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jev: Function-to-Question Decorator

@jev.fn compiles a Python function definition (parameters, docstring, return annotation) into a state plus typed questions, then validates the answers back into Pydantic models.

Category
Tools & Integrations
Published by
Community
Author
Added
2026-09-18
Tagscommunitypythondecoratorpydantic

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Run a Pydantic AI agent on Jev: each field of the output type becomes one question, the prompt is the state, and confidence comes back per field.
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A Python asyncio runtime that puts Jev in a supervision loop above Codex or OpenCode workers: it judges completion, test sufficiency, and stuck loops, then steers, retries, or stops the job.
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Render a live Jev meter over any video: Whisper transcribes, every sentence is scored, and ffmpeg exports a 16:9 edit with flags and a scoreboard. Presets cover debates, calls, and pitches.
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From the community

Posts from builders shipping with Jev right now.

A Claude session from 1M to 86K tokens

This is actually insane. This uses @typesafeai Jev model, as a plugin in Claude to review all the un-nesseasary tool calls, and it takes 1s to run! Like, literally, 1 second to take my Claude session from nearly 1M to ... 86K tokens! 😮 Ask your claude to install it and be  Show more

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tamara
tamara
@tamarajtran

found the perfect use case for @typesafeai Jev: instant compaction in 2026, why is compaction still a summarization prompt? Jev can make it instant by scoring every tool call and dropping what’s irrelevant

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Vercel's fx safety reviewer, 18x faster

We're seeing extraordinary results from @typesafeai. Default mode in 𝚏𝚡 is auto, with a safety reviewer analyzing every command. That reviewer runs on GPT Luna today. Jev is up to 18x faster (p95) *and* more accurate. It's coming to @vercel AI Gateway and likely new default.

Pranit
Pranit
Vercel
@fazxes

We benchmarked fx auto mode (safety) classifier with @typesafeai's Jev. tl;dr: ~5-18x faster and more accurate than 𝚐𝚙𝚝-𝟻.𝟼-𝚕𝚞𝚗𝚊, our current top choice

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