Jev-Moderation-Bot

brainstormity/Jev-Moderation-Bot on GitHub

Stars: 44
Forks: 5
Language: Python
License: MIT
Last push: 22 Sep 2026

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

How it uses Jev

    sdk_response = await sdk_client.system_one(
        state=state,
        questions=sdk_questions,
        model=target_model,
    )

results: Dict[str, QuestionResult] = {}
for qid, ans in sdk_response.answers.items():
    raw_dict: Dict[str, Any] = {"type": ans.type}
    if hasattr(ans, "choice"):
        raw_dict["choice"] = ans.choice
    if hasattr(ans, "confidence"):
        raw_dict["confidence"] = ans.confidence
    if hasattr(ans, "probabilities"):
        raw_dict["probabilities"] = getattr(ans, "probabilities", {})

View in typesafe/__init__.py

Open on GitHub

Use casesLLM guardrails 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

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

AnyJev: Jev-style decisions from any open LLM

Python library from Nokia researchers that reads typed Choice, Noul and Score answers with probabilities from an open LLM's logits or hidden states, without fine-tuning. Its zero-label level reduces option-order bias, and a head fit on 100 to 300 labels can run behind vLLM.

Avi Chawla (@_avichawla)696 starsOpen

AlternativeConfidence-gated actions

jev-review

Staged code-review workflow with a local dashboard, where Jev judges code changes or a whole codebase.

devagrawal09582 starsOpen

ProjectSemantic code linting+1

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