What people are building with Jev

Automater

@automater_ai

Claude Code Jev as MCP tools the agent can skip is not a gate. Put it in a PreToolUse hook that fails closed, with deny rules behind it. automater.ai/intel/jev-clau…

Dain

@Dain0x

A beautiful pattern can still be noise. Here’s a JEV × Opus 5.5 workflow I’d test: → A data pipeline identifies candidate signals. → JEV selects which candidates to investigate using defined criteria. → Opus helps explore those candidates and propose explanations. → Statistical checks and human review test whether those explanations hold up. The critical question: what evidence would change our mind? That question belongs inside the workflow, before a promising pattern becomes a confident conclusion. This animation illustrates the concept. The particles and activity are simulated.

frevana

@frevana_ai

Jev made competitor ad research 30x faster and ~90% cheaper. Typed “game” as the category. 20 seconds later: 294 top TikTok ads across 68 brands analyzed. Jev: 1/ pulled the top ads from the past 180 days 2/ scored every ad on stop-scroll, hook, format + trust 3/ clustered them into creative patterns 4/ classified winners, watches + dogs 5/ built a leaderboard of what’s winning 6/ recommended what to make next 20 sec · ~$0.02 For gaming: Winning hooks: offer/promo, challenge accepted Winning format: gameplay + UGC This is what we’re building at @frevana_ai with Jev: decode your category’s ads → know what to make next, in seconds. Try it free. Link in the first comment 👇

nicolay

@nicolaycz

Jev is the semantic if in your agent loop. LLM writes. Jev (TypeSafe System One) answers typed questions on the same state (Noul, Choice, Score) with calibrated probs. Your code branches. Pull the cheap decisions out of the LLM. Early access: console.typesafe.ai

leiro

@leiroops

the lead engineer at a research company used Jev + GPT-6 Astra to build a system that analyzes 12,000,000 source updates every 6 hours the system processes massive streams of information, a process like this used to cost $1,000,000+, now Jev engineering does it for $200 the first version relied on GPT-6 Astra to interpret every result it worked, but processing millions of routine decisions through a large model created unnecessary latency and huge inference costs then the engineer rebuilt the decision layer around Jev instead of explaining every source, Jev reads the saved task state and decides what should happen next: Research, Write or Review Astra handles the open-ended work: comparing evidence, resolving contradictions and preparing the client brief code controls the queue, permissions, budget, retries and files at the core of this system is the same architecture I break down in the article: Jev routes the work, GPT-6 Astra handles the open-ended tasks, and code keeps the process running continuously the full step-by-step build of a client research process that runs 24/7 is below ↓ would you let AI decide which findings deserve further research?

gabidev

@GabiDev98

Here is another use case for Jev in DeFi. Vault risk classification. I pulled the signals that matter for Morpho vault risk (allocations, LLTV, utilization, idle, oracle, curator, APY sanity, liquidity and more) across 50 @Morpho vaults on Base, then asked Jev to score each one LOW / MEDIUM / HIGH / EXTREME. 50 judgments in 22.6s, ~452ms avg, ~60% median confidence, around $0.006 in costs Result mix: 30 MEDIUM - 12 HIGH - 8 EXTREME - 0 LOW. Jev also ranked curators and vaults by trust, who looks safest, who looks riskiest. Claude, Codex and other models can do this as well but it takes much more time and money.

keno

@kenonews

JEV can’t see images. So how do you give it eyes? What’s possible? What can run locally? How far can you get for free? The answers (and the catches) are in the full breakdown below:

Hassan

@nutlope

Just trained Tev1 0.8B, a tiny Jev-like classifier. Here it is running completely locally on my mac with @ollama & classifying some tasks. It's extremely fast: only ~50ms E2E latency. Video is not sped up! Releasing weights & benchmarks very soon so you can try it yourself :)

Viv

@Vtrivedy10

Jev for RAG in almost all cases you trust the semantic matching capability of Jev more than dot product similarity very useful as the direct similarity metric in small data cases and a great reranker with big data

Jarek

@jarekceborski

I rebuilt CAPTCHA with Jev The browser measures how you fill in the form. Server turns that into a few plain sentences, and Jev decides: person or bot. Blog post with source code in the comments:

OpenRouter

@OpenRouter

Introducing typesafe/jev-router: a cache-aware model router powered by Jev and @typesafeai The Jev Router picks the best model and reasoning effort for each request, balancing quality, speed, and cost. Here's how it works 👇🏻

Peter Steinberger 🦞

@steipete

"Today, we’re releasing two Cloudflare-trained decision models, Clef and Clef-flash" blog.cloudflare.com/clef-decision-… Never seen an idea spreading so fast.

Victor M

@victormustar

Alert: Cloudflare just dropped a Jev alternative on Hugging Face 🚨 (Apache 2.0) huggingface.co/Cloudflare/clef