Writing

LLM features become operable when prose is separated from typed output contracts, validation, retries, and refusal states.

Why AI code review should end in a check against the real system, not a confident comment thread.

A short note on using AI to compress human context without sanding off judgment, voice, and uncertainty.

An AI agent's claim is useful only after it is tied to a check the real system can pass.

How lightweight decision records keep agent work recoverable without turning every chat into a source of truth.

In a multi-machine agent workflow, pushing to origin is only the first half of keeping work recoverable.

A note on keeping agent runtime inspectable without pretending Git is the whole memory system.

Why continuous adaptation is an operator problem, not only a model capability.

Long-form context is valuable, but onboarding needs a smaller package people can actually use.

Useful agent systems need routing, state, and operator visibility more than another chat box.

Quota limits should be visible product behavior, not a surprise runtime failure.

Live tasks reveal what agent coding tools handle well and where operators still need stronger checks.

Typed task artifacts give agents a stable source of truth while boards and dashboards stay projection-only.