feedback

These results show that a model’s capabilities are compatible, generalizable and can carry over to other harnesses. Providers do report to optimize some models for their own coding environments: OpenAI, for example, describes GPT-5-Codex as optimized for agentic software engineering in Codex.12 Yet we observe that a shared provider does not guarantee the best pairing. The practical question remains which harness delivers the best balance of cost and task success for a given model and workload.

For humans, cleverness and intelligence are correlated; for machines they are largely decoupled, and today's tools are best viewed as stochastic generators of sometimes-clever, often-useful outputs — which yields the characteristic "useful yet unsatisfying" feeling of a magic trick once explained. He resists a one-dimensional picture entirely: the space of cognitive tasks is extremely high-dimensional, so no "sub-human to super-human" scale captures it, and the frontier that still eludes machines is data-scarce reasoning — extrapolating from five or six facts and a vague analogy, exactly where human mathematicians excel.

Large language models can develop novel social biases previously unseen in training data by simply going through an iterative decision making process and learning from spurious outcomes, which they do at a rate even more frequent than humans.

Agents doing judgement-heavy work need a way to learn from the people whose judgement they are trying to approximate. 1. Principles beat rules, because rules overfit and principles transfer. 2. Agents need to learn how to learn, or feedback turns into brittle exceptions. 3. The feedback loop has to live where the team already works, or people stop participating.

Feedback is not learning unless the agent can generalize

When an agent does work repeatedly, the prompt starts to become the thing you review. If those instructions determine production behaviour, they should live in a repo, with version history, review, and rollbacks. The daily learning agent does not directly change production behaviour. It opens a PR showing what feedback it reviewed, what principle it thinks should change, and the exact diff to the skill file. A human reviews it like any other change.