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GG5533/README.md

I make AI systems survive production

Not model quality — the plumbing around it. Retries that charge twice, provider calls with no deadline, agent loops with no ceiling, spend nobody capped, and prompt changes that quietly degrade output until a customer notices.

Same failure classes as any distributed system, wearing an AI hat.

10 ways your AI feature breaks in production → The full checklist I audit against. Free, no signup.


Reviews of other people's production code

changedetection.io (33.6k★) — reviewed the LLM client's retry handling in #4383. The maintainer called the points "spot on" and shipped Retry-After handling per RFC 9110, backoff jitter and a delay ceiling in #4384; the third point was traced and closed as already safe.

Follow-ups: regression tests for the retry-budget behaviour that fix introduced (open), and #4385 — the AI intent filter fails closed on a malformed model response, suppressing the change and caching that verdict, while every other failure path in the same function fails open.


Work

stripe-fulfilment-kit — fenced, leased order claims that survive retries, crashes and concurrent webhook deliveries. Cross-process atomic via SQLite. 59 tests, all offline.

ai-review-gate — two models review a coding agent's output and block it before it ships. Includes an honest record of the eight real errors it caught in one session.

cardamom-quant — quantitative model, 37-trial ledger.


The bug I'm most known for is one I wrote myself

A lease that passed 78 tests and was still wrong. A provider call that outlived its two-minute lease wrote its failure over a completed order — hiding a product the customer had paid for, and re-opening the order for a third charge.

Every test settled an order from its current owner. None covered a write from a superseded one, which is precisely the state a lease exists to create.

Full write-up — how 78 tests missed it →


Available for contract work · gg5533.github.io · samihabbal5@icloud.com

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