What people are building with Jev

spect

@spectnfa

The founder of Jev just dropped a 1-hour masterclass on how Jev actually works LLMs → Decisions → Verification → Coding Agents 0% → 0:40 - why LLMs alone aren't enough 25% → 20:27 - the 3 primitives behind Jev 50% → 26:15 - turning huge AI tasks into tiny reliable decisions 75% → 45:04 - real systems: data, agents, verification 100% → 56:33 - the future of coding agents, memory and subagents Most people are still learning prompts. This explains the layer that decides what the model should do, checks whether it worked, and decides what happens next. Basically: how you go from “AI chatbot” to software you can actually trust to run itself. And it's explained by the guy building it. Bookmark this. Watch the full hour tonight. Then read the guide below and steal the architecture for your own agents ↓

Raph Guilhem

@raph_guilhem

Opus 5.5 + Jev = Automatic scoring of 120 competitor ads, shortlisting the 10 worth adapting, and creating versions of each. Both models came out in the recent days, so I tried them together on a marketing use case, playing to what each one is built for: → Claude Opus 5.5 built the pipeline: it found the competitors, ran the http://Pletor.ai workflow "Meta ads spyer", wrote the scoring script and built the interface. → Jev made the judgement calls on every ad The use case: I picked Grüns, the greens gummy brand, as a test case. I pulled 120 ads from 12 of its competitors on the Meta Ad Library, and had Jev score each one on 7 questions: hook, clarity, message, concept fit, tone, angle, health claim risk. Code then combined the scores and picked the 10 worth adapting, and created them, via Pletor MCP.

FrankD

@FrankDa18249347

支撑这个交易机器人的Jev账户快没钱了，我准备把机器人关停后拿最后的Jev额度做一个实验，在polymarket上实盘反买。因为这个机器人亏钱亏的实在是太稳定了，甚至没有一丝起伏，仿佛每个决定都是奔着击穿账户去的，所以我也不打算搞什么dry run了，直接实盘小金额开怼，看看把剩下额度跑完结果如何

Mnimiy

@Mnilax

tool which made me over $2,000 is open now my Jev + Grok Bot copytrading bot finally has a front door 50k+ lines written. 270+ contributions. four weeks of it. every buy walks the same floor before it can become yours: > watch only the wallets you picked > match the move against the rule you wrote > check it on two providers first > hold it to your caps, allowlist, slippage and expiry the full build is in the article below.

Gabriel Pauli

@gabrielpavli

Working on a simple tool that uses JEV to help organise my bookmarks. The UI is awful and it’s nothing crazy, but it’s been nice to learn about JEV and how it works.

Ed Plese

@edplese

Jev but for images? I built a local model to try it out! 63ms for a 512x384 image and fast enough for 15fps video. Birthday 94%. Golden retriever 78%. Sprinkles 93%. Candles lit 67%. Dog about to eat the cake? 52%.

Coach Shweta Bajaj

@shwetabjaj

What stands out to me in the Jev + Grok Bot setup is the division of work: Jev decides. Grok Bot acts. Human stays in control for anything irreversible. That’s a much cleaner agent architecture than asking one model to do everything. Cheaper, faster, and easier to reason about. @typesafeai @grok

kayato

@_kayato

Jev使ってネタバレ防止のChromeアドオン作ってみた。 例） ガンダムシリーズの投稿は見たいけど、閃光のハサウェイの新作のネタバレは見たくないというシチュエーションでのデモ 結構いい感じに動いてる…！これなら不意打ちのネタバレくらわないのでは…！ @typesafeai #Jev

