Jev for resume screening — a daily feed

jev is insane !!! 🤯 i built an ai casting system for ecommerce videos with @typesafeai . 199 ai model profiles. 15 product categories. 661 judgments in ~4 seconds in my tests. one brief and it: → scores all 199 profiles for the campaign → narrows them down to 6 finalists → ranks 45 products, 8 hooks and 8 scenes luxury watches? beauty? cars? change the brief. watch the entire shortlist change. the

@CompleteSkeptic Jev-powered resume screening tool , Define job requirements, upload resumes in bulk, and get structured match/no-match judgments — every conclusion traceable back to the original resume text.seconds level finished it. @typesafeai https://t.co/di5H4psVnK

They said Jev can't hallucinate. It just can't say anything that isn't on the menu. I ran 150 résumés through "the frontier model that doesn't write" — 1,800 decisions in 3.35 seconds for 1.4 cents. Then I read the fine print. One guy's trading bot lost $31,680. 👇 https://t.co/2OPxe4hE5N

Is Jev racist? For identical resumes, Jev shows slight preference for black sounding names and against asian sounding names. Very small diff but seemingly statistically significant. https://t.co/yHH2kpgKy7

Jev scans a stack of 100 resumes making candidate recommendations in seconds! try it https://t.co/a4LuOy20Qe

12k+ singapore job listings. 16 questions each. jev screened them for an estimated US$2.84 in model usage. turned the results into a map of what companies keep asking for. https://t.co/hDjmrpOoKv

요즘 뜨는 Jev AI를 활용해서 이력서 합격 확률을 분석해주는 토스앱 만들어봤습니다!! 써보시고 이상한 점이나 개선 아이디어가 있으면 편하게 알려주세요!! https://t.co/PoWcLmb16V #Jev #토스앱 #AI #토스 #이력서 https://t.co/ru2Rey6Wpm

My time with a US-based remote startup recently came to an end. So I made every YC company interview my resume. Jev read it against all 6,245 YC companies in 25 seconds. Cost: $0.37. 156 founders should take my call. The list, the workflow, and the tool 🧵 https://t.co/bjZEWSj9l2

Jev-powered Agent #Jev Built a local resume screening workbench for HR teams and early-stage founders. You define the bar → upload resumes in bulk → it checks every condition → every verdict has a quote from the resume to back it up. Not "AI thinks it's a fit." More like: which conditions passed, which failed, and exactly where in the resume. 👇 demo

The tool is live, bring your own key: https://t.co/ndRrXDRL3p Resume PDF in, your TypeSafe key. Jev picks your criteria from your resume, or type your own: "only climate", "team under 30", "sponsors visas". Nothing gets stored.

Per company, Jev answered: • would the founders take a first call • best-fit role, and am I under/at/over level • stage: dead, too early, seed, growth, late • hires remote from India? sponsors visas? • is the product truly AI-native • which ONE resume line opens the cold email

הארכיטקטורה הסופית שלי: 1) מודל Jev שהוא Screener: מקבל טקסט, ושופט במקביל עבור כל הטקסטים האחרים מה ההסתברות שהם מתארים את אותו הדבר. 2) עבור אלה שהוא לא היה בטוח: מודל Jev נוסף שמקבל זוג-זוג, עם עוד קונטקסט 3) קוד שמאחד את כל הזוגות הרלוונטיים 4) מודל Sonnet/Opus שמקבל את כל אלה שלא היינו בטוחים לגביהם ומחליט 5) קוד שעושה Validation בסוף

One Jev use case with HUGE value 100 people apply for one open role. Normally someone has to review every profile manually. Jev can classify all 100 almost instantly. In our benchmark, it finished in just 0.827 seconds. That was 4.26x faster than the next fastest option. It was also 2.6x cheaper in that test.

話題のJev（TypeSafe System One）を、法令検索「条文くん」の本番に入れました。 条文くんでは、検索で集めた候補の中から「この質問に本当に必要な情報か」を判断する処理に使っています。 こういう処理はLLMでもできます。ただ、検索では候補が100件、200件と出ることもあり、その一つ一つをLLMに判断させると時間もコストもかかり、判断も揺れます。Jevは文章を生成するのではなく、決められた問いに対して判定することに特化しているので、こういう細かい判断を大量に安定して入れる用途とかなり相性がいいです。 実際に使ってみて良かったのは、単に速くて安いだけではなく、「検索結果として近いか」ではなく「この質問にこの情報が必要か」という判断を直接させられるところでした。 ただ、「なんとなく良さそう」という理由では本番には入れていません。条文くんでは検索ロジックを変更するとき、基本的にベ

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 stil

自己做了个小工具：资金费套利组合筛选。 看哪边做多/做空、扣费后大概剩多少、为什么先看这组。 还很早，网页功能先免费。欢迎挑刺、提需求。 https://t.co/8AIzbZHKHs 用了 @typesafeai 的 Jev，支付接 @0xinfini。 有 Bug 或想法回这帖也行，TG：https://t.co/ZiQcVYMeZA https://t.co/eBTB9tOC0N

I read this post in reddit - i went for freshers interview but they were asking for experience ... so i built something which can help students when they go for interview and i used jev api to buils this.. https://t.co/NdpP1M7sp1

I combined ChatGPT browser + JEV for job portal applications. The result? 2-3 Times faster results compared to Browser mode only https://t.co/b3GzCmDIAp

Sígueme, comenta JEV y te mando el link (gratis hasta el 25) Ordena 100 entrevistas en 12,8 segundos: pasa / reserva / descarta + scores. Coste del ejemplo: 0,005 $. ¿Útil o preocupante? #Jev #TypeSafe #IA #Contratacion #RRHH https://t.co/PtATCsg9Ws

