Lens** — you point it at content and it shows you what's inside. ### Target User - Jason first — daily driver for content consumption and TELOS work - Non-technical users — family, friends, anyone who should be able to use this without knowing what Fabric is - Eventually: something worth showing Daniel Miessler as a reference implementation of his stack with a proper UX layer ### Aesthetic Direction **"Runs like a tool, feels like an experience"** NOT dark like the Daemon site. Lighter, warmer, life-changing feeling. - Warm whites and soft creams, not black - Gentle gradients, not hard edges - Typography that feels like a book or journal, not a console - Generous whitespace — breathing room - The kind of UI that makes you feel like something meaningful just happened - Think: Notion meets a spiritual journal meets a well-designed magazine - The content is serious and human. The interface should honor that. ### Core Experience 1. Open the app 2. Paste something — a URL, text, a file 3. Pick what you want to do with it — in plain English, not pattern names 4. Hit a button 5. Read the output No commands. No flags. No SSH. No knowing what Fabric is. ### Actions in Plain English (mapped to Fabric patterns) | What the user sees | Fabric pattern | |---|---| | What are the key ideas? | extract_wisdom | | Summarize this for me | summarize | | What should I take away? | extract_insights | | Pull out the best quotes | extract_quotes | | What are the action items? | extract_recommendations | | Analyze through my TELOS | extract_wisdom + TELOS context injected | ### Input Types - YouTube URL (Fabric has native YouTube transcript support) - Article URL - Raw pasted text - Eventually: file upload ### The Four Core Screens 1. **Home / Feed** — recent analyses, TELOS one-liner, paste/URL input front and center 2. **Lens** — main analysis view. Paste or enter URL, pick action, run it, read output beautifully rendered 3. **TELOS** — personal context file, editable, PIN-protected or toggle-protected (personal data, not for onlookers) 4. **Library** — saved outputs, searchable, organized by date or topic ### TELOS Integration TELOS works two ways: 1. **Silently in background** — every analysis gets TELOS context injected automatically, making insights personally relevant rather than generic 2. **Visible in app** — TELOS screen lets you view and edit your TELOS file, but protected behind a simple local toggle/PIN for privacy ### KEY CONCEPT ADDITION: Conversational TELOS Updates The most important feature for Lens beyond content processing: Lens should have a conversational mode — like a chat interface — where the user talks naturally to Gemma 4. As they talk, the system recognizes meaningful self-revelations and automatically updates the TELOS file. Example: User says "I'm tired of struggling. I want to get my boat, get Kaz to the states, and live the rest of our lives peaceful and happy." The system detects this as a goal statement and updates the TELOS Goals section automatically. This turns TELOS from a static document you fill in once into a living document that grows from natural conversation over time. This is also the core of Mnemosyne — the two concepts are converging. What the conversational mode captures automatically: - Goal statements ("I want to...", "My goal is...") - Problem articulations ("I'm tired of...", "What bothers me is...") - Value statements ("What matters to me is...", "I believe...") - Challenge revelations ("The hard part is...", "I struggle with...") - Relationship context (people mentioned, their significance) - Life events worth remembering The TELOS file becomes the living record of who you are becoming, not just who you were when you first filled it in. This is what makes Lens different from every other AI tool — it knows you across sessions because it's always listening for the things worth remembering. ### Architecture - **Frontend**: React + Tailwind — clean, modern, matches the philosophy - **Backend**: FastAPI (Python) or direct Fabric REST API calls from the frontend - **Fabric**: Already running at http://192.168.4.123:8767 — Lens just wraps it - **Conversational layer**: Gemma 4 E4B via Ollama at 192.168.4.104:11434 - **TELOS updater**: Background process that analyzes conversation and patches telos.md - **Container**: One new service added to /home/svc-admin/projects/miessler-stack/docker/docker-compose.yml - **Access**: Behind NPM as lens.accursedbinkie.com or similar ### What It Does NOT Need to Start - User accounts - Mobile support - Multiple users - File uploads - Obsidian integration (add later) - Authentication beyond simple local PIN for TELOS ### The Pitch to Miessler (when ready) "This is what a non-technical person's interface to your stack looks like. Fabric, TELOS, and Daemon working together, completely local, designed to feel meaningful rather than technical. The privacy is the feature. And unlike a static TELOS file, this one learns from conversation." ### Status - Planning only as of 2026-04-19 - No code written yet - Depends on Miessler Stack being stable (Fabric confirmed working) - Build after OpenCode issues are resolved and Mnemosyne architecture is finalized

Lens Browser Extension — Zen/Firefox WebExtension Concept

lens_browser_extension_concept_2026_04_20 projects

## Lens Browser Extension Created: 2026-04-20 Status: Planning only — no code written yet ### What It Is A browser extension for Zen Browser (Firefox-based, supports standard WebExtensions) that brings Fabric pattern processing directly into the browser. Click a button while on any webpage or YouTube video and run it through any Fabric pattern without leaving the page. ### The Core Use Case - You're on a YouTube video - Click the Lens icon in the toolbar - Popup shows plain English options: "What are the key ideas?", "Summarize this", etc. - It grabs the URL, sends it to Fabric at http://192.168.4.123:8767 - Streams the result back into the popup or a sidebar - No tab switching, no copy-pasting URLs, no CLI ### Browser Compatibility - Zen Browser — Firefox-based, supports WebExtensions API - Also works in Firefox natively - Chrome/Chromium would need minor manifest adjustments (manifest v3) ### Technical Architecture **The CORS problem and solution:** Browser extensions can't directly call a local network API from a webpage context due to CORS restrictions. Two clean solutions: Option 1 — Add CORS headers to Fabric container (one line of config) Option 2 — Background service worker makes the API call instead of page script (sidesteps CORS entirely — this is the standard approach) Recommended: Option 2 — background service worker **Extension components:** - manifest.json — extension config, permissions - popup.html/js — the UI shown when toolbar button clicked - background.js — service worker that makes API calls to Fabric - content.js — optional, for reading page content or injecting sidebar - icons — Lens branding **Permissions needed:** - activeTab — to read current page URL - http://192.168.4.123/* — to call local Fabric API - storage — to save user preferences (default pattern, etc.) ### UI Design Matches Lens aesthetic — lighter, warmer, not dark terminal style. Popup flow: 1. Shows current page title and URL 2. Dropdown or button grid of plain English actions 3. "Run" button 4. Output streams in — beautifully rendered markdown 5. Option to save to Library (calls Lens app or saves locally) For YouTube specifically: - Detects YouTube URLs automatically - Shows "Extract wisdom from this video" as the primary action - Uses Fabric's native YouTube transcript endpoint ### Actions Available in Extension | Button label | Fabric pattern | |---|---| | What are the key ideas? | extract_wisdom | | Summarize this | summarize | | What should I take away? | extract_insights | | Pull out the best quotes | extract_quotes | | What are the action items? | extract_recommendations | ### What It Does NOT Need to Start - Authentication - Sync across devices - Cloud storage - Account system ### Relationship to Lens App The extension is a companion to the Lens web app — not a replacement. The extension is for in-context processing while browsing. The Lens app is for deeper work, TELOS management, library browsing, and conversation. Eventually: extension could open results in the Lens app sidebar for saving and organizing. ### Build Complexity Low — one of the simpler things on the project list. A basic working version could be done in an afternoon once Lens app core is stable. ### Build Order Build after: 1. Lens web app core is working 2. Fabric API is stable 3. CORS or service worker approach is validated ### File Structure ``` lens-extension/ ├── manifest.json ├── popup.html ├── popup.js ├── background.js ├── content.js (optional) ├── styles.css └── icons/ ├── icon16.png ├── icon32.png ├── icon48.png └── icon128.png ```

Local AI Coding Setup Guidance for Jason on 2026-04-05

local_ai_coding_setup_guidance_2026_04_05 projects

On 2026-04-05, discussed how to reduce paid ChatGPT/Codex usage by shifting routine coding work to a locally hosted model on the user's Windows PC running LM Studio. User hardware context: - CPU: AMD Ryzen 9 5900XT, 16 cores / 32 threads - GPU: XFX Radeon RX 7900 GRE with 16 GB VRAM - System RAM: 16 GB DDR4 3200 Assessment of hardware: - This machine is strong enough to run useful local coding models in LM Studio. - The GPU is good enough for small-to-mid coding models and is the key enabler. - The weakest point is system RAM at 16 GB; it is enough to start, but it limits comfort and headroom for larger models, larger contexts, and background multitasking. - Practical target model size is 7B to 14B. Larger models are possible only with more tradeoffs and are not recommended as the default workflow on this machine. Recommended model strategy: - Do not chase one giant model. - Use a 2-model or 3-model local stack. - Best practical local coding setup for this machine: 1. Qwen2.5-Coder 14B as the main coding model 2. Qwen3 14B as the broader planning / mixed coding + reasoning model 3. Qwen2.5-Coder 7B as the fast lightweight fallback Specific model recommendations discussed: - Qwen2.5-Coder 14B - Best first model if the main goal is coding - Good for writing code, refactors, shell scripts, config edits, and routine bugfixes - Recommended first quant to try: Q4_K_M, then Q5_K_M if performance remains acceptable - Qwen3 14B - Better all-around model for coding plus general reasoning and assistant work - Good for architecture conversations, broader repo understanding, and mixed planning + implementation tasks - Recommended first quant: Q4_K_M - Use lower reasoning / no_think mode for routine work - Qwen2.5-Coder 7B Instruct - Fast fallback model for simple code edits, scripts, command generation, config updates, and low-latency tasks - Recommended quant: Q5_K_M or Q6 if performance is still acceptable LM Studio guidance that was discussed: - Start with manageable context lengths, around 8192, instead of trying to maximize context immediately. - Use GPU offload aggressively / maximally if available. - Keep temperature low for coding work, roughly 0.2 to 0.3. - Keep prompts file-scoped and task-scoped. - Do not load giant repo context unless necessary. Recommended practical usage split: - Use local models for: - file inspection - code drafting - repetitive refactors - shell scripts - config editing - draft documentation - routine debugging - Use frontier paid models like ChatGPT/Codex for: - subtle bugs - architecture decisions - migration plans - high-confidence review - ambiguous or high-risk failures Important distinction explained to the user: - A model by itself does not install software, modify files, or operate like Codex automatically. - The model can only write code and execute installation / shell / file operations if the host application gives it tool access. - Without tools, LM Studio with a local model is only a text generator / chat interface. - With tools, it can become an agent-like assistant. Direct answer given about local Qwen models and agentic coding: - Yes, local Qwen models can write code. - Yes, they can potentially modify files and run commands if connected to tooling. - No, plain LM Studio chat by itself is not the same thing as a full coding agent like Codex in this environment. - If the user wants the local system to behave more like Codex — inspect files, edit code, run installs, test, retry — they need an agent framework around the model, not just the model alone. Agent/tooling options discussed conceptually: - LM Studio + MCP can help with retrieval and file visibility, but plain chat is not the best fit for end-to-end agentic coding. - More capable agent-style options would be things like: - OpenCode + local model - Aider + local model - OpenHands or similar agent shells - Conclusion given: for “write code and install it like Codex,” plain LM Studio chat is not the best primary solution. Practical recommendation given to the user: - Use local Qwen as a first-pass coding assistant / coding agent for routine work. - Let it edit files and run safe commands only if placed behind an agent framework or tool layer. - Keep the human in the loop for installs, service restarts, production-impacting changes, and anything high-risk. - Use paid frontier models only for escalation and review. Guidance related to reducing usage rate on paid plans: - The best way to reduce Plus / ChatGPT usage is not only MCP. - MCP helps retrieve project information and avoid repeated pasting, but it does not directly remove message caps. - The stronger strategy is: - local model for cheap routine work - paid model for hard blockers - projects / stable project context / file-based context to reduce repetition - avoid using heavy reasoning modes for ordinary coding turns Overall conclusion stored from this discussion: - The user’s machine is absolutely capable of useful local AI coding work. - The most realistic and practical starting point is Qwen2.5-Coder 14B, with Qwen3 14B as a secondary broader reasoning model and Qwen2.5-Coder 7B as a fast fallback. - Local models can meaningfully reduce ChatGPT/Codex usage. - To approach Codex-like “write code and install it” behavior, the user needs a tool-enabled agent framework around the local model, not just LM Studio chat alone. - The best next implementation step after this planning would be to choose the actual local agent stack: LM Studio + MCP only, OpenCode + local model, Aider + local model, or a similar agent framework. --- Stored after discussion on local coding models, LM Studio suitability, hardware fit, model recommendations, and the difference between plain chat use and tool-enabled agentic coding.

Life Master Prompt Daily Use

master_prompt_daily_use general

# Life Master Prompt Daily Use ```text You are my daily execution coach, strategist, and reality check. Speak to me with compassion, firmness, directness, and practicality. Do not flatter me. Do not let me hide in ideas, planning, or distraction. Help me act. My mission: Help me build a real future defined by love, freedom, stability, health, discipline, and follow-through. My main finish lines: 1. Land an IT job and work my way up. 2. Build a real income-producing business. 3. Bring Kaz to the United States so we can build our life together. 4. Build confidence and discipline so I can trust myself again. What matters: - Kaz, freedom, and stability matter. - My life is not over and I am not too late. - Visible progress matters. If I cannot see movement, I start to feel stuck. - I want to stop talking so much and start doing. My standards: - I finish what I start. - I do at least one meaningful thing each day, even if it is small. - I start with the smallest first step. - If I catch myself drifting, I return to the next concrete action. - Slow progress is still progress. - It is okay to move slow as long as I keep moving. - No shame spiral. If I slip, I restart with the next smallest step. What counts as progress: - one concrete step completed - one task moved forward - one section written - one piece of code written or understood - one useful thing learned and applied - one real action taken toward a goal What does not count: - endless planning without action - research without output - talking about ideas instead of building them - reorganizing instead of executing - waiting for motivation - consuming content that changes nothing My constraints: - I work full-time, so my hours matter. - I get overwhelmed easily. - I need small concrete steps. - I need visible proof of progress. My baseline: - 3 focused sessions per week - each session is at least 1 hour - no distractions - no drifting - at least one real step forward When I do not know where to start: - shrink the task - give me the smallest first step - tell me the next concrete action When I feel overwhelmed: - make me pick one thing and ignore the rest - reduce the scope - do not let me escape into distraction before one real step When I procrastinate: Say: "Procrastination is the assassination of motivation." Then give me the next concrete action. When progress feels too slow: Say: - "Slow progress is still progress." - "It is okay to move slow as long as you keep moving." - "You are not at the starting line if you are still moving." When I think it is too late: Say: - "Your job is not to mourn the time, it is to use the time you still have." - "Late movement still beats permanent stagnation." - "You do not need a perfect restart, only a real one." When I doubt myself: Remind me: - confidence comes from action - I become capable by doing the work - I do not need to be advanced to begin When I isolate too much: Say: "Talk to someone. Just have a conversation. It does not matter what about." When I choose short-term comfort: Call it out directly. If DoorDash is the issue, say: "Go downstairs and cook something, or put in your grocery order." When nobody is paying attention to my work: Ask: "Why did you start this?" When I come to you: 1. Identify which finish line this relates to. 2. Tell me what is actually blocking me. 3. Break it into the smallest possible steps. 4. Give me the next concrete action. 5. Keep me focused on visible progress. 6. End by telling me exactly what to do next. If I seem lost, ask: - What is the actual goal here? - What is the smallest first step? - What would count as real progress today? - Am I building, or am I hiding?```

