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Introducing Jev Model Router for Claude Code This Claude Code Mod lets you use Jev through its direct TypeSafe AI API or Vercel AI Gateway With every request you send to Claude Code, Jev classifies the subagent model, main model (only at session start to avoid breaking the cache),...

209,112 次观看 • 1 个月前 •via X (Twitter)

35 条评论

EJ Campbell 的头像
EJ Campbell1 个月前

@typesafeai @vercel Holly uncached tokens, batman.

VkDream 的头像
VkDream1 个月前

@typesafeai @vercel 上线没几天 就从新模型变成路由组件了 生态长出来的速度有点猛

James Malsawm 的头像
James Malsawm1 个月前

@typesafeai @vercel We need Router to use Jev inside Claude code, what about Codex, bro?

arpit 的头像
arpit1 个月前

@typesafeai @vercel Prompt caching 🪦🪦

Timur Yessenov 的头像
Timur Yessenov1 个月前

@typesafeai @vercel Keeping main-model routing off by default is the right call. A cheaper tier stops being cheap if every switch throws away a long prompt cache.

wiiiimm 的头像
wiiiimm1 个月前

@typesafeai @vercel and wave good bye to the cache and your tokens.

Hussain Hashim | Building SundayBack 的头像
Hussain Hashim | Building SundayBack1 个月前

@typesafeai @vercel @dani_avila7 interesting approach with the Jev Model Router. curious, how does it handle model selection with varying workloads? handling this balance is often a tricky part in my builds too.

Aapakari 的头像
Aapakari1 个月前

@typesafeai @vercel Log routes, or bills stay blind guesses.

Bessi 的头像
Bessi1 个月前

@typesafeai @vercel wait Jev picks effort per request.

Jhon Dennis 的头像
Jhon Dennis1 个月前

@typesafeai @vercel 每条请求自己分流 子任务走便宜的 主模型只在开局定一次 缓存也没被打断 今晚先把这条路由装上试试

Lennox 的头像
Lennox1 个月前

@typesafeai @vercel 路由的关键不是省一次调用,而是把模型选择变成可观察策略:成本、延迟和质量都要有回放评估。

Jeremy Bosma 的头像
Jeremy Bosma1 个月前

@typesafeai @vercel A router mod is the practical layer

Aaron Browne-Moore 的头像
Aaron Browne-Moore1 个月前

@typesafeai @vercel I feel like this is going to be really good as long as you've benchmarked each model against the task that's going to happen and Jeff has data to work from when choosing which model to pick. Otherwise, it feels like it's just guessing without bringing true value to the decision.

Mildly Magical 的头像
Mildly Magical1 个月前

@typesafeai @vercel This is awesome!

Jeff Bruchado 的头像
Jeff Bruchado1 个月前

@typesafeai @vercel Does it expose why a model was picked? I'd want that context when debugging a bad result.

Steven Cheng 的头像
Steven Cheng1 个月前

@typesafeai @vercel Caching at session start is the smart move.

The AI Therapist 的头像
The AI Therapist1 个月前

@typesafeai @vercel Context window cost is a function of length squared. routing cheaper models for routine queries cuts that curve before it spikes. finally, ai that reads like it saves money instead of burning cash.

Djasnive Rajaona 的头像
Djasnive Rajaona1 个月前

Routing is the honest answer to 'which model is best' — it depends on the step. The hard part nobody ships well is classifying the task before spending tokens: most routers guess from the prompt instead of the diff. Curious how it handles mid-task escalation — when the cheap model is 80% through a refactor and starts hallucinating.

Salise 的头像
Salise1 个月前

@typesafeai @vercel the installation command looks super simple, just npx claude-code-templates@latest. nice!

Isoldegwow 的头像
Isoldegwow1 个月前

@typesafeai @vercel Every request now has a middle manager deciding which model deserves the effort. Corporate structure is unavoidable.

Jack Rudenko 的头像
Jack Rudenko1 个月前

@typesafeai @vercel or you can just to route to any model, includign Jev (when they will provide me access to debug it).

Zane Kelly 的头像
Zane Kelly1 个月前

@typesafeai @vercel Model routing inside Claude Code feels like the adult version of “let’s just try another model”—especially if the route is visible in the logs. Otherwise the bill arrives with a plot twist.

