#codegeneration

TL;DR Governance in an AI code generation tool comes down to six checks: who can sign in,...

A planner answered `movies` for a request containing the word `movie`. The generator singularised that to `movy` and its own checker then rejected the result. Two candidates round-trip and the order decides, and the same two words defeat both obvious repairs.

Over 88 requests, with everything downstream held constant, the model's plan rescued zero rows an empty seat lost, under all four ways of scoring the run. The one reading in which it looked resolved was resting on two rows the scorer could not see.

The headroom over nine build configurations came out zero, and then zero again. The first zero meant eight of the nine were invisible to the driver; the second, from 1,584 builds through a driver that reaches all nine, means they return the same verdict on all 88 requests.

A primitive algebra searched from examples solved five of twenty-eight exercises, and every one already had a hand-written implementation in the same repo. A behavioural index recognised one function in forty-eight, and zero of eleven when handed other people's correct solutions to the same exercises.

Originally published at norvik.tech Introduction Explore the implications of...

Fifty-six percent of organizations say they're not well prepared to detect or contain unintended...

Discover how to unlock Claude AI coding with 5 essential templates. Boost your developer speed, automate workflows, and master code generation today.

Eleven of twenty-one operations never passed my frozen checks, and I wrote that up twice as a limit of the generator. A wrapper that only re-expressed each program's own stdout repaired 90 of 102 failures, changing no algorithm.

Umair shares his llm agent flutter ui generation comparison, detailing 5 setups, common failures like RenderFlex overflows, and fixes based on 100+ generated...

Key Takeaways Developers using GitHub Copilot completed coding tasks 55.8% faster in a controlled...

The simplest agent that works: spec and tests by hand, the model writes the code, it runs, and the failure feeds back as context. A complete write-test-fix loop in code that runs.