Compaction keeps your coding agent running while quietly deleting the why. How lossy compression makes you the agent's memory — and the local-first fix.
LangChain OpenWiki validates repo documentation for coding agents. But repo orientation is not the same as evidence-graded product understanding.
Your vibe-coded app caught on and now people pay for it — but you can't fully explain it. Build a mental model without reading every line, top-down.
Your product works. On launch day someone asks what it does at the edges — and you'd have to open the file to answer. That gap is the real launch risk.
Vibe-coding got you a working product you can't speak for. Reclaiming it isn't slowing down — it's re-authoring what you shipped, behavior by behavior.
You own a product you can't explain — acquired, inherited, or vibe-coded with AI. Here's how to read code you didn't really write, behavior by behavior.
You vibe-coded a working product. Users use it. You can't speak for it without reading every line. The product is yours — you just don't know it yet.
Auto-extracted specs read the code, not the decisions behind it. They quietly promote placeholders and fallbacks as if settled. Sign-off is the missing step.
AI-native teams need three layers of durable memory — behavior, decisions, execution. Execution has tooling. Most teams lack the behavior layer entirely.
AI makes rebuilding cheap. But product decisions are expensive to recreate from memory. Here's how a behavior spec makes 'start fresh' a feature, not a failure.
Paste a prompt into Claude or ChatGPT, describe your product module briefly, and get a .pbc.md behavior spec you can view, edit, and commit to your repo.
CLAUDE.md and AGENTS.md tell agents how to work in your repo. They don't tell agents what your product promises. That's a different artifact — the PBC layer.
A step-by-step guide to writing a .pbc.md file for your product's most critical module. Start with plain Markdown; add structured blocks agents can read.
Shipping fast with AI agents feels productive. But the costliest mistake isn't bad code — it's building confidently when nobody wrote down what was decided.
PRDs capture intent. Tests verify assertions. Between them, there's no artifact tracking what the product promises — grounded in code, confirmed by humans.
AI agents have AGENTS.md, memory banks, harnesses, and monitors. They still lack the product context layer — what the product promises and what must hold.
AI can extract product logic from your codebase. Stewie builds a living behavior spec your whole team can read — no code, no docs, no waiting on engineers.
Your coding agent ships correct-looking code that breaks product promises. The fix isn't capability — it's the product context layer agents lack.
Your repo has workflow instructions, session context, and feature specs. None of them answer what the product promises to do. That's the PBC layer.
Shipping fast with AI coding tools is genuinely good. The problem isn't the speed — it's what gets left behind. Product reasoning doesn't survive the vibe.
