Vibe Coder Expansion
You can already get AI to build things. This is how you turn what it builds into something real people can rely on — stable, secure, and launch-ready, no CS degree required. Spot where vibe-code breaks before your users do, make AI's output trustworthy (TDD, code review), close the security holes it quietly leaves, then ship and run it the way product teams do: release engineering, CI, observability, cost control. It's the in-the-job layer the free vendor academies — Anthropic Academy and the OpenAI Academy — never cover.
Anthropic and OpenAI teach you their house. This track teaches you the rest of the job — anti-patterns, decision frameworks, and in-the-job wisdom for running real agents in production.
PART 1 — Vibe Coding Fundamentals
Build the judgment and recovery loop behind good vibe coding
Start with the work every vibe coder needs: decide what is safe to ship, recover a session that has drifted, direct visual work, improve prompts, and leave the codebase easier for the next agent to read.
- Prototype vs Production Framework (1:42)
- Vibe Debt Recovery Moves (7:09)
- The Vibe-to-Production Pipeline (2:22)
- Visual / UI Vibe Coding Patterns (8:16)
- When Vibes Break: Failure Pattern Catalog (9:00)
- Common Prompting Anti-Patterns
- Why AI Loves Consistent Patterns (6:29)
- Write Tests First, Let AI Implement (6:27)
- When AI Tests Are Wrong: Validation Patterns (6:45)
- Code Review Checklist for AI Output
- Where Attackers Get Into Your AI App
- Find Security Holes While You Code, Not After You Ship (10:54)
- Teach Your Agent Once, Not Every Session
PART 2 — Reliable Agent Workflows
Turn a successful session into a repeatable engineering workflow
Choose the right amount of agency, hand work off cleanly, make the repository legible, and use tests and hierarchical instructions to keep an agent inside the lines.
PART 3 — Production Safety
Review, secure, release, and recover AI-authored code
Move from plausible output to controlled production changes with code review, threat modeling, security gates, single-source conventions, reversible releases, and scoped credentials.
PART 4 — Agent Systems
Build and operate tool-using, multi-agent systems
Work at the system boundary: retrieval choices, MCP servers, database access, bounded multi-agent patterns, observability, cost, audit, and continuous evaluation.
- Long Context vs RAG vs Fine-Tuning (2026)
- Building MCP Servers (6:08)
- Client-Side MCP Hygiene
- Database Integration Patterns (MCP) (5:06)
- Security Considerations for Tool Access (6:05)
- The Critic Agent Pattern (8:18)
- When Multi-Agent Adds Chaos: Anti-Patterns (8:38)
- Parallel Agent Supervision (4:54)
- Observability & Cost for AI-Augmented Systems
- Audit, Compliance & Continuous Evals
Run the patterns on your real codebase
The modules above are the map. Pair them with a 1:1 session or a live class and walk out with the workflow already wired into your week.
// running fleets of agents at the manager / platform layer? see the Vibe Management track · experimenters only