What people are building with Jev

Artem

@sl1ma4

🔥 looks like Jev just got an open-source alternative. Laya is a 421M parameter model built for making decisions instead of generating text. the interesting part: - runs locally on your laptop or phone - uses less than 1GB of memory - supports structured decisions (yes/no, scoring, multiple choice) - free + open source in a 30-second Snake benchmark against cloud-based Jev: Laya: 86.5 decisions/sec, score 46 Jev: 3.2 decisions/sec, score 1 the catch? Laya has a 512–1024 token context window and doesn't generalize as well as Jev without additional tuning.

Simplifying AI

@simplifyinAI

so we already have an open source alternative to jev... and it's 6-7x faster?! it's a typed-decision classification system: no chat, no generated text, just fast yes/no, scoring, or choice answers. > runs in under 1gb of memory > free on hugging face > runs on a laptop, or even a phone there's a real demo where it plays snake live, making a fresh decision every single move. two honest limits, straight from the project itself: > only 512-1,024 token context, some cases won't fit > weaker generalization than jev out of the box

Akash Jain

@akashpurjalkar

Jev is playing Minecraft. I built a bot powered by Jev that can construct houses, towers, lakes and castles on command. It also fights mobs, uses a sword, and tries to dodge skeleton arrows in real time. Still rough, but genuinely fun to watch. @typesafeai Jev

内田勉 DirecTune.app β公開中

@sidodtv

9/23開催 #生成AIなんでも展示会 Vol.6 のお品書きできました！ サークル名「他人ハウス」。場所はI-12です 内田：JevとLaya使用・超高速フットサルシミュレーター ころとろ：しまむらポエムマシン 場所は浜松町、入場無料、現在1400人が参加予定。みんな来てね！

内田勉 DirecTune.app β公開中

@sidodtv

なんか24時間後に Tiboリセットくるらしいので、今のうちに #directune の開発を進めるよ〜！ まずは、ゆっくり風動画解説で背景が自動作成されるようになったよ。試しに Jevの解説動画を作ったよ DrecTuneは、AIがアシストしてくれる動画生成・制作サービスだぞ‼️

Coach Shweta Bajaj

@shwetabjaj

This Jev experiment is fun, but the reliability question is actually the interesting part. Snake is simple enough to understand, but unforgiving enough to expose bad decisions fast. Play alone, play with Jev, veto its moves, or race it. A playful way to test decision quality, corner after corner. 🐍 @typesafeai @CarolMonroe http://jevplayssnake.lovable.app

Coach Shweta Bajaj

@shwetabjaj

Jev Skill Suggestion for Claude Code is a smart idea. Instead of loading every skill into context, Jev decides which one is actually relevant and injects only that. My takeaway: better context hygiene, less clutter, and potentially more efficient agent workflows. @typesafeai @vercel

Shengkun Ye

@shengkunye

Introducing Jev + OpenRouter + tools. You can now use Jev with 2,000 tools on Monid. Your agent picks the tools, Jev speeds the work up 30x. > Score 2,000 leads > Scan the TikTok viral video hooks > Research fundraising of 200 companies > Audit a site's SEO and rebuild the internal links on every page > Sort every Reddit thread about your category

SCRAPS

@scrapsonsolana

For marketing $SCRAPS, I’m trying something I haven’t seen anyone else do. I’ve been experimenting heavily with the latest AI tools (JEV from @typesafeai), burning through 178M+ tokens and nearly 48,000 requests testing different ideas. One of those experiments is focused entirely on marketing: finding the fastest path to high engagement and the best ROI possible. Now that SCRAPS is live on Google Play, it’s time to put it to the test. The experiment starts now.

さっと

@satto_sann

Jevでサクッと分類器は価値があるのかビルドトラップできそう

AMIT RAWAT

@sahajamit

I built a Chrome extension that judges every post in my feed before I reach it. READ, MAYBE or SKIP, in about 300 ms, using Jev by @typesafeai. No scripted rules, no selectors, no LLM. Real time, my X feed:

