Ideas Weave Every Narrative with AI.
The new Agent Detective tool promises to find which agent broke in a workflow. But the top comment reveals a fatal flaw: ‘broke’ is subjective. The real value isn’t detection—it’s forcing teams to define what ‘good’ looks like. That conversation reveals deeper design flaws than any bug ever could.
Developers obsess over accuracy while ignoring cost—but the real bottleneck to production AI is cost predictability. Maverik gives you a systematic way to benchmark agent performance and predict costs, so you can decide whether a 5% accuracy gain is worth a 10x cost increase. Stop flying blind.
In an AI startup, nobody tells you what to do. You survive by building information asymmetry, testing before analyzing, and treating your own career like an Agent architecture: define your profile, your skills, and your tools. These 10 lessons from my first week show how to turn chaos into influence.
Smevals, a collaboration between Simon Willison and the Superpowers plugin developer, isn’t just another eval tool — it’s a lightweight sanity check that evaluates your evaluators themselves. Most AI teams are flying blind, relying on manual tests disguised as ‘evaluation.’ Smevals sits in the gap between expensive enterprise suites and guesswork, giving solo developers and small teams a cheap, fast way to validate prompt changes before they become public embarrassments.
Most teams treat AI agent evaluation like a final exam: pass a few test cases, ship, and pray. But agents are non-deterministic, black-box, and cascade errors. The real framework turns evaluation into a closed-loop system where every failure generates regression tests, root-cause labels, and repair tickets. This is the only way to survive production.