delost

@thedelost

Jev has been blowing up lately. If you already have API access but don't know where to start, just copy this checklist: 1. jev-browser Drives a real headless browser through MCP, CLI or a library. Jev picks one action per step from the page's clickable and typeable elements, and scores whether the goal is met or the run is stuck. https://github.com/jkudish/jev-browser 2. jev-browser-agent Per-step browser decisions at about a second. One request asks "which operation" and "which target" together, and confidence under 0.6 escalates to a bigger model or to you. https://github.com/smartdio/jev-browser-agent 3. typesafe-jev-bridge Zero-dependency OpenAI-compatible bridge. Use typed yes/no, choice and score judgments from Claude Code, Cursor, Cline or any OpenAI SDK, over CLI or HTTP. https://github.com/RevocGG/typesafe-jev-bridge 4. pilot-typesafeai-jev Jev inside LangGraph and Deep Agents. One request asks three questions, plain code picks the route, and only the winning route spends chat model tokens. https://github.com/jyje/pilot-typesafeai-jev 5. building-with-jev-skill An agent skill for writing programs that call Jev: question design, state structure, thresholds, and how to diagnose a question that answers wrong. https://github.com/dbreunig/building-with-jev-skill 6. jev-skill Guided setup plus evaluation workflows, with a read-only presence check and no silent provider switches or fabricated probabilities. https://github.com/wuyoscar/jev-skill 7. awesome-jev Source-backed catalogue of what people actually shipped, sorted by category, with limitations noted per entry. https://github.com/yibie/awesome-jev 8. awesome-typesafe-jev Field guide with SDKs, live demos, agent tools and independent evaluations, including where the vendor numbers came from. https://github.com/AbdelStark/awesome-typesafe-jev 9. awesome-jev-by-typesafe Evidence-backed use cases, patterns and starter code, with the 70 to 500 ms latency claim marked as vendor-reported. https://github.com/Anil-matcha/awesome-jev-by-typesafe 10. awesome-jev (cobanov) The widest catalogue right now: SDKs in Go, Java and JS, MCP connectors, guardrail experiments and studies. https://github.com/cobanov/awesome-jev Copy these complete Jev blueprints - then read the full Jev setup below ↓

silentguy

@silentguyy66

Grok Bot does the job, Jev decides where the job goes next That's the ai company split: a work model and a decision model In this free 22-min tutorial they walk through jev, typesafe's "system one" model that makes decisions instead of chatting You ask one question, Jev answers from a fixed menu: noul, choice, score The demo: customer messages in, frustration score out. usable right in code Not another chat model, it's the fork in the pipeline Grok Bot keeps generating Jev keeps deciding Humans keep the approvals The teams wiring in that third layer are quietly becoming one-person companies Save this before your competitors put a decision layer between their bots

Geek Lite

@QingQ77

把本地 MLX 推理的 Laya 模型和托管 API 的 Jev 放在同一个 T-Rex 跑酷赛道上对打，用延迟、存活和回放数据对比本地推理与云 API。 github.com/virajbhartiya/…

Just AI

@thelazyyogii

上次说的 Jev： 不会陪聊，只负责拍板。 这几天又冒出个 Laya。 活差不多，路子不一样。 一起想成： 流水线上专门判题的人， 不写长文，只答「过不过 / 归哪类」。 Jev = 外面请来的裁判 开箱就能用，按量付钱， 选项多、材料长时更稳。 适合懒得自己搭、先要结果的人。 Laya = 自己厨房里养的裁判 权重能下，Apache 开源，本机跑， 更快更省隐私。 但要自己微调；选项一堆时容易糊。 适合有机器、要数据不出门、愿自己调的人。 一句人话： 不是谁取代谁—— 要省事找 Jev，要自己掌控找 Laya。 都是选择题机器，不是聊天搭子

Jason Torres

@jasontorres

I launched a chatbot yesterday and used JEV as a 17-in-1 router, filter, classifier, intent judge for efficient tool calling. Then further downstream is a relevance/context loop system. Worked really well. No more unwanted spams and prompt injections.

امیرحسین اسلامی

@AHSeslami

اولین تجربه‌ی خودم با JEV 👀 این تجربه برای ۳ روز پیشه؛ وقتی JEV هنوز توی حالت Waiting List بود. من کلاً هر ابزاری که سخت‌افزارم بتونه بکشه رو تست می‌کنم 😄 برای همین توکن JEV رو از OpenRouter گرفتم و به Grok Bot دادم. ماجرا چی بود؟ از GITEX کلی ویدیو گرفته بودم و نمی‌دونستم کدوم‌ها رو اول منتشر کنم. از اون طرف هم برای هرکدوم کلی کاور طراحی کرده بودم و واقعاً حوصله انتخاب و مرتب‌کردنشون رو نداشتم 😂 به JEV گفتم: «خودت انتخاب کن، دسته‌بندی کن و فولدربندی‌شون کن.» در کمتر از چند دقیقه همه‌چیز رو مرتب کرد و حتی پیشنهاد داد کدوم ویدیوها رو اول منتشر کنم. Grok Bot هم بقیه‌ی توضیحات و تحلیل‌ها رو انجام داد. برای اولین تجربه، واقعاً از JEV راضی بودم 🔥 هنوز Laya-Max چینی رو هم تست نکردم؛ نصبش هنوز تموم نشده. اونم که آماده شد، تجربه‌ام رو می‌گم 👀

Arthur Marques

@ArthurLabMRP

was trying to figure out how to make these 2 models fight each other, so I put jev vs laya in chess lol script picks the 10 best moves, captures, and moves that put pieces under attack. still wip but jev feels way smarter