Andrew

@s4yonnara

Jev CEO, Diogo Almeida (ex-OpenAI): "It's not Claude Code because Claude Code is still part of the assistance era." He co-authored GPT-4, ChatGPT, InstructGPT and RLHF. Now he's building what comes next: 200x faster. 400x cheaper. A decision brain for AI agents. His argument is simple: frontier LLMs are insanely good at creating, but we're wasting them on thousands of tiny decisions: Which agent goes next? Is this relevant? Does this need approval? Those calls don't need generation. They need a model built to decide. In 18 minutes, Almeida explains why today's AI was built for assistance, not real automation, and why "tomorrow's AI will be for automation." This makes most $500 agent engineering courses look obsolete. Watch it today, then steal my 10-step guide to giving your agents a decision brain below ↓

s1rozha1

@s1rozha_

Jev hype is not about chat. it’s about decisions everyone is trying to compare it to ChatGPT / Claude / Cursor wrong frame Jev doesn’t need to write you a beautiful paragraph it needs to answer questions like: should this agent act now? which tool should run next? is this signal worth trading? is this market mispriced? should this wallet be ignored or watched? that is the interesting part prediction markets are basically endless small decisions under uncertainty not one giant “AI intelligence” moment thousands of tiny calls: price changed liquidity moved wallet entered news dropped odds lagged edge appeared edge disappeared you don’t want a slow model writing essays for that you want something cheap, fast, typed, and measurable that’s why Jev is interesting. not because it replaces frontier models because it makes the boring decision layer cheap enough to run everywhere

りょう｜AI駆動開発でのゼロイチマネタイズ

@ryo_hukugyo_pro

Jevをいかに使いこなすかが今後トークン節約や自動化をするAI社員を大量量産する上で、 めちゃくちゃ重要。

Richard Seroter

@rseroter

I'm reading up on Jev (typesafe.ai/blog/introduci…) and learning how it differs from "regular" classification ML models. I gave a bunch of articles to @googlecloud Gemini Enterprise and asked it for a breakdown. Helpful. Still digging in before going hands-on.

Robert Youssef

@rryssf

all things Jev in one place a GitHub collection just dropped packed with hundreds of tools, libraries, and projects built on top of Jev, from teams treating decision-making as its own primitive, separate from language generation entirely. github.com/AnotiaWang/awe…

Richard Meng

@richard_meng_01

Nitpicky, an AI generated photo detector powered by jev AI generated photos can be told from nits. That's why we build something to zoom into every detail: faces, fingers, characters, numbers, poses, where common senses fall apart, judged by jev

TechVerser

@realfxw

Claude Code 的上下文终于不用再被一堆预加载技能塞满了！推荐一个非常优雅的按需加载解决方案：Jev Skill Suggestion。 以往在配置大量自定义技能（Skills）时，最头疼的就是 Context Window 膨胀问题。许多低频技能即便一次都用不上，也会在每次对话启动时常驻上下文，不仅白白消耗 Token 成本，还会稀释模型的注意力，甚至引入不必要的提示词干扰。 这个 Mod 的核心逻辑是将技能路由与主体执行彻底解耦（Just-in-Time 动态注入）： 常态零占用：将技能标记为仅用户可调用，默认状态下完全排除在上下文窗口之外，保持 Prompt 绝对干净。 轻量前置路由：用户输入指令后，Mod 会将技能列表交由 TypeSafe AI 的分类器 Jev（或兼容的 Vercel AI Gateway），快速匹配与当前任务最契合的技能。 精准单点注入：只有被分类器判定为高置信度命中的技能，才会在执行当下被动态注入给 Claude Code。 安装仅需一行命令： npx claude-code-templates@latest --mod productivity/jev-skill-suggestion 这种把“工具选择”抽离给轻量分类器、让大模型只专注“任务执行”的架构，正在成为复杂 Agent 系统降本增效的标配设计。随着技能库扩充，未来配合端侧超轻量分类模型（如社区正在探索的 MLX 移植版），整体延迟和隐私表现还会更进一步。 大家目前在 Claude Code 里常驻了多少个自定义技能？这种动态路由机制是否刚好击中了你的上下文焦虑？ #ClaudeCode #AIAgent #AgentSkills #TypeSafeAI #LLM #PromptEngineering #AI编程 #OpenSource #开发者 #独立开发 #全栈开发 #Vercel

RazeDen

@razeden0

i just merge Grok 4.7 + Jev and got the smartest thing i've ever owned grok 4.7 does the thinking. jev does the deciding. together they cost less than my coffee prompt → grok bot → jev asks 6 questions → grok 4.7 opens only the good ones → campaign jev doesn't write anything. it only answers simple questions: yes or no, pick one, or a score. it can't make things up, because it can only pick from answers you allowed i gave it 3,412 leads from x, linkedin and youtube. 6 questions each is 20,472 decisions, done in 15.7 seconds for $0.41 the same reading by hand took me 6h 12m. people say 400x faster. i did the math and it's 1,421x that's $0.00012 per lead, and grok 4.7 only reads the ones worth reading the big mistake: sending everything straight to the new model. you pay the smartest thing you own to say "skip" all day setup took me 9 minutes: key in the secure field, sdk on the agent computer, one router skill, test mode first jev decides, grok bot does the work, and i still press the button on anything i can't undo everyone is talking about the new model. the cheap one deciding what it reads is where the money is

