Batching many questions into one request instead of many calls, which is dramatically cheaper and faster with identical answers.
Every question in a request is evaluated in parallel against the same state, and the answer to one question never becomes context for another. The parallel-questions cookbook batches a 13-question regulatory briefing into one call and reports 12.2x lower cost and 10x lower latency with no change in answers.
Coding agents fall into the one-question-per-call habit more than people do, which is why TypeSafe's agent skill explicitly tells agents to put many questions in each call.
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Posts from builders shipping with Jev right now.
Support answers in a Mac app
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 Show more
A local Jev build with room to get faster
ローカルTypesafe Jev、できたんやないか？ もうちょい速度は改善余地ありそう
typesafeのJev、これローカルLLMで割といけるんじゃないか？ ていうかそういう使い方するのもローカルLLMの醍醐味だったわけで！
jev-review: a local-first score loop for coding agents
built `jev-review` @typesafeai it's an experimental, local-first MCP plugin that gives coding agents a score quality feedback loop across different metrics. agents call jev while they work, get scored, make improvements, and repeat the loop try below 👇
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
The X algorithm, rebuilt with Jev
rebuilt the X algorithm with Jev - uses real weights - simulates virality of your post - has a global feed (you see everyone) it's insanely accurate
Hard-coded rules moved to Jev
Now using @typesafeai Jev in aiseotracker.com, linkdr.com, genppt.com, etc AI ends up vibe coding so much AI regex slop if you don't read the code, so I can finally move all this hard-coding to Jev and it's insanely fast! Also for regular LLM Show more
A chief of staff for bots
got @typesafeai's new model Jev as a chief of staff for bots Jev reads the task, wakes the right teammates off the bench and gives each one the right model It is possible on OpenMausBot as it supports all the LLMs from your existing subscriptions Jev as a decision engine is Show more
