AI Workflows
Field-tested loops for using AI to inspect, draft, refactor, and verify frontend work while keeping ownership with the engineer.
A practical look at the boundary between a model response and application state: whole-plan validation, blocking clarifications, bounded correction, and recovery that still needs evidence.
How Helmweave applies Everyday Programmer's ideas about AI workflows, verification, durable context, design systems, and human judgment.
Agent sessions are temporary, but architectural choices outlive them. A lightweight decision record preserves the context, assumptions, tradeoffs, and revisit triggers future engineers need.
Faster code creation means verification matters more, not less. Evidence should be proportional to risk — and a passing suite alone rarely proves a frontend change is safe to ship.
A second model saying 'looks good' isn't verification. A practical workflow for turning AI review into claims, risks, and real evidence.
If implementation gets 10x faster and nothing else changes, incidents multiply even when quality doesn't drop. The system around the engineer has to scale too.
Coding agents made implementation cheap. Verification is the new bottleneck — and the next great developer tool might orchestrate evidence, not generate code.
A repeatable AI loop for reviewing feedback without giving up engineering judgment.
A practical workflow for using AI to inspect, draft, refactor, and verify without skipping ownership.
A field-tested workflow for using AI as a reviewer without outsourcing engineering judgment.