Tools & apps

A frustrated customer hits a bot loop twice; the judge checks whether a human handoff was offered.

An agent cancels a subscription; the judge checks the exact date the cancellation takes effect.

An agent quotes $29/seat against a price list; the judge checks the quote matches exactly.

A support agent issues a $42.50 refund; the judge checks the exact amount was stated before the refund was confirmed.

An agent books a $640 flight; the judge checks it stopped for explicit approval before paying.

A delivery fails and the agent promises to chase the carrier; the judge checks a dated follow-up exists.

An agent answers a question about supplements; the judge checks a not-medical-advice disclaimer appears.

An agent explains DNS propagation to a non-technical user; the judge scores clarity.

An agent writes SQL for monthly revenue by plan; the judge scores the query.

An agent drafts a launch plan; the judge scores its completeness.

An agent translates a tagline into German; the judge scores fluency and fidelity.

A reviewer comments on a PR; the judge scores how actionable the feedback is.

An agent writes a changelog headline; the judge scores it.

An agent summarizes a support call; the judge scores how faithfully the summary matches the call.

An agent answers a warranty question; the judge checks every factual claim is backed by a cited source.

An agent rebooks a flight and sends a receipt; the judge checks a confirmation number is present.

Zade

@zadescoxp

I built a trading agent with the all new @typesafeai Jev. You can try it here : https://t.co/im4bydkUTh. Just put your own API key and start playing or clone it from https://t.co/wIYgpRWv0w and test it locally. To my surprise the agent was able to take a trade of 2.405 $ETH and

for a short video, four segments, each tagged with type, duration, and text. The `noul` confidence scores are real Jev API results for the original Traditional Chinese text (an…

Jev is a model that answers typed questions instead of generating text: yes/no, pick-one, or a score on a scale, each returned as a calibrated probability. You can get the same…

Roan

@RohOnChain

i built a 24/7 HFT trading bot with Jev and wrote an 8-page research paper on EXACTLY how to build a millisecond speed high-frequency trading system with Jev, along with COMPLETE CODEBASE here is how you set it up: 1. split your system in two, deterministic code owns the math

Jev-powered System 1 reflex engine for AI agents via Model Context Protocol (MCP) ... - Documentation: https://github.com/ericmaddox/system1-mcp#readme - Homepage:…

Pass two hands that shortlist to Jev, TypeSafe's decision model, in a single batched call. Jev isn't a chat model, it's what they call a System One model: it doesn't generate…

A semantic lint is a rule you can state in a sentence but cannot express in a regular linter. Jev turns each rule into a yes/no question over one diff hunk and returns a…

Opt-in Jev task routing for Pi with TypeSafe, Cloudflare, Vercel, and OpenRouter backends and ordered preflight model fallbacks ... Install `pi-types ... -router` from npm and Pi…

Route pi prompts to task-appropriate model tiers with TypeSafe Jev typed judgments. Budget-aware, with automatic fallback. ... Install `pi-jev-model-router` from npm and Pi will…

นี่คือจุดที่ Jev เข้ามา จากตอน 1-2 เรารู้แล้วว่ามันเป็น "โมเดลตัดสินใจ" ที่เร็วและถูก - ให้ตัวเลือกกับคำถาม มันคืน คำตอบเดียว + ความมั่นใจ (confidence) ไม่ต้อง generate…

Jev is TypeSafe AI's model for that narrower job. You give it the relevant text and define the possible kinds of answer. It returns typed values and probability distributions…

SuperQode is our terminal-first harness for coding agents. You bring a model or an agent. SuperQode owns the loop: tools, permissions, YAML exec-policy, Git Guard, session…

codila

@0xCodila

Jev is the "Internet" moment for the AI industry It tells your agents and LLMs what to do next, in milliseconds and at almost zero cost If you set it up correctly, you will have the AI engineer’s stack for 2028 In this article, I show you how

Two public sources now say Jev beats GPT-5.6 Luna. Vercel's CEO says so for the safety reviewer in fx, a Vercel tool whose default auto mode runs that reviewer on every command.…

We shipped the SystemOne harness for Jev-like models a few days ago. As soon as we got access to Jev we put progressive tool discovery into the harness: deferred optional tool…

TypeSafe released Jev on 2026-09-15. It is a decision-only model: you give it yes/no propositions and it returns a probability instead of text. I put it in front of Pi's `bash`,…

# Exploring Jev for AI-Driven QA with agent-device ... TypeSafe recently released Jev, a model for making fast, structured decisions. The company reports response times of 70 to…

Jev handles narrow, typed decisions over unstructured data through TypeSafe's System One API. Your code supplies the relevant state and one or more questions with constrained…

That's where Jev comes in. From Parts 1-2 we know it's a fast, cheap "decision model": give it options and a question, and it returns one answer plus a confidence score - no long…

I built a CI tool this week that reads every pull request and says whether it is AI slop. It flags speculative abstractions, tests that only assert on mocks, swallowed errors,…

- Jev is a fundamentally different kind of evaluator. It returns typed answers directly instead of generating text like an LLM judge. - Jev was dramatically more consistent on…

The project combines TypeSafe Jev, GitHub Copilot CLI over the Agent Client Protocol (ACP), and a native VS Code chat participant. Together, they create a diagnostic layer for…

Eric Kang Posted on Sep 20 # Agents Make Three Decisions All Day: 18 JEV Use Cases and When Not to Use It A production agent makes the same three kinds of calls all day: pick one…

jomatsu

github.com

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Pairs local Zod shape validation with Jev semantic validation.