Passing null as a Choice option description when the option name needs no extra explanation.
Choice criteria values accept null, which tells the model the option name is self-explanatory. It keeps short, unambiguous option sets from filling the request with redundant descriptions.
The rest of the criteria map still carries whatever contrast the options need; null is a signal to spend description budget only where options could be confused.
Definitions that connect to this one.
Posts from builders shipping with Jev right now.
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.
Custom Jev-style models for agent workflows
Prediction: millionaires will be made using custom Jev style models (parallel constrained decoding) to make the agent systems companies already run more token efficient. Let me explain with a scenario: Imagine a company already has an agent workflow running where an llm reviews Show more
They were building in stealth for 2 years, I was building in stealth for 2 hours… Happy to open source Qwen-2.5-1B-RLCD, 5x faster on-device inference for JSON workloads that need to be type-safe. ⚡️Demo below on a M4 MacBook⚡️ every LLM has the ability to efficiently batch
Live viral post analyzer
Just created this with Jev by @typesafeai. A live viral post analyzer. As soon as you stop typing for .5 seconds it analyzes the viral potential. Going to try and actually make this good, will need to scrape a lot of twitter data... Notice how it also categorizes the tweet Show more
Jev plays Tetris: 134 lines in two minutes
Puse a Jev, la nueva IA de @typesafeai, a jugar al Tetris en modo súper difícil. Decidió cada jugada en unos 0,3 segundos. Acomodó 357 piezas e hizo 134 líneas en solo 2 minutos. Increíble.
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！