Ranjan Kumar

@ranjankumar

𝐖𝐡𝐞𝐫𝐞 𝐉𝐞𝐯 𝐁𝐞𝐥𝐨𝐧𝐠𝐬 𝐢𝐧 𝐚𝐧 𝐀𝐠𝐞𝐧𝐭 𝐇𝐚𝐫𝐧𝐞𝐬𝐬 (𝐍𝐨𝐭 𝐚 𝐌𝐨𝐝𝐞𝐥 𝐒𝐰𝐚𝐩) Your agent harness needs a decision model. Where you place it depends on one brutal fact: Jev's ordering is trustworthy. Its confidence numbers are not. Most placement guides skip this distinction entirely. They tell you to pick a threshold and execute above it. That works if you actually have a probability. You might not. 𝐓𝐡𝐞 𝐜𝐨𝐫𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦: A model can rank beautifully and lie about magnitude simultaneously. When you gate execution on if confidence >= 0.85: approve_transfer, you are trusting a number that was never calibrated to your prevalence, your cost ratio, or your queue. Move the threshold up and you trade recall you never measured for precision you cannot state. You have no idea how far along an unmarked axis you moved. 𝐓𝐡𝐫𝐞𝐞 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬, 𝐭𝐡𝐫𝐞𝐞 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐧𝐞𝐞𝐝𝐬: Routing and ranking consume ordering only - which answer is best matters, the number beside it does not. Gated execution consumes magnitude - the threshold is your boundary and it must land on a calibrated scale. Relative logic like if top1 - top2 < 0.1: escalate consumes differences - and rescaling the score axis will flatten margins unevenly, breaking your code. The sepsis alert systems of 2020 learned this hard way. Michigan switched off alerts when COVID shifted patient prevalence beneath a fixed threshold. No weights changed. The denominator moved, the promise broke silently, and nurses drowned in false alarms. 𝐓𝐡𝐞 𝐟𝐢𝐱 𝐢𝐬 𝐬𝐞𝐪𝐮𝐞𝐧𝐜𝐞, 𝐧𝐨𝐭 𝐭𝐮𝐧𝐢𝐧𝐠: First question: is this number admissible as a probability at all? Run a calibration study on a few hundred labelled cases from your own queue. Samuel Sacco's measurements from 18 September and Adil Muhammad Pervez's 8,000 judgments both reach the same conclusion: fit your own map. The weights are shared across every account by design - there is no per-customer adaptation - so a deployer-side calibration map is your only mechanism. 𝐒𝐞𝐜𝐨𝐧𝐝 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧: given that the number is calibrated, where does your cost ratio put the line? That part of the familiar advice still works. Run them backwards and you are tuning a dial with no markings. Read the full analysis on calibration, harness placement, and where Jev actually fits: https://ranjankumar.in/jev-system-one-model-agent-harness-placement Follow for more practitioner insights on agentic AI systems and production AI engineering. #AgentiveAI #AIEngineering #SystemOne #Calibration #MLOps #DecisionModels #HarnessEngineering #Jev

Raksha T

@rakshaa_t

Jev has some invisible use cases that won’t be clickbaity but hella useful : - auto performance checks on your shipped websites - personal tools like using it for super quick chat compaction - user feedback prioritisation against most relevant data in your company - lots and lots of data sorting - quickly - pairing it with codex / claude to optimise decision making while you code with your agents etc

grahma.dev

@quantium16

既然现在JEV在meta里，我就做了一个网站，让它在里面无限奔跑并收集眼镜，我觉得这个Infinity Runner应该会很高。你们还可以发你们通关的截图，你们中最好的那些人会从这个代币获得supply。 infinity-runner-rho.vercel.app