Spent some time testing #Jev from @typesafeai on a use case I care about: matching a resume against real job descriptions. I built a small demo called Rolewise. @metix_ai handles job retrieval; Jev handles the fit decision. The setup: - Parse the resume and generate editable search filters. - Retrieve jobs and full descriptions through the Metix AI API. - Send each resume–JD pair to Jev, with 20 r

New @typesafeai Jev + @ExaAILabs use case We manually triage every candidate that comes through @Getthecheckpod's recruiting pipeline, which some days is 100+ To test Jev's decision-making, we had Exa enrich each LinkedIn profile, then Jev scored whether to schedule an intro call + rated candidate strength 0-2 Within seconds it triaged 400 candidates for $1.80 ($1.75 Exa, $0.05 Jev)

I recently got access to Jev by TypeSafe AI. They have several real-time use cases, including resume evaluation. So, naturally, I had to test it. I submitted two versions of my CV: • One from the pre-AI era • One from the post-AI era It was fascinating and a little funny to compare the scores. Can you guess which one is which?

@billlee i know https://t.co/cjObaGSJEP is a game changer right! Here's the free resume scanner tool powered by Jev - in action https://t.co/a4LuOy20Qe https://t.co/GtmCa9e1mg

Built a small tool with Jev AI that analyzes your chances of passing resume screening. I tested it on applications I actually passed and failed, and the results were surprisingly accurate. Still in beta — limited to 5 uses per day. https://t.co/11WjQniMH0 #Jev #AI https://t.co/2oz5jYrBL7

با #jev یه ابزار ساختم که رزومه رو در مقابل شرح شغلی می‌ذاره و تو کسری از ثانیه امتیاز دقیق می‌ده. (همون ATS) سرعتش دیوونه‌کننده‌ست. https://t.co/cLD6Rewf65

Jevで履歴書の一次スクリーニングを試してみた。「必須条件が書類上そろっているか」と「一次通過か、人が精査するか」は別判定にした。必須条件の判定が用意した答えと合ったのは50.0%なのに、通過／精査の判定は84.2%まで出せた。良い候補を見送る方が痛いので、自動で不採用にはしない、というのが今回のポイント。 サンプルは合成76件・プロンプト第4版。候補が職種にどれくらい合うかの段階判定は、ぴったり一致と1段階ずれまで含めると92.1%。 個人情報の実データは未使用。実務で使うには、業務担当者との期待値のすり合わせ、リアルなデータでの検証、個人情報の取り扱いの検討がいる。 この結果だけで使える・使えないは判断できない。 詳細は記事を参照。https://t.co/5p3127vfsm #Jev

Quick test of JEV in finance applications. Early proof of concept. Brute-force peer selection, ranking 6K+ potential companies in ~40 seconds, with detailed criteria behind each score. 👇 #JEV #FinTech #CapitalWorkbench https://t.co/BRJZAiuLFP

Jev can't write a single word. So I gave it two real company jobs instead. - labels an inbox, 50 emails in 2 seconds - screens candidates, ranks them by fit Built both. Full video below 👇 cc @CompleteSkeptic @typesafeai https://t.co/kNiAOrPckU

Used Jev (@typesafeai's decision model, via OpenRouter) in production on likethisgame, my game recommendation site. It rates candidates before the LLM writes recommendations. Write-up with 5 small A/B tests and the cost breakdown: https://t.co/FLFsg8n1Lp

Paste your resume and the job you're applying to. Get back what the job asks for that your resume doesn't prove - with a number on each one. No score out of 100. Those are fakeable. This is evidence. Built on @typesafeai Jev. https://t.co/461mbCacwD #jev #ai #resume #ats https://t.co/ckO8opS9Yd

I combined ChatGPT browser + JEV for job portal applications. The result? 2-3 Times faster results compared to Browser mode only https://t.co/y6vP9bHXSf

JEV、すごい。面接議事録100人分（架空データ）を渡したら ・12.8秒で全員を「通過／保留／見送り」に仕分け ・専門スキル、コミュニケーション、志望度をスコア化 ・「重大な懸念あり」の議事録も検出 コストは全部で$0.005。1人あたり0.01円以下。 文章生成ではなく評価・判断専用モデルなので、速くて安い。 採用文脈なら「どの部署に適性があるか」の判定、一次スクリーニングの補助、面接官ごとの評価のばらつきチェックなど、使い道が広そう。

jev kullanarak t24'ün daron acemoğlu ile yağtığı röportajı sınıflandırdım. toplamda 3.7 saniye sürdü. yarım saatlik röportajın neresinde duygusal, neresinde tartışmalı konular konuşulduğu çıkardı. nerede anekdot, komik bir şey varsa onları belirledi. nerede bir iddia varsa onları işaretledi. jev'in kullanım alanına çok güzel bir örnek daha

用JEV做简历筛选这个案例确实有趣且实用，我自己一vibe 了这个实验。500份面试，每份纪要都让 Jev 分别回答 6 个问题，一共3000次判断，用时 31.4 秒，估算 $0.03173 一次请求详细字段见评论 https://t.co/mblXTmbska

Built a jev demo for finance Screens 10 teasers against investment criteria in 15.9 seconds for $0.0106 https://t.co/eHquha80gE

中途採用の書類選考について、JevとClaudeでそれぞれ評価し、処理速度の差を比較してみた 100人分の書類の判断に、Jevが4秒、Claude(Haiku)が20秒と5分の1の時間で完了 それだけでなく、コストはなんと1円、、、30分の1の値段でした それで評価ほぼ変わらずとなかなか利用余地ありそうです オカムラさんの動画を参考にさせていただきました！