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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

Jev can outperform the rules many companies use to prevent fraud. We found that without any pre-training, it accurately detected 93% of a fraud ring. In contrast, an LLM in a similar set up only achieved 62%. https://t.co/ronCD7hLvb

I just classified a month of my Gmail for eight hundredths of a cent. 150 emails → 9 folders. Read-only, nothing written back to my inbox. Built with Jev. Repo below 👇 https://t.co/PaUcxpcDE1

Using JEV by @typesafeai to classify the 100s of inbound deals we get, side-by-side with a small open model we’ve been using for our in-house AI rating module that self-trains. impressive how close JEV gets while being significantly cheaper and faster to run for classification :) https://t.co/6c6I9SHQuY

There's a fun mix of excitement and cope on my timeline re-Jev: - OMG I made Jev a year ago this is dumb - You can fine tune this encoder into a better, faster, local classifier, etc Re the "just train your own small classifier" crew - Voyage trains great, SOTA pointwise rerankers that are obviously specialized to the reranking task and have been at it for years, likely also using a strong synthetic data recipe... Exact setup in the response tweet. Do note that using Jev is definitely more expensive than using Voyage rerank-3 bc of the tokens in the lengthy rubric included in every request (t

Built a Claude Code mod using Jev that watches what you're asking and routes the turn to the right model. Quick renames go to Haiku. Real implementation? Opus. Architecture planning? Fable. @claudeai @ClaudeDevs @bcherny @lydiahallie @trq212 https://t.co/DgIgIGpHLq

Built a Ticket Triage Copilot using Jev (@typesafeai) instead of a general LLM for support ticket classification. 🔗 GitHub: https://t.co/zQ9oB7t7Eq

I think Jev really flourishes inside your code more than in the actual product. Right now, I’m using Jev to classify the thinking mode of our AI agent, so it can select a high, medium, or low mode depending on the prompt.

Built an AI dispatcher for Zammad tickets using Jev (TypeSafe AI). New ticket comes in, Jev decides which team owns it with a confidence score attached. Not a chatbot, a structured decision. Bridge is a tiny Ruby/Sinatra service. Open source, MIT. https://t.co/bXdJpGWHuw #jev

Built an SLO-aware inference router using Jev It uses Jev as a typed decision model to select the optimal LLM for each request based on predicted quality, latency, cost, and live backend load Releasing full walkthrough video soon https://t.co/XUjKj9Ryyn

Built jevmetrics — an OTel Collector component using Jev inference to score metric relevance, redundancy, and keep/reduce/drop decisions. Fail-open, with a deterministic policy still in control. https://t.co/1zLhyNjoEp #OpenTelemetry #Observability #Jev #Typesafe

everyone's using jev to route between models. we put it on inbound sales replies the question isn't which model handles this turn, it's whether "sure" means yes. ours used to log that as a rejection and bin the lead [N] real replies through ai gateway. [x]ms p50, whole batch cost [z] papercut on the way: experimental_evaluate doesn't exist in ai v6, evaluation modality is v7 only, and nothing on the gateway model page says so. @vercel worth a line

i just shipped a small repo that will help you understand it perfectly.its a Real-time support triage + response bot using Jev and cerebras https://t.co/l4gFvr0GdW PS if you like it,approve my entry to the hackerhouse. I applied with rewantgoenka87076@gmail.com

exactly!! just shipped a real-time support triage + response bot using Jev and cerebras. https://t.co/l4gFvr0GdW

youre right. For example in a support ticket jev can classify way faster that which agent should get the ticket. I built a Real-time support triage + response bot using Jev and cerebras https://t.co/l4gFvr0GdW

just shipped a Real-time support triage + response bot using Jev and cerebras: https://t.co/l4gFvr0GdW this should help you get started!

I just built a Real-time support triage + response bot using Jev (from @typesafeai) and @cerebras Jev routes the ticket to a specialist agent (general / account / billing / technical) and decides whether a human should take it instead ,Cerebras then drafts the reply using whichever agent Jev picked. Repo: https://t.co/p6y4IQ2VhZ @callmeshuklaji @communidiyi @hackgoofer @dgo_almeida @sydneyrunkle

Jev join the race 🔥 Jev read the top 250 Hacker News stories 250 stories 2000 typed judgments in 16 seconds cost: $0.0095 about AI 32% Negative Tone: 41 ( 16% ) Built with Jev by @typesafeai https://t.co/RY3GiLTks9

DiffJury — TypeSafe Jev PR risk router + code review coach.

Automatic model routing for Pi using TypeSafe's Jev through Vercel AI Gateway.

Typed, confidence-aware agent skill routing with TypeSafe Jev.

TypeSafe (Jev) skill routing for Hermes Agent: names the one skill worth loading, before the model call. Opt-in, stdlib only, ~$0.001 per routed turn.

MCP server for TypeSafe Jev: typed classify, score, check, match and screen for any agent, with confidence on every answer.

Semantic tool routing and typed System One decisions for the Pi coding agent using TypeSafe Jev.

Route HTTP requests by meaning. A semantic router for Hono powered by Jev.

Per-turn model & reasoning routing for Codex, driven by Jev (TypeSafe System One): picks the model, thinking depth and speed mode for every turn.

A lightweight Jev-powered router for models, tools, and subagents.

Route to the cheapest model in claude code for your task using jev-router.

LlamaIndex reranker + router powered by TypeSafe Jev — typed scores/choices, cheaper than LLM-as-judge.

Neon Function proxy for the Neon AI Gateway with TypeSafe Jev routing.

Async LangGraph workflow that gets a typed Jev Choice (invoice or general) and routes each inbound email to the matching handler.

I made an experimental model router for @pidotdev using Jev as the decision maker. It's not a generic "pick a cheaper model" router. It isolates work into logical threads and prices cache reads/writes, cold context, expected output + the cost of switching back. @CompleteSkeptic @typesafeai This wouldn't of been possible without Jev, it's an amazing primitive to make software with 💪🏻

Using Jev from @typesafeai to improve matching performance, accuracy and cost in our automated @blackbaud RE NXT CRM integration. https://t.co/TVdfZOa3wI

Doc-OCR router using Jev @typesafeai A Jev-powered router that looks at a PDF page by page, decides which pages actually need OCR, extracts the rest locally. Result: save cost on # OCR pages + speed https://t.co/ZjXqHSjSGh

my video on the routing tool blew up on YT! i crossed 10k views in less than 19 hours, i still can't believe it - my most viewed video till date yayee have been consistently doing YT and content in the ai space for last 7 months and finally get to feel this😇 i built this tool yesterday called agent router using jev + herdr, i was awake until midnight doing recording, and publishing the video. today it feels all the efforts are worth it

Put together a quick video of using Jev with ReqLLM I cover the new `evaluate/4` method, why I went that route, and make a real API call to Jev to classify an issue https://t.co/wvSoeI07AD

IRS O*NET job classification using Jev (1,016 possibilities) Query: "I scoop scoops and sprinkle sprinkles" Result: 35-3023.00 Fast Food and Counter Workers https://t.co/SSXUqQwNo3

I'm using Jev (from @typesafeai) to categorize expenses for my company! Our office manager used to do this. It's all in Mongolian and we have to do it using bank records. It is very unique to our company, and not something a software would handle easily. We also have about a years worth of Excel files (training data) that I'm using to give Jev guidance on classification (few shot style). It really works, it's stupidly cheap, and when it's not confident it says so. Pretty great! This isn't anything new, classification in ML is extremely "solved". But this is a general classification model