Dark Factory Operator - Level 1: Personal AI Assistant

Self-paced course: build a personal AI assistant with memory that survives every session, on your own machine. No coding, your agent does the building. $79 one-time.

Price: $79 one-time.

Modules

Module 00: What You're Actually Building (30 min)

• Demo: assistant wakes up, reads briefing, references last week's decisions
• What separates this from a chatbot: autonomy, persistent memory, scheduled ops
• How agent skills work, SKILL.md as your assistant's identity (OpenCode or Claude Code)
• What you'll have by the end of this course

Module S: Tool Setup: Meet Your Agent (~45 min)

• Two ways to drive it: OpenCode desktop app (no terminal) or the terminal, your choice
• What an AI agent actually is: and what it isn't
• Basic usage: sessions, context, and commands
• The config file: settings that persist

Module 01: Environment Setup (~1.25 hr)

• Agent install: OpenCode desktop app or Claude Code (Mac, Windows, Linux)
• Workspace structure: ~/ai-workspace/: what lives where
• Local LLM setup: Ollama (CLI) or LM Studio (GUI), same models, different experience
• API key management and rate-limit proxy, budget tracking from day one
• First run: verify your agent connects

Module 02: Your Agent's Identity (60 min)

• SKILL.md, name, description, how your agent (OpenCode or Claude Code) finds your assistant
• AGENTS.md, behavioral contract: startup sequence, tools, rules
• SOUL.md: persona, tone, model selection
• USER.md: who you are, what the agent should know about you

Module 03: Memory Without Infrastructure (65 min)

• Why every session starts blank, and three approaches that fix it without a database
• Option 1: Organized markdown, daily notes, decision log, project state. Zero setup, works today.
• Option 2: Obsidian vault, local knowledge base your agent reads and writes. Links build context over time.
• Option 3: Ripgrep search, fast text search across a notes directory. No embeddings, no config.
• How to structure notes so the agent loads the right context at session start
• When you've outgrown this: the signal that it's time for Level 2's RAG stack

Module 04: Day-to-Day Operations (115 min)

• Daily notes: YYYY-MM-DD.md, what gets written, when, by whom
• The daily briefing: yesterday's decisions, today's tasks, any blockers
• Heartbeat cron: polling on a schedule, automatic status updates
• Telegram integration: the agent reports to you, not the other way around
• Proactive monitoring: three scripts (error alerting, disk monitoring, usage reporter) with scheduled cron jobs. The system tells you when something breaks
• The morning health check: whether the scheduled briefing actually fired, not just whether it is scheduled

Module 05: Hooks: Automated Triggers (65 min)

• on-session-start: injects relevant context before you type anything
• on-session-end: writes the daily note and a structured handoff for next session
• on-compaction: archives full conversation history, then verifies nothing valuable was dropped
• prune-tool-results: stops context bloat before it slows everything down
• Telegram notify: pings you when a task completes

Module 06: Putting It All Together (~1.75 hr)

• Start from zero, end with a working personal assistant
• Configure memory files, briefings, and three core hooks
• Run a simulated week: watch memories accumulate, see the briefing improve
• Troubleshooting: what breaks, why, and how to read errors

Module 07: Your Assistant's Graph (~45 min)

• Nodes, edges, state, and reducers, named on the assistant you already built
• The four kinds of node, and why most of yours should stay plain code
• Draw your own assistant, then read the drawing for the edges you never declared
• Declared cycles are features, discovered cycles are bugs, and every cycle needs a bound
• Why a lock file and a marker file solve different problems when your briefing runs twice

Module 08: Why You're Building This (~22 min)

• From prompts to loops: why every automation in this course is secretly a loop
• The Minimum Viable Loop: the smallest cycle worth automating
• The 4-condition checklist for whether a task deserves a loop at all
• The Loops Stack: where Level 1 ends and the Dark Factory begins

Module ★: Bonus Library ()

• The skill library: 16 production skills the Dark Factory runs on, plus a catalog indexing the 232 in the wider library
• Six specialist subagents (hephaestus, sisyphus, looper, fast-build, quality, critique) each one a single job you delegate
• Identity templates, all six course hooks, the helper scripts, and a memory quick reference
• agentvault, secure credential storage, so your agent can use your keys and never read them

Frequently asked questions

Why not just use free tutorials?

You could. But you'd spend months doing what this course covers in a weekend. The advice online is scattered, contradictory, and often untested. This is the collection, curated, tested, production-proven. Everything that works is here, organized and ready to use.

Do I need to be a developer?

No terminal needed, OpenCode's desktop app covers everything, and the terminal is optional. The identity files are Markdown. The hooks are Python but provided as templates. If you can read a config file and follow a README, you can do this. Complete beginners welcome, you just need to install the app, follow the lessons in order, and answer your agent's questions.

What do I need to run this?

Any Mac, Windows, or Linux computer from the last few years, no GPU, no special hardware. Your agent: OpenCode or Claude Code, both offer desktop apps; the terminal is there if you prefer it. Want fully offline? A local model via Ollama or LM Studio works throughout. Level 1 runs on all three platforms; if you continue to Level 2, its factory stack needs a Linux environment: Windows via WSL2 or 64-bit Linux (both tested; the setup script targets Debian/Ubuntu and RHEL-family distributions), or on a Mac a Linux VM or a small VPS.

How is this different from using ChatGPT or Claude.ai?

ChatGPT forgets everything when the session ends. Your Level 1 assistant remembers decisions, project state, and preferences across every session. It runs on your infrastructure. Switch models tomorrow. Your context comes with you.

Does this require a server?

No server required. Everything runs on your laptop or desktop. Local LLMs are fully supported. Nothing leaves your machine unless you choose a hosted model (your prompts and file contents go to that provider) or turn on the Telegram notifications (the notification text goes to Telegram).

What about privacy?

Every component works with local models (Ollama or LM Studio, llama3.2:3b, qwen3:8b). Your assistant's memory, daily notes, decision logs, all of it stays on your hardware. This isn't a compromise mode. It's the default.

How long do I have access?

Lifetime access. Once you buy, you get every update to the program for free.

What is a 'harness'?

The course's word for what you are actually building. The model (Claude, a local Llama) does the raw thinking; the harness is everything around it, identity, memory, permissions, triggers, skills. Nearly all of what makes an agent useful is harness, and here every piece of it is a file you own. That is why the assistant survives model changes: the model is one line in an identity file, swap it, and the rest of the harness stays.

What is founder's pricing?

The current prices are held for the first 50 students, in exchange for an honest review once you've built. After the first 50, pricing moves to the regular rate.

What if it doesn't work for me?

30-day guarantee. Read all 10 modules in the browser, risk-free. Not for you? Email support@darkfactory.education for a full refund. Downloading the build kits makes the purchase final. The files are the product. Full policy: /refund.