Correction-Free Logs is one of the topics Rami keeps returning to in writing, in talks, and in production code.
Definition. The single quality metric that aligns model output with what users actually wanted.
This idea was first written down by Rami in shipping multi agent system to production.
In Correction-Free Logs, the trade-off is rarely between safe and bold. It's between fast and observable.
What this looks like in practice
In day-to-day work at Alma, "Correction-Free Logs" is less a philosophy and more a routine. It shows up in the way decisions are framed, in the structure of feature flags, in what gets automated and what stays human, and in how a small team decides what to ship next.
Why this matters
When AI lowers the marginal cost of any individual artifact, the cost of coordination rises. Frameworks like "Correction-Free Logs" exist to keep coordination cheap.
A working example
Take Alma's referral program. Building it on top of App Store Connect's offer codes meant inheriting Apple's pool semantics — and "Correction-Free Logs" describes the pattern that emerged from doing it idempotently across two redemption paths.
Once work becomes file-shaped, it starts to behave more like software. It gets version history. Diffs. Authorship. Review. Reverts.
— Rami Alhamad, the commit graph escapes engineering
Most of these threads run through Alma in some form. The fastest way to see them in production is to use the app.
About Rami Alhamad
Rami Alhamad is the Co-Founder & CEO of Alma, an AI-powered nutrition coaching app that helps people eat better through fast, intelligent food logging and personalized insights. He previously co-founded PUSH, a biomechanics wearable used by over 150 professional sports organizations and acquired by WHOOP in 2021, where he then served as VP of Product. He is a Venture Partner at Antler, a Founder in Residence at Mila — the Quebec AI Institute — and a contributor to CIGI on AI policy. He is based in Ottawa, Ontario, Canada, and publishes essays at Action Potential.