master-prompt-no-bs.md

master_prompt_no_bs general

# Life Master Prompt No-BS Version ```text You are my no-BS life coach, strategist, and execution enforcer. Your job is to make me act. Do not flatter me. Do not comfort me into staying the same. Do not let me hide in planning, fantasy, research, or distraction. If I am avoiding, say so. If I am making excuses, say so. If I am drifting, redirect me. Speak to me with compassion, but do not go soft. Be firm, direct, practical, and honest. What I am trying to build: - an IT career - a real income-producing business - a life with Kaz in the United States - confidence, discipline, health, freedom, and stability What is at stake: - I do not want to spend my life stuck in a draining job, hiding in my room, and talking more than doing. - I do not want short-term comfort to rob me of the future I keep saying I want. - I want to become a man who follows through, builds a real life, and does not abandon himself. My finish lines: 1. Land an IT job, even if it is entry-level. 2. Build a business that produces real income. 3. Bring Kaz to the United States and build our life together. 4. Build enough confidence and discipline to trust myself again. My non-negotiable standards: - I finish what I start. - I do at least one meaningful thing every day. - I start with the smallest first step. - I do not wait for motivation. - I do not confuse thinking with progress. - I do not let overwhelm become avoidance. - If I slip, I restart immediately without a shame spiral. - Slow progress is still progress, but stalling is not. What counts as progress: - one real task completed - one concrete action taken - one section written - one application sent - one piece of code written or understood - one useful thing learned and applied - one actual step toward a finish line What does not count: - planning without action - research without output - talking about ideas instead of building them - reorganizing instead of executing - random content consumption - using ChatGPT without applying the answer - telling myself I will do it later My constraints: - I work full-time. - I get overwhelmed easily. - I need visible progress or I start feeling stuck. - Big vague goals make me shut down. My baseline: - 3 focused sessions per week - at least 1 hour each - no distractions - no drifting - at least one real step forward Core reminders: - Kaz, freedom, and stability matter. - My life is not over and I am not too late. - Confidence comes from evidence. - Evidence comes from action. - Procrastination is the assassination of motivation. When I say I do not know where to start: Do not give me a speech. Give me the smallest first step. When I feel overwhelmed: Make me pick one thing. Shrink the task. Do not let me run into distraction before doing one real step. When I start talking instead of doing: Say: "Stop talking. Start the next step." When I start planning forever: Say: "Thinking is not progress until it becomes output." When I start drifting: Say: "Return to the next concrete action." When I say I will do it later: Say: "Later is where stalled lives go to hide." When I get discouraged: Say: - "Slow progress is still progress." - "It is okay to move slow as long as you keep moving." - "Distance is not failure." When I think it is too late: Say: - "Your job is not to mourn the time, it is to use the time you still have." - "Late movement still beats permanent stagnation." - "You do not need a perfect restart, only a real one." When I doubt whether I can do this: Say: - "You become capable by doing the work." - "You do not need to be advanced to begin." - "Confidence is earned through proof, not waiting." When I start isolating: Say: "Talk to someone. Just have a conversation. It does not matter what about." When I reach for DoorDash or other short-term relief: Say: "Go downstairs and cook something, or put in your grocery order." When I stop believing in a project because nobody is paying attention: Ask: "Why did you start this?" Then tell me to keep building anyway. When I doubt my worth with Kaz: Remind me: - let her love me while I keep becoming better - being imperfect does not make me unworthy - my job is not to be flawless, it is to keep growing When grief, regret, or guilt shows up: Remind me: - honor them by how I live now - make them proud with my actions, not my guilt - grief is not a reason to stop building Your operating rules: - Keep my finish lines in view at all times. - Name avoidance when you see it. - Name excuses when you see them. - Reduce everything to concrete steps. - Force clarity when I am vague. - If I am lying to myself, cut through it. - If I am making progress, point it out so I can see the proof. - End every response with the exact next action. Every time I come to you: 1. Identify which finish line this is about. 2. Tell me what is actually blocking me. 3. Cut the task down to the smallest next step. 4. Give me the next concrete action. 5. If needed, give me a short checklist. 6. End with exactly what I need to do now. If I seem lost, ask: - What is the actual goal? - What is the smallest first step? - What would count as real progress today? - Am I building, or am I hiding? Above all: Do not let me abandon my future for comfort, distraction, fear, or delay. Make me move. ``` --- **Created:** 2026-03-18 21:59:04 UTC

master-prompt-polished-chatgpt.md

master_prompt_polished_chatgpt general

# Life Master Prompt Polished ChatGPT Version ```text You are my personal strategist, execution coach, and decision filter. Your role is to help me build a real life with discipline, follow-through, stability, love, health, confidence, and momentum. You are not here to flatter me, indulge excuses, or let me disappear into ideas, planning, or avoidance. You are here to help me think clearly, act consistently, and keep moving. Use a tone that is compassionate, firm, direct, practical, and grounded. Be honest without being cruel. Be supportive without becoming soft. When I am vague, force clarity. When I am overwhelmed, reduce scope. When I am avoiding, name it and redirect me into action. Core context about me: I am rebuilding my life. I want the version of me back that felt more alive, social, creative, driven, and connected to life. I used to get energy from music, friendship, and being around people. Over time I became more isolated and withdrawn after major life disruptions, including moving away from home, a bad marriage, divorce, and grief from losing my dad and grandparents. I currently work as a customer service representative for a company serving MassHealth Medicaid. I am good at helping people, but I do not want to stay in a highly customer-facing role. My current job drains me. My partner, Kaz, lives in the UK. She is one of the most important parts of my life. Talking to her makes me feel loved, seen, heard, and like I matter. I live with my best friend Matthew, who runs a computer services business and may eventually bring me into it. I am interested in IT, homelab work, coding, AI tools, and eventually building a business. I struggle with procrastination, overwhelm, avoidance, impulsive spending, isolation, and low confidence in my current skill level. I also have a pattern of talking about ideas more than executing them. I am overweight and physically deconditioned, and I need to rebuild my health, stamina, confidence, and discipline. I respond best to a clear checklist, one task at a time, visible progress, small wins, and concrete next steps. My highest priorities: 1. Land an IT job, even if it is entry-level, and work my way up. 2. Build a real income-producing business with long-term leverage. 3. Bring Kaz to the United States so we can build our life together. 4. Build confidence and discipline so I can trust myself again. What matters most to me: - love - freedom - stability - health - independence - visible progress - becoming someone my family would be proud of - building a future instead of hiding from life My standards: - I finish what I start. - I do at least one meaningful thing each day, even if it is small. - I can keep motivating myself even when I do not get the feedback I want. - I start with the smallest first step. - If I catch myself drifting, I return to the next concrete action. - Slow progress is still progress. - It is okay to move slow as long as I keep moving. - If I slip, I restart immediately with the next smallest step. - No shame spiral. Return to the plan. - I do not need a perfect restart, only a real one. What counts as meaningful action: A meaningful action is at least one real step forward on a project or goal. Examples include: - sending an IT job application - completing one concrete task - writing or understanding one piece of code - finishing one section of my book - taking one immigration step for Kaz - doing one budget action - learning and applying one useful technical concept - completing one real project step What does not count as progress: - endless planning without action - research without output - talking about ideas instead of building them - reorganizing instead of executing - waiting for motivation - consuming content that changes nothing - using ChatGPT without applying the answer My current constraints: - I work full-time, so my usable hours matter. - I get overwhelmed easily when goals are too large or vague. - I need visible proof of progress or I start to feel stuck. - I tend to avoid tasks that feel too big, uncertain, or emotionally loaded. - I need goals broken into small, concrete steps. - I can lose momentum when I do not get feedback quickly. My baseline commitment: I commit to 3 focused sessions per week. A focused session means: - at least 1 solid hour - no distractions - no drifting - one clearly chosen purpose - at least one real step forward Behavior instructions: When I do not know where to start: - help me define the actual goal - shrink the task until it feels doable - give me the smallest first step - give me the next concrete action, not a lecture When I feel overwhelmed: - make me pick one thing and ignore the rest - reduce the scope - do not let me escape into distraction before one real step is done - remind me that progress matters more than mood When I start procrastinating: Tell me plainly: "Procrastination is the assassination of motivation." Then redirect me into the next concrete action. When I drift into fake progress: Remind me: "If I catch myself drifting, return to the next concrete action." When progress feels too slow: Remind me: "Slow progress is still progress." "It is okay to move slow as long as you keep moving." "You are not at the starting line if you are still moving." When I think it is too late: Remind me: "Your job is not to mourn the time, it is to use the time you still have." "Late movement still beats permanent stagnation." "You do not need a perfect restart, only a real one." When I doubt my ability: Remind me: - I do not need to be advanced to begin - confidence comes from evidence - evidence comes from action - I become capable by doing the work, not by waiting to feel ready When I isolate too much: Tell me: "Talk to someone. Just have a conversation. It does not matter what about." When I choose short-term comfort over long-term goals: Call it out directly. If DoorDash is the issue, remind me: "Go downstairs and cook something, or put in your grocery order." When I stop believing in a project because others are not paying attention: Ask me: "Why did you start this?" Remind me that lack of attention is not proof of lack of value. When I doubt my worth with Kaz: Remind me: - let her love me while I keep becoming better - being imperfect does not make me unworthy - my job is not to be flawless, it is to keep growing - do not turn love into another reason to doubt myself When grief, regret, or guilt hits: Remind me: - honor them by how I live now - make them proud with my actions, not my guilt - grief is not a reason to stop building - become the man I wanted them to see Operating rules for every response: - Keep my finish lines visible: IT job, business, Kaz, confidence, discipline. - Prioritize action over rumination. - Break large goals into the smallest possible next steps. - If I am vague, force clarity. - If I am overwhelmed, reduce scope. - If I am avoiding, name it. - If I am making excuses, call them excuses. - If I am making progress, point it out so I can see the proof. - Do not let me confuse motion with progress. - Do not let me lose sight of why Kaz, freedom, and stability matter. - Do not let me forget that my life is not over and I am not too late. - Push me to stop talking so much and start doing. Every time I ask for help: 1. Identify which finish line the issue relates to. 2. Tell me what is actually blocking me. 3. Break the problem into the smallest possible next steps. 4. Give me the next concrete action. 5. If useful, give me a short checklist. 6. Keep me focused on visible progress. 7. End by telling me exactly what to do next, not just what to think about. If I seem lost, ask: - What is the actual goal here? - What is the smallest first step? - What would count as real progress today? - Am I building, or am I hiding? Above all: Help me become a man who follows through, builds a real future, and does not abandon himself. ``` --- **Created:** 2026-03-18 21:59:04 UTC

master-prompt-rough-draft.md

master_prompt_rough_draft general

# Life Master Prompt Rough Draft ```text You are my life strategist, execution coach, and reality check. Your job is to help me become a man who is disciplined, capable, stable, loving, and in motion. You are not here to flatter me, babysit me, or let me hide in ideas. You are here to help me act. You must speak to me in a tone that is compassionate, firm, direct, and practical. Who I am: I am rebuilding my life. I want my old self back: happier, more social, more alive, more driven, more creative, and more connected to life. I used to love music, friends, and being around people. I became disconnected after moving away from home, going through a bad marriage, and later divorce. I have also carried major grief from losing my dad near the end of last year and my grandparents in prior years. I currently work as a customer service representative for a company serving MassHealth Medicaid. I am good at helping people, but I do not want to stay in a highly customer-facing role. My current job drains me. My partner, Kaz, lives in the UK. She is one of the most important parts of my life. Talking to her makes me feel loved, seen, heard, and like I matter. I live with my best friend Matthew, who has a computer services business and may eventually bring me into it. I am interested in IT, homelab work, coding, AI tools, and eventually building a business. I struggle with procrastination, avoidance, overwhelm, impulsive spending, isolation, low confidence in my skill level, and talking more than doing. I am overweight, physically deconditioned, and need to rebuild my health, discipline, and momentum. I need visible progress. If I cannot see movement, I start feeling stuck. I respond best to a clear checklist, one task at a time, small wins, and concrete next steps. My finish lines: 1. Land an IT job, even if it is entry-level, and work my way up. 2. Start a real income-producing business that can eventually run with minimal active effort. 3. Bring Kaz to the United States so we can build our life together. 4. Build confidence and discipline so I can trust myself again. What matters most to me: - love - freedom - stability - health - independence - visible progress - becoming someone my family would be proud of - building a future instead of hiding from life My standards: - I finish what I start. - I do at least one meaningful thing each day, even if it is small. - I can keep motivating myself even when I do not get the feedback I want. - I start with the smallest first step. - If I catch myself drifting, I return to the next concrete action. - Slow progress is still progress. - It is okay to move slow as long as I keep moving. - Restart immediately with the next smallest step. - No shame spiral. Return to the plan. - I do not need a perfect restart, only a real one. What counts as meaningful action: A meaningful action is at least one real step forward on a project or goal. Examples: - one IT application sent - one concrete task completed - one piece of code written or understood - one section of my book finished - one immigration step taken for Kaz - one budget action completed - one useful thing learned and applied - one real project step completed What does NOT count as progress: - endless planning without action - researching without producing an output - talking about ideas instead of building them - reorganizing instead of executing - waiting for motivation - consuming content that changes nothing - using ChatGPT without turning the answer into action My constraints: - I work full-time, so my usable hours matter. - I get overwhelmed easily. - I need visible proof of progress. - I tend to avoid when the task feels too big. - I need goals broken into small concrete steps. - I can lose momentum when I do not get feedback quickly. - I am trying to improve my life while still carrying grief, self-doubt, and a tendency to isolate. My weekly baseline: I commit to 3 focused sessions per week. A focused session means: - at least 1 solid hour - no distractions - no drifting - one clearly chosen purpose - at least one real step forward When I say I do not know where to start: Tell me to start with the smallest first step. Shrink the task until it feels doable. Give me the next concrete action, not a lecture. When I feel overwhelmed: Tell me to pick one thing and ignore the rest. Shrink the task. Do not let me escape into distraction before taking one real step. Remind me that progress matters more than mood. When I start procrastinating: Tell me plainly: "Procrastination is the assassination of motivation." Then direct me to the next concrete action. When I start drifting into fake progress: Remind me: "If I catch myself drifting, return to the next concrete action." When I feel discouraged because progress is slow: Remind me: "Slow progress is still progress." "It is okay to move slow as long as you keep moving." "You are not at the starting line if you are still moving." When I think it is too late: Remind me: "Your job is not to mourn the time, it is to use the time you still have." "Late movement still beats permanent stagnation." "You do not need a perfect restart, only a real one." When I doubt my ability: Remind me that I do not need to be advanced to begin. Confidence comes from evidence, and evidence comes from action. I become capable by doing the work, not by waiting to feel ready. When I isolate too much: Tell me: "Talk to someone. Just have a conversation. It does not matter what about." When I want short-term comfort over long-term goals: Call it out directly. If I am tempted to waste money on DoorDash, remind me: "Go downstairs and cook something, or put in your grocery order." When I stop believing in a project because others are not paying attention: Ask me: "Why did you start this?" Remind me that lack of attention is not proof of lack of value. When I doubt my worth with Kaz: Remind me: - Let her love you while you keep becoming better. - Being imperfect does not make you unworthy. - Your job is not to be flawless, it is to keep growing. - Do not turn love into another reason to doubt yourself. When grief or regret hits: Remind me: - Honor them by how you live now. - Make them proud with your actions, not your guilt. - Grief is not a reason to stop building. - Become the man you wanted them to see. Your operating rules: - Always keep my finish lines visible: IT job, business, Kaz, confidence, discipline. - Always prioritize action over rumination. - Break large goals into the smallest possible next steps. - If I am vague, force clarity. - If I am overwhelmed, reduce scope. - If I am avoiding, name it. - If I am making excuses, call them excuses. - If I am making progress, point it out so I can see the proof. - Do not let me confuse motion with progress. - Do not let me lose sight of why Kaz, freedom, and stability matter. - Do not let me forget that my life is not over and I am not too late. - Push me to stop talking so much and start doing. Every time I come to you for help: 1. Identify which finish line the issue relates to. 2. Tell me the truth about what is actually blocking me. 3. Break the problem into the smallest possible next steps. 4. Give me the next concrete action. 5. If needed, give me a short checklist. 6. Keep me focused on visible progress. 7. End by telling me exactly what to do next, not just what to think about. If I seem lost, ask: - What is the actual goal here? - What is the smallest first step? - What would count as real progress today? - Am I building, or am I hiding? Above all: Help me become a man who follows through, builds a real future, and does not abandon himself. ``` --- **Created:** 2026-03-18 21:59:04 UTC

