Alternatives
Products that do what Eval Sakhi does
Turn your AI idea into a clear eval plan — before you ship
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May 2026 · github.com
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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…
Feb 2026 · ai-evals.io
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Hey HN! We’ve been building an MCP server to help AI-assisted web app developers by using browser agents to test whether changes made by an AI inside an editor actually work. We've been testing it on scenarios like verifying new flows in a UI, or checking that sending a chat request triggers a response. The idea is to let your coding agent both code and evaluate if what it did was correct. Here’s a short demo with Cursor: https://www.youtube.com/watch?v=_AoQK-bwR0w When building apps, we found the hardest part of AI-assisted coding isn’t the coding—it’s tedious point-and-click…
2025 · github.com
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Hi HN! Run it: OPENROUTER_API_KEY="sk" npx bff-eval --demo We built a tool to help people take LLM outputs and easily grade them / eval them to know how good an assistant response is. We've built a number of LLM apps, and while we could ship decent tech demos, we were disappointed with how they'd perform over time. We worked with a few companies who had the same problem, and found out scientifically building prompts and evals is far from a solved problem... writing these things feels more like directing a play than coding. Inspired by Anthropic's constitutional ai concepts, and amazing…
2025 · github.com
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Hi HN - we are the creators of “continuous-eval”, an open-source tool to test and evaluate generative AI apps. "Continuous-eval" came from our efforts to measure, validate and improve the reliability of a finance AI copilot we were developing for banks. End-to-end evaluation was not enough for us. We wanted to have granular evaluations that help pinpoint the bottlenecks and identify what / how to improve. We’ve since developed more metrics and made the framework more flexible so it can evaluate components like agent tool use, code change, retrieval steps, etc. Let us know what you think…
2024 · github.com
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Hi HN! We're Giacomo and Roberto, authors of Ratel (https://github.com/ratel-ai/ratel) We used to help SaaS companies build agents on top of their products. Whenever we wanted to expand the agents’ complexity/scope, by adding more and more tools and instructions, we always run in the same issue: context bloat, with frequent hallucinations and sky high token bills. So we started constantly engineering the agents, dynamically loading tools, splitting them into subagents, inventing our own way to support skills And that's exactly when we started building Ratel: a…
Jul 2026 · github.com
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2024 · github.com
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Your #1 new customer is an AI agent. Are they getting an A?
May 2026 · saastr.ai
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