Patterns and strategies for effective human-AI collaboration
BetaCrafting effective prompts that get reliable, high-quality results. Techniques for system prompts, few-shot examples, chain-of-thought reasoning, and structured outputs.
FundamentalsUsing AI as a coding partner — from scaffolding projects to iterative debugging. How to delegate effectively, review AI-generated code, and maintain quality across sessions.
Development Claude CodeWhen AI code doesn't work the first time. Systematic approaches to error diagnosis, context sharing, and collaborative problem-solving with AI assistants.
TroubleshootingPlanning and structuring projects for AI-assisted development. Breaking work into AI-friendly chunks, managing context windows, and maintaining consistency across files.
Planning Best practicesGoing from idea to working prototype in hours, not weeks. Real examples of full-stack apps built through human-AI collaboration — including MediaLog, Tweetster, and Mad Patrol.
Speed Case studiesUsing AI for drafting, editing, and research without losing your voice. Techniques for maintaining authenticity while leveraging AI's speed and breadth.
CreativeStart with a clear, specific outcome. "Build a media tracking dashboard" beats "make something cool." The human sets direction.
Use AI to generate the initial structure — database schemas, file layouts, boilerplate. Review everything before moving forward.
Work in focused 1–2 hour sessions. Each session has a clear goal. Fresh context keeps AI outputs sharp.
Share error messages, screenshots, and context. AI is great at diagnosing issues when given the right information.
Ship early, improve continuously. AI helps with deployment scripts, configuration, and the final 20% of polish.
Primary AI partner
Terminal-based coding
Game dev & prototyping
Hosting & deployment
Database
Backend logic