◆ Use cases
Where engineered loops already work.
Each domain has different actions, different feedback, and a different standard of truth. What carries across is the discipline: reason, act, measure, evaluate, repeat — until the work passes or the attempt fails honestly.
A capability is a domain we engineered, not a prompt we wrote.
A general model given a hard job in an unfamiliar domain produces plausible work and no way to tell whether it holds. The fix isn't a longer prompt — it's a world: tools that do the domain's real operations, state you can measure after every move, and an evaluator outside the model that decides what survives. That world is the product; the reasoning is what searches it.
Every capability below was built the same way, and each one ships with its own record of what failed on the way there.
Bound the domain
A capability starts as one job with a clear edge: the actions it may take, the artifact it produces, and what counts as done. Narrow beats general — the loop is only as good as the world built around it.
Build the instruments
Deterministic tools do the work the model shouldn't guess at: a simulator, a compiler, a benchmark, a corpus of what good looks like. Every action returns a measurement, not an opinion.
Put truth outside the model
An evaluator the model cannot talk its way past decides whether the work holds — replayed physics, a held-out metric, a human expert's blind review. Failures name what to change next.
The catalog
1 of 6 written up
Level designer
Writes levels in your native schema, simulates each one, and reads the playthrough back. Failed pacing or reachability checks change the next layout; only levels that pass the simulation reach your editor.
90% acceptance · 200+/dayRead the write-up →Game play
Plays your build and measures what a player would feel — difficulty, pacing, fairness. Each session's telemetry redirects the next run, and the report lands with the traces behind it.
10k sessions per nightWrite-up comingQA / chaos monkey
Drives the build toward failure, watching crash logs and state assertions. What breaks steers where it probes next, and every finding arrives as a reproducible case.
63 crashes reproduced · one releaseWrite-up comingQuery optimization
Rewrites plans and indexes, then runs them against your real benchmark. Measured latency decides what survives, and only changes that are faster on your data are handed back.
p95 latency −41%Write-up comingModel training
Proposes a configuration, trains it, and evaluates against a held-out metric. The result sets the next configuration, and the run returns the checkpoint with its evaluation history.
2.3× faster to target metricWrite-up comingKernel development
Rewrites kernels and compiles them against your target hardware. Real timings and correctness checks decide the next candidate; you receive the kernel that measurably won.
1.8× throughput on target GPUWrite-up comingFIGURES ARE SINGLE-DEPLOYMENT RECORDS, NOT GUARANTEES
Have work that needs its own loop? Bring us the domain and the standard of truth.
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