AI-Evals.io – Evaluate this site with the tools it reviews
I've been working on a site [1] to give people control of their LLM workflows through AI evals - automated checks that, once defined, let you move fast without regressions and cut through hype with proof. That one-liner is aimed at software engineers, but I've spent my career helping cross-functional teams collaborate, and that's really what this is about. AI agents make powerful workflows very plausible, but only if teams can grow them incrementally without losing control - no vendor lock-in, no discipline silos, no blind trust in outputs. The site tries to meet different audiences where…
AI-Evals.io – Evaluate this site with the tools it reviews launched on February 15, 2026 with 5 votes, #1,882 of 16,627 launches that month and more than 75% of that year's launches.
What it does
Evals give you control, once you define them, you can move fast without regressions and cut through hype with proof. Evals make PoCs verifiable so you see real progress instead of one-off demos, and quality doesn't vanish over time. Evals reduce manual checking and keep experiments grounded, even when you use AI to move faster. Evals help you keep control and use AI tools with more confidence, even without deep coding skills. Evals are provable and auditable, letting you move fast while reducing risk across thefrom ai-evals.io
In the maker’s words, at launch
I've been working on a site [1] to give people control of their LLM workflows through AI evals - automated checks that, once defined, let you move fast without regressions and cut through hype with proof. That one-liner is aimed at software engineers, but I've spent my career helping cross-functional teams…
Does a similar job
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Argmin AIJul 2026 · argminai.com · ▲32Hey, HN community! We've built Argmin AI after shipping LLM features where the demo worked, then the bill and latency got unpredictable in production. Prompts expanded, context grew, retrieval got noisy, retries appeared, and agent workflows added loops. Argmin AI optimizes LLM-related expenses as a system: 1. prompt and context efficiency 2. model selection and routing 3. RAG inefficiencies and caching opportunities 4. agent workflows (tool calls, retries, loop control) Changes are validated with evals and guardrails (tests, gates, judges), tailored to your quality definition and goals.…
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