Morgan

@morganlinton

It has been a really interesting experience to build an eval suite for System One Models like Jev. My first two attempts didn't work out. But I learned a lot in the process, and since Jev is so fast, and cheap, it's a lot easier to run quick tests than with an LLM. I'm on attempt number three with this eval suite, and thought I'd share more about it. The eval suite is called @VulcanBench Verdict, and here's the high-level on how it works:

Akshat

@akshatnerella

I vibe coded more fun apps using @typesafeai AI jev on the backend. My fav one is probably JevedIn, which is an extension for linkedin that automatically detects and blocks AI slop (thank me later). But I am lowk afraid of my linkedin feed being empty at this rate lol. Source code: JevedIn: https://github.com/akshatnerella/jevedin JevTube: https://github.com/akshatnerella/jevtube (currently building jevvit for reddit lol) comment what you want me to build next! #TypesafeAI #jev #classificationmodel #extensions #aislop

Thinh Le

@thinh_lvv

Can someone make Laya and Jev work? 😅 I created simple Flappy Bird game and let Jev and Laya control it. But it look weird 😬

Timothy Kassis

@TimothyKassis

We built a really nice free tool on top of @typesafeai Jev to see how rigorous an academic paper is.

Yannis

@yannnis

Enough is enough! Is it that LinkedIn sucks or what? Let's settle this. I go viral on LinkedIn for slop (attached) Then I spend 2 days to build a JEV product to scan Reddit for leads. And get tumbleweed!?! How??

Georgios Konstantopoulos

@gakonst

We index, rank and curate paid services powered by MPP and x402 to achieve this. Mercator is the apex aggregator I wish I had when we first launched MPP. This works by a pipeline which took a lot of work to get done, and most recently got turbocharged by integrating Jev by @typesafeai (come see Diogo's talk at Frontiers!)! See how running your first job works: https://mercator.sh/docs#run-your-first-job

Omid Sayfun

@OmiidSayfun

Couldn't find my important unread emails, so I built a Chrome extension. Jev finds them and labels them by what they're about. You can check it out here: github.com/iamomiid/jev-i…

Ackerman

@Yarilo7brigada

98% of my feed is junk. Now I don’t even see it I built a filter that reads my feed for me. It runs on Jev - a model that doesn’t generate text; it just makes binary decisions, in milliseconds and for pennies. For every post, it evaluates four questions: Is it relevant to my niche? Does it provide real value? What format is it (breakdown, news, ad, meme)? And does it make loud claims with zero proof? Out of 1,000 posts, only 20 remained. 97 milliseconds per post. 1.2 cents for the entire morning. The most frustrating takeaway, nearly half of my feed was ads and memes, not the creators I originally followed for substance. An hour of mindless scrolling turned into five lines over breakfast.

Rohan Mahtani

@itsrohanm

made this fun little word game so I could test Jev: clue me it's similar to charades and taboo. you describe your word (without using it), Jev tries to get it yorohan.com/clue-me @CompleteSkeptic @typesafeai i can't believe how fast Jev is 🔥

AI Insider

@TheAIInsiderN

JEV + OPUS 5.5 IS INSANE. I built an AI system that analyzes an X post before you publish it. Paste any X link. Press Start. Get a virality score in minutes. The entire production version was built in 9 minutes. Here’s what happens: Jev analyzes 800 viral posts as a live baseline Groups them by hook type: demo, launch, proof, contrarian take Runs 12 structured checks: hook, numbers, media, CTA and more Finds the 5 most similar viral posts Opus 5.5 explains why those posts spread Then your post receives: Virality score out of 100 Estimated likes and views Percentage of the viral baseline it beats Direct comparison with similar winners It works on drafts too. So instead of posting and hoping, you can identify weak hooks, missing proof and unclear CTAs before anyone sees them. Hover over any result to inspect the original post, its media, every check and the final score. JEV DECIDES FAST. OPUS EXPLAINS WHY. #JEV

Avid

@Av1dlive

Jev + GPT-6 Astra just built the most TERRIFYING AI trading setup on the internet... [this article covers 90% of what is required to build quant-level systems] /1 GPT-6 Astra reads the order book, the tape and 3 correlated futures /2 Jev turns the signal into a trade and checks the risk limit /3 computer use clicks the order screen, no broker API needed /4 the full loop runs in 6 ms, signal to fill steal this setup in the article below👇

Daniel Raeder

@DanRaeder

While Jev is down for new invites, I worked with Claude last night to build Vej (a jev clone that runs locally.) It's weakest at score types, but almost 1:1 for noul and choice. You can use it to build and play with jev-based apps until you can get in. github.com/draeder/Vej

Proliquid

@proliquid_xyz

Did you know that we have @typesafeai Jev AI giving you the sentiment on the news? Don't miss the next ethereum:0xfaba6f8e4a5e8ab82f62fe7c39859fa577269be3 like move. One click long on bullish news only on app.proliquid.xyz/terminal 🦈

Jetwani Avinash

@jetwaniavinash

First thing I built on Jev: a memory gate for Claude Code. Every message gets one question: is this worth remembering? 0.3s later it's either skipped or saved as one line in JEVMEM.md in the repo. Open source, link in my pinned post.

