Jev ecosystem projects — page 71

Find the LLM calls in your codebase that are really decisions, see what they'd save on TypeSafe Jev, and prove it on live traffic before you swap. TypeScript, JavaScript, Python.

Windows Forms playground for Jev System One — visualize and evaluate TypeSafe primitives (Choice, Score, Noul) with a 3-pane editor, preset builders, and simulated API evaluation.

Prototype: AI-assisted NZQA marking from rubric criteria alone, using TypeSafe Jev for guardrails, criterion scores and confidence-based triage. NOT ENDORSED BY NZQA - CONCEPT ONLY

Jev-trading is a desktop application that combines the ultra-fast neural network model Jev (by TypeSafe AI) with an intuitive visual interface. We've created a platform where the power of algorithmic trading is accessible without writing a single line of code, messing with Python scripts, or configuring servers in the terminal.

Independent benchmark of TypeSafe's Jev against GPT-4.1-mini on 200 labelled enquiries. Finding: a calibrated confidence score tells you when to ask a human, not when to ask a bigger model.

Unofficial Jev-powered Zig terminal agent with deterministic policy gates and encrypted audit trails.

No project description available.

an Information-Geometric Analysis Toolkit for any System-one (Jev, Jevlike) agent systems

Is Jev an instrument? Use Jev to finally answer the ultimate question of life: What is an instrument?

Self-contained showcases of JuL (Juste un LLM) — local typed decisions reproducing jevable.com ideas, running on the wemm-4b-4bit model.

Branch on plain-English yes/no questions, batched into TypeSafe System One calls

The asking side of Jev (TypeSafe System One: noul, choice, score) in Rust: client library, mjev CLI, evaluation harness, and Jev reverse engineered from its docs. Talks to Intel-Phi-Jev.

An abstraction for System One models, currently supporting TypeSafe's Jev and Laya. Typed questions, probabilities, and "don't know" instead of a guess, from Python, the command line, or MCP

Find where a folder of Markdown notes contradicts itself, and ask it anything. Every pair of notes crosschecked, every note asked, with Jev via OpenRouter.

Evaluate content for slop and guide revisions with 15 TypeSafe Jev scores. Not an AI-authorship detector.

System 1 vs System 2 AI models, measured: a chain test comparing one-pass Jev with Qwen 3.8 thinking on and off

Chrome-Extension für viele offene Tabs: Schnellsuche, Gruppen, Sortieren, Doppelte, Wächter und Verlauf. Optional mit Jev über OpenRouter oder TypeSafe.

Jev / TypeSafe AI 驱动的 X 信息筛选：按你的规则发现值得读的帖子。开源判断规则库 + 免费 Chrome 扩展。Filter X with your own criteria.

Agent make dozens of silent judgments a turn: is this done, is this correct, do I have enough to decide, which way now. Made inside your own text stream, those judgments are uncalibrated and biased toward finishing, you tend to certify your own work. judge hands one such judgment to Jev.

jev typesafe-ai implementation to enhance pure deterministic decisions and other modeling

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The Jev projects actually worth your time — picked, compared, kept current.

Agent skill for writing programs with Laya and laya-cli — local typed decisions (Choice/Score/Noul), port of building-with-jev-skill to Laya

Classification of human/agent messages using Jev in Claude-Code/OpenCode