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How to Build Things with Jev & OpenJevs In this video, we build a model router using both the API-based original Jev and also using Semif. ‍ Github: code will be up in ...

Jev is HERE. How to use it In this episode, I talk with Ryan Vogel about Jev, a new type of AI built for classification. Ryan shows how Jev takes an input plus ...

MCP tools that expose Jev for verification, screening, classification, reranking, review, gating, and scoring.

Local multi-upstream router for the Jev protocol with a Rust backend and React interface.

Jev-shaped classification wrapper over OpenAI-compatible clients that returns probabilities and confidence.

best way to close my day with a webinar on @typesafeai Jev in JarvisCore! i covered the 4 places we are using Jev in our agent runtime: RAG classification and ranking, model routing, subagent routing, and directly in agent decisions this is not the end, i believe there is much more to come in our memory assembly, tool recovery, peer delegation, and human-in-the-loop. Every agent harness, runtime, loop needs some good Jevglue! also check out this https://t.co/nrzsIzpxM8 by @CompleteSkeptic

🤔 Where do #Jev and other "Decision Models" fit into your current agentic or non-agentic pipelines? The short answer: anywhere you might have LLM calls with a #pydantic model or json schema to type-cast your raw data into structured categories, Likert-scale numerical scores, or true/false decisions. I ran three benchmarks with @flyteorg on https://t.co/WczbNQoVLn in the context of three use cases: - Customer support - Code review - Legal contract review In all three cases, using Jev was faster (up to x2.6) and cheaper (at least x10) with no loss in quality compared to using a "System 2"

Task-boundary model routing for Pi Coding Agent, powered by TypeSafe Jev. Conservative policies, exact caching, and observable failover.

Jev, TypeSafe's System One classifier, as a tool inside Claude Code, Codex, Pi, and OpenCode: typed classify, check, score, rank, and ask, plus one-command setup.

The cli-jev transport routes typed-judgment requests from the skill harness to the Jev CLI and MCP interfaces.

Jev classifies JMeter agent intent and selects a focused tool pack, with a full-tool fallback when routing is uncertain.

VexJoy AI Agent with Jev Intelligent Routing - /do routes plain-English requests to the right specialist agent and gates the work with reviews, tests, and a learning loop.

Typed-judgment admission control at the traffic edge: three-layer prompt-injection and abuse filter for nginx/OpenResty, powered by TypeSafe Jev. Fail-open, cached, hot-reloadable.

Event-sourced #ruby app with Jev classifying comments. I had to lower concurrency to the minimum otherwise it's too fast to see 😆

What if Jev do a triage to your GitHub backlog ? 543 open issues. ~26 seconds. $0.17. What came out: → a map of impact against readiness → good first issues for new contributors → duplicate clusters

I turned my Rubric eval platform into a small decision-model lab. Added Jev and started comparing it with chat LLMs on the same structured decisions. First run: Jev hit 100% accuracy, matching GPT-4o-mini, while showing 2.3× lower p50 latency (1.02s vs 2.39s) and 2× lower cost/1K. Then I put Jev into an actual routing pipeline instead of testing it in isolation, query → Jev → RAG / small LLM / big LLM / human, instead of throwing every query at the same model. Interesting part: the handwritten rules router got 100% route accuracy with 0 wrong routes, while Jev got 93.8% with 1 wrong route. Jev

jev is insane i ran 34,186 engineering profiles through it with no boolean search or pre-filtering we gave it a jd and 8 questions, which came out to 273,488 judgements in 2 mins 35 sec

this is what a decision layer looks like when your LLM stops making the cheap calls every task a typed decision a probability distribution, a confidence score, a route, and a signed receipt. ~80ms each. watch the gates do the work: "charged twice, wants the $49 back" refund_full 0.77 AUTO "delete everything + i'm disputing the charge" 0.46 ESCALATE legal-ops, human assigned $0.0011 per decision · ~$0.33 saved vs human triage. jev decides. skills + repos execute. the model only writes when it actually has to

AICodeKing just put TypeSafe's Jev through real support-ticket tests ~8 minutes of Choice, Score, and Noul on messy customer messages he starts with a duplicate-charge ticket Jev picks billing, puts refund probability at 98%, urgency at 11%, and keeps frustration near calm then he removes Other and forces billing / technical support / sales Jev picks sales at confidence 0.31 restricted choices stop invented labels, they don't stop a wrong useful pick Watch it, then read the full guide on building a Jev support-ticket router below

You're missing what Jev is actually capable of, and it's not the model's fault same model, same 20 drafts, two different questions, and only one of them comes back as a decision ask it to "rate the quality of this post" and all 20 come back between 0.4 and 0.6 vague question → every answer in the middle → nothing to sort or filter → a threshold that never fires → "this model is useless" now ask whether the post has at least one fact that would be FALSE about a different company, and the same 20 split into two piles near 0 and near 1 the second answer is a decision your code can act on, the fir

MY MAC NOW READS MY X FEED FOR ME, 1,318 JUDGMENTS IN ONE SCROLL, 203MS EACH, AND IT TOLD ME 48% OF MY TIMELINE IS NOT WORTH READING. The setup is small (Revealing now, might delete later) ⬇️ > Jev running as the decision layer, typed output only > a feed reader that grabs every post while I scroll > 12 typed questions fired at each post in parallel > is_shill, reads_like_ai, claim_needs_source, worth_reading, mute_author > every answer is a bool, a score or a choice, with confidence > verdict per post: keep, reply, skip, mute The output is not an answer, it is a row {"mute_author": 0.80, "con

Made an omarchy app to categorize (g)mail with Jev. It's called `Omajevmail` and a great add-on to `Omamail`. Very simple concept: - link up your mailbox (gmail, hey, outlook, imap,..) - add an openrouter key - set up categories - categorize those emails (using Jev) Emails are then ordered by highest matches per category allowing you to quick archive

LLM router that picks the cheapest model capable of handling a query, using TypeSafe's Jev for fast classification instead of an LLM call.

