5 articles
Dan Luu's new agentic coding essay is not another vibe check. It is a useful reminder that coding agents only compound when the test loop, review loop, and task-selection loop are stronger than the code generator.
A Show HN project claims large agent-cost cuts by rendering bulky context as images. The useful lesson is not the trick itself. It is that compression needs evals, byte-safety rules, and per-request accounting.
OpenAI's June deprecations put Agent Builder, hosted Evals, and reusable prompts on a November 30 shutdown path. Here is the practical migration plan: Agents SDK, repo-owned prompts, and eval receipts.
The Bayer and Thoughtworks PRINCE case study is a useful reminder that reliable agentic AI comes from context routing, traces, evals, monitoring, and human review, not from a better prompt alone.
Hex's data-agent lab shows the practical eval pattern AI teams should copy: compare candidates against stable baselines, keep receipts, and judge changes by task behavior.
Showing 4 of 4 articles
New tutorials, open-source projects, and deep dives on coding agents - delivered weekly.
Explore 688 topics
Browse All Topics