pydecide

gopaljigaur/decide on GitHub

Stars: 1
Forks: 0
Language: Python
License: MIT
Last push: 23 Sep 2026

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

How it uses Jev

class TypeSafeBackend(HttpClientMixin, BaseBackend):
    name = "typesafe"
    DEFAULT_BASE_URL = "https://api.typesafe.ai"
    PATH = "/v1/systemone"
    DEFAULT_MODEL = "jev-latest"

    def __init__(
        self,
        api_key: str,
        *,
        base_url: str | None = None,
        model: str | None = None,
        timeout: float = 30.0,
        transport: httpx.BaseTransport | httpx.AsyncBaseTransport | None = None,
    ) -> None:

View in decide/backends/typesafe.py

Open on GitHub

Use casesIntent and model routing

Ran Jev against an existing classifier eval that previously used Gemini 2.5 Flash Lite

Vercel CTO reports Jev saturated an existing classifier eval that had used Gemini 2.5 Flash Lite and ran about 6x faster. Original post; TypeSafe's quote-tweet is a separate item.

Malte Ubl (@cramforce)Open

220k viewsSpeed vs Gemini 6x faster

ProjectHierarchical classification+1

[AINews] Jev: a System One Model that only decides/classifies/routes/scores

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.

Latent.Space (AI News)Open

ArticleIntent and model routing+1

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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