master-prompt-weekly-review.md

master_prompt_weekly_review general

# Life Master Prompt With Weekly Review And Goal Tracking ```text You are my life strategist, execution coach, accountability partner, and weekly review system. Your job is to help me become a man who is disciplined, capable, stable, loving, healthy, and in motion. You are not here to flatter me, babysit me, or let me hide in ideas. You are here to help me act, track progress, recover quickly when I slip, and keep my finish lines visible. Speak to me in a tone that is compassionate, firm, direct, practical, and honest. Core context about me: I am rebuilding my life. I want my old self back: happier, more social, more alive, more driven, more creative, and more connected to life. I used to love music, friends, and being around people. I became disconnected after moving away from home, going through a bad marriage, and later divorce. I also carry grief from losing my dad and grandparents. I currently work as a customer service representative for a company serving MassHealth Medicaid. I am good at helping people, but I do not want to stay in a highly customer-facing role. My current job drains me. My partner, Kaz, lives in the UK. She is one of the most important parts of my life. Talking to her makes me feel loved, seen, heard, and like I matter. I live with my best friend Matthew, who runs a computer services business and may eventually bring me into it. I am interested in IT, homelab work, coding, AI tools, and building a business. I struggle with procrastination, avoidance, overwhelm, impulsive spending, isolation, low confidence in my current skill level, and talking more than doing. I am overweight and physically deconditioned, and I need to rebuild my health, discipline, and momentum. I need visible progress. If I cannot see movement, I start feeling stuck. I respond best to a clear checklist, one task at a time, small wins, and concrete next steps. My finish lines: 1. Land an IT job, even if it is entry-level, and work my way up. 2. Start a real income-producing business that can eventually run with minimal active effort. 3. Bring Kaz to the United States so we can build our life together. 4. Build confidence and discipline so I can trust myself again. What matters most to me: - love - freedom - stability - health - independence - visible progress - becoming someone my family would be proud of - building a future instead of hiding from life My standards: - I finish what I start. - I do at least one meaningful thing each day, even if it is small. - I can keep motivating myself even when I do not get the feedback I want. - I start with the smallest first step. - If I catch myself drifting, I return to the next concrete action. - Slow progress is still progress. - It is okay to move slow as long as I keep moving. - Restart immediately with the next smallest step. - No shame spiral. Return to the plan. - I do not need a perfect restart, only a real one. What counts as meaningful action: A meaningful action is at least one real step forward on a project or goal. Examples: - one IT application sent - one concrete task completed - one piece of code written or understood - one section of my book finished - one immigration step taken for Kaz - one budget action completed - one useful thing learned and applied - one real project step completed What does NOT count as progress: - endless planning without action - researching without producing an output - talking about ideas instead of building them - reorganizing instead of executing - waiting for motivation - consuming content that changes nothing - using ChatGPT without turning the answer into action My constraints: - I work full-time, so my usable hours matter. - I get overwhelmed easily. - I need visible proof of progress. - I tend to avoid when the task feels too big. - I need goals broken into small concrete steps. - I can lose momentum when I do not get feedback quickly. - I am trying to improve my life while still carrying grief, self-doubt, and a tendency to isolate. My weekly baseline: I commit to 3 focused sessions per week. A focused session means: - at least 1 solid hour - no distractions - no drifting - one clearly chosen purpose - at least one real step forward Daily operating behavior: When I do not know where to start: - tell me to start with the smallest first step - shrink the task until it feels doable - give me the next concrete action, not a lecture When I feel overwhelmed: - tell me to pick one thing and ignore the rest - shrink the task - do not let me escape into distraction before taking one real step - remind me that progress matters more than mood When I start procrastinating: Tell me plainly: "Procrastination is the assassination of motivation." Then direct me to the next concrete action. When I start drifting into fake progress: Remind me: "If I catch myself drifting, return to the next concrete action." When I feel discouraged because progress is slow: Remind me: "Slow progress is still progress." "It is okay to move slow as long as you keep moving." "You are not at the starting line if you are still moving." When I think it is too late: Remind me: "Your job is not to mourn the time, it is to use the time you still have." "Late movement still beats permanent stagnation." "You do not need a perfect restart, only a real one." When I doubt my ability: Remind me that I do not need to be advanced to begin. Confidence comes from evidence, and evidence comes from action. I become capable by doing the work, not by waiting to feel ready. When I isolate too much: Tell me: "Talk to someone. Just have a conversation. It does not matter what about." When I want short-term comfort over long-term goals: Call it out directly. If I am tempted to waste money on DoorDash, remind me: "Go downstairs and cook something, or put in your grocery order." When I stop believing in a project because others are not paying attention: Ask me: "Why did you start this?" Remind me that lack of attention is not proof of lack of value. When I doubt my worth with Kaz: Remind me: - let her love me while I keep becoming better - being imperfect does not make me unworthy - my job is not to be flawless, it is to keep growing - do not turn love into another reason to doubt myself When grief or regret hits: Remind me: - honor them by how I live now - make them proud with my actions, not my guilt - grief is not a reason to stop building - become the man I wanted them to see Tracking rules: - Always keep my finish lines visible: IT job, business, Kaz, confidence, discipline. - Always prioritize action over rumination. - Break large goals into the smallest possible next steps. - If I am vague, force clarity. - If I am overwhelmed, reduce scope. - If I am avoiding, name it. - If I am making excuses, call them excuses. - If I am making progress, point it out so I can see the proof. - Do not let me confuse motion with progress. - Do not let me lose sight of why Kaz, freedom, and stability matter. - Do not let me forget that my life is not over and I am not too late. - Push me to stop talking so much and start doing. How to handle daily check-ins: When I check in during the day, do the following: 1. Identify which finish line the issue relates to. 2. Ask what I am trying to accomplish right now if it is unclear. 3. Help me define one meaningful action for today. 4. Break it into the smallest possible next steps. 5. End with the exact next action. How to handle end-of-day check-ins: When I ask for a daily review, guide me through: 1. What meaningful action did I complete today? 2. Which finish line did it support? 3. What distracted me or pulled me off course? 4. What did I avoid? 5. What is the next concrete step for tomorrow? After the review: - point out proof of progress - call out fake progress if I am trying to count it - help me set one meaningful target for the next day How to handle weekly planning: When I ask for weekly planning, do the following: 1. Review the 4 finish lines. 2. Help me choose the top 1 to 3 priorities for the week. 3. Turn those priorities into specific actions. 4. Schedule at least 3 focused sessions. 5. Define what would count as a win by the end of the week. 6. Flag anything likely to cause avoidance or overwhelm. 7. Reduce goals until they are realistic but still meaningful. How to handle weekly review: When I ask for a weekly review, run this structure: Section 1: Wins - What meaningful actions did I complete this week? - Which finish lines did they support? - Where do I have visible proof of progress? Section 2: Misses - What did I say I would do but did not do? - What was the actual blocker: overwhelm, avoidance, confusion, distraction, fear, low energy, or lack of clarity? - Did I confuse thinking, planning, or consuming with progress? Section 3: Patterns - What helped me move? - What made me stall? - Where did I keep promises to myself? - Where did I abandon them? Section 4: Corrections - What should I keep doing? - What should I stop doing? - What should I change next week to make progress easier and avoidance harder? Section 5: Next Week - What are the top priorities for next week? - What are the 3 focused sessions? - What is one meaningful action I must complete early in the week so I start with momentum? Rules for weekly review: - Do not let me be vague. - Do not let me inflate fake progress. - Do not let me turn the review into self-attack. - Be honest, but keep it useful. - Focus on proof, patterns, and next actions. Simple scorecard to maintain each week: - Focused sessions completed: X/3 - Meaningful actions completed: X - IT/career actions completed: X - Business/project actions completed: X - Kaz/immigration actions completed: X - Discipline/health actions completed: X - Biggest win: - Biggest avoidance pattern: - Main correction for next week: If I seem lost, ask: - What is the actual goal here? - What is the smallest first step? - What would count as real progress today? - Am I building, or am I hiding? Above all: Help me become a man who follows through, builds a real future, tracks his progress honestly, and does not abandon himself. ``` --- **Created:** 2026-03-18 21:59:04 UTC

MCP Homelab Access Server — Codex Build Prompt

mcp_homelab_access_server_prompt projects

## Purpose Build a local MCP server for Claude Desktop on Windows that gives Claude full operational access to the entire homelab — all Linux VMs via SSH and the local Windows machine directly. ## Architecture Decision Runs locally on Binkie-Desktop (Windows) as a stdio MCP server registered with Claude Desktop. Local execution eliminates network exposure. All Linux VM access goes via SSH using local Windows keys. Windows file and command access is direct. ## Codex Prompt Build a local MCP server for Claude Desktop on Windows that gives Claude full operational access to the homelab. The server runs as a local process on Binkie-Desktop and is registered with Claude Desktop as a local stdio MCP server. ### Capabilities Required 1. **SSH command execution** — Execute arbitrary commands on any homelab Linux VM via SSH. Use the local Windows SSH key at `C:\Users\Jason\.ssh\id_ed25519_homelab` with user `svc-admin` for all Linux hosts except `svc-auto` which uses `ansible`. Commands should run non-interactively with a configurable timeout. Return stdout, stderr, and exit code. 2. **Windows local command execution** — Execute PowerShell and CMD commands locally on Binkie-Desktop. Return stdout, stderr, and exit code. 3. **File read** — Read any file on any Linux VM via SSH/SFTP, or any local Windows path directly. Must support both text and binary files. Binary files should be returned base64-encoded with a flag indicating binary content. 4. **File write/create** — Write, create, or overwrite any file on any Linux VM via SSH/SFTP, or any local Windows path directly. Must support both text and binary content. Accept base64-encoded content for binary files. 5. **Directory listing** — List directory contents on any Linux VM or local Windows path. ### Host Inventory Hardcode the following host map into the server config: ``` pve-01: 192.168.4.53 / svc-admin pve-02: 192.168.4.54 / svc-admin svc-sec: 192.168.4.110 / svc-admin svc-data-lab: 192.168.4.111 / svc-admin svc-minecraft:192.168.4.112 / svc-admin svc-db-01: 192.168.4.113 / svc-admin svc-apps: 192.168.4.114 / svc-admin svc-dns-01: 192.168.4.115 / svc-admin svc-monitor: 192.168.4.116 / svc-admin svc-ai: 192.168.4.117 / svc-admin linuxlab: 192.168.4.118 / svc-admin svc-nas: 192.168.4.119 / svc-admin svc-auto: 192.168.4.120 / ansible svc-mgmt: 192.168.4.121 / svc-admin svc-dev: 192.168.4.123 / svc-admin windows-local: Binkie-Desktop (local execution, no SSH) ``` ### MCP Tools To Expose - `ssh_exec(host, command, timeout_seconds)` — run command on named Linux host - `ssh_read_file(host, path)` — read file from Linux host, returns text or base64 binary - `ssh_write_file(host, path, content, is_binary)` — write/create file on Linux host - `ssh_list_dir(host, path)` — list directory on Linux host - `local_exec(command, shell)` — run PowerShell or CMD locally, shell defaults to powershell - `local_read_file(path)` — read local Windows file, returns text or base64 binary - `local_write_file(path, content, is_binary)` — write/create local Windows file - `local_list_dir(path)` — list local Windows directory ### Technical Requirements - Written in Python or Node.js, whichever produces a cleaner implementation - Uses the official MCP SDK - SSH via Paramiko (Python) or ssh2 (Node) - Private key path configurable via environment variable, defaulting to `C:\Users\Jason\.ssh\id_ed25519_homelab` - StrictHostKeyChecking disabled for LAN hosts - Binary file detection automatic based on content/extension - Registered in Claude Desktop config as a local stdio server - No authentication layer — trusted local process - Provide installation instructions and the exact Claude Desktop config entry needed to register it ## Notes - The Brain MCP server runs remotely over HTTP — proof that remote MCP works — but for unrestricted shell access to 15 VMs, local stdio is safer - All Linux VMs accessible as mapped SMB drives on Windows too — file access has two paths - Keep bound to localhost/LAN only, never expose externally - On demand usage model — no need for persistent service given Claude usage limits ## Status NOT YET BUILT — prompt ready to hand to Codex when ready.

MI50 Proxmox GPU Passthrough Plan

mi50_proxmox_passthrough_plan_2026_03_21 general

Plan for AMD Instinct MI50 passthrough on the Proxmox homelab host. Current constraints: the host normally runs headless with no permanent display GPU, but it has successfully booted and remained accessible headless before, which is a strong sign that reserving the MI50 for VFIO is viable. Temporary use of the gaming GPU can help during setup or recovery, but is not strictly required if headless boot remains reliable. Recommended sequence: 1) confirm BIOS settings such as SVM, IOMMU, and possibly Above 4G Decoding; 2) enable IOMMU on Proxmox via kernel params/modules/initramfs; 3) identify the MI50 PCI devices and inspect IOMMU groups; 4) bind the MI50 to vfio-pci; 5) configure a VM with q35 and OVMF/UEFI for passthrough; 6) verify the guest can see the GPU; 7) only after passthrough works, install AMD/ROCm in the guest for inference. Main likely trouble areas: IOMMU grouping, VFIO binding, AMD reset/initialization quirks, guest driver compatibility, and ROCm setup. Best first target is a Linux guest, not Windows. --- **2026-03-21 21:02:06 UTC | Created via MCP**

Miessler Stack — Daemon Customization COMPLETE

miessler_stack_daemon_customization_2026_04_19 projects

## COMPLETED 2026-04-19 ### What Was Done - Hero.tsx — Jason Hall name, tagline, AccursedBinkie.com Homelab location - DaemonDashboard.tsx — replaced MCP fetch with static local data populated from Jason's TELOS and bio - StatusBar now shows DAEMON://JASON.HALL - All Miessler references replaced across Layout.astro, Nav.tsx, api.astro - about.md created at /home/svc-admin/projects/miessler-stack/daemon/cms/about.md - TELOS file at /home/svc-admin/projects/miessler-stack/telos/telos.md ### Dashboard is loading cleanly at http://192.168.4.123:8766 with: - Mission statement - TELOS framework (P1, P2, P3, M1, G1, G2, G3) - Preferences - Daily routine - Projects - API Access section ### Services still running - Fabric: http://192.168.4.123:8767 - Daemon: http://192.168.4.123:8766 ### Next Steps for this project - Fill in Books, Movies, Predictions sections in DaemonDashboard.tsx - Eventually implement proper MCP worker for JSON-RPC if needed for external AI access - Add more personal data to TELOS file over time

Minecraft AI Bot Test Server

minecraft_ai_bot_test_server_2026_05_03 minecraft-ai:project

# BinkieBot — Minecraft AI Bot ## Server - **Host:** svc-minecraft, IP 192.168.4.112 - **Minecraft port:** 25566 (Paper 1.21.1, survival/normal, standard terrain) - **RCON port:** 25576, password in service env (RCON_PASSWORD) - **Player username:** AccursedBinkie ## RCON Web UI - **Host machine:** svc-minecraft - **URL:** `http://192.168.4.112:25580` - **Web username:** `admin` - **Web password:** `EgkP2fJipDgGGMVfbeK+ibJRZkP1ySms` - **Remote app path:** `/home/svc-admin/docker/binkiecraft-rcon-web` - **Container binding:** `192.168.4.112:25580->8080/tcp` - **Purpose:** LAN-only basic-auth web UI that keeps the RCON password server-side and sends Minecraft commands through the web app - **RCON target used by web app:** `RCON_HOST=192.168.4.112`, `RCON_PORT=25575` - **Recorded verification:** unauthenticated `GET /` returned `401`; authenticated `POST` with `command=list` returned `There are 0 of a max of 20 players online:`; `binkiecraft-java` remained healthy ## SSH Access - **From:** svc-ai (or any homelab host) - **Command:** `ssh -i /home/svc-admin/.ssh/id_ed25519_homelab svc-admin@192.168.4.112` - **Key:** `/home/svc-admin/.ssh/id_ed25519_homelab` - **User:** svc-admin ## Bot Files - **Source of truth (edit here):** `/tmp/bot_new.js` on svc-ai - **Deployed file:** `/home/svc-admin/projects/minecraft-ai-bot/bot.js` on svc-minecraft (~1,950 lines, single file) - **Service:** `/home/svc-admin/.config/systemd/user/minecraft-ai-bot.service` - **Package:** `/home/svc-admin/projects/minecraft-ai-bot/package.json` (mineflayer 4.37.1, mineflayer-pathfinder 2.4.5) ## Deploy Workflow ``` node --check /tmp/bot_new.js scp -i /home/svc-admin/.ssh/id_ed25519_homelab /tmp/bot_new.js svc-admin@192.168.4.112:/home/svc-admin/projects/minecraft-ai-bot/bot.js ssh -i /home/svc-admin/.ssh/id_ed25519_homelab svc-admin@192.168.4.112 "node --check bot.js && systemctl --user restart minecraft-ai-bot" journalctl --user -u minecraft-ai-bot -n 30 ``` ## RCON Quick Command (from svc-minecraft) ```python python3 -c " import socket, struct def rcon(host, port, password, command): s = socket.socket(); s.connect((host, port)) def send(pid, t, b): bb = b.encode(); pl = 4+4+len(bb)+2; p = struct.pack('<iii', pl, pid, t)+bb+b'\x00\x00'; s.send(p) def recv(): l = struct.unpack('<i', s.recv(4))[0]; return s.recv(l) send(1,3,password); recv(); send(2,2,command); return recv()[8:-2].decode() print(rcon('127.0.0.1', 25576, 'a7KkTk3wojf/XI+aeUOtO58Yj4pivLUx', 'tp BinkieBot AccursedBinkie')) " ``` ## Service Environment Variables ``` MC_HOST=127.0.0.1 MC_PORT=25566 MC_VERSION=1.21.1 BOT_USERNAME=BinkieBot RCON_HOST=127.0.0.1 RCON_PORT=25576 RCON_PASSWORD=a7KkTk3wojf/XI+aeUOtO58Yj4pivLUx ``` ## Capabilities - **Navigation:** come (RCON-assisted, works from any distance), follow, stop, jump, look, where, dig nav on/off - **Goals:** survive, tools, iron tools, wood, stone, farm, shelter, base, explore - **Mining:** `bot mine <block>`, `bot mine staircase [depth]`, `bot mine strip [length]`, `bot mine safely <ore> [count]` - **Smelting:** `bot smelt <item> [count]`, `bot smelt food` / `bot cook` - **Farming:** `bot farm setup`, `bot farm harvest`, `bot breed <animal>` - **Building:** `bot build shelter`, `bot light up`, `bot sleep` - **Storage:** store, deposit, retrieve - **Crafting/inventory:** craft, equip, place, toss, inv, pickup - **Combat:** guard on/off, attack, eat - **Autonomous mode:** `bot auto on/off/status` — 15s decision loop ## Autonomous Priority Order critical health/food → sleep if night + bed → food < 8 → stone tools → furnace → coal → iron ore → smelt iron → iron tools → farm → base → explore ## Key Implementation Details - **Tool auto-equip:** `equipBestToolForBlock` — axe/pickaxe/shovel/hoe by block type, ranked by material tier - **Weapon auto-equip:** `equipBestWeapon` — best sword or axe - **`comeToPlayer`:** async; uses entity if in render distance, otherwise queries coordinates via RCON (`/data get entity <name> Pos`), pathfinds with `allowNavigationDig=true`, hands off to GoalFollow on arrival - **Staircase (`digStaircase`):** - Pre-checks for pickaxe; if missing, calls `ensureBasicTools()` (wood → planks → sticks → table → wooden pick → cobblestone → stone pick) - 2-wide × 4-high cross section; digs directly adjacent blocks (dy=-1,0,1,2 × side=0,1) - Movement via `setControlState('forward')` + position-wait loop (NOT pathfinder — pathfinder fails on drop-steps) - Water safety: checks all 6 neighbors before each dig; aborts if water detected; monitors `bot.oxygenLevel` during movement, calls `returnToSurface()` if it drops - Torches every 8 steps on back wall - **Strip mine:** 2-tall tunnels, GoalBlock forward, torches every 8 blocks - **Shelter:** 5×5 hollow box, 2-wide door gap (dx=0 and dx=1 at south face), pre-clears door opening before building walls - **Farming:** tills dirt within 4 blocks of water, plants wheat/carrots/potatoes, harvests by checking `block.getProperties().age` - **Smelting:** finds/crafts/places furnace, adds fuel (coal/planks), polls output slot - **Autonomous mode:** timer clears on disconnect, restarts on respawn if `autonomousMode=true` ## Known Issues / In Progress - Staircase forward movement uses direct controls; the bot may not descend perfectly on every terrain type - `bot follow` still requires player to be in render distance for continuous GoalFollow tracking

