A chaptered walkthrough with a Python demo repo covering Noul, Choice, and Score, parallel multi-question calls, and an autonomous triage loop.
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X: Intent-based search in Gmail
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)
Another crazy @typesafeai Jev example: Predictive spreadsheets Spreadsheets recalculate numbers, not meaning. Jev reads intent. Type "Urgency" at the top of a column and, as you type, it figures out you want each row rated from "no follow-up needed" to "urgent" in ~100 ms.
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
X: Watching 25 customer groups on WhatsApp
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. 🔥
X: 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.
OpenByteInc/QuantDingerPython
fhshaik/typesafe-marioPython
FBddcz/embodied-jevPythonPosts from builders shipping with Jev right now.
dmx.to reshapes the timeline in plain language
the next version of dmx.to will let you fully customize the timeline both with dumb rules (how did i not think of these until now lol) and smart rules using Jev (BYOK also supports vercel gateway) 🤓 you can filter 3 lvls of usefulness, tweet types, topics, etc
GPT and Jev compared live
Built a live tool using @typesafeai JEV ! See live comparison btwn GPT & JEV at gptvsjev.vercel.app Get benchmarks like Money Spent ! Time Taken ! Tokens Used!
LitJev: Jev-style decisions on any local LLM
开源了 LitJev：我们对 Jev 类型化决策 API 实现方式的猜想，可接入任意现成 LLM。 小测试：Qwen3.8-27B，10 道 MMLU-Pro 一次完成，0.3 s。比让模型只生成答案快 2.9x；比生成完整 schema JSON 快更多（显然）。更多测试马上来。 非官方，基于公开信息。
Secret detection with Jev, tested
Built a quick Secret Detection Test with Jev by @typesafeai Results seem promising so far, ran it a few times and it's pretty consistent as well. github.com/teyhouse/jev-s…
Jev Driving Lab: a playground for System One models
@typesafeai (and System One models in general) have a lot of uses, and fun to play with. New paradigm open to public now. This is a self-driving sim I spun up to test Jev today. Structured sensor state in, typed driving decisions out, steer, brake, overtake, pedestrians, speed Show more
200 rows, 32 template families, and traps, judged in one call
First test with Jev (TypeSafe System One): 200 rows, 32 template families, traps — 'yes' rows with "we'll pass", 'no' rows with 'absolutely, happy to meet'. The single Jev question scored 100%. My 12 dimensions scored 98%. I had designed a task my own question could ace.
