A practical guide for non-technical people who want to ship code with AI.
- Product Managers with strong technical literacy and can articulate technical systems in natural language
- Sales professionals who want to build the feature requests they get from customers
- Marketers who want to build the demo & landing page for the product they're promoting
The core 4-step workflow: Plan → Issues → Implement → Review. Start here.
My go-to technologies for shipping apps: Next.js, React, Supabase, Tailwind, TypeScript, Vercel.
A log of how my coding practices are evolving over time.
Deep dives into the mental models that make AI coding work:
| Framework | What You'll Learn |
|---|---|
| Comprehensible Code | Why you must understand every line the AI writes |
| Context Engineering | Managing the AI's memory for better output |
| Atomic Features | Ship small, ship often, stay reversible |
| Emergent Abstractions | Why you shouldn't over-architect early |
| Agent Phalanx | Orchestrating multiple AI models (advanced) |
Quick reference guides for daily work:
| Cheatsheet | What It Covers |
|---|---|
| Prompting Patterns | How to "talk" to AI effectively |
| Claude Code Tips | Tool-specific tips and workflows |
| Git Survival Guide | The "social protocols" of version control |
Sample templates you can copy and adapt:
Real-world notes on AI models for coding—updated as new models drop:
| Model | Best For |
|---|---|
| Claude Opus 4.5 | Everything—my daily driver |
| GPT 5.X | Deep context gathering, strict code review |
| Gemini 3.0 | Front-end design, asset generation |
"The hardest part wasn't learning to review AI-written code... despite what everyone told me. The hill was learning how to work with other engineers on the same repo, where we all rely on the engine running smoothly."
- from my post reflecting on "90 Days at Complex"
Learning to code with AI is surprisingly tractable. The "hard" part is mastering the ancillary practices that developers take for granted: git, version control, branch management, PR etiquette, and CI/CD pipelines. These are social protocols encoded in technical systems.
This repo is designed to help you learn both.
If you're brand new: Start with QUICKSTART.md, then read Comprehensible Code.
If you're already coding with AI: Skim the frameworks for mental models you might be missing, then keep the cheatsheets open while you work.
If you want to go deep: Read everything in order. The frameworks build on each other.
These frameworks have emerged from my experience evolving from product manager, to startup founder, to building as a "collapsed" talent–owning product, design, code, and adoption as a single contributor.
Your mileage may vary. But the frameworks are battle-tested.
This repo is free. If you want more hands-on help:
→ Work with me — Workshops, 1:1 coaching, and enterprise training.
Or just say hi
Some links in this repo are referral links.