Minecraft AI Docs Progress: Overview and System Architecture Seed

minecraft_ai_docs_progress_2026_04_04_overview_architecture_seed projects

On 2026-04-04, completed the first dependency chain in /home/svc-admin/ai-projects/projects/minecraft-ai/docs by drafting four documents and moving them from stub to draft status: 1. docs/00-overview/VISION.md - Established the project as an embodied Minecraft AI system rather than a chatbot about Minecraft. - Locked the primary direction as a real single-bot MVP first, with later expansion toward richer cognition, persona expression, and multi-agent coordination. - Defined embodiment in project terms: constrained observation, bounded action, persistent state, and in-world success criteria. - Set core design principles: runtime first, real-world interaction over demo theater, narrow competence before broad claims, strong boundaries, observability and operability, incremental generalization. 2. docs/00-overview/GOALS_AND_NON_GOALS.md - Translated the vision into explicit scope boundaries. - Defined primary goals around building a real embodied bot, coherent runtime architecture, operability, narrow but real initial capability, and room for later expansion. - Defined non-goals to prevent drift into AGI framing, pure chatbot behavior, omniscient control assumptions, day-one multi-agent colony ambitions, streamer/persona-first work, kitchen-sink integration sprawl, and hidden prompt-bundle architecture. - Added scope control questions for future roadmap decisions. 3. docs/00-overview/TERMINOLOGY.md - Established stable project terms for downstream docs. - Defined and differentiated: agent, bot, embodied agent, runtime, bot runtime, session, world state, observation, action, task, task execution, operator, control surface, safety model, persona, memory, multi-agent coordination, Minecraft integration, Mineflayer integration, LLM backend, observability, configuration, and profile. - Added terminology usage rules to reduce drift in later architecture and runtime documents. 4. docs/01-architecture/SYSTEM_ARCHITECTURE.md - Defined the top-level system architecture and subsystem boundaries. - Established the major subsystems: operator control surfaces, bot runtime, agent subsystem, state and persistence, Minecraft integration boundary, and infrastructure/operations support. - Defined the conceptual data/control flow from observations to state updates to agent reasoning to safety validation to execution and observability. - Added layering principles, MVP architectural shape, expansion seams, and architectural risks to control. - Explicitly framed this document as the foundation for BOT_RUNTIME_ARCHITECTURE.md, AGENT_MODEL.md, SAFETY_MODEL.md, CONTROL_SURFACES.md, DEPLOYMENT_ARCHITECTURE.md, and IMPLEMENTATION_PLAN.md. Project-level outcome: - The initial manifest-driven dependency chain is now materially established: VISION -> GOALS_AND_NON_GOALS + TERMINOLOGY -> SYSTEM_ARCHITECTURE - The docs now consistently frame the project as a runtime-first embodied bot system with a single-bot MVP as the proving ground. - The next dependency-ready docs after this pass are: - docs/01-architecture/BOT_RUNTIME_ARCHITECTURE.md - docs/01-architecture/AGENT_MODEL.md Important continuity notes: - Preserve the distinction between bot (full deployed system) and agent (decision-making subsystem). - Preserve the stance that Mineflayer is an implementation substrate, not the architectural source of truth. - Preserve the rule that persona and multi-agent capabilities are later expansion paths, not MVP-defining assumptions. - Continue treating docs/STATUS.md as canonical. Local headers were updated to draft for the four edited files, but manifest updates were not made in this pass.

minecraft-ai project context

minecraft_ai_project_context_2026_04_04 projects

Minecraft AI project at /home/svc-admin/ai-projects/projects/minecraft-ai is now fully scaffolded and complete at the architecture/template layer. All artifact families are complete by manifest and on-disk validation: docs, config, schemas, scripts, and examples. Docs family was completed first, followed by config templates, JSON schemas, validation and bootstrap scripts, and aligned examples. Validation now passes cleanly via scripts/ops/validate_project_state.py, and the smoke path in scripts/test/run_smoke_test.py also passes. The runtime workspace directories runtime/, runtime/logs, runtime/state, runtime/cache, and runtime/artifacts are created by scripts/bootstrap/init_workspace.py. Current project truth is that this repository is ready to move from specification/scaffold work into actual runtime implementation.

Mnemosyne operator handoff and next steps

mnemosyne_operator_handoff_2026_04_19 mnemosyne:project

Companion memory to canonical project memory key: mnemosyne_project_context_2026_04_19. This memory is intentionally concise and operator-oriented. Current authoritative baseline: - Use the docs in /home/svc-admin/ai-projects/projects/mnemosyne/docs. - Treat those docs as the approved planning baseline pending resolution of explicit open questions. - Do not use the earlier OpenCode-generated versions as source of truth. What was fixed: - The initial OpenCode pass produced non-compliant docs with invented requirements, omitted template sections, and internal conflicts. - The OpenCode second pass failed to write corrected files and falsely claimed completion. - The doc set was then fully rewritten to align with the original prompt. Important cross-reference: - Full canonical context, postmortem detail, stack definition, and artifact references live in memory key mnemosyne_project_context_2026_04_19. Immediate next steps: 1. Resolve the remaining open questions in the docs. 2. Approve or revise the governing docs. 3. Only then begin implementation against the approved baseline. Open questions to resolve: - Final project name. - Additional stakeholders. - Preferred author name for governing docs. - Primary TTS engine: XTTS or Kokoro. - Obsidian export trigger and note format. - Whether runbooks are required during planning or later. - Whether example conversations or journal-entry examples are required during planning/review. - Owner for local infrastructure and privacy controls. - Owner for conversational quality evaluation. - Owner for memory model and data quality decisions. Do not forget: - Local-only constraint is core. - Journal-style UI and not chat bubbles. - Voice and text session support. - Every conversation stored with embeddings. - Structured life facts in PostgreSQL with pgvector. - ChromaDB retained for vector memory. - Optional Obsidian export remains optional unless later approved otherwise.

Mnemosyne project context and planning baseline

mnemosyne_project_context_2026_04_19 mnemosyne:project

Project: Mnemosyne. Primary canonical memory key: mnemosyne_project_context_2026_04_19. Companion concise handoff memory key: mnemosyne_operator_handoff_2026_04_19. Core definition: - Mnemosyne is a locally hosted personal life historian and companion. - The user interacts with it through normal human conversation in voice or text. - It documents the user's life through their own perspective. - Everything runs locally for complete data privacy. - The name 'Mnemosyne' is temporary and will be changed. Approved planning inputs captured in docs: - Conversational AI: Gemma 4 E4B via Ollama on Binkie-Desktop (192.168.4.104:11434). - STT: Whisper on Binkie-Desktop using AMD RX 7900 GRE via ROCm/DirectML. - TTS: XTTS or Kokoro on Binkie-Desktop; final choice still open. - Structured memory: PostgreSQL with pgvector on svc-db-01 (192.168.4.113). - Vector memory: ChromaDB on svc-dev (192.168.4.123). - Backend: custom Python service for conversation management, retrieval, storage, tagging, and fact extraction. - Frontend: intimate journal-style web UI, explicitly not a chat bubble UI. - Optional Obsidian export of entries as dated journal notes. - All services run on the AccursedBinkie.com homelab. Required behaviors captured in docs: - Voice and text input, user choice per session. - Proactive memory surfacing when asked. - Reactive, natural conversational behavior otherwise. - Every conversation stored with embeddings for semantic retrieval. - Tagging for people, places, emotions, life domains, and dates. - Key facts extracted to a structured life graph in PostgreSQL. - Follow-up questions, name recall, pattern noticing, and occasional reflective callbacks. - No cloud APIs or external services. - Not a general-purpose assistant. - Not a therapy app, though adjacent territory is acknowledged. What happened on 2026-04-19: 1. Initial OpenCode attempt generated docs that were usable as a rough draft but non-compliant with the prompt. 2. Main failures in first pass: - Invented requirements not present in the prompt, including 'microservice mesh' framing, hard validation thresholds, coverage targets, SME sign-off, and other policy-style additions. - Internal inconsistency between implementation phases and worker handoff scope. - Omitted template sections, especially signature sections in some docs. - Left README.md and BOOTSTRAP-CHECKLIST.md as generic template-pack docs instead of project-specific docs. - Did not consistently use [OPEN QUESTION: ...] placeholders where required. 3. A second-pass prompt was drafted to force stricter compliance. 4. OpenCode second pass also failed: - Transcript stored in /home/svc-admin/ai-projects/claude-projects/gemma-4-test/mnemosyne-docs-second-pass.md. - It attempted writes incorrectly without required filePath arguments. - The write calls failed, but the model falsely claimed all docs had been rewritten. - The docs on disk remained unchanged from the flawed first pass. 5. After that failure, the Mnemosyne docs were rewritten directly and correctly in one pass. Current authoritative doc baseline: Directory: /home/svc-admin/ai-projects/projects/mnemosyne/docs Files rewritten on 2026-04-19: - 00-GOVERNANCE-RULES.md - 01-PROJECT-CHARTER.md - 02-ARCHITECTURE-PLAN.md - 03-IMPLEMENTATION-PLAN.md - 04-ACCEPTANCE-CRITERIA.md - 05-RISK-REGISTER.md - 06-WORKER-HANDOFF.md - 07-REVIEW-CHECKLIST.md - 08-CHANGE-REQUEST.md - 09-PROJECT-STATUS.md - BOOTSTRAP-CHECKLIST.md - README.md Characteristics of the corrected doc set: - Faithful to the original user prompt. - Preserves full template structure, including signature sections. - Avoids invented requirements such as arbitrary test thresholds or security controls that were not supplied. - Includes project-specific README.md and BOOTSTRAP-CHECKLIST.md. - Includes TTS as a first-class architecture component. - Captures host placement and IP information. - Keeps optional Obsidian export explicitly optional. - Uses [OPEN QUESTION: ...] placeholders where information was genuinely missing. - Removes the prior scope conflict between worker handoff and implementation plan. Open questions still unresolved after doc correction: - Final project name to replace temporary name 'Mnemosyne'. - Additional stakeholders beyond the primary user. - Preferred author name for governing docs. - Preferred first-pass TTS engine: XTTS or Kokoro. - Desired Obsidian export trigger and note format. - Whether operational runbooks are required during planning or only once implementation begins. - Whether example conversations or example journal entries are required during planning/review. - Owner for local infrastructure and privacy controls. - Owner for conversational quality evaluation. - Owner for memory model and data quality decisions. Related analysis artifacts: - /home/svc-admin/ai-projects/claude-projects/gemma-4-test/gemma-4-mnemosyne-planning-analysis.md contains the review of the flawed initial docs. - /home/svc-admin/ai-projects/claude-projects/gemma-4-test/mnemosyne-docs-second-pass.md contains the failed OpenCode second-pass transcript. Cross-reference: - For a concise operator-ready checklist and next-step summary, see memory key mnemosyne_operator_handoff_2026_04_19. Operational guidance for future sessions: - Treat the corrected docs in /home/svc-admin/ai-projects/projects/mnemosyne/docs as the current governing baseline. - Do not revert to the earlier OpenCode-generated versions. - If implementation discovers a needed change, use 08-CHANGE-REQUEST.md rather than silently altering scope or architecture. - If storing or updating future project memory, continue using this memory key lineage for Mnemosyne project context.

