Jev for email triage and support tickets

Riley Brown

@rileybrown

Yeah Jev by @typesafeai is very cool. It classified 500 emails in seconds. And it costed 3.5 cents.

vogel

@ryanvogel

this model is actually insane at email classification i tested it on 1500 of my own emails to see how well it works and I am blown away

Hassan

@nutlope

Jev + Kimi K3 for fraud detection! TLDR: Jev classified 100 emails in 1.42 seconds, then I routed the uncertain cases to Kimi K3. The full pipeline got 96/100 correct for only ~$0.07. Video is not sped up, check out the live run! Here was my process: I gave Jev 100 emails to classify (a mix of 50 legit & 50 fraudelent emails). It classified all of them in 1.42 seconds. An underrated feature about Jev is it will give you the confidence score for a classification, so I routed any prediction under 95% confidence to Kimi K3 to be fully sure. 31 emails fell below that threshold. After routing those to Kimi K3, the combined pipeline reached 96% accuracy. The full run took 16 seconds & ~$0.07 in inference costs: - $0.068 from Kimi K3 on @togethercompute - $0.003 (1/3 of a cent) from Jev on @typesafeai. I think this is a really interesting pattern: use a fast specialized model like Jev for the narrow task, then route the uncertain cases to a larger LLM. I feel like this kind of approach could be a game changer for use cases like fraud or anything realtime. You can use the speed & low cost of Jev while having a larger LLM as a fallback to ensure high accuracy.

John Yeo

@johnyeo_

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

nader dabit

@dabit3

Jev is really good at intent-based search! How it looks in Gmail: (for a huge inbox you'd prob let semantic search / embeddings pull first but still much better experience)

Malek Ould-Oulhadj

@malekoo

First @typesafeai use case, live in our Mac app: setup and troubleshooting help when no model is loaded. Model downloading, load failed, API returning 503, phone won't pair: the user asks, Jev reads the question with the whole built-in manual as state and decides, with probabilities, what it is and which article answers it, or that nothing does. The app then shows the real documentation and live status. Jev decides, the app answers from its own docs. No model loaded, nothing invented. 42/42 on a held-out set: paraphrases, typos, French, German, Spanish, features that don't exist, follow-ups. Median 0.93 s. Great breakthrough by the TypeSafe team. Thank you.

t0t0

@t0t0_build

Un JEV me está monitoreando más de 25 grupos de whatsapp con clientes en tiempo real y evaluando si tengo que saber algo por urgencia problemas, pedidos irresueltos etc. Si tengo algo que saber, le avisa a una LLM que me escriba. 🔥

Versun

@VersunPan

刚看了下这个jev wechat项目，模型是用 jev + deepseek 通过 Android 无障碍服务读取App 的节点树，兜底是用截图 + 本地 OCR 上下文只有最近 10 条消息 微信能监测到，有封号危险

Zohaib Tanwir

@zohaibtanwir

Jev is incredible - built an email classifier that is capable of classifying 50 emails in under 2 secs. That too, when Jev is hosted in us-west, and I am in India. Idea credit - @ryanvogel @typesafeai #Jev

Yujie Zha

@zhayujie

Had CowAgent build a batch ticket analysis tool on Jev. Jev doesn't generate text. You send content and typed questions, it returns classifications, scores and booleans, each with a probability distribution. One call per ticket, 7 judgments, ~500ms.

日本で一番社長にAI研修する21歳

@zeetakou

今、GPT Live APIを使って作っているテレアポ用のAIに、Jevを組み込んでみています。どの判断を任せると一番活きるのか、実際に動かしながら検証中です。 僕ら、AI研修だけやってると思われがちなんですが、実はこういう開発もかなりやってます。リストと営業シナリオを設定するとAIが自動で電話をかけ、見込みがある相手だけ担当者に転送。通話結果や要約、会話ログも残せます。 ここにJevを入れて、「この相手は見込みがあるか」「今、担当者につなぐべきか」「再架電するなら優先度はどれくらいか」といった判断を任せたい。電話中や通話後に何度も発生する細かい判断とは、かなり相性がよさそう。 研修で教えるだけじゃなく、自分たちでも毎日作って試してます。日々研究。こういうのが一番楽しい

よたん

@yo_ta_n

Difyで作った身内用LINE Bot。家族だけはLINE ID判定して自宅のライトやエアコン操作がチャットから行えるように対応できた。極力コストを減らすため分類器はJev、チャット応答はLunaを選択。#Dify #Jev #ChatGPT #LINE

MajdAICode

@MajdAICode

One coding-agent pattern I’m exploring: /ticket-triage asks Jev where to look; Cursor checks the repo; Jev flags possible test gaps after the diff. PHPUnit/Playwright verify behavior, and I review before commit. Video: x.com/MajdAICode/sta…

Edwin Mesa

@edwinfmesa

Explorando qué se puede hacer con Jev y Laya, que (los de moda), me pregunté si podrían ayudar a un agente de soporte a preparar el contexto mientras el usuario escribe su mensaje. El contexto es clave para un LLM, así que armé una startup ficticia con 7 documentos. 🧵

web5kol 48club

@web5_kol

Jev微信聊天助手已开源，可作为手机端对话副驾，帮助用户分析微信群聊和私聊意图。 Jev通过无障碍服务读取屏幕（非侵入式），结合AI模型判断对方真实意图、危险等级，并生成3条候选回复，支持一键填入但不自动发送 x.com/li9292/status/…

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…

nikhil mudholkar

@nikhilmudholkar

I put OpenAI’s new Decisions API through the same 1,565-email benchmark I’ve been running on Jev, Perplexity and Clef. 6 models from 4 companies now. Perplexity v1.1 came out best overall 🚀 OpenAI was the fastest⏱️ But the confidence behavior is still the most interesting part to me. Jev was the only model that said “≥99% sure” hundreds of times without getting one of those answers wrong. Its the cocky guy in the class who is also right most of the time🥲 Clef has massive imposter syndrome! it's much better than its confidence suggests. And Perplexity changed a lot in just 4 days. v1 gave just 3 answers at ≥99% confidence. Four days later, v1.1 gave 323. It also got slightly more accurate and cut the price in half. kudos @AravSrinivas and @perplexity_ai for making such massive strides and shipping so quickly! There's no doubt now that @typesafeai has opened a pandora's box and as @CompleteSkeptic said, forked a new branch in the AI timeline with these decision models!