We build with AI, and mostly trust it.
Where it fails, a strict pipeline catches it.
Left alone, a model writes plausible code — code that compiles, demos, and can be quietly wrong. For software that sends orders to the market or performs pre-trade risk checks, that's an expensive failure mode.
- 01
Mechanical gates the model can’t argue with
Hooks that return an error on a layer violation, an oversized file or an off-convention SQL migration. Gates are enforced, not requested.
- 02
A forced process
Plan-first, test-first, one concern at a time. The design is a two-page contract written before any code, and a failing test is never edited to pass.
- 03
Adversarial review
Independent agents that trace every ripple effect and are instructed not to spare the author. Any medium-or-worse finding blocks the change.
- 04
The stage we don’t automate
What reaches us is distilled — the two-page design to approve or reject, and the findings ranked by severity. Our judgment is spent where machines are weakest: architecture and business correctness.
- 05
A learning loop
Every correction we make becomes a permanent rule the planner applies on the next task. The system gets harder to catch out over time.
AI makes us faster. Unsupervised, it produces technical debt; constrained and reviewed, with a feedback loop, it raises both productivity and quality — and nothing ships without our sign-off.
We write about how this works in practice — the gates, the bugs, the trade-offs — in Quietly Wrong
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