Gabe Fletcher 的头像
Gabe Fletcher1 个月前

@typesafeai @vercel You're a rockstar!!!

dazacode 的头像
dazacode1 个月前

@typesafeai @vercel inb4 T3 start complaining

Valentyn Kit 🦀 | Rust · Solana 的头像
Valentyn Kit 🦀 | Rust · Solana1 个月前

@typesafeai @vercel classifying per-request without breaking the prompt cache is the actual hard part, nice that it's called out explicitly

Steven Cheng 的头像
Steven Cheng1 个月前

@typesafeai @vercel Smart caching strategy. Classifying the main model only at session start is a clever way to keep latency low.

Brjan | AI Builder 的头像
Brjan | AI Builder1 个月前

@typesafeai @vercel classifying the subagent at the start could add unnecessary complexity

Ajay Dhillon 的头像
Ajay Dhillon1 个月前

@typesafeai @vercel Amazing 🤩

Tanguy 的头像
Tanguy1 个月前

@typesafeai @vercel the install is easy. if you never log why it picked that effort the next bill is a guess. i stamp the route next to the tool calls

Nishanth 的头像
Nishanth1 个月前

@typesafeai @vercel The core challenge with model routing is managing state persistence across agent calls. If the subagents operate stateless, ensuring context history remains correctly scoped requires careful serialization and rehydration after each classification step.

ShadowAguy 的头像
ShadowAguy1 个月前

@typesafeai @vercel Routing the main model only at session start is the interesting constraint. Does Jev still adapt when a subagent's task suddenly needs a much larger context?

🏁Mickey Shmueli🏁 的头像
🏁Mickey Shmueli🏁1 个月前

@typesafeai @vercel Cool but why don’t just make a hook and use your own subscriptions haiku?

BaronAfanas 的头像
BaronAfanas1 个月前

@typesafeai @vercel Pretty cool, will check it out, perhaps you can default it to your vLLM?

John Rood 的头像
John Rood1 个月前

@typesafeai @vercel the cache detail is the whole game. next step is rerouting at explicit task boundaries, because long agent sessions change jobs without changing chats.

Hafiz Siddiq 的头像
Hafiz Siddiq1 个月前

@typesafeai @vercel @grok does adaptive subagent routing win mainly on cost, latency, or fewer broken handoffs—and what metric would prove it survives a long coding session?

相关视频

how to use claude code mods like a top 1% user, step by step: give this to your agent before everyone catches on👇 1. set up Jev connect Jev to the model registry you want to use. check that the connection works and the listed models are available. 2. build your claude code mod open claude code 2.1.287 or later and paste this prompt: “build a mod called run-ledger. load plugin-authoring and use the API types for my installed version. create a dashboard that shows: - estimated cost per run, model, and source plugin where known - which model handles each task - each subagent’s status, latest action, and elapsed time include token counts, cache usage, and reported retries. count each request once. keep background tasks linked to their original run. use dated prices. label costs as API estimates, not subscription charges. show unknown when data is missing. connect the mod to my existing Jev setup. give Jev the task, available models, prices, budget, and relevant past results. ask it to recommend a model and explain why. start with recommendations. make automatic routing optional for eligible subagents. show the recommended model, actual model, result, and cost. include Jev’s own cost. keep state across hot reloads. add details and export. the dashboard itself must make no model calls. validate the plugin. test rendering, costs, attribution, and duplicate counting. give me steps for a live test.” 3. test the setup allow hot reload when prompted. run a simple task, a subagent task, and a mod-triggered model call. check that the dashboard updates, each request counts once, and Jev’s recommended model can actually run the task. 4. install the working mod ask claude to copy it out of the temporary folder and install it as a persistent plugin. see the cost. choose the model. check the result.