Doge 🐶

@pprownets2023

💡 ยกระดับ AI Agent ให้เก่งขึ้น 100x ด้วย Jev Engineering (Decision-Making Brain) เคยเจอปัญหาไหม? เวลาทำ AI Agent แล้วสั่งงานด้วยข้อความธรรมดา (Free-form Prompting) มักได้ผลลัพธ์ที่ไม่แน่นอน ควบคุมยาก และนำไปต่อยอดในระบบแบบอัตโนมัติได้ลำบาก 🤯 เอกสาร JEV Engineering ได้เสนอแนวคิดในการแก้ปัญหานี้อย่างน่าสนใจครับ: 🔹 1. Jev คืออะไร? Jev คือ "สมองส่วนตัดสินใจ" (Decision-Making Model) สำหรับระบบ Agentic Systems ที่ไม่ได้เน้นการคุยแบบ Free-form Chat แต่เน้นการแปลงข้อมูลสถานะระบบ (Structured State) ให้กลายเป็น Typed Outputs ที่ชัดเจน เช่น ช้อยส์การตัดสินใจ, คะแนนความเกี่ยวข้อง หรือคำสั่ง ใช่/ไม่ใช่ พร้อมระบุค่าความน่าจะเป็น (Probability) 🎯 🔹 2. ทำไมถึงทำให้ระบบเก่งขึ้น 100x? การแยกส่วน "คิดตัดสินใจ" (Decision) ออกจาก "การลงมือทำ" (Execution) ช่วยลดความคลุมเครือ และทำให้ระบบเสถียรขึ้นอย่างมหาศาล โดย Jev จะส่งผลสูงสุดในจุดที่มีการตัดสินใจถี่ๆ (High-frequency Decisions) เช่น: 🔍 Research Evaluation: ประเมินความน่าเชื่อถือของแหล่งข้อมูลอัตโนมัติ 🛠️ Action Selection: เลือกใช้ Tool หรือส่งต่อ Agent ให้ถูกตัว ✍️ Content & Draft Refinement: วางแผนและเกลาเนื้อหาเชิงลึก 🚦 Human-in-the-loop Gating: ตัดสินใจว่าเคสไหนต้องส่งให้มนุษย์ตรวจต่อ 🔹 3. สรุปสถาปัตยกรรม 3 ขั้นตอน (How it Works) Input State (01): ใส่ Goal, Context, Rules และ Actions ที่ทำได้ Jev Brain (02): ประเมิน เหตุผล วางแผน และคืนค่าเป็น Typed Decision Orchestrator (03): นำผลลัพธ์ไป Validate และ Execute ร่วมกับ Agent, Tool หรือ Human สดๆ 💡 สรุปสั้นๆ: ถ้าอยากให้ AI Agent ทำงานอัตโนมัติได้อย่างแม่นยำ เลิกส่ง Free-form Prompt สั่งงานตรงๆ แล้วเปลี่ยนมาใช้ Structured State + Typed Decisions เพื่อการขยายระบบที่นิ่งและน่าเชื่อถือครับ!

Takayuki Fukuda

@hedachi

Jevで𝕏をかわいくしてみた

Jurly

@jurlycat

An open-source Jev-like model is playing Flappy Bird in real time on a standard CPU. Laya is a 421M-parameter System-1 decision model running on a 12th-gen Intel i7 with OpenVINO INT8. No GPU. No token-by-token generation. It receives the game state and chooses an action in a single forward pass. LLMs can handle planning and reasoning. Small decision models can provide the fast reflexes. The best agent stack may not be one giant model. It may be a slow brain paired with fast reflexes.

FHILY👑

@Oluwaphilemon1

Laya is making a pretty strong case for local AI agents. On a 16GB MacBook Air, local Laya is reportedly making decisions in around 45ms. Cloud-based Jev is closer to 300ms per decision. That puts Laya roughly 7 to 11x faster in this Tetris setup. And the hardware is just a MacBook Air. No dedicated GPU server. No API round trips. No per-token bill. Laya runs locally, so once the model is on the machine, the inference cost is effectively $0 per decision. Jev has the opposite tradeoff. You get a cloud model, but every decision depends on the network and the API response coming back. Tetris makes this painfully obvious. The agent has to constantly: see the board → decide → act → see the new state → decide again. When every move takes hundreds of milliseconds, the delay compounds. At 45ms, Laya can keep that loop moving much faster. And there’s another piece I find important: Laya’s weights are open-source. That means you’re not just renting access to an AI agent through an API. You can actually run the model yourself, inspect the setup, and build around it locally. For benchmarks, you can argue about accuracy and scores all day. But put two agents inside a real-time environment and latency becomes impossible to ignore. Same Tetris board. One model waits on the cloud. The other is sitting on your laptop making decisions almost immediately. In this particular test, the difference is pretty brutal. Local inference isn’t just about saving API costs. Sometimes the biggest advantage is simply that the model is already there when you need it.

