Reading up on Typesafe.ai
8 deep · digging since sep 21
- How Jev works: calibrated decision models
Jev is a low-latency decision model that scores options via LLM forward passes to output calibrated probabilities without text generation.
- i hate the future: the normalization of inexplicable failures
Argues that AI-driven development normalizes unexplained failures, turning accountability into shrug‑off attitudes like “stupid thing sucks,” undermining proper debugging software.
- Introducing System One Models & Jev - TypeSafe AI Blog
TypeSafe AI releases Jev, a System One model that provides structured, hallucination-free decisions at sub-500ms speeds, aiming to replace LLMs for automation workflows.
- tokens too cheap to meter
The price of running machine‑learning intelligence is falling by orders of magnitude yearly, making AI tokens as cheap as electricity and enabling pervasive local and hosted AI use.
- Jev, week one · 100 things people built
During its first week, Jev from TypeSafe AI was used to build 100 diverse projects, showcasing its decision-model capabilities across various applications.
- Jev introduces a new shape of LLM—System One, aka Decision Models
TypeSafe AI launched Jev, a decision model that outputs probabilistic numbers for yes/no, choice, and score queries, priced at $0.042 per million input tokens.
- Jev introduces a new shape of LLM—System One, aka Decision Models
The piece presents Jev, a TypeSafe AI decision‑model that turns text inputs into probabilistic scores for yes/no, choice, and rating tasks, explores its low cost, bias risks, and community hacks.
- Jev, Prolog, Pi, and the dream of probabilistic logic programming
The integration of Jev with DeepClause and its Pi extension enables scalable probabilistic logic programming for expert‑system applications like SOP2AGENT.