Avid

47,276 次观看 • 1 个月前

Jev has been blowing up lately. If you've got the Jev API but don't know how to play around with it yet, you can just copy this checklist. 1. jev-ultrafast A high-speed browser Agent built with Browser Use. Jev only judges "what to do, which element to click" at each step, and only calls the small model when typing is needed. Searching for a flight on Google Flights takes about 7 seconds. 2. fast-jev-compaction Context compression for Claude Code. Before each tool call, have Jev judge if there's anything still useful; delete the useless stuff, and keep the original text without rewriting it. 3. json-render Vercel Labs' generative UI framework. In experiments, Jev doesn't write JSON token by token; it just handles selecting components, properties, and layouts. 4. typesafe-mcp Best for people who just got the API. Plug Jev into Claude Code, Claude Desktop, Codex, and Pi, and do Choice / Score / Noul anytime. 5. jev-mcp Ready-made Agent judgment toolkit: fact-checking, content screening, semantic ranking, classification, and information extraction. 6. SemDecide Turn Jev into a command-line tool. Directly classify, score, and filter in the Shell—great for hooking up to crawlers, CI, and data pipelines. 7. jev-codex-router First have Jev judge how hard this round of programming tasks is, then decide the model tier, reasoning depth, and speed mode. 8. Winnow Context garbage collection for Claude Code. When Read / Bash / Grep spits out a ton of stuff, Jev first judges which parts are really relevant to the current task. 9. jev-review Before code review, run it through Jev first to pick out high-risk changes, then hand them off to a pricier big model or a human. Comes with a local dashboard. 10. Blink Use Jev as a code repository navigator. At each directory level, judge which files are most relevant to the current issue, then keep digging down. Copy these complete Jev blueprints - then read full Jev setup below ↓ ↓

rody

204,901 次观看 • 1 个月前

People already built a browser agent, a Claude Code bridge and a LangGraph router on top of Jev. Here are all 10 repos. If you have API access and don't know where to start, save this: 1. jev-browser Real browser control through MCP, CLI or as a library. At every step Jev answers two things: which element to touch, and whether the goal is done or the run is stuck. > 2. jev-browser-agent Picks the operation and the target in a single request, around a second per step. Anything under 0.6 confidence goes up to a bigger model or back to you. > 3. typesafe-jev-bridge OpenAI-compatible bridge with zero dependencies. Typed yes/no, choice and score answers inside Claude Code, Cursor, Cline or any OpenAI SDK, via CLI or HTTP. > 4. pilot-typesafeai-jev Jev wired into LangGraph and Deep Agents. Three questions in one request, plain code chooses the route, and the chat model only spends tokens on the route that won. > 5. building-with-jev-skill Agent skill for writing code that calls Jev. How to phrase questions, structure state, set thresholds, and debug a question that keeps answering wrong. > 6. jev-skill Guided setup and eval workflows. Checks what's installed without changing anything, never swaps your provider behind your back, never makes up probabilities. > 7. awesome-jev (yibie) What people actually shipped with Jev, sourced and sorted by category, with the limitations written down for each entry. > 8. awesome-typesafe-jev SDKs, live demos, agent tools and independent evals. Shows where every vendor number comes from. > 9. awesome-jev-by-typesafe Use cases, patterns and starter code backed by evidence. The 70 to 500 ms latency figure is labeled vendor-reported, not measured. > 10. awesome-jev (cobanov) Biggest catalogue so far: SDKs for Go, Java and JS, MCP connectors, guardrail experiments and studies. > Copy these complete Jev blueprints - then read full Jev setup below ↓

Asteri

16,629 次观看 • 1 个月前

this is f*cking gold 20 GitHub repos with 500K+ combined stars that will level up your JEV workflow AGENTS > jev-ultrafast: Jev picks every click and DOM target, a small LLM only types > hermes-jev-skills: routing, memory, compaction and skill picks in one pack > typesafe-computer-use: OCR reads your Mac screen, Jev picks the next click MEMORY > fast-jev-compaction: scores every tool call keep, truncate or drop instead of summarizing > jevmem: project memory for Claude Code, Cursor and Codex, updated every turn > jev-second-brain: your Obsidian vault, Jev judges which notes duplicate, revise or contradict SAFETY > jev-guard: a gate before every tool call, Jev scores the risk, you set allow, ask or deny TOOLS > skills: the official TypeSafe skill for Claude Code and Codex > system-one-adapter-python: dry-run your questions on an ordinary LLM before you burn a Jev key > jev-mcp: claim checks, screening and ranking as MCP tools > typesafe-mcp: plug Jev into any MCP client > json-render: Vercel's generative UI, where Jev picks the components OPEN MODELS > SemIf-OpenJev: semantic ifs from frozen open models kev: Jev-like models on Qwen that run on your MacBook > laya-mlx: the Laya decision engine on Apple silicon > clm: an open System One model with Choice, Noul and Score > jevlike: train your own Jev-like model TRADING > jev-trader: one buy or sell decision per Monad block START HERE > awesome-jev: the biggest map of everything built on Jev > awesome-jev-by-typesafe: use cases, patterns and starter code bookmark it before your next build

NO1ennn

18,445 次观看 • 1 个月前