We moved our production database from SQLite to PostgreSQL without slowing down
A 33-minute cutover window and 99 of 99 tables verified, shipped while the same codebase absorbed 637 merges and a dozen other initiatives.
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Topic
Quality
Fast code is not the same as safe code. Here is how we keep both.
Quality covers the review and verification steps between an agent's first draft and a merged change. It answers what a reviewer should check, and what a test suite should cover. It also shows where a written rubric beats a gut feeling.
An agent can produce working code quickly and still get the wrong thing done well. The fix is not to slow every task down. Put a real gate where it catches the failure: tests, a second agent's review, a human check on the risky parts. Skip the gate where it only adds delay.
This hub is for anyone deciding how much to trust a merge an agent proposed. It covers the checks that earn that trust, without turning review into a bottleneck.
Request access →Anatomy of an $8 feature: the itemised receipt for code no human wrote
944 user stories shipped in a week, with no human writing the code. Here's what the median one cost, and what each quality gate caught.
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Can you trust AI-generated code? A practical guide to reviewing agent work at scale
The real question isn't whether AI agents can write code. It's whether you can trust code you didn't watch get written. Here's how gates make that answer yes.
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Cost-aware AI engineering: keeping agent spend visible and bounded
Token spend is easy to lose track of with more than one agent running. Here's how to keep it visible per task, agent, and model.
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Round up your herd.
Bring every human and every agent onto one table. Watch the work move. Costs update as it happens.