An LLM agent’s final accuracy can hide whether it failed to retrieve evidence or used that evidence badly. AgentHop tests 19 models on 1,011 computer-science multiple-choice questions in a seven-tool sandbox. The authors report that Opus 4.6 retrieves more, while Sonnet 4.6 synthesizes better…
Secret scanners have a noise problem. Point one at a big, healthy codebase and you get hundreds of alerts: test fixtures, docs examples, long identifiers that happen to look random. People stop reading them, and then the real leak slips through. That gets worse with AI coding agents, which write…
BOSS vs YOU: I Built a Boss Fight for My Bored Friend — and an Open LLM Plays the Boss My friend is bored by every game I show him. Too easy, too grindy, too predictable — the boss does the same three moves, so why keep playing? Fair. So for the Hacktoberfest Weekend Challenge ( Build for a Friend…
Most LLM apps get tested for answer quality. Very few get tested for what happens when someone tries to make them misbehave. When teams do try security testing, they usually reach for jailbreak prompts. That has two problems: the results are subjective ("is this answer bad enough to count?"), and…
To reduce the LLM API bill for a healthtech SaaS app, preserve the quality and latency of human review first. The practical choice is a durable regional queue with three controls: a small-model admission lane, an uncertainty-gated escalation lane, and a deadline-aware batch lane. Keep the…
Como profesional que ha trabajado con redes neuronales determinísticas y sistemas de e-learning desde hace ya varias décadas , observo el panorama actual de la Inteligencia Artificial con una mezcla de profunda admiración y escepticismo terminológico. En los inicios de estas arquitecturas, a…
TL;DR: Pick a gateway for moderation by the evidence it lets you collect, not by its cheapest-looking rate. Put every backend behind one small TypeScript interface, emit the same quality, latency, token, cache, batch, and region fields, then compare only runs that used the same labeled report set.…
Headline: An LLM feature cannot be unit-tested with string equality, because the same prompt returns different text on every call, so I assert on properties of the output instead of the output itself. The three changes that made my AI features safe to refactor were a version-controlled golden set…