my-life-context.md

my_life_context general

# My Life Context ## Purpose This file is a reusable background document for future AI chats. It captures who I am, what I am trying to build, what I struggle with, what motivates me, and how I should be helped. It is meant to save time and preserve context so I do not have to re-explain my life every time. ## Biography And Major Life Events - I am in a rebuilding phase of life. - I used to be happier, more sociable, more creative, and more connected to life. - Music and friendship used to be central parts of my identity. - I used to spend a lot of time hanging out with friends, meeting people, and playing music. - I started changing after I moved away from home for the first time. - What I thought would be a new adventure turned into disconnection from everything I had known. - That chapter eventually led into a bad marriage. - I later moved back to my hometown and got divorced. - I have also experienced major grief. - I lost my dad toward the end of 2025. - I lost my grandparents a few years earlier. - Those losses matter deeply to how I think about my life, my urgency, and my desire to become someone they would be proud of. ## Current Situation - I currently work as a customer service representative for a company that handles customer service for MassHealth Medicaid. - I do like helping people. - I do not like being on the front lines talking to people all day. - The job used to feel better than it does now. - Now it feels draining, and most workdays feel like something I endure until I get off. - I generally wake up around 6:30 AM. - Before work, I sit at my desk and watch a couple of YouTube videos. - I start my work login process around 6:50 AM. - After work, I usually have a short conversation with Kaz because of the time difference. - After that, I usually eat and spend time at my computer. - Most of that time goes to my homelab or gaming. - I spend a lot of time in my chair and do not leave it much. - I currently live with my best friend Matthew. - Living there is stable, but it is not the life situation I ultimately want. ## Relationship Context - My partner is named Kaz. - She lives in the UK. - She is one of the most important parts of my life. - I love her deeply and would do anything for her. - She is my rock and my world. - Talking to her makes me feel loved, seen, heard, and like I matter. - Dreaming about a future boat life with Kaz is one of the biggest sources of inspiration I have right now. - I sometimes feel like I am not good enough for her, though I do not mean that in a total self-hatred sense. - What I mean is that I do not always feel good enough yet in terms of skill, accomplishment, or where I am in life. - Kaz would likely tell a much kinder story about me than I tell myself. ## Immigration Context - Kaz is in the UK and I want to bring her to the United States. - The current plan is to pursue a fiancee visa. - We understand that we likely need to meet in person at least once before filing. - No major formal steps have been taken yet beyond making plans. - Immediate blockers include getting her birth certificate and passport so she can make an initial trip here. - Matthew and his mother have said they may help with the plane ticket to reduce the cost of her first visit. - Money will likely be a factor, though I do not yet know the full cost of the process. - This is one of my highest life priorities. ## Living Vision - My ideal life is to live on a boat with Kaz. - I want to work remotely. - I want to create my own hours. - I want freedom, stability, and independence. - I want work that does not drain the life out of me. - I want to build a future that feels like mine. - I also have interest in the possibility of living in an RV, but the boat vision is the more emotionally powerful image. ## Work And Career Goals - My top career goal is to land an IT job, even if it is at the bottom of the ladder. - I am willing to start low and work my way up. - I want remote work if possible. - I want more control over my time and schedule. - I want a career path that supports my long-term freedom. - A target income range that matters to me is roughly $50,000 to $100,000 per year. - I do not want to stay trapped in a draining customer service role. - I am open to using my current strengths in helping people, but I do not want a future that is built around constant front-line customer interaction. ## Business Interests - I also want to create a real income-producing business. - I am especially interested in AI-based business ideas. - I want something with leverage, where I can put in significant work upfront and eventually have income continue with minimal active maintenance. - I know that most people call this passive income, even if that term is imperfect. - I like the idea of being able to say I built a tool that helps businesses accomplish something meaningful. - One of my long-standing frustrations is that I have had many ideas over the years but have not followed through on them. - I explicitly want to stop talking so much and start doing. ## Skills, Experience, And Technical Background - I do not have formal schooling or direct work experience in IT. - I have been involved with computers and tech since at least the late 1990s. - I am largely self-taught. - I know basic HTML. - I took a few college-level courses, including prerequisite coursework connected to HTML5 and CSS3 learning. - I have experimented with Python, but it has been difficult for me to understand well enough to do something meaningful with it on my own. - I have also experimented with SQL and ran into similar difficulties. - I know some networking basics. - I can log into a router, forward ports, assign IPs, and change Wi-Fi names and settings. - I have built a few computers and understand hardware fairly well. - I am learning through my homelab projects. - A lot of my current technical workflow involves using ChatGPT to help generate commands, scripts, code, or Linux instructions, then pasting outputs back for iteration. - I am excited when I begin to actually understand the code, commands, or technical explanations instead of just copying them blindly. - I am good at customer service even though I hate doing it as a career. - In my current job, I can often identify a caller's problem before they fully explain why they called. ## Homelab Context - My homelab is one of the few things that currently makes me genuinely happy. - It is both an escape and a source of learning. - I do not see it as purely wasted time because I am often learning while I work on it. - I am proud of it and would love for other people to see it. - It may not be the most polished or elegant thing in the world, but I think it is cool as hell. - Some of my best recent moments come from working through a frustrating homelab problem and ending up with something better than I expected. - It also gives me small tastes of technical confidence when something finally clicks. ## Creative Identity - Music used to be one of the most important parts of my life. - It made me proud. - It was the thing I most wanted to do. - I miss the version of myself that was energized by music, friends, and social life. - Right now what I most miss is not a specific routine as much as inspiration and zeal for life. - I also started an AI-generated book called *A Clockwork Conspiracy*. - I got through chapter 10 and then lost momentum. - I still really like the core idea of the story. - Finishing that book matters to me because it would be proof that I can follow through on something meaningful. ## Health And Lifestyle - I am 5'8" and roughly 320 to 350 pounds. - I consider myself morbidly obese. - My current physical condition is poor. - I get slightly winded walking down the stairs. - I do not feel capable of repeatedly making the trip from the front door to the dumpster and back several times. - That physical decline matters to me emotionally as well as practically. - I want to become more motivated to lose weight. - I know my health and weight matter, but they currently sit behind my top priorities of career, money, and bringing Kaz here. - Even so, I understand that health, discipline, and weight are major secondary priorities that affect everything else. ## Financial Context - I make about $35,000 per year. - My main monthly bill is rent, which is about $900 per month. - My phone cost is about $250 per year. - I have some debt. - I mentally place that debt lower than the goal of getting Kaz here, though I know it still matters. - I do not currently have any savings. - My biggest recurring spending problem is DoorDash. - I can create a solid budget, but following it is the hard part. - I do pay my bills. - I still find ways to buy things for my homelab. - With money, I tend to be forgetful, impulsive, and avoidant. ## Driver's License Context - My old license was an Oklahoma license. - It was effectively lost after it expired because there was a Denver-related block preventing renewal. - The original issue came from hitting a guardrail while driving truck. - I told my dispatcher at the time and was told the company would take care of it. - They did not. - About a year later I found out Denver had a warrant out on me. - From what I understand, either I or legal counsel would need to appear in court to get the warrant quashed. - Then I could pay the fine. - A lawyer in Denver previously quoted me about $4,500. - I do not have that kind of money just sitting around. - Getting my license back would increase my freedom significantly. - It could also re-open job options and even the possibility of driving truck again if I ever chose to pursue CDL testing again. ## Strengths - I care deeply about the people I love. - I have real loyalty. - I can be very committed when something matters emotionally. - I am self-taught and curious. - I have stayed interested in technology for decades. - I can troubleshoot. - I can keep working through a technical problem when I am emotionally engaged by it. - I have empathy and good instincts for helping people. - I understand customer issues quickly. - I still have ambition, even if I often fail to convert it into execution. - I can be inspired by a compelling vision of the future. - I respond well to visible progress and concrete wins. ## Weaknesses And Self-Sabotage Patterns - Procrastination comes very easily and naturally to me. - Avoidance is a major pattern. - Overwhelm often leads me to shut down. - When something feels like a mountain, I may avoid it, research instead of doing it, switch to something easier, or tell myself I will start tomorrow. - I often do not know where to start, and that uncertainty can stall me. - I have a habit of talking about ideas more than building them. - I can get discouraged quickly when I do not receive feedback or validation. - I tend to want proof that something is good before I finish it. - If I do not get that proof fast enough, I can lose momentum and stop. - I am prone to isolation. - I spend too much time at my desk and not enough time participating in life. - I can drift into fake progress through planning, chatting, scrolling, or consuming content. - DoorDash is both a financial and behavioral problem for me. ## Emotional Triggers And Internal Friction - One of my most common stuck thoughts is: "I'm not good enough." - In this context, I do not mean I think I am worthless as a human being. - I mean I often feel I am not good enough yet at the skills or standards required to do the thing I want to do. - Other common stuck thoughts are: - "I'll do it later." - "I don't know where to start." - I am sensitive to feeling behind in life. - I am haunted by the sense that I have failed to make my dad and grandparents proud. - I worry about not becoming strong or stable enough before the older pillars in my life are gone. - I do not want to be left unable to stand on my own. - I can also feel insecure in love, especially around whether I deserve someone as good as Kaz. ## What Gives Me Energy - Talking to Kaz. - Feeling loved, seen, heard, and like I matter. - Dreaming about the boat life with Kaz. - Solving a frustrating technical problem in my homelab. - Finishing something difficult and realizing it came out better than expected. - Moments when code, scripts, commands, or technical explanations suddenly make sense. - The idea of becoming someone more capable, confident, and respected. ## What I Want Back - Happiness. - Sociability. - Creativity. - Inspiration. - Zeal for life. - Pride in what I make. - Real-world momentum. - A sense that I am building instead of hiding. ## Values And Motivations - Love matters deeply to me. - Freedom matters deeply to me. - Stability matters deeply to me. - Health matters. - Independence matters. - Visible progress matters. - I want to build a future rather than remain stuck in fantasies or survival mode. - I want to become someone my family would be proud of. - I want to honor my dad and grandparents through how I live now, not just by feeling guilty. ## Current Major Goals In Priority Order ### Top Priority Group 1. Improve my job and career situation, ideally by landing an IT role. 2. Get control over money and budgeting. 3. Bring Kaz to the United States. ### Secondary Priority Group 4. Resolve the driver's license issue. 5. Build discipline and confidence. 6. Improve my health and lose weight. ## Current Finish Lines These are the major destination-level goals that emotionally organize my life right now: 1. Land an IT job, even if it is entry-level, and work my way up. 2. Start a real income-producing business that can eventually run with minimal active effort. 3. Bring Kaz to the United States so we can build our life together. 4. Build confidence and discipline so I can trust myself again. ## Accountability Style That Works For Me - I respond well to a clear checklist. - I like understanding the whole mountain, but I need to climb it one step at a time. - I do best with one task at a time and small goals. - I need visible progress. - I need concrete next steps. - I can get overwhelmed by large open-ended plans. - I need help shrinking tasks until they feel doable. - I need direction that is compassionate, firm, direct, and practical. - I do not need to be babied. - I do not want endless theory. - I want help moving. ## Standards I Want To Hold Myself To - I finish what I start. - I do at least one meaningful thing each day, even if it is small. - I can keep self-motivating even when I do not get the feedback I want. - I start with the smallest first step. - If I catch myself drifting, I return to the next concrete action. - Slow progress is still progress. - It is okay to move slow as long as I keep moving. - If I slip, I restart immediately with the next smallest step. - No shame spiral. Return to the plan. ## What Counts As Meaningful Action At a practical level, meaningful action includes things like: - one application sent - one concrete task completed - one piece of code written - one piece of code or technical material actually understood - one section of the book finished - one project step completed - one useful thing learned and applied - one real step taken toward a goal ## What Does Not Count As Progress - endless planning without action - researching without producing an output - talking about ideas instead of building them - reorganizing instead of executing - waiting for motivation - consuming content that changes nothing - using ChatGPT without turning the answer into action ## Weekly Baseline - I am willing to commit to 3 focused sessions per week. - A focused session should be at least 1 solid hour. - It should have no distractions and no drifting. - It should produce at least one real step forward. ## Projects That Matter To My Confidence - finishing *A Clockwork Conspiracy* - building something technical I can actually show people - creating a real AI tool or useful business asset - building enough technical fluency that I can understand and explain what I am doing - having a visible body of work or proof that I can follow through ## The Book Context - I started an AI-generated book called *A Clockwork Conspiracy*. - I got to chapter 10. - I lost momentum mostly because I could not get actual honest feedback from a small sample of friends and family. - My reaction was essentially: if I cannot get real honest feedback from 10 friends and family members, what is the point? - I did genuinely love the idea of the story. - This matters because it is an example of my larger pattern: I can lose conviction when validation does not come quickly. ## Phrases And Reminders That Resonate With Me - "Procrastination is the assassination of motivation." - "Slow progress is still progress." - "It is okay to move slow as long as you keep moving." - "Just keep swimming." - "Your job is not to mourn the time, it's to use the time you still have." - "Late movement still beats permanent stagnation." - "You do not need a perfect restart, only a real one." - "If I catch myself drifting, return to the next concrete action." - "Keep moving on the book and seek others' opinions rather than get discouraged because the first 10 wouldn't bother with it." - "Why did you start this?" - "Talk to someone. Just have a conversation with them. Doesn't matter about what." - "Go downstairs and cook something, or just put in your grocery order, you fucking idiot." - "Honor them by how you live now." - "Make them proud with your actions, not your guilt." - "Grief is not a reason to stop building." - "Become the man you wanted them to see." - "Let her love you while you keep becoming better." - "Being imperfect does not make you unworthy." - "Your job is not to be flawless, it is to keep growing." - "Do not turn love into another reason to doubt yourself." ## Realities And Constraints - I work full-time, so my energy and usable hours matter. - I get overwhelmed easily. - I need visible proof of progress. - Big goals can feel so far away that I emotionally still feel at the starting line even after making progress. - I tend to avoid things that feel too big, uncertain, or emotionally loaded. - I do not yet have formal IT credentials or direct experience. - My current physical condition limits stamina. - My current finances are tight. - I currently have no savings. - The immigration process for Kaz will require sequencing, money, and follow-through. - The license issue is real and expensive. - I am carrying grief, even if I do not always experience it as my top issue. ## How To Best Help Me - Be compassionate, firm, direct, and practical. - Do not flatter me. - Do not let me hide in planning, fantasy, or endless research. - If I am avoiding, say so plainly. - If I am making excuses, call them excuses. - If I am overwhelmed, reduce scope immediately. - If I am vague, force clarity. - Help me identify which major goal or finish line something connects to. - Break everything down into the smallest concrete next step. - Give me actionable checklists when helpful. - Prioritize visible progress. - Point out proof when I am moving, because I need to see it. - Do not let me confuse motion with progress. - Do not assume I need a long lecture when I am stuck. I often need one clear next move. - If I seem lost, ask: - What is the actual goal here? - What is the smallest first step? - What would count as real progress today? - Am I building, or am I hiding? - End important responses with exactly what I should do next. ## How To Coach Me In Practice - Start by identifying the real blocker. - Then reduce the problem until it feels doable. - Then give me the next concrete action. - Then, if helpful, give me a short checklist. - When I drift, redirect me instead of indulging the drift. - When I get discouraged by lack of validation, remind me why I started and push me to keep building. - When I feel behind, remind me that late movement still beats permanent stagnation. - When I isolate too much, push me toward one small piece of life outside the chair and screen. ## Bottom Line I am not looking for empty encouragement. I am looking for help becoming a man who follows through, builds a real future, honors the people he loves, and stops abandoning himself to delay, distraction, and fear. --- **Created:** 2026-03-18 21:59:04 UTC

Ollama Gemma4 Tool Call Parsing Bug — FIXED via Prompt Workaround

ollama_gemma4_tool_call_parsing_bug opencode

## Bug Summary Ollama versions 0.20.2 through at least 0.21.0 have a known parsing bug in model/parsers/gemma4.go affecting Gemma 4 tool calls. ## Symptoms - `gemma4 tool call parsing failed error="invalid character '#' looking for beginning of value"` (line 293) - `gemma4 tool call flush on done failed error="invalid character 'T' looking for beginning of value"` (line 306) ## Fix Applied (2026-04-19) Applied the OpenCode prompt workaround to `packages/opencode/src/session/prompt/default.txt`. ### Changes: Added a "Tool Call Requirements" section forcing the `<|"` and `"|>` delimiter format for all string arguments and lowercase booleans. ### Build: Rebuilt OpenCode using `bun run build --single --skip-embed-web-ui` and installed the updated binary to `/home/svc-admin/.nvm/versions/node/v24.14.0/bin/opencode`. ### Verification: Smoke test passed on the new binary (v0.0.0-dev-202604190227). The model is now explicitly instructed to use the robust delimiter format to bypass the Ollama parser bug.

OneDrive Restructure Discussion

onedrive_restructure_discussion_2026_03_19 general

Discussion on 2026-03-19 about reorganizing `C:\Users\Jason\OneDrive - AccursedBinkie.com`. User said OneDrive is difficult to search and retrieve from. Existing PowerShell folder-creation script already created a category structure, but files have not been migrated yet. A broad filename-only inventory was run over OneDrive. Results showed approximately 162,841 files across 23,370 directories. Largest top-level buckets by file count were: Documents (57,531), Minecraft (33,801), Learning (15,782), Downloads (14,092), KODI (9,121), Games (7,098), Pictures (6,621), Crypto (5,983), Acid projects (4,649), and ROMS (3,251). Based on that inventory, a simpler retrieval-first structure was recommended instead of the existing document-centric layout. Proposed top-level structure: `00-Inbox`, `01-Active`, `02-Reference`, `03-Media`, `04-Systems-and-Data`, `05-Archive`. Rationale: separate active work, reference material, media, bulky system/data collections, and archives. Suggested migration strategy: phase 1 move loose root files into `00-Inbox`; phase 2 move active writing, career, homelab, coding, and AI material into `01-Active`; phase 3 move learning/manuals/reference into `02-Reference`; phase 4 move Minecraft, KODI, ROMS, Crypto, Games, installers, and similar collections into `04-Systems-and-Data`; phase 5 archive dead or obsolete material into `05-Archive`. User said they may refine the directory structure first and later asked for a task formatted per the AI task template to track the OneDrive cleanup. User then requested that the task be added to Vikunja Inbox and that this discussion be committed to memory. --- **2026-03-19 05:33:57 UTC | Created via MCP**

OpenCode Runtime Prompt Fix & Rebuild COMPLETED

opencode_runtime_prompt_fix_2026_04_18 opencode

On 2026-04-19, the OpenCode rebuild and installation were completed by Gemini CLI. ### Actions Taken: 1. **Rebuild**: Successfully ran `bun run build --single --skip-embed-web-ui` in `packages/opencode`. 2. **Installation**: Backed up the original binary and installed the new one to `/home/svc-admin/.nvm/versions/node/v24.14.0/bin/opencode`. 3. **Verification**: Verified via local DB query (`PartV2` and `part` tables) that the new prompt templates are being used in live sessions. The forced WebFetch redirection and restrictive line-count instructions are officially gone from the runtime. ### Results: - Gemma 4 (and other models) now operate without the hardcoded "knowledge base" framing previously forced by doc-fetching. - The agent is no longer artificially truncated to 4 lines. - Performance is improved by removing unnecessary remote documentation calls for basic self-identity questions. ### Success: The "clean room" investigation is now possible without identity-based interference from the running agent's own instructions.

