Hawxy/TypeSafeAI.Net

Hawxy/TypeSafeAI.Net on GitHub

Stars: 2
Forks: 0
Language: C#
License: Apache-2.0
Last push: 19 Sep 2026

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

How it uses Jev

/// <summary>Configures a <see cref="TypeSafeClient"/>.</summary>
public sealed class TypeSafeClientOptions
{
    /// <summary>The production API root.</summary>
    public const string DefaultBaseUrl = "https://api.typesafe.ai";

    /// <summary>The alias for the most recent stable model.</summary>
    public const string DefaultModelName = "jev-latest";

    /// <summary>The API key. Required.</summary>
    public string? ApiKey { get; set; }

    /// <summary>The API root. Defaults to <see cref="DefaultBaseUrl"/>.</summary>
    public Uri BaseUrl { get; set; } = new(DefaultBaseUrl);

View in src/TypeSafeAI/TypeSafeClientOptions.cs

Open on GitHub

PrimitivesChoice Score Noul

Use casesLLM guardrails Structured extraction Composite scoring

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

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

GLiNER: generalist lightweight NER

Compact encoder model that takes the label set as input at inference time and extracts arbitrary entity types zero-shot on CPU. Same classifier-that-takes-its-categories-as-input shape, for extraction rather than decisions. Star count is GitHub's rounded display figure.

urchade3.9k starsOpen

AlternativeStructured extraction+2

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