27 Tracks. 221 Lessons.
Every track is built from real production systems — not synthetic demos. Pick your tier and start building.
From zero to your first web page — install Claude Code, learn the core workflow, and ship your first project with AI as your copilot.
The art and science of communicating with AI — write prompts that get results, build system prompts that shape behavior, and master the techniques that separate operators from novices.
The OpenAI ecosystem from API to production — master GPT-4o, o1, function calling, structured outputs, Assistants API, and the patterns for building reliable OpenAI-powered applications.
Google's multimodal powerhouse — master Gemini 2.0 Flash, Pro, and Ultra, harness the 1M token context window, process images/audio/video natively, and build production pipelines with the Gemini API.
From prompt to pixel — master AI image generation across every major platform, build production fallback chains, and learn the craft of visual prompting that separates operators from amateurs.
From script to screen with AI — master Veo, Sora, Runway, HeyGen digital twins, and the production pipelines that turn ideas into published video content without a film crew.
The engineering discipline that separates builders who ship from builders who generate. Quality checkpoints, testing that catches real bugs, CI/CD as enforcement, structured debugging, the Two-AI Architecture, and incident response — taught through real production war stories.
Build the mental model that separates operators from casual users. Understand AI as an operating system — persistent, routed, and compounding.
Set up the persistent agent platform, MCP servers, memory layer, watchdogs, and the full service architecture that runs 24/7.
Build content flywheels, cron-AI pipelines, model routing, and git-based deployment — systems that produce output while you sleep.
The operational rules that prevent catastrophic failure: stop-and-replan, E2E validation, compound learning, security, cost discipline, and ticket hygiene.
The testing discipline that let us fix 100 bugs across 11 projects overnight — autonomously. Quality gates, E2E testing, Playwright as development eyes, multi-agent code audits, visual QA retros, and the delivery checklist that separates shipped from broken.
We build 90% of our tools from scratch. Not stubbornness — sovereignty. Learn the framework for deciding when to build, when to adopt, how to security-scan, how to wrap external tools without creating dependency, and how to maintain exit strategies.
Beyond the basics — MCP servers, hooks, parallel agents via worktrees, CLAUDE.md mastery, remote sessions, and the operational patterns that 10x your output.
The operational layer that separates power users from everyone else — hidden settings, hook architecture, model routing, subagent configuration, agent teams, and fleet deployment across machines.
From chatbot to employee — install OpenClaw, wire up Discord and Telegram, build your first automations, design a Skills library, architect persistent memory, and ship Mission Control. Eight lessons from someone who actually runs this in production.
The compound cost of neglected repos: bloated CLAUDE.md files burning tokens on every agent session, stub test files gaming quality gates, and CI jobs wasting minutes on every PR. Six lessons covering the 200-line rule, stub detection, CI cost engineering, and the systematic audit workflow — taught through real production examples.
Six self-contained patterns for finding and fixing bugs at scale with AI agents. The 3-role audit swarm, tested-but-unwired dead code, fail-open defaults, verify-before-fix discipline, autonomous overnight runs, and integration guides as first-class outputs — each a standalone pattern drawn from real production audits.
The methodology that resolved 84 code audit findings across security, architecture, performance, and testing in a single session — audit swarms, prioritized fix order, parallel agent dispatch, CI gates, and the math of compound velocity.
Ten hard-won lessons from operating a multi-machine homelab over Tailscale — merge gaps, lying health checks, exponential drift, permission bombs, ghost processes, network ambiguity, Docker caching traps, singleton enforcement, version observability, and building automated drift detection. Every lesson draws from a real incident.
From single-agent to fleet — design orchestration layers, coordinate parallel agents, manage shared state, and build systems where AI agents hand off work to each other.
The framework for running AI autonomously without babysitting — validation agents, swarms with consensus, code review agents, confidence scoring, escalation protocols, and kill switches. Trust is earned, not assumed.
Competitive intelligence as modern prophecy — build AI-powered systems that monitor markets, extract signals from noise, track competitor moves, and synthesize intelligence into decisions.
Prepare for the Claude Certified Architect — Foundations certification. Master all five exam domains: agentic architecture, tool design & MCP, Claude Code configuration, prompt engineering & structured output, and context management & reliability. 60 questions, 720 to pass, zero shortcuts.
Build the connective tissue that lets AI agents talk to each other — deterministic routing, org-based authority, audit-before-dispatch, and the SDK pattern. Drawn from a real production broker running 24/7.
Authority ceilings, escalation over hard blocks, a 4-level kill switch with CLI fallback, recovery protocols, and the non-negotiable 100% safety test coverage rule. Built for the 2am incident you hope never comes.
Per-agent daily budgets, model tier routing, loop detection, cost attribution events, and the CFO daily report — the complete FinOps stack for autonomous AI agents. Prevent the $200 weekend before it happens.
Reasoning traces, behavioral baselines, drift detection, goal alignment, decision replay, and the automated 1:1 protocol — the observability stack that treats AI agents like real employees with real performance reviews.