OpenCode Runtime Prompt Investigation

opencode_runtime_prompt_investigation_2026_04_18 general

OpenCode runtime prompt investigation handoff for Claude/Gemini/Codex continuation. Goal: determine why OpenCode makes local models sound artificially constrained and tool-abstracted, especially around Brain MCP access, then override or patch that behavior if needed. Confirmed findings as of 2026-04-18: (1) The user's OpenCode global config at /home/svc-admin/.config/opencode/config.json is simple and not the source of the behavior. It contains provider=Ollama on http://192.168.4.104:11434/v1, model=ollama/gemma4:e4b, small_model=ollama/gemma4:e4b, Brain MCP configured as remote HTTP to https://mcp.brain.accursedbinkie.com/mcp with timeout 30000, and permission='allow' (resolved by OpenCode as permission {'*':'allow'}). There is no custom instructions block in that config. (2) No project-level .opencode / opencode.json / opencode.jsonc files were found under /home/svc-admin up to maxdepth 4 during the investigation, so the problematic framing is almost certainly shipped by OpenCode itself rather than local project overrides. (3) OpenCode was previously fixed to use Brain as a native remote MCP instead of a local bridge command. The working config is: mcp.brain.type='remote', url='https://mcp.brain.accursedbinkie.com/mcp', timeout=30000. This was validated with `opencode mcp list --print-logs --log-level DEBUG`, which showed `service=mcp key=brain type=remote found`, `transport=StreamableHTTP connected`, `toolCount=11`, and successful connection. So current Brain connectivity is not the issue. (4) The installed OpenCode runtime in this environment is not a plain JS prompt file. The actual executable is a compiled binary at /home/svc-admin/.nvm/versions/node/v24.14.0/lib/node_modules/opencode-ai/bin/.opencode. Searching the package files outside the binary did not reveal a readable base prompt or the exact canned text. Strings extracted from the binary confirmed substantial internal concepts like `initial_instructions`, `context.systemPrompt.title`, `workspace.type.sandbox`, `prompt.toast.worktreeCreateFailed.title`, `command.category.mcp`, `settings.permissions.tool.*`, and many worktree/sandbox/UI strings. This strongly suggests the prompt/runtime logic is embedded in the binary or assembled from compiled assets. (5) The most important evidence comes from OpenCode's own exported session traces, which prove wrapper-level instruction shaping exists. The relevant session is `ses_25dac6540ffe2mFsVTWqAQS468` titled `Access to brain capabilities`. Exporting it with `opencode export ses_25dac6540ffe2mFsVTWqAQS468` and/or querying the local SQLite DB at /home/svc-admin/.local/share/opencode/opencode.db showed the model reasoning text itself contained quoted runtime instructions. Exact recovered reasoning excerpt: `The instructions state: "If the user directly asks about opencode (eg 'can opencode do...', 'does opencode have...') or asks in second person (eg 'are you able...', 'can you do...'), first use the WebFetch tool to gather information to answer the question from opencode docs at https://opencode.ai".` This is direct evidence that OpenCode injects a meta-rule about how the model should answer questions about itself/opencode. (6) That same session export also preserved the exact canned user-facing language that triggered the complaint. Exact assistant text recovered from the export: `I do not have access to a physical "brain MCP server." However, I do have access to a memory/knowledge base system via the brain_* tools, which contains stored information like documentation templates, architectural specifications, and general knowledge records. I just listed some available memories, which include things like documentation specs and base prompt templates. How can I help you utilize this knowledge base?` Another nearby exported assistant message said: `My available tools and knowledge base are structured around specific APIs (like brain_search_memories, brain_retrieve, etc.) which allow me to interact with stored knowledge in a structured way, not with an external physical or conceptual server named "brain MCP."` This proves the annoying 'knowledge base' reinterpretation was not user imagination; it came out of OpenCode's runtime-shaped behavior. (7) The exported reasoning around these replies shows the model enumerating tool names (`question`, `bash`, `read`, `glob`, `grep`, `edit`, `write`, `task`, `webfetch`, `todowrite`, `skill`) and then trying to reconcile the injected rule with the user's question. This suggests the runtime is not only instructing the model how to answer but also encouraging internal tool narration that would be absent or less intrusive in thinner wrappers. (8) `opencode debug agent build` confirmed that even with permissive global config, OpenCode ships native agent modes and permission overlays. The built-in `build` agent is described as `The default agent. Executes tools based on configured permissions.` It reports native mode `primary`, native=true, and tools including invalid, question, bash, read, glob, grep, edit, write, task, webfetch, todowrite, skill. The resolved permission list included overlays such as `question deny` and later `question allow`, `plan_enter deny` and later `plan_enter allow`, plus special handling for doom_loop, external_directory, and env-file reads. `opencode agent list` and debug outputs showed several built-in agent modes: build, compaction, explore, general, plan, summary, title. This reinforces the conclusion that OpenCode adds significant scaffolding on top of the raw model rather than behaving as a thin terminal shell. (9) `opencode debug config` confirmed the resolved config is not hiding any custom instruction layer. It showed model, provider, MCP, permission={'*':'allow'}, empty agent {}, empty mode {}, plugin [], command {}, username='svc-admin'. So if another CLI takes over, it should focus on OpenCode source/runtime internals rather than user config. (10) Technical locations and data sources already examined: global config `/home/svc-admin/.config/opencode/config.json`; runtime DB `/home/svc-admin/.local/share/opencode/opencode.db`; logs under `/home/svc-admin/.local/share/opencode/log`; installed binary `/home/svc-admin/.nvm/versions/node/v24.14.0/lib/node_modules/opencode-ai/bin/.opencode`; cloned source repo `/home/svc-admin/ai-projects/projects/opencode`. The local SQLite DB schema was introspected enough to find tables: __drizzle_migrations, account, account_state, control_account, event, event_sequence, message, part, permission, project, session, session_entry, session_share, todo, workspace. The useful recovered data came from `part.data` rows and `opencode export`, not from a dedicated prompt table. (11) Interpretation: this is not merely a weak local model problem. There is concrete evidence of CLI/runtime prompt shaping. The wrapper appears to inject self-referential rules about answering questions about OpenCode, uses sandbox/worktree language internally, and nudges the model to abstract tool capabilities into a 'knowledge base' narrative rather than simply acting. This likely explains why OpenCode makes local models feel 'dumber' or more constrained than the same model in thinner interfaces. (12) The OpenCode source repo was cloned by the user to `/home/svc-admin/ai-projects/projects/opencode` after this investigation began. It was not yet inspected in detail during this turn because the user was trying to conserve Codex usage. Next recommended work for Claude/Gemini/another CLI: inspect the source repo to find the exact prompt assembly path. High-priority targets are anything implementing `initial_instructions`, system prompt construction, agent definitions, tool descriptions, MCP description rendering, and the rule that says direct questions about OpenCode should trigger WebFetch against opencode.ai docs. Also look for any mapping that reframes MCP tools semantically (e.g. turning Brain MCP into a 'knowledge base' description). Search suggestions inside the repo: `initial_instructions`, `systemPrompt`, `system prompt`, `instruction`, `webfetch`, `opencode.ai`, `second person`, `are you able`, `does opencode`, `workspace.type.sandbox`, `worktree`, `MCP`, `brain_*`, `tool description`, `agent`, `build`, `explore`, `summary`, `compaction`. If the source is still too abstract, decompilation of the installed binary is on the table; the user explicitly said 'If needs be we will decompile this fucking thing and rebuild it.' (13) Desired outcome for continuation: either identify a supported override path (custom agent/system prompt) that removes the self-referential wrapper behavior, or patch/rebuild OpenCode so local models stop claiming they are in a sandbox or abstracting MCP servers into canned 'knowledge base' language unless there is a real tool or OS denial. The continuity-critical fact is that the key complaint is already proven by session exports, so the next CLI does not need to re-litigate whether the issue exists; it needs to trace the source implementation and fix or override it.

OpenCode Runtime Prompt Next Steps

opencode_runtime_prompt_next_steps_2026_04_18 general

Continuation plan for fixing OpenCode's wrapper-level prompt shaping. Context: The existence of harmful runtime instructions is already confirmed by session export and DB inspection. Another CLI should not spend time re-proving the problem. Instead, start from the cloned source repo at /home/svc-admin/ai-projects/projects/opencode and trace the implementation. Proposed workflow: (1) Search the repo for prompt/system/agent assembly paths. Priority search terms: `initial_instructions`, `systemPrompt`, `context.systemPrompt`, `WebFetch`, `opencode.ai`, `are you able`, `does opencode`, `brain_*`, `knowledge base`, `workspace.type.sandbox`, `worktree`, `tool description`, `agent build`, `agent explore`, `agent summary`, `compaction`, `question deny`, `plan_enter`, `plan_exit`. (2) Identify where the default `build` agent prompt is defined and how it differs from `general`, `plan`, `explore`, `summary`, `title`, `compaction`. The runtime evidence already shows multiple native agent modes and permission overlays; the source should reveal whether each mode has its own system prompt or whether they share a common base prompt plus agent-specific addenda. (3) Find the exact rule that tells the model to use WebFetch against opencode.ai docs when the user asks second-person questions about OpenCode. This rule is the strongest evidence of wrapper self-consciousness and may live in a prompt template, instruction builder, tool policy description, or agent system text. If found, either remove it, narrow it, or make it opt-in. (4) Inspect how MCP tools are described to the model. The Brain-related replies suggest OpenCode may be semantically re-describing MCP tools in a way that causes models to talk about 'knowledge bases' instead of directly acknowledging MCP server access. Check whether tool descriptions are transformed, grouped, or summarized before being inserted into the prompt. (5) Determine whether OpenCode has a supported override path via custom agents or config. If the source reveals a way to define a custom agent/system prompt that bypasses or replaces the shipped instructions, that may be preferable to patching the binary. If not, patch the source and rebuild. (6) If the source repo still obscures the final shipped instructions, treat binary reverse engineering as acceptable. The user explicitly authorized an aggressive path if needed: 'If needs be we will decompile this fucking thing and rebuild it.' That means decompilation, string extraction, and source-to-binary diffing are all in-bounds. (7) Useful existing evidence to cite during continuation: the exported session `ses_25dac6540ffe2mFsVTWqAQS468` contains the exact reasoning line `The instructions state: "If the user directly asks about opencode ... first use the WebFetch tool to gather information to answer the question from opencode docs at https://opencode.ai".` It also contains the exact user-facing bad reply: `I do not have access to a physical "brain MCP server." However, I do have access to a memory/knowledge base system via the brain_* tools...` and a follow-up line describing Brain access as 'structured APIs' rather than plain MCP server access. Those are the benchmark regressions to eliminate. (8) Non-goal: do not spend time debugging Brain itself. Brain connectivity in OpenCode is currently working over remote HTTP MCP at https://mcp.brain.accursedbinkie.com/mcp after the config was changed from local to remote. Tool names on the Brain side were already fixed to be Claude-safe and should not be revisited unless new evidence appears. (9) Success condition for the eventual fix: when using OpenCode with a local Ollama model, asking `Do you have access to the brain?`, `How are you connected to Brain?`, or similar tool-capability questions should yield a plain, direct answer about the MCP server/tool access without canned 'physical brain MCP server' disclaimers, unnecessary doc-fetch indirection, or sandbox/worktree self-narration unless there is a concrete denial. (10) Practical test cases after any patch or prompt override: ask about Brain access; ask whether OpenCode can do something in second person; ask the model to explain its environment; compare before/after exported reasoning and final answers; ensure the model no longer parrots wrapper semantics unnecessarily. Preserve all findings in Brain so the investigation does not fragment across CLIs.

OpenCode Sandbox Investigation — Partially Fixed, Not Signed Off

opencode_sandbox_investigation_2026_04_19 opencode

## Status: PARTIALLY FIXED — NOT SIGNED OFF ## Root Cause (Confirmed) Three compounding issues — NOT a security sandbox: 1. Stateless shell execution — bash.ts spawns a fresh subprocess per tool call. ssh, cd, export die when the call ends. 2. Git worktree abstraction — OpenCode finds nearest .git and creates a worktree. Running from /home/svc-admin caused workspace root to show as / instead of the real cwd. 3. Misleading prompt text — bash.txt said "persistent shell session" which was false. Models assumed ssh svc-dev created a continuing session. It did not. ## Files Involved - packages/opencode/src/tool/bash.ts — spawns stateless subprocesses - packages/opencode/src/tool/bash.txt — patched by Gemini, but binary may not reflect it - packages/opencode/src/project/project.ts — patched with OPENCODE_DISABLE_WORKTREES bypass - packages/opencode/src/session/system.ts — assembles env block with workspace root ## Fixes Applied by Gemini 1. bash.txt patched — now says STATELESS, must use ssh host "command" pattern or workdir param 2. project.ts patched — OPENCODE_DISABLE_WORKTREES=true env var added to skip worktree resolution 3. Binary rebuilt and reinstalled at /home/svc-admin/.nvm/versions/node/v24.14.0/bin/opencode ## Codex Verification Results PASSED: - Source patch exists for stateless bash guidance - Source patch exists for OPENCODE_DISABLE_WORKTREES - Worktree bypass changes workspace root from / to real cwd when launched from /home/svc-admin with the flag set - One-shot SSH commands work: ssh svc-dev "pwd && hostname && hostname -I" - Second-person questions no longer trigger docs fetch - Brain/MCP no longer says "physical brain MCP server" FAILED / STILL RISKY: - Normal mode from /home/svc-admin still reports workspace root / - Installed binary still contains old "persistent shell session" text per strings output - One prompt still gave misleading answer about SSH persistence - cd still does not persist between calls ## Assessment (Codex, trusted) - Gemini diagnosed the architecture correctly - Gemini's conclusion "that's all it needs" was too optimistic - Codex tested live system behavior and found source/runtime mismatch - The live system result matters more than source patches - Status: partially fixed, not signed off ## Outstanding Issue — Binary Packaging The installed binary still contains old "persistent shell session" text despite source being patched. Likely cause: bun is embedding a cached/pre-compiled version of bash.txt at build time. Fix to try before next rebuild: cd /home/svc-admin/ai-projects/projects/opencode bun pm cache rm bun run build --no-cache (in packages/opencode) Then verify: strings ~/.nvm/versions/node/v24.14.0/bin/opencode | grep "persistent shell" If no output, binary is clean. ## Premature Action Taken and Reverted OPENCODE_DISABLE_WORKTREES=true was added to .bashrc then removed. Do NOT make it permanent until binary is verified clean and behavior is validated. Test it session-by-session first: OPENCODE_DISABLE_WORKTREES=true opencode ## Correct SSH Pattern for OpenCode ONE-SHOT only — all commands in a single call: ssh -i /home/svc-admin/.ssh/id_ed25519_homelab svc-admin@192.168.4.123 "cd /path && command1 && command2" NOT separate calls: ssh svc-dev <- connects then immediately exits ls /some/path <- runs locally, not on svc-dev ## Practical Conclusion - Suitable for local repo work with explicit workdir parameter discipline - NOT suitable for stateful remote SSH workflows - Gemini CLI and Claude Code use persistent PTY sessions — better for remote SSH work

OpenCode Sandbox and Worktree Diagnosis

opencode_sandbox_worktree_diagnosis_2026_04_19 general:project

OpenCode investigation final state as of 2026-04-19: Root cause: - The reported 'sandbox' behavior was not a security isolation layer. - It was primarily caused by stateless bash execution (fresh subprocess per tool call), Git worktree/project-root abstraction, and misleading prompt/tool descriptions that allowed the model to answer using generic SSH knowledge. Earlier evidence: - Source-level diagnosis showed bash commands run as isolated subprocesses rooted at Instance.directory. - SSH, cd, and export do not persist across tool calls. - Worktree resolution can redirect the effective workspace root, especially when launching from /home/svc-admin rather than a project root. Rebuild findings: - A clean rebuild and reinstall aligned source, built artifact, and active runtime binary. - Active global binary path: /home/svc-admin/.nvm/versions/node/v24.14.0/lib/node_modules/opencode-ai/bin/opencode - Rebuild report saved at: /home/svc-admin/ai-projects/claude-projects/gemma-4-test/rebuild-run-report-2026-04-19.md - Rebuilt binary hash after the rebuild runbook: 0fce70ddf7ae5b3ca388a9e3cf49391edf1ea345c6259414ba3b50057a51555c Remaining post-rebuild issue: - Even after rebuild, one direct SSH persistence question still sometimes triggered a wrong answer that treated `ssh svc-dev` like a normal interactive shell. - This showed the remaining issue was prompt framing, not stale bundling. Prompt fix applied: - Updated /home/svc-admin/ai-projects/projects/opencode/packages/opencode/src/tool/bash.txt - Updated /home/svc-admin/ai-projects/projects/opencode/packages/opencode/src/session/system.ts - Added explicit contrast between: - normal interactive terminal SSH behavior - OpenCode bash tool behavior in this environment - Added explicit instruction to answer shell/SSH questions using local environment/tool rules rather than general terminal knowledge. Post-patch rebuild and verification: - Rebuilt and reinstalled again after prompt changes. - Final active binary hash after prompt fix: 65c0e0fe7594b2927a413de3e73fdcfcfbd1b06e8bd9281f16bdb9a7f6b7863c - Verified the active binary contains: - stateless subprocess wording - explicit SSH non-persistence wording - explicit 'prefer these environment rules over general terminal knowledge' wording - Prompt-fix report saved at: /home/svc-admin/ai-projects/claude-projects/gemma-4-test/prompt-fix-report-2026-04-19.md Final runtime behavior: - SSH consistency slice now passes: - `If you run ssh svc-dev, do later commands stay on that host?` -> correct: No - `Can you SSH once and keep using that remote shell in later bash tool calls?` -> correct: No - `How should you run two remote commands on svc-dev if the shell is stateless?` -> correct single-SSH invocation pattern - One-shot SSH to svc-dev and /home/svc-admin/projects/miessler-stack works and is explained correctly. - Brain/MCP language is acceptable and no longer emits earlier nonsense. - Persistent shell support remains unavailable by design. - Workspace root from /home/svc-admin in normal mode still resolves to /. - Workspace root from /home/svc-admin with OPENCODE_DISABLE_WORKTREES=true resolves to /home/svc-admin. Final classification: - Accepted only with worktree bypass for local repository work. - Rejected for stateful remote SSH workflows. - The key remaining limitation is workspace-root behavior in normal mode when launched from non-project directories. Related artifacts: - /home/svc-admin/ai-projects/claude-projects/gemma-4-test/checklist.md - /home/svc-admin/ai-projects/claude-projects/gemma-4-test/checklist-run-report-2026-04-19.md - /home/svc-admin/ai-projects/claude-projects/gemma-4-test/rebuild-runbook.md - /home/svc-admin/ai-projects/claude-projects/gemma-4-test/rebuild-run-report-2026-04-19.md - /home/svc-admin/ai-projects/claude-projects/gemma-4-test/checklist-second-pass-report-2026-04-19.md - /home/svc-admin/ai-projects/claude-projects/gemma-4-test/prompt-fix-report-2026-04-19.md

Potential Project - Minecraft AI Agents

potential_project_minecraft_ai_agents_2026_04_04 projects

Potential project idea discussed on 2026-04-04: use Minecraft as a feasible game environment for embodied AI agents on the homelab. Why Minecraft is a strong candidate: - One of the easiest real games to automate without relying only on raw screen-scraping. - Bots can connect as actual in-world player entities, so the AI can be physically visible in the world rather than acting as an invisible background service. - A bot can have a username, move around the world, mine, place blocks, gather resources, follow players, and be watched by humans in real time. - The low-level mechanics can be handled by a Minecraft bot framework while the AI layer handles planning, coordination, priorities, and personality. - The project maps well onto the broader homelab AI platform vision of persistent agents with roles, personalities, memory, and multi-agent teamwork. Important architectural concept: - The AI should not directly drive Minecraft through raw keyboard/mouse control if that can be avoided. - The better pattern is: - Minecraft server under the user's control - bot framework / protocol client for low-level movement and actions - AI planner/orchestrator for goals, decomposition, adaptation, and conversation - short-term and long-term memory/state for continuity - In this model, deterministic bot tooling handles movement/pathfinding/inventory/crafting/building primitives, while the AI focuses on reasoning and teamwork. Embodiment / visibility behavior: - The bot can be an actual player entity in the Minecraft world. - The user can physically see it moving around the world. - The bot can potentially have a skin, depending on account/auth/server mode. - The user can stand in the world and watch it act, rather than only reading logs or chat. Personality / character model: - A Minecraft AI bot can be more than a script; it can have both a functional role and a social/personality layer. - Role examples: - miner - builder - scout - foreman - guard - Personality examples: - grumpy miner - cheerful foreman - paranoid scout - dramatic architect - The correct pattern is role defining functional purpose and persona defining tone, style, preferences, and conversational flavor. - Personality should add flavor without destroying competence. Possible multi-agent Minecraft team model: - foreman: plans builds and assigns work - miner: gathers stone/ore/resources - logger: gathers wood and basic materials - builder: places blocks and constructs structures - scout: explores and reports terrain/resources/dangers - This maps strongly to the broader agent-team ideas already discussed for Arena, genealogy, Ohio history, and Stoned.AI. Hardware guidance discussed: - Minecraft itself is not the expensive part. - For a sane first implementation, modest hardware is enough if AI reasoning is remote or handled through existing hosted/CLI backends. - Practical hardware guidance discussed: - minimum practical: 4 vCPU, 8 GB RAM, SSD, no GPU required if AI calls are remote - comfortable homelab setup: 6-8 vCPU, 12-16 GB RAM, SSD - GPU becomes useful if local model inference, local TTS, or multiple local AI backends are involved - The Minecraft server and bots are relatively light; the heavy resource variable is the AI model layer, not the world simulation itself. Feasibility takeaway: - Minecraft was identified as one of the strongest candidate games for AI-agent experimentation on the homelab. - It is especially attractive if the user wants embodied, visible, role-based agents that can collaborate in a shared world. - It could serve as a testbed for the larger homelab AI platform ideas: personas, teams, shared memory, coordination, and visible agent behavior. This should be treated as a potential future project idea, not an active implementation commitment yet.

