kazz187/jev-sdk-go on GitHub
- Stars
- 0
- Forks
- 0
- Language
- Go
- License
- MIT
- Last push
- 24 Sep 2026
Read from GitHub on . Counts change daily.
How it uses Jev
return &jev.Response{RequestID: "req_jevtest", Model: "jevtest", Answers: answers}, nilkazz187/jev-sdk-go
Go 1.27 client with generic typed questions (jev.Noul, jev.Choice, jev.Score, plus jev.Raw). Supports batching, retries, and Vercel AI Gateway as an alternative backend.
More like this
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AI News daily roundup leading with the Jev launch, summarising RLCD and TypeSafe's 20-200x speed and 40-400x cost claims. Its recap of reactions notes posters using Jev as a structured classifier, judge and routing policy where text generation is unnecessary.
Introducing System One Models and Jev (Hacker News launch thread)
The 1,978-point, 513-comment launch thread. The top comment calls the speed comparison misleading, since Jev only returns structured output, and disputes the cannot-hallucinate claim because a valid type can still hold a wrong value. Others note ordinary LLMs can already be forced into structured output.
DSPy: programming, not prompting, language models
Declares typed input/output Signatures for LLM modules and optimizes the underlying prompts and weights against a metric. Its Signature abstraction is the closest widely-used open equivalent of Jev's typed-question interface. Star count is GitHub's rounded display figure.
Outlines: structured outputs for LLMs
Guarantees valid structured output during generation by constraining decoding to a grammar or schema. The mechanism HN commenters repeatedly cited as already covering Jev's cannot-produce-a-type-error guarantee on ordinary LLMs. Star count is GitHub's rounded display figure.
Instructor: reliable JSON from any LLM
Pydantic-based library that extracts typed, validated structured data from LLMs across providers with automatic retries. The established way teams get Jev-style typed answers today, at LLM latency and cost. Star count is GitHub's rounded display figure.
