Argmin AI
Hey, 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.…
Argmin AI launched on July 22, 2026 with 32 votes, #698 of 24,623 launches that month and more than 95% of that year's launches.
What it does
For teams and founders shipping agentic AI workflows and features. Generic metrics don't speak your product's language, so a new prompt, model, or RAG change can pass them and still ship real issues. A quick manual check catches even less. You don't need annotated data, an ML team, or a month to be safe from regression. Drop in your agent's task, business rules, docs, and examples, and Argmin AI builds an evaluation you run before every change.
Argmin AI turns your AI workflow, rules, docs, and a few examples into an evaluation you can run before every release. No ML team or evaluation code required.
You launched an AI feature. Argmin AI turns your workflow, your rules, and a few examples into an evaluation that tells you it still works, before every release. Hand checks, scattered judgment, and one-off judge prompts all fail at the same place: the full release surface your customers actually hit. A few hand checks can look fine while the broken answer sits in the thousands you never opened. A few hand checks can look fine while the broken answer sits in the thousands you never opened. The standard lives in docs and memory. Quality changes with whoever reviewed the answer last. A judge built quickly in a chat gives the same reply a different score on each run, with no rubric and nothing…from argminai.com
In the maker’s words, at launch
Hey, 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…
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