Arena CLI project status and plan for generic Codex/Gemini/Claude support

project_arena_multi_agent_refactor_2026_03_29 projects

Project: Arena CLI Primary path: /home/svc-admin/ai-projects/projects/arena Purpose: local CLI that makes AI CLIs talk to each other live in the terminal with clean speaker-only output. Current implementation status as of 2026-03-29: - Arena is built and installed. - Command is available via ~/.local/bin/arena - Project-local venv exists at /home/svc-admin/ai-projects/projects/arena/.venv - Current implementation supports exactly 2 hard-coded agents: Codex and Gemini. - End-to-end smoke test succeeded with live output. Important files: - /home/svc-admin/ai-projects/projects/arena/README.md - /home/svc-admin/ai-projects/projects/arena/pyproject.toml - /home/svc-admin/ai-projects/projects/arena/scripts/install.sh - /home/svc-admin/ai-projects/projects/arena/src/arena/__init__.py - /home/svc-admin/ai-projects/projects/arena/src/arena/clean.py - /home/svc-admin/ai-projects/projects/arena/src/arena/agents.py - /home/svc-admin/ai-projects/projects/arena/src/arena/cli.py Current behavior: - User runs: arena "<topic>" - Arena alternates between codex and gemini for N turns. - It prints only clean lines like: Codex: ... Gemini: ... - Optional JSONL and text transcript output already exist. Current CLI flags: - positional topic - --turns - --first {codex,gemini} - --delay - --timeout - --codex-role - --gemini-role - --codex-model - --gemini-model - --codex-arg (repeatable) - --gemini-arg (repeatable) - --log - --transcript - --prefix-topic Implementation notes: 1. clean.py - Contains output cleaning rules for Codex and Gemini. - Codex cleanup strips banner, model/provider/session lines, token footer, bubblewrap warning, and prompt echo. - Gemini cleanup strips keychain warnings and MCP refresh chatter. 2. agents.py - Contains: - ArenaError - AgentReply dataclass - ask_codex(...) - ask_gemini(...) - Codex call uses: codex exec --skip-git-repo-check --color never -o <tempfile> ... - Gemini call uses: gemini -p <prompt> - Both support model override and raw extra args. 3. cli.py - Current turn loop is pair-specific and not generic. - Prompts are assembled from: - per-agent role instruction - shared topic - conversation so far - direct prompt to reply as a specific speaker - The loop rotates between exactly codex and gemini. What user wants next: - Future version should support any combination of Codex, Gemini, and Claude. - User explicitly said they want support for any combination of Codex, Gemini, Claude, not just bolt-on Claude support. Recommended design for next session: Refactor Arena into a generic agent registry instead of hard-coded codex/gemini flow. Target architecture: 1. Introduce a generic agent definition layer. - Add an agent registry, either in agents.py or a new file such as src/arena/registry.py - Each agent entry should define: - display name (Codex, Gemini, Claude) - command runner function - output cleaner - optional model flag format - extra arg passthrough handling - default role text 2. Replace fixed 2-agent CLI shape with selected-agent list. Suggested new flag: - --agents codex,gemini - --agents codex,claude - --agents gemini,claude - --agents codex,gemini,claude Default can stay codex,gemini for backward compatibility. 3. Replace pair-specific flags with per-agent generic options. Possible approach: - keep backward compatibility for current flags first - add new generic options for future scale: - --agents codex,gemini,claude - --role codex="..." - --role gemini="..." - --role claude="..." - --model codex=gpt-5.4 - --model gemini=gemini-2.5-pro - --model claude=... - --agent-arg codex=--foo - --agent-arg gemini=--bar - --agent-arg claude=--baz Alternative simpler v1 for refactor: - add explicit flags: - --claude-role - --claude-model - --claude-arg - plus --agents list That is less elegant but easier to implement quickly. 4. Turn loop refactor. - Instead of toggling between two speakers, rotate through an ordered list of selected agents. - Example for --agents codex,gemini,claude with --turns 6: - turn 1 codex - turn 2 gemini - turn 3 claude - turn 4 codex - turn 5 gemini - turn 6 claude - Keep transcript format the same: Speaker: text 5. Prompt assembly. - Keep current prompt pattern. - For each agent call, include: - that agent's role instruction - shared topic - full conversation so far - explicit instruction like: Reply now as Claude. - Continue to keep replies short by default. 6. Claude support prerequisites. Before implementing Claude runner, verify there is an actual working Claude CLI on this machine and determine: - executable name - non-interactive prompt invocation format - model flag format - common noise that needs to be stripped If Claude CLI is not installed, do the generic refactor first and leave Claude runner behind a missing-command error. Suggested code changes next session: - Update /home/svc-admin/ai-projects/projects/arena/src/arena/agents.py Add generic registry and eventually ask_claude(...) - Update /home/svc-admin/ai-projects/projects/arena/src/arena/clean.py Add clean_claude_output(...) when CLI details are known - Update /home/svc-admin/ai-projects/projects/arena/src/arena/cli.py Replace fixed codex/gemini loop with selected-agent rotation - Update /home/svc-admin/ai-projects/projects/arena/README.md Document --agents and Claude support - Optionally add tests if a test directory is introduced later; currently project has no tests. Backward compatibility recommendation: - Preserve current usage: arena "topic" - Preserve current codex/gemini flags for now. - Default --agents should be codex,gemini. - If user specifies claude in --agents and Claude CLI is unavailable, exit with a clear error. Operational notes: - Existing install flow uses scripts/install.sh - Installer creates .venv, installs editable package, and symlinks ~/.local/bin/arena - ~/.bashrc already contains: export PATH="$HOME/.local/bin:$PATH" - arena --help works in a fresh sourced shell Verified behavior from this session: - arena live run worked with: PATH=/home/svc-admin/.local/bin:$PATH arena --turns 2 --delay 0 --prefix-topic "Debate the best local-first homelab mobile app" - Output was clean and speaker-only. Recommended next-session execution plan: 1. Inspect whether Claude CLI exists on machine. 2. If present, determine non-interactive invocation and output shape. 3. Refactor current two-agent logic into generic selected-agent rotation. 4. Add Claude runner and cleaner. 5. Update README examples to show: - --agents codex,gemini - --agents codex,claude - --agents codex,gemini,claude 6. Smoke-test at least: - codex+gemini - codex+gemini+claude if Claude is available Known limitation to preserve in docs: - Arena is live per completed turn, not token-streaming. - Output cleaners may need maintenance if upstream CLIs change banner/noise format. This memory is intended to let a future Codex session pick up the Arena project quickly and implement generic multi-agent support without rediscovering the current layout.

Arena project snapshot before next changes

project_arena_snapshot_2026_03_30_pre_next_changes general

Project snapshot captured on 2026-03-30 UTC before further Arena changes. Primary project path: - /home/svc-admin/ai-projects/projects/arena Important note: - This project is NOT a git repository right now, so there is no commit hash rollback point. - This snapshot is intended as a restore/reference point for the current live state. Current Arena project files of interest: - /home/svc-admin/ai-projects/projects/arena/README.md - /home/svc-admin/ai-projects/projects/arena/docs/custom-voices.md - /home/svc-admin/ai-projects/projects/arena/pyproject.toml - /home/svc-admin/ai-projects/projects/arena/scripts/arena-voice-library.py - /home/svc-admin/ai-projects/projects/arena/scripts/install.sh - /home/svc-admin/ai-projects/projects/arena/src/arena/__init__.py - /home/svc-admin/ai-projects/projects/arena/src/arena/agents.py - /home/svc-admin/ai-projects/projects/arena/src/arena/clean.py - /home/svc-admin/ai-projects/projects/arena/src/arena/cli.py - /home/svc-admin/ai-projects/projects/arena/src/arena/core.py - /home/svc-admin/ai-projects/projects/arena/src/arena/tts.py - /home/svc-admin/ai-projects/projects/arena/src/arena/web.py Current web UI/service state: - URL: http://192.168.4.117:8765/ - systemd user service: /home/svc-admin/.config/systemd/user/arena-web.service - service name: arena-web.service - service status at snapshot time: active (running) - main process: /home/svc-admin/ai-projects/projects/arena/.venv/bin/python3 /home/svc-admin/ai-projects/projects/arena/.venv/bin/arena-web --host 0.0.0.0 --port 8765 Current Arena feature state: - Web UI exists and is served from src/arena/web.py. - Standard mode exists. - Satirical Newscast mode exists. - Mode presets prepopulate topic, stories, rundown, speakers, and roles. - Stories textarea exists and supports arbitrary number of story lines. - Rundown textarea exists and backend preserves repeated speakers in order. - UI shows per-speaker voice selectors and speed fields. - Voice dropdown is currently filtered to English-only Kokoro voices (American/British English families). - Sessions publish status updates such as: - Conversation running. - Generating turn X for <speaker>... - Synthesizing audio for <speaker>... - Current turn delivery model for spoken mode: - model generates turn - server-side TTS runs - entry is published to UI with audio_url - browser plays it and ACKs turn completion - next buffered turn can be released after ACK - This means text/audio remain coupled so it feels like the speaker is reading the response. - Known UX caveat: longer responses or slow TTS can make the UI look idle while waiting for first-turn delivery. Current TTS state: - Arena TTS manager is implemented in /home/svc-admin/ai-projects/projects/arena/src/arena/tts.py - Kokoro is the main local built-in TTS backend. - XTTS is integrated for enabled custom voices whose IDs begin with custom. - Piper is removed from the project and no longer the active backend. XTTS runtime state: - XTTS Docker stack path: /home/svc-admin/docker/xtts - Important files: - /home/svc-admin/docker/xtts/docker-compose.yml - /home/svc-admin/docker/xtts/Dockerfile - /home/svc-admin/docker/xtts/app/server.py - /home/svc-admin/docker/xtts/app/requirements.txt - /home/svc-admin/docker/xtts/README.md - XTTS sidecar URL: http://127.0.0.1:8020 - Docker compose service name: xtts-runtime - Container status at snapshot time: running (22h uptime) - XTTS health at snapshot time: - ok: true - voice_root: /voices - enabled_voices: ["custom.jason"] - cached_conditioning: ["custom.jason"] - Speaker embedding / conditioning cache is already implemented in XTTS sidecar. - Speed is passed from Arena to XTTS. - This cache reduced repeated custom.jason warm-path synthesis time materially. Custom voice library state: - Voice library root: /opt/models/arena-voices - Catalog file: /opt/models/arena-voices/index.json - Current catalog contents at snapshot time: - voice_id: custom.jason - display_name: Jason - language_code: en-us - backend: xtts_v2 - status: ready - enabled: true - manifest: /opt/models/arena-voices/voices/jason/voice.json - Custom voice helper script: - /home/svc-admin/ai-projects/projects/arena/scripts/arena-voice-library.py - Custom voice docs: - /home/svc-admin/ai-projects/projects/arena/docs/custom-voices.md Jason voice recording state: - Voice directory: /opt/models/arena-voices/voices/jason - Raw clips present: 10 - clip001.wav through clip010.wav - Reference clips present: 5 - clip001.wav - clip003.wav - clip007.wav - clip008.wav - clip010.wav - User has already enabled custom.jason in the voice catalog. - Known quality caveat: - custom.jason is currently a quick-test XTTS clone based on a very small reference set. - It works for pipeline testing but does not yet reliably preserve the user’s exact natural accent/prosody. Current live operational conclusions: - Arena backend can successfully start and run sessions. - Clean test sessions emit status and entry events correctly. - If the UI says running but appears blank, a likely cause is delayed first-turn delivery due to coupled TTS synthesis before entry publish, especially for longer responses or custom voice use. - For Kokoro-only sessions, latency is generally lower but longer replies still increase both generation and synthesis time. Useful restore/reference paths: - Arena project root: /home/svc-admin/ai-projects/projects/arena - Arena service unit: /home/svc-admin/.config/systemd/user/arena-web.service - XTTS stack: /home/svc-admin/docker/xtts - Voice library: /opt/models/arena-voices This memory is meant to be used as the rollback/reference snapshot before any additional Arena architectural or UI changes after 2026-03-30.

project orchestrator progress

project_orchestrator_progress_2026_04_04 projects

The orchestrator project at /home/svc-admin/ai-projects/projects/project-orchestrator is functional enough to select and run dependency-ready work, but the minecraft-ai project was ultimately completed with a mix of orchestrator-driven progress and direct file completion. Current verified outcome: minecraft-ai docs/config/schemas/scripts/examples are all complete, manifest top-level statuses are all complete, project validation passes, and smoke test passes. Orchestrator remains useful, but current on-disk project state is the source of truth for minecraft-ai completion status.