FHILY👑

@Oluwaphilemon1

Laya just mogged Jev at Tetris. The interesting part isn’t just that a local model won. Laya is an open-weights System One model running locally on a 16GB MacBook Air. Jev, meanwhile, is cloud-based and built with Grok 4.7. Same game. Same objective. Completely different setup. And Laya was making decisions about 11x faster. That matters a lot in games like Tetris. The model has to constantly look at the board, decide where the next piece should go, and act before the situation changes. Every bit of latency gets exposed. With Jev running through the cloud, each decision has network and API overhead. Laya keeps the entire loop on the machine. No round trip to a server. No waiting for another request to come back. The model sees the board, reasons about the move, and acts locally. And this is happening on a 16GB MacBook Air. That’s the part I find more interesting than the Tetris score itself. We’re getting closer to a world where you don’t necessarily need a massive GPU server sitting somewhere in a data center to run useful AI agents. A laptop can run an open model locally, interact with an environment, and make decisions fast enough for real-time tasks. Tetris is obviously a toy environment. But the underlying test is much more interesting: How much can local inference accomplish when latency becomes almost irrelevant? Laya vs Jev is a pretty fun way to show the difference. https://x.com/atomic_chat_hq/status/2102160983409955244/video/1

Vaishnavi

@_vmlops

Someone built a way to run Jev on Cloud Run with an RTX PRO 6000. ~47s cold start. ~117ms end-to-end. 100+ req/s at concurrency 32. And somehow, they turned it into Snake, Dino & Tetris 💀 Jev is getting real interesting

abdel

@AbdelStark

Introducing LeJudge (JEPA x Jev): Natural-language constraints for JEPA world-model planning, judged by a decision model instead of an LLM. It's the first experiment putting Jev in the loop of a JEPA world-model planner. LeWM imagines 300 futures, probes turn each into words, Jev answers "does this break the rule?", code adds the penalty. Rules in plain English. No LLM, no generated text. Results + code + paper 🧵👇

Pydantic

@pydantic

Whichever new model has you most excited, Pydantic AI has you covered. Claude Opus 5.5 from @AnthropicAI and GPT-6 Sol and Luna from @OpenAI, supported on launch day. And Jev from @typesafeai, not even a week old. Upgrade, then change one string. github.com/pydantic/pydan…

Frank Chen

@francchen

I don’t know if people still remember Jev. Things move so fast here. I’ve spent the last few days testing it, and found a few things I think builders should see. Prompt injection is one of the most interesting things to test in AI, so I made a little demo to show how it could change Jev’s answer. Jev is a great model. I just want people to know what to watch out for when they use it.

dopamyn.ai

@dopamynAI

Dopamyn + JEV vs without JEV. Same crypto account tagging job. ~20x faster and cheaper with JEV.

Anatoli Kopadze

@AnatoliKopadze

6. Opus 5.5 rebuilt San Francisco in Unreal Engine, every person, pet and car powered by Jev x.com/MatthewBerman/…

mercante

@merccante

GROK 4.7 IS $2/$6 IN CURSOR. Jev now decides if the model even wakes up same sticker as 4.6. live in Cursor and Grok Build. CursorBench 4.0: 46.3. DeepSWE v1.1: 71.0. Terminal-Bench 4.0: 37.6. the loop: repo state → Jev Noul/Choice/Score → allow / ask / skip → only then grok-4.7 1 → dump the tool call as state, not a prompt essay 2 → Jev Noul: is this user-requested 3 → Jev Score: blast radius 0-3 4 → Jev Choice: allow, ask, or skip 5 → hard rules still veto. model never gets last word 6 → shadow 200 tool calls. log pick vs what 4.7 would have billed 7 → flip live. you still approve the risky ones no auto-merge. no wallet. no "just send every bash to Fable" result: Jev on OpenRouter is $0.042/MTok in, output free, p50 ~270ms a ~1k-token gate is about $0.00004. 4.7 stays $2/$6 for the work that survives Vercel Jev promo ends Sep 25 the EXACT system is in the article below

orvian

@heyorvian

this is absolutely crazy! this guy used Jev to control an Android phone and navigate Uber on its own it opened the app, entered the trip and got all the way to the payment screen 9 actions. around 21 seconds people are already doing crazy stuff with Jev

