Learn · Debugging
From hypothesis to defensible root cause.
A methodology for production debugging in the AI era. Six modules. Each paired with replay-reviewed challenges on the platform.
Modules
Six modules.
01
Hypothesis before search
The single biggest mistake in AI-assisted debugging: searching before forming a hypothesis. A protocol for not doing that.
02
Bisecting with intent
git bisect, but for behavior. Working a bisect against production telemetry instead of unit tests.
03
Memory leaks in long-running services
Node and Python. Heap snapshots, retainers, the classes of leak AI usually misdiagnoses.
04
Race conditions and silent loss
Why concurrent code resists AI suggestion fixes — and what to do about it.
05
Distributed debugging
Correlating across services with traces, structured logs, and the rare time it's worth a debugger.
06
Defending the fix
A protocol for writing the PR description that survives a review six months later when the bug recurs.