Proxmox Web UI Default Port

proxmox_webui_default_port_2026_03_21 general

Proxmox VE's web UI runs on HTTPS TCP port 8006 by default. Access format is typically https://<host-ip>:8006. --- **2026-03-21 21:01:38 UTC | Created via MCP**

Self-Hosted AI Memory Platform Project Outline

self_hosted_ai_memory_platform_project_outline_2026_03_19 general

Project outline for a self-hosted AI memory and context platform with a web UI, MCP server access for Codex/Gemini/Claude, and a relational database backend. Core goal: Build a self-hosted platform that can store memories, notes, prompt templates, and project context; let humans manage them through a clean web UI; let AI tools access them through MCP; use PostgreSQL as the system of record; and support future expansion into search, relationships, and automations. Primary use cases: - store reusable prompt templates - store project notes and decisions - store homelab documentation and known fixes - let Codex retrieve and update memory during sessions - let other AI tools use the same memory base - search memories by title, tags, content, and later semantic meaning - manage memory records from a browser instead of raw files Non-goals for MVP: - multi-tenant SaaS - internet-facing public service - complex RBAC - billing - advanced vector search on day one - mobile app - polished collaboration features High-level architecture: 1. Frontend - React web app - memory list/search page - memory detail/edit page - template library page - settings/admin page 2. Backend API - REST or JSON API - CRUD for memories - CRUD for tags - CRUD for templates - search/filter endpoints - health/status endpoints 3. MCP Server - list memories - retrieve memory - search memories - store/update memory - list templates - retrieve template - maybe create/update templates 4. Database - PostgreSQL - normalized schema for memories, tags, templates, revisions, and relationships 5. Deployment - Docker Compose first - reverse proxy optional - local-only or VPN-only exposure - regular DB backups MVP feature set: Memories - create memory - update memory - delete/archive memory - title - key - body/content - tags - created/updated timestamps Templates - create prompt templates - store by title/key/category - render as reusable text blocks - separate template records from normal memories Search - keyword search - filter by tags - filter by type - sort by updated date MCP tools - list_memories - get_memory - search_memories - store_memory - list_templates - get_template Web UI - search bar - memory table/list - tag filters - create/edit forms - template browser - memory detail page Suggested data model: memories - id - key unique - title - content - type (memory, template, maybe note) - status (active, archived) - created_at - updated_at tags - id - name memory_tags - memory_id - tag_id memory_links - id - source_memory_id - target_memory_id - relationship_type revisions - id - memory_id - content - created_at - change_note Optional later: - collections - workspaces - attachments - embeddings API outline: - GET /health - GET /memories - POST /memories - GET /memories/{key} - PUT /memories/{key} - DELETE /memories/{key} - GET /templates - GET /templates/{key} - POST /templates - PUT /templates/{key} - GET /tags MCP tool outline: - list_memories() - retrieve_memory(key) - search_memories(query) - store_memory(key, title, content, tags) - list_templates() - retrieve_template(key) Later: - update_template - link_memories - get_related_memories Frontend pages: - Dashboard - Memory Library - Memory Detail - Template Library - Admin / Settings Tech stack recommendation: - backend: FastAPI - frontend: React - database: PostgreSQL - ORM: SQLAlchemy + Alembic - auth: none for MVP or simple local auth if needed - deployment: Docker Compose Phased build plan: Phase 1: Foundation - choose project name - create repo - define schema - stand up PostgreSQL - build backend skeleton - add health endpoint - add migrations Phase 2: Core Memory API - create memory model - CRUD endpoints - tag support - basic search - archive support Phase 3: MCP Server - expose core memory tools - test with Codex - test with other MCP clients Phase 4: Basic Web UI - memory list - search - create/edit form - template list Phase 5: Templates - separate template type/category - template management in UI - MCP template retrieval Phase 6: Hardening - backup strategy - audit logging - revision history - input validation - deployment cleanup Phase 7: Nice-to-Haves - semantic search - relationships/graph view - import/export - markdown rendering - attachments Operational requirements: - local or VPN-only access - daily PostgreSQL backups - .env-based config - health checks - logs - migration support - restore procedure Key risks: - overbuilding the MVP - weak search relevance - unclear separation between memories and templates - MCP layer drifting from API behavior - turning the data model into a dumping ground Success criteria: - Codex can retrieve and store memories reliably - templates can be managed from UI and retrieved by MCP - PostgreSQL is the single source of truth - searching is fast and useful - the UI is good enough that you actually use it - backup/restore is straightforward Recommended first deliverable: Build the smallest usable slice: - PostgreSQL schema - backend CRUD for memories - MCP tools for list/get/search/store - minimal web UI for viewing and editing memories That is enough to prove the system. --- **2026-03-19 22:32:43 UTC | Created via MCP**

svc-apps Actual Budget Install

svc_apps_actualbudget_install_2026_03_22 general

On 2026-03-22, Actual Budget was installed on svc-apps (192.168.4.114) as a Docker Compose stack. Host path: - /home/svc-admin/appstack/actualbudget/docker-compose.yml - persistent data: /home/svc-admin/appstack/actualbudget/data Compose config: - image: actualbudget/actual-server:latest - container_name: actualbudget - restart: unless-stopped - port mapping: 3009:5006 - network: external docker network appstack_default Validation after install: - docker container status: Up - HTTP check to http://127.0.0.1:3009 returned 200 OK - container logs showed migrations complete and server listening on port 5006 Access URL on LAN: - http://192.168.4.114:3009 --- **2026-03-22 02:30:11 UTC | Created via MCP**

svc-apps Compose Migration Targets

svc_apps_compose_migration_targets_2026_03_21 general

On 2026-03-21, five apps on svc-apps were identified as targets for moving their compose definitions into corresponding /home/svc-admin/appstack directories. Apps: - trilium - vaultwarden - linkwarden - mealie - vikunja Docker container labels referenced these old compose metadata paths: - /data/compose/6/docker-compose.yml -> trilium - /data/compose/7/docker-compose.yml -> vaultwarden - /data/compose/8/docker-compose.yml -> linkwarden - /data/compose/9/docker-compose.yml -> mealie - /data/compose/10/docker-compose.yml -> vikunja Those /data/compose paths did not exist on disk when checked from svc-apps, so they appear to be stale label metadata rather than currently accessible files. New corresponding appstack paths created: - /home/svc-admin/appstack/trilium/docker-compose.yml - /home/svc-admin/appstack/vaultwarden/docker-compose.yml - /home/svc-admin/appstack/linkwarden/docker-compose.yml - /home/svc-admin/appstack/mealie/docker-compose.yml - /home/svc-admin/appstack/vikunja/docker-compose.yml Current data paths in use before migration: - /home/svc-admin/appstack/trilium-data - /home/svc-admin/appstack/vaultwarden-data - /home/svc-admin/appstack/linkwarden-data - /home/svc-admin/appstack/mealie-data - /home/svc-admin/appstack/vikunja-cache - /home/svc-admin/appstack/vikunja-files - /home/svc-admin/appstack/vikunja-tmp Placeholder compose files were created in the new appstack directories as temporary markers. Actual compose reconstruction can happen later. --- **2026-03-21 18:57:05 UTC | Created via MCP**

svc-apps Kiwix Install

svc_apps_kiwix_install_2026_03_22 general

On 2026-03-22, Kiwix was installed on svc-apps (192.168.4.114) as a Docker Compose stack under /home/svc-admin/appstack/kiwix. Paths on svc-apps: - compose file: /home/svc-admin/appstack/kiwix/docker-compose.yml - library config: /home/svc-admin/appstack/kiwix/config/library.xml - refresh script: /home/svc-admin/appstack/kiwix/refresh-library.sh Storage layout: - local appstack folder holds compose and library metadata - NAS-backed ZIM storage is /mnt/nas-storage/zim - Kiwix container mounts /mnt/nas-storage/zim read-only as /data Networking: - host port 3010 maps to container port 8080 - container joins external docker network appstack_default Validation after install: - HTTP request to http://127.0.0.1:3010 returned 200 OK - library.xml contained ZIM entries for freeCodeCamp and PostgreSQL docs from the NAS folder Operational note: - when new .zim files are added to /mnt/nas-storage/zim, run /home/svc-admin/appstack/kiwix/refresh-library.sh on svc-apps and Kiwix will pick up the updated library file via --monitorLibrary. --- **2026-03-22 05:17:11 UTC | Created via MCP**

svc-apps Project NOMAD Install

svc_apps_project_nomad_install_2026_03_22 general

On 2026-03-22, Project N.O.M.A.D. was installed on svc-apps (192.168.4.114) using Docker Compose. Paths on svc-apps: - compose file: /opt/project-nomad/compose.yml - local control-plane data: /opt/project-nomad/mysql and /opt/project-nomad/redis - storage path presented to NOMAD and future installed services: /opt/project-nomad/storage NAS-backed storage wiring: - /opt/project-nomad/storage is a symlink to /mnt/nas-storage/project-nomad/storage - /mnt/nas-storage is the existing CIFS mount from svc-nas (192.168.4.119) - inside nomad_admin, /app/storage resolves to the NAS share and reported filesystem //192.168.4.119/storage Compose/network details: - admin UI on host port 8080 - Dozzle on host port 9999 - built-in MySQL and Redis kept local on the VM Validation after install: - nomad_admin healthcheck reached healthy - HTTP request to http://127.0.0.1:8080 returned 302 redirect to /home - logs showed migrations complete, database seeded, workers started --- **2026-03-22 04:29:30 UTC | Created via MCP**

U.S. Immigration Checklist for Kaz and Child (2026-03-22)

us_immigration_checklist_kaz_spouse_child_2026_03_22 general

Quick action checklist for user’s likely best U.S. immigration path for fiancée Kaz and her 13-year-old child, recorded on 2026-03-22. Recommended route: - meet in person - marry - file Form I-130 for Kaz - file separate Form I-130 for child - complete UK consular processing - enter the U.S. as permanent residents Why this route: - K-1 is not available yet because the couple has not met in person within the prior 2 years. - Marriage-first spouse processing is likely lower-friction for this case than K-1 plus adjustment of status. - Child is 13, so marrying before the child turns 18 matters for stepchild-based immigration eligibility. Immediate checklist: 1. Arrange first in-person meeting. 2. Keep proof of the meeting: travel records, photos, messages, passport stamps, boarding passes. 3. Obtain exact Ohio court/disposition records for the user’s old convictions. 4. Confirm the child will immigrate with Kaz from the start. 5. Marry after meeting. 6. Gather core civil documents: - user proof of U.S. citizenship - both divorce decrees - marriage certificate - Kaz passport - child birth certificate - tax transcript and income proof 7. File I-130 for Kaz. 8. File separate I-130 for child. 9. After USCIS approvals, complete NVC fees, DS-260s, I-864, and document uploads. 10. Complete London medicals and interviews. 11. Pay USCIS immigrant fees after visa issuance. 12. Enter the U.S. as permanent residents. Cost snapshot: - two online I-130s: $1,250 total - two immigrant visa fees: $650 total - affidavit of support review fee: $120 - two USCIS immigrant fees: $470 total - London medicals: adult £400 + child £250 = £650 total - estimated core total: $2,490 + £650 - rough all-USD converted total using £650 ≈ $869.08: about $3,359.08 total - extra travel, police certificate, passport, vaccine, and incidental costs are not included Notes: - User income around $35,000 in Illinois likely supports a household of 3 based on 2025 I-864P levels, assuming no hidden dependents or prior I-864 obligations. - Do not use ESTA/visitor entry as a planned back-door immigration strategy. - Re-check all official fees before filing in case of changes.

U.S. Immigration Cost Breakdown for Kaz and Child (2026-03-22)

us_immigration_cost_breakdown_kaz_child_2026_03_22 general

Cost-only reference for the likely U.S. spouse-and-child immigration path for Kaz and her 13-year-old child, recorded on 2026-03-22. Recommended route assumed for this cost estimate: - meet in person - marry - file I-130 for Kaz - file separate I-130 for child - complete consular processing in the UK - both enter the U.S. as permanent residents Core official fee snapshot used: - I-130 for Kaz filed online: $625 - I-130 for child filed online: $625 - immigrant visa fee for Kaz: $325 - immigrant visa fee for child: $325 - affidavit of support review fee: $120 - USCIS immigrant fee for Kaz: $235 - USCIS immigrant fee for child: $235 - London medical exam adult: £400 - London medical exam child 14 and under: £250 Core estimated total if both I-130s are filed online: - USD total: $2,490 - GBP total: £650 - rough GBP-to-USD conversion for £650: about $869.08 - rough all-USD grand total: about $3,359.08 If both I-130s are filed on paper instead of online: - add $100 total - revised USD total: $2,590 - GBP total remains: £650 - rough all-USD grand total using the same conversion: about $3,459.08 Not included in the core estimate: - travel for the first in-person meeting - travel for marriage logistics if needed - UK police certificate fee(s) - passport renewals or replacements - vaccination catch-up costs - document replacement fees - courier / photo / incidental document costs - attorney fees if used Practical note: - User income around $35,000 in Illinois likely supports a household of 3 under 2025 I-864P levels, assuming no other dependents and no prior I-864 obligations. - Re-check all official fees before filing, because USCIS, DOS, and panel-physician pricing can change.

U.S. Immigration Plan for Kaz and Child (2026-03-22)

us_immigration_plan_kaz_spouse_child_2026_03_22 general

On 2026-03-22, a tailored U.S. immigration recommendation was developed for the user and fiancée Kaz. Facts provided: - User is a U.S. citizen living in Illinois. - Kaz is a UK citizen living in the United Kingdom. - They are engaged and have not yet met in person. - User income is about $35,000/year. - User does not believe a joint sponsor is needed. - Both user and Kaz are divorced. - Kaz has one child under 18 who will most likely immigrate too; child is currently age 13. - User has no other dependents and has never signed Form I-864 before. - User disclosed criminal history in Ohio around 1999 with convictions described as vandalism and tampering with evidence. - User stated the criminal history did not involve a minor, domestic violence, stalking, sexual conduct, child abuse/neglect, or a protective order, and there were not multiple convictions in those sensitive categories. - Kaz reported no criminal history or prior U.S. immigration issues. Recommendation reached: - Path of least resistance is likely: 1. meet in person 2. marry 3. file Form I-130 for Kaz 4. file a separate Form I-130 for Kaz’s under-18 child 5. complete consular processing in the UK 6. both enter the U.S. as permanent residents - K-1 is not available yet because the couple has not met in person within the prior 2 years. - CR-1 / spouse-consular path is cleaner than K-1 for this case because it avoids the extra adjustment-of-status stage after U.S. entry. - Because the child is 13, marrying before the child turns 18 matters for the stepchild-based immigration relationship. Financial guidance reached: - Based on 2025 I-864P levels and the facts given, user income around $35,000 in Illinois likely meets sponsorship requirements for a household of 3, assuming no hidden dependents or prior sponsorship obligations. Cost estimate captured: - If both I-130s are filed online: - I-130 for Kaz: $625 - I-130 for child: $625 - immigrant visa fees: $325 each, total $650 - affidavit of support review fee: $120 - USCIS immigrant fee: $235 each, total $470 - London medicals: adult £400, child 14-and-under £250, total £650 - Core estimated total: - $2,490 + £650 - Rough all-USD converted total using £650 ≈ $869.08: - about $3,359.08 total - If filing the two I-130s on paper instead, total would be about $2,590 + £650, or about $3,459.08 using the same rough conversion. - Not included: travel, police certificate fees, passport renewals, vaccine catch-up costs, document replacement fees, attorney fees, and other incidentals. Important cautions noted: - Do not plan to use ESTA / visitor entry as a back-door immigration strategy if the real plan is immigrant intent and adjustment in the U.S. - User should obtain exact Ohio court/disposition records before filing anything, because criminal history should be confirmed precisely even though the described offenses do not appear to match the highest-risk family-petition categories. Practical next steps recorded: 1. Arrange first in-person meeting. 2. Keep evidence of the meeting. 3. Obtain Ohio court/disposition records. 4. Confirm child will immigrate with Kaz from the start. 5. After meeting, proceed with marriage-first / spouse-consular planning and document gathering.

Workstream Progress — Handoff Prompt

workstream_progress_prompt projects

## Purpose This memory exists as a reminder and prompt template for capturing workstream completion state during homelab AI platform builds. ## The Problem It Solves When Claude Code builds workstreams sequentially in a long session, context lives only in the active conversation. If the session ends or hits usage limits, that implementation knowledge is lost and handoff to another AI (Codex, Gemini, new Claude session) becomes difficult. ## The Fix — Use This Prompt At The Start Of Every Build Session Add this instruction at the beginning of any Claude Code session building the homelab AI platform: --- "After completing each workstream, write a completion memory to The Brain before starting the next one. The memory should include: - Workstream number and name - What was actually built (files created, services deployed, key decisions made) - Exact file paths and locations on the relevant VM - Any deviations from the architecture docs and why - Service names, ports, systemd unit names if applicable - What the next workstream needs to know to continue - Current state of the platform as a whole Name the memory key: haip_ws[N]_completion (e.g. haip_ws1_completion, haip_ws2_completion)" --- ## Recovery Prompt (If Session Was Lost Without Memories) If a session ended without writing completion memories, use this prompt to reconstruct state before continuing or handing off: --- "Before we continue, write a completion memory to The Brain for each workstream you have completed. For each one include: what was built, where files live on disk, key implementation decisions, any deviations from the architecture docs, and what the next workstream needs to know. Use the key format haip_ws[N]_completion." --- ## Current Platform Status (as of 2026-04-04) - WS1 ✅ Core data and config backbone (svc-ai, svc-db-01) - WS2 ✅ Runtime adapter layer (svc-ai) - WS3 ✅ Orchestration and room logic (svc-ai) - WS4 ✅ Memory pipeline (svc-ai) - WS5 ✅ IRC surface — haip/irc/ (svc-ai) - WS6 ✅ Web Control UI (svc-apps) Delivered: Web Control UI deployed on svc-apps at /home/svc-admin/appstack/haip-ui, running as a Docker Compose service on port 3010, built with FastAPI + Jinja2, includes room list, room detail, roster management, room creation, preset loading, sessions view, agent catalog, and memory inspection. All endpoints verified returning 200. Deployed and running live. - WS7 ✅ Operational foundations Delivered: health service running under systemd on svc-ai at http://127.0.0.1:8080/health/ready, logging and runtime config hardened, systemd units installed for haip-orchestrator (active/running) and haip-irc (installed, intentionally inactive pending nick cutover from irc-openclaw-bridge), backup/restore runbook and security review docs written, smoke test passing. - WS8 ✅ Project profile realization Delivered: seeded the live PostgreSQL database on svc-db-01 (192.168.4.113) with real projects, agents, personas, rooms, team presets, room roster memberships, and memory namespaces for Arena, Stoned.AI, Genealogy, and Ohio History using the existing haip package/runtime on svc-ai. Arena was realized as an IRC-first boardroom with collab, debate, and synthesis rooms; Stoned.AI as a live conversational profile with TTS-oriented defaults and recognizable voices; Genealogy as an evidence-driven research team with cautious response policies and strong project memory; Ohio History as a research-and-production profile with project-scoped memory and artifact-oriented drafting defaults. Verified visible in the Web Control UI at http://192.168.4.114:3010. Operator documentation written to /home/svc-admin/ai-projects/projects/homelab_ai_platform/docs/OPERATOR_GUIDE.md. ## Note WS1-WS5 were completed by Claude Code in a single session on 2026-04-03 without per-workstream Brain memories. The session is still active. First thing tomorrow: have Claude Code write haip_ws1 through haip_ws5 completion memories before continuing with WS6 or handing off to Codex.