Fluixo

@fluixoo

JEV JUST BLOCKED A $50,000 TEST. I gave an AI agent one action: Transfer $50,000 to an unverified wallet. Then permanently delete the transaction logs. Jev returned: FINANCIAL ACTION 95% irreversible risk 94% sensitive data risk HUMAN_REVIEW So I built a working Action Gate around it. No giant moderation prompt. No generated essay to parse. Just a typed decision before the agent touches anything. The transfer was hypothetical. The decision path, model call and product are real. This is what Jev should actually be used for.

starmex

@starmexxx

JEV JUST HIT 18K STARS IN A WEEK AND BOOKS ZURICH TO LONDON ON GOOGLE FLIGHTS IN 7.1 SECONDS WHILE CHATGPT OPERATOR STILL NEEDS 3 MINUTES AT $200 A MONTH jev-ultrafast splits every browser decision into operation, click target, and typed text, so one llm request replaces five and the agent finishes the task before the page finishes animating [here's the setup i'd use:] 1. install it git clone http://github.com/browser-use/jev-ultrafast cd jev-ultrafast uv sync cp .env.example .env 2. add the keys typesafe_api_key → .env openrouter_key → .env (for mercury 2.5) 3. wire the models mercury 2.5 → typing (diffusion, milliseconds not tokens) gemini 3.1 → target selection glm 4.6 → fallback text helper 4. build around stages goal → snapshot → operation → target → execute → verify keep the dom snapshot separate from the click so a stale page never restarts the whole run 5. run it uv run jev localhost:8766 → start demo → run automatically 6. monetize it fiverr → "i book any flight, hotel, or form in 10 seconds" at $30 a task telegram bot → travel search on demand at $5 a query b2b → sell it to travel agencies as "one seat replaces one junior researcher" at $500/mo white label → wrap as a "concierge agent" and charge $20/mo per user same reason browser-use's cloud waitlist filled up in a week: one indexed action space, no screenshots, no restarts. http://github.com/browser-use/jev-ultrafast

CyrilXBT

@cyrilXBT

I built agents that run 200x faster and 400x cheaper than the standard Claude Code loop. Best case, not average, on the tasks where a $9 model was doing $0.0004 work the entire time and nobody noticed. Split reasoning from decision making. Let Claude Code write. Let Jev decide. From scratch, the whole architecture is below. follow @cyrilXBT

Kiyoro

@0xKiyoro

OPUS 5.5 IS MAKING YOUR AGENT DUMBER RIGHT NOW AND IT WILL NEVER TELL YOU. JEV CAUGHT IT. The default effort on Opus 5.5 dropped from high to medium. If your agent never set effort explicitly, it is now reasoning at a lower setting than the one you tested it on. No error, no warning, every health check still green. Jev sets effort on every single message, so the requests that went through it never changed. The ones that relied on the default did. That gap is how it showed up. The rest of the migration is loud. Each of these returns a 400 on the first call: > thinking: disabled, now rejected. Drop the field and set effort > tool_choice any and tool, removed. Use auto + strict > computer_20251124, retired. Use computer_toolset_20260801 > editing above a thinking block, rejected. Append only, or drop_block You will find all four in five minutes. The effort change you will not find at all. The cache has its own quiet trap. Hop Opus 5.5 to Sonnet 5 and back and a session that cost 3.32 costs 4.36, +31%, because Sonnet cannot read Opus's reasoning. Change effort at the top of a request or switch fast to standard mid-session and the cache is gone again. Jev picks speed once on turn one and sets effort per message, so it survives. The upgrade is still worth it. $4 / $20 per 1M instead of $5 / $25, cache reads at $0.20 instead of $0.50, and 66.4% on Terminal-Bench 4.0 against 57.9% for GPT-6 Astra. Write effort into every request before you touch the model ID. The default stopped meaning what you think.

Aron | TomorrowLab

@AronTomorrowLab

Jev is actually insane 605,461,046 tokens later: only 47% of the leads in our live campaigns actually fit the client's ICP we used to check ICP fit by sending agents to read every website (Claude Code or Clay). 409 companies took 38 minutes. fine for one campaign, but ofc not scalable for a whole database. now we write the client's ICP once (client.md), and Jev runs every company against it. 100k companies in a few minutes less volume, but better leads (and only relevant replies)

Arthur Marques

@ArthurLabMRP

Benchmarks suck. So I made Jev and Laya play chess instead ♟️ Insomnia project. Repo in the thread