Kevin Dubois

@kevindubois

With all this hype around #Jev and open source derivatives like #Laya I wanted to see how these kinds of decision models would work in Agentic Workflows with #LangChain4j. (tl;dr it's pretty intuitive!) kevindubois.com/2026/09/23/rou…

Prasad Pilla

@prasad_pilla

AGREED. team built an internal tool using Jev to qualify candidates, and Jev itself is genuinely impressive at what it’s designed todo. but recruiting is hard. there are too many variables that don’t fit neatly into a rubric: context, trajectory, ownership, quality of work, potential, and the signals you only catch by actually digging into someone’s work tbh https://x.com/idleshubh/status/2102429846660079971?s=20

morph

@morpphhhaw

TypeSafe founder Diogo Almeida introduced Jev, and the field I would inspect first in any agent built around it is not model. It is state An agent can have room for a long conversation and still miss the one line that matters. A refund request needs the current order, the charge record and the policy that applies today. It does not need twelve earlier attempts to sound helpful Here is the state packet I would put in front of Jev: step 1 → name the decision the code needs to make now, not the entire job the agent was given step 2 → pull facts from the system of record: order status, amounts, timestamps and permissions step 3 → include the customer's actual words as evidence, without asking Jev to reconstruct the whole conversation step 4 → state the constraints that can change the route, such as a refund window or an account hold step 5 → build the available actions from live code, so a closed account never appears as a valid destination step 6 → put the question in the question field. State is evidence, not a second prompt hiding instructions step 7 → leave the sums and date arithmetic to code, then send Jev the result it needs to judge step 8 → keep the exact packet with the answer, because you cannot debug a decision from its label alone The point is not to compress everything until it looks clever. The point is to make it obvious which piece of evidence could change the answer The document below shows that packet on one page. The article goes further into shaping state without turning it back into a transcript

Yurii Oksamytnyi

@YuriODev

🛡️ @LangChainAI shipped Jev as a tool-call gate and model router. Key detail: tool output is excluded from the classifier input, so fetched content can't authorise its own execution. Steal that rule. langchain.com/blog/building-…

OrcDev

@orcdev

TanStack AI just shipped subagents. Jev picks which models / agents should run for the task, in parallel, in sequence, or mixed. Best Jev use case I've seen so far. TanStack team cooked. 🔥

Benchmark Heaven

@benchmarkheaven

Hopper (#3) beats Jev 1.13.0 on three of four columns: Calibration 79.1 vs 76.3, Speed 86.8 vs 83.3, Cost 58.7 vs 52.0. Jev keeps #1 on the fourth: Intelligence 53.1 vs 48.0, hard tier 74.1% vs 65.0%. Under 50, the gate applies. Score 63.29 vs 59.43. benchmarkheaven.com/jev-models/v1.…

rewind

@rewind02

built a full production app with opus 5.5 and jev... fed it a url, it screenshots the site, scores it against a list of known patterns, and ships a shareable report here's the actual pipeline: - gemini flash takes the screenshot, that's the only vision step needed - jev scores it against a fixed list of criteria in a fraction of a second, no text generation, just structured ratings - opus 5.5 writes the entire app, front to back, off a single planning prompt - product os turns the plan into a spec, then a roadmap, then working code, task by task - code review, security audits, and testing happen automatically as it builds, not after - a deploy checklist gets generated too, walks you through auth setup, hosting, env variables, step by step - cost to run per scan lands in fractions of a cent, both models chosen specifically because they're cheap at their job what actually makes this work: opus 5.5 handles everything generative, jev handles every decision that doesn't need generation, and neither model gets used for a job it's not built for that split is why the whole thing runs on pennies instead of frontier-model pricing pair that with a structured build system instead of one long freeform prompt, and a non-coder ships a live, secured, production app in an afternoon, not a sprint

aiasssistantstore

@aiassistantstor

CLM-8B Just Dropped: The New Open AI Model That Claims to Be Up to 9× Faster Than Jev for Agents

