mattneel/typesafe (Elixir)

mattneel/typesafe on GitHub

Stars: 1
Forks: 0
Language: Elixir
License: MIT
Last push: 17 Sep 2026

Read from GitHub on 24 Sep 2026. Counts change daily.

How it uses Jev

%TypeSafe.Response{
  model: "jev-1.13.0",
  request_id: "req_01a0ac97618c76898e35b41bf6d9aa5e",
  raw: %{...},
  answers: %{
    is_urgent: %TypeSafe.Answer.Noul{noul: 0.95},
    department: %TypeSafe.Answer.Choice{
      choice: :billing,
      confidence: 0.81,
      probabilities: %{billing: 0.87, technical: 0.13, sales: 0.0}
    },
    frustration: %TypeSafe.Answer.Score{
      score: 1.05,
      confidence: 0.93,
      legend: %{0 => "Calm", 1 => "Frustrated", 2 => "Very angry"},

View in README.md

Open on GitHub

PrimitivesChoice Score Noul

Use casesStructured extraction Confidence-gated actions

jev-trader

Makes one AI trade decision every Monad block on the Kuru MON-USDC market using Jev. The public deployment runs in dry-run mode with a mock model.

jarrodwattsOpen

2.2k starsLoop, mock model p50 100 msDecision window ~300 ms/block

ProjectConfidence-gated actions+1

jkudish/jev-mcp

MCP server that gives Claude Code, Codex, Amp, OpenCode and other agents ten Jev judgment tools, each returning typed probabilities they can branch on. The author calls it early software.

jkudishOpen

320 starsPer call 150-500 ms

ToolAgent routing and skill selection+2

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.

albelfioOpen

DiscussionIntent and model routing+3

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.

stanfordnlp38k starsOpen

AlternativeStructured extraction+3

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.

dottxt-ai16k starsOpen

AlternativeStructured extraction+2

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.

567-labs14k starsOpen

AlternativeStructured extraction+1

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