Promptx
A type system for LLM prompts. Ship with confidence.
Promptx launched on April 11, 2026 with 3 votes, #2,709 of 21,248 launches that month and more than 50% of that year's launches.
What it does
A type system for LLM prompts. Define schemas, write behavioural contracts, stress-test with adversarial AI cases or your own ground-truth CSV, and diff fingerprints across versions. Catch prompt regressions before they hit production. Built on Claude Code. No API keys. No dependencies.
A type system for LLM prompts. Define schemas, write behavioural contracts, stress-test with adversarial AI cases or your own ground-truth CSV, and diff fingerprints across versions. So you catch prompt regressions before they hit production. Built entirely on Claude Code. No API keys. No dependencies. - GitHub - ayush-mongia/promptx: A type system for LLM prompts. Define schemas, write behavioura
Does a similar job
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Prompt Engine – Auto pick LLMs based on your prompts2024 · jigsawstack.com · ▲93Nowadays, a common AI tech stack has hundreds of different prompts running across different LLMs. Three key problems: - Choices, picking from 100s of LLMs the best LLM for that 1 prompt is gonna be challenging, you're probably not picking the most optimized LLM for a prompt you wrote. - Scaling/Upgrading, similar to choices but you want to keep consistency of your output even when models depreciate or configurations change. - Prompt management is scary, if something works, you'll never want to touch it but you should be able to without fear of everything breaking. So we launched Prompt…
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I built a system that lets LLMs automatically learn and improve problem-solving strategies over time, inspired by Andrej Karpathy's idea of a "third paradigm" for LLM learning. The basic idea: instead of using static system prompts, the LLM builds up a database of strategies that actually work for different problem types. When you give it a new problem, it selects the most relevant strategies, applies them, then evaluates how well they worked and refines them. For example, after seeing enough word problems, it learned this strategy: 1) Read carefully and identify unknowns, 2) Define…
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Find & Try Awesome Prompts on the Most Popular LLM Models!🔥

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