Abdullah

@Abdullah_Ops1

التجربة الثانية مع JEV 👇 سويت له Vibe Coder Launch Inspector تحط رابط مشروعك يفحصه ويطلع لك هل جاهز للإطلاق ولا يحتاج شغل يعطيك Score + المشاكل + الأدلة + وش اللي يحتاج تعديل والأحلى كل مشكلة معها Fix with Codex يطلع لك برومبت جاهز ترسله لكودكس #AI #JEV #Codex #VibeCoding

apolinario (poli)

@multimodalart

congrats on the release! i've included xor on the Decision Index 0.2 benchmark of jev-like models we don't have a vision benchmark yet, but the text-only version got 8th place, strong in arts & human taste x.com/multimodalart/…

PeterY

@yudeewxy

用JEV + Codex + TG Bot，开发了一个小工具，用于实时判断9个coin的价格走势和概率。交易信号来自前几个月开发的trading信号平台。同时给这个Bot授权了1万刀，看看他围绕这些信号来实时交易的表现会怎样。 开发过程： - JEV开通，获取API，配置到本地 - 配置TG Bot - 连接Binance和OKX的API - 信号聚合平台：http://pytrading.vercel.app - 或者找到trading view或者自己的一套交易因子 - Bot配置钱包，授权10K usdt，在OKX上进行交易 JEV的优势是：快速高效的反馈价格及进行交易行动、相比LLM大量节省Token

apolinario (poli)

@multimodalart

congrats on the release, i've added it to the jev decision index 0.2, it's the #2 open weight jev-like model 🥳 and the strongest in chess and for its size x.com/multimodalart/…

ImRobot

@th3nolo

Been building an agent-agnostic permission gate: Jev (TypeSafe) + hooks. Many harnesses copy Claude Code's hook format. Some barely have hooks at all. This makes me happy: agy in YOLO mode. chmod -R gets blocked, the agent asks me, I say yes, it runs once. Again? Blocked again.

Muskan Paliwal

@PaliwalMuskan19

built this skill-picker for myself because apparently having 30+ agent skills also means remembering which one does what 😭 jev (@typesafeai), being the god that it is, ranks them for a given task and tells me and my agents which skills are actually worth using phew. one less thing for my brain to cache. https://github.com/MuskanPaliwal/skill-picker

Cartwright

@CartwrightApp

Built Cortex, a local MCP server: Claude plans, a fast layer does the clicking. Same Mac-app task: computer use 23 s → Cortex 0.8 s One action: ~5 s Claude round → 0.25 s, no model 24 tools: browser, Mac apps, GitHub search, safety gate Measured on my Mac. @typesafeai #JEV

TactiX Trading Panel

@TactiXPanel

The Jev-powered AutoScalper passed all Tests during the Testnet Live Test with an average win rate of 82% trading on the 1m chart, using a 120 candle lookback window. So 2h structures get scalped top-to-bottom and bottom-to-top Now its time to aim for mainnet deployment!

Awais.

@abbas_kazmi066

plenty of use-cases for JEV. here's one: Hover Explanations. This simple demo testing cost me around: $0.006. Definitely very cheap and fast.

Cuth

@ItsCuthulhu

Now that I've collected so much data on System One models on my DGX Spark. Lets see if I can beat Jev on size, speed, and accuracy. It will be MIT for anybody to build on. Everything I do on here is open source.

Browser Use

@browser_use

Luna (planner) -> Jev (actor) playing poker and winning

Chris Brownridge

@chrisbrownridge

another @treg_ai /Jev demo to analyze data SUPER fast used treg to pull meta ads and then Jev to teardown the landing pages so you can understand where brands are sending traffic to categorizes the type of page, offers they're using and language used to sell also shows ad creative launch velocity, creative mix takes a few seconds to do it all end to end.

OpenMed

@OpenMed_AI

Four typed questions across four authored fictional notes, labels written before the calls. Jev 16/16, Laya 13/16. On the medication-conflict question alone: 4/4 vs 2/4. A useful diagnostic, not a clinical accuracy estimate.

Ricker

@0xRicker

Jev Engineering turns a static agent workflow into a graph that can rewrite itself while running. the video is basically the problem at scale: hundreds of routes → thousands of crossings → different agents → different tools → different confidence levels Jev Engineering doesn’t control every step. it controls the crossings. when two paths compete: → score both → kill the weak route → reroute the task so instead of one fixed chain, you get a live braid: state → decision → parallel paths → crossings → Jev → next state that’s the point of Jev Engineering: more parallel execution without letting the system lose the objective. the agents create the paths. Jev decides which paths survive. full breakdown below ↓

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: