GitHub's official AI review guide is a mostly-human checklist

A three-check method for deciding whether a dead project deserves a revival, with the Atom editor as the worked example.

Meta description: Configure TickDB MCP to give Claude access to live market data with millisecond...

Using agent-browser for goal-oriented UI exploration, independent assertions for verification, and turning discovered failures into cheaper regression checks.

When I started building my AI technical interviewer, I did what most people do: I threw a big system...

Moving beyond naive prompt loops: how to build production-grade, verifiable AI agent runtimes using explicit state machines, verification gates, and deterministic replay in TypeScript.

Newly recovered data examined by Search Engine Land offers an unusually detailed view of the systems...

Most "best AI code review tool" comparisons start and end with a benchmark score. That score hides...

Seven checks that turn an opaque AI agent run into something reviewable: diff first, pinned working directory, isolated workspace, logged commands, scoped credentials, reproducible run, explicit stop reason.

It means giving the agent enough context to do the right thing For this week's Agent Factory...

I saw someone tell this story recently, so I am going by their telling of it. They were in an...