I rebuilt my job crawler with Jev. The task: start at a company's official homepage, find Careers, and identify jobs that match my profile. Before, with an LLM: ~5 minutes. After, with Jev: just over 20 seconds in my test. Every company organizes its website differently. Jev Show more
CommunityCookbooks & Demos
3,518 intern applications checked in 4:54
Alan's team built an intern-review console on Jev after 4,000+ applications: it checked 3,518 applications in 4 minutes 54 seconds and surfaced 20 for humans.
- Category
- Cookbooks & Demos
- Format
- Post
- Published by
- Community
- Author
- Shubh Srivastava
- Added
- 2026-09-25
- Last verified
- 2026-09-25
Highlights
- 3,518 intern applications checked in 4 minutes 54 seconds.
- 20 applications surfaced for human review out of more than 4,000.
- Every square in the console is a real SDE intern application.
Watch out
A company demo post; no precision, recall, or cost figures are published.
Reactions & coverage
Posts, threads, and videos about this entry from around the web.
X: A job crawler that reads careers pages with Jev
X: 400 companies matched to one candidate
JEV is INSANE. We gave it 400 companies and one candidate profile. In 12 seconds, it predicted which jobs the candidate had the highest chance of getting, assigned a confidence score and detected job-candidate mismatches. All for just $0.0005 It can also score Show more
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From the community
Posts from builders shipping with Jev right now.
Jev plays Super Mario Bros.
got @typesafeai’s new model Jev to play Super Mario Bros. fast inference + structured outputs makes it surprisingly good for real time use cases. I'm excited to see what can be done with these new models!
LLMs generate answers, Jev makes decisions
this is the easiest way to understand Jev: LLMs generate answers. Jev makes decisions. that sounds like a small difference, but it actually changes the entire use case. say you give a normal LLM this: “here’s a user, their account history, payment behavior, support chats, Show more
@CompleteSkeptic
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x
Arbitrary classification as a primitive
Jev is cool not because it re-invented classification, but because it makes ARBITRARY classification into a type-safe programmable primitive. A general purpose zero shot decision model whose native interface is RUNTIME-DEFINED typed decisions, optimized for that exact interface
If you called Yann LeCun an idiot for saying we need to move beyond LLMs and build something new, you are banned from using Jev.
How Jev makes agents faster and cheaper
Jev solved local harness/model routing I use a combination of Claude Code, Codex and Opencode as my local agentic stack and routing to other harnesses was always enforced in the system prompt/rules With a deterministic hook that Claude Code can decide before delegation, Jev Show more
500 emails for 3.5 cents
Yeah Jev by @typesafeai is very cool. It classified 500 emails in seconds. And it costed 3.5 cents.
Headless Chromium agent
Acabo de terminar la implementación de @typesafeai + Chromium Headless para que mis agentes puedan navegar por internet a una buena velocidad! En este ejemplo le pido al agente que entre a la página del término "Café" en Wikipedia y navegue por los hipervínculos hasta terminar Show more
Tremendo esto!! Voy a trabajar en una implementacion propia.