Triage & routing

I built a CLI that sorts a folder by what each file actually says, using Jev from @typesafeai. 2,225 BBC news articles, anonymous filenames, sorted into 5 topics by content in 4.9 seconds for $0.05 at 97% accuracy. Fast & cheap 🚀 https://t.co/WQBzwGRcoD

Jev made our Slack agent 2x faster ⚡️ Our agent can be quite slow because it needs to read skills and figure out which tools to call. We used @typesafeai's new model to speed this up by first passing it the prompt and classifying the best skill, tool and params to use before handing it to the agent

Jev + deepseek v4 flash 非常棒的组合，我把我的项目，一堆意图识别的东西用 Jev 重构，效果特别好，也特别快，非常省钱。再搭配 DeepSeek V4 Flash 的速度，简直快到飞起。 https://t.co/gybIxcVUzO

Jev will be super helpful for agents to make split second decisions in workflows, data classification, judgment calls, and hundreds of other use-cases in the enterprise. Here's a quick demo with Box and Jev to make that real. The demo pulls an incident report from Box, asks whether it's customer-facing and how severe it is, moves the file into escalate, monitor, or review folders, and sets a metad

Tried Jev for this use case : On one side: a JD → extract the most important requirements. On the other: upload multiple resumes → see which candidate fits the JD best. Jev was surprisingly fast at this. I will test this model on a few other use cases I have in mind. https://t.co/b0IkQ3zDjM

Jev + Higgsfield = solved auto-routing for genAI models. In this demo, @typesafeai’s Jev evaluates prompt and picks the most fit models for video and image generations on Higgsfield API. https://t.co/MyTRdqlZ3o

What does it cost to sort 1,700 emails with Jev? I gave JEV each full email object: - Subject - body - sender No special changes. Jev sent back 4 values for each email: - Category: work, shopping, finance, security - Priority: low to urgent - Spam score: a percentage - Reply score: how much the email needs a reply from me One user wrote that their account had a violation. JEV gave it a 90% reply s

The ideas have been flowing the last 48 hours. I built a way to load and classify YouTube comments with Jev to understand which ones need my immediate attention 🎉 https://t.co/cKOFKv1ZjX

人気配信者のコメント欄もAIが監視する時代に！ 判断特化のAIモデル「Jev」を、配信コメントの自動モデレーションを試してみました！ コメントごとに「問題なし／要注意／10分停止／1時間停止／永久ブロック」の5レーン振り分けと スパム・人格攻撃・脅迫・個人情報の該当確率、視聴者の感情の判定を同じ1リクエストで取っています 確信度が返ってくる0.5未満は自動処分せず要注意に回す運用も可能でした 例えば、平均同説5~10万人ほどの超人気配信者が5時間の配信を行った場合、50万件ほどのコメントを捌いたとして常時回しっぱなしでコスト試算は$30~$50ぐらいになるイメージです 実務上は信頼性が重要になるので、安全弁的な措置はもっと必要ですが、実用性がありそうな気がしました

I've been playing with jev and I've made this fun model router, a bit like Tinder. Meet Consort → https://t.co/GNsnu0i5Tb I'm super interested in what your input and what your output is like. https://t.co/yqRuiG7Fpq

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