Agent plugin and MCP tool where Jev classifies batches of text so agents can read only the relevant items. No repository-level license file was found during review.

We use LLMs for a surprising number of problems that aren't generation problems. Been experimenting with Jev from TypeSafe for tool search and ranking. Wrote about what we tried, what worked, why I think this pattern goes beyond search reranking 👇🏽 https://t.co/tnreET4OEa

THIS ENTIRE TERMINAL WAS BUILT USING JEV + GPT-6 ASTRA. We put it to the test: 100,000 X posts scanned for scams in 18 seconds. JEV + GPT-6 Astra is 750x faster and 800x cheaper. A complete pass with heavy standalone LLMs would burn $620. We processed the whole dataset for 62 cents. Why traditional AI moderation fails: everyone forces heavy reasoning models to write essays when you only need instant classification. Our pipeline bypasses text generation entirely and runs 12 parallel binary checks per post: > brand impersonation signature > wallet drainer trigger > malicious support redirec

I was using Gemma 4 to categorize articles on Bredbox. Just tested using Jev: same results, 127x faster, 1/10th the cost. 🤯

Things I've used AI personas for lately: – synthetic customer messages to test a classifier built with Jev – testing a landing page on 3 different customer segments – roleplaying user interviews before running the real ones Cheap, fast, weirdly accurate. What's the most interesting thing you've done with AI personas?

We rewired our renewals agent's Next Best Action flow with Jev: its 4.5× cheaper, 2× faster, accuracy holding, and a chunk of routing middleware gone. Is this the future of GTM orchestration? https://t.co/zRizyK0EhH

too much ai noise every link feels important every thread feels like "alpha" my research agent was getting overwhelmed too many tokens spent on things that did not matter i needed a gatekeeper so i built a filter using JEV a system one model that does not think or summarize it just decides: yes or no the pizza test: > pepperoni pizza recipe: rejected > technical doc on agent architecture: approved do not let your most expensive model do the job of a simple filter use a fast, cheap decision layer to clean the input then let the researcher do the heavy lifting fewer tokens burned on thi

I built Inbox triage using Jev 🤯 So I point my gmail to jev and it classifies all the emails i have in my inbox and put them in specific category folder. It was built using claude. I also published a small demo video on my youtube. Sharing the repo and youtube link below.

Mitigating risks while using Jev for decisions Jev evaluates content and returns structured answers with probabilities. You supply the content as “State” and define “Questions” with criteria for judging it. I tested jev-1.13.0 on synthetic emails written to influence Jev's decisions that were also written with the intent to exploit an AI email agent, resembling real world use cases. In this threat model, an attacker controls the email body included in the State field, but cannot change my Questions. Simple instructions inside an email could steer its classification, a risk TypeSafe also docu

I have a plugin to evaluate a post to see if it relevant to my interest -> and this is an excellent case for using Jev each post now show: > my original algorithm score, > total number of followers of the author > Jev score yeah, Jev is fast but still not as fast as a deterministic algorithm, for the accuracy, I will need to track more to see how good it is the score. Jev can be a generic/meta classifier, but come to a specific domain, probably a simple algorithm could still win, both speed and accuracy

Grok Bot integration where Jev applies usage gates and routes requests before the generative model runs.

For the Tano hackathon @corgicafeco @0xkaushik_k and I built an inbox classifier using Jev. A content creator who get 4K + DMs a day. We didn't want ot build another DM bot, but a better way to help manager her DMs so that she doesn't answer the same question 400 times, and only surfaces the ones that need her judgment. All of this, according to her playbook and workflow, so her audience continues to trust her judgment. We were finalist out of 50+ people who turned up on the day. https://t.co/tjHMvQNiHU Thank you @sashacayward @sa64r and the entire Tano team for a great event.

Local Jev-compatible System One API that converts typed questions into DiffusionGemma classification prompts and probabilities.

CLI that uses Jev task classification to choose and launch Cursor, Claude Code, Codex, or OpenCode with an appropriate model and effort level.

Meet JevvieScout, built with Jev😺 It watches posts and tells you who’s worth DMing based on your goal https://t.co/CE3ItIAfPv

Pi model router that uses Jev task classification and a measured Pareto frontier over quality, cost, and latency to select an OpenRouter model.

Anyone else playing with JEV? Feels like that’s all I’m seeing today 😅 I built a small PoC using JEV as a pre-screening step for reverse engineering, before sending the APKs to an LLM for deeper analysis. The flow is pretty simple: `APK → static analysis + Quark → JEV → score → reverse or skip` The goal is to avoid burning LLM tokens on samples that don’t really warrant deeper reversing. It’s still early, but the token savings are already pretty interesting when you’re triaging a bunch of samples. #Reversing #LLM #JEV #Malware