@ts_floydTypeSafe just shipped the first model built for machines, not for people. Jev doesn't write text like an LLM; it decides based on input state and outputs typed results. The system relies on three primitives: Choice picks from a list, Score places items on a rubric, and Noul returns a probability between 0 and 1. The architecture splits roles clearly: an LLM generates content, Jev makes decisions, code controls execution, and humans handle uncertain cases. Effective usage demands stating facts with one atomic question and exact criteria while skipping personas or preambles entirely. Descriptive criteria beat simple labels, so defining outcomes like technical failures as broken behavior or integration failure works better than vague tags. Each query must focus on a single judgment; complex multi-part questions like asking if something is valuable, urgent, and likely to buy need to be combined in code rather than asked separately. The model never provides explanations; it returns only decisions, probabilities, and confidence levels, which forms the contract. State management must stay clean, containing only the object, relevant context, and changing facts without extra noise. While the model supports a 64k token limit, accuracy shifts as state size increases, making smaller useful states an upgrade. Queries can be batched to judge multiple dimensions in parallel with minimal latency impact, allowing one call to assess thirteen dimensions at once. Users should include all potential questions in requests since output tokens are free. Decision gating should rely on confidence thresholds rather than the answer itself, acting above 0.85, escalating between 0.55 and 0.85, and routing below 0.55 to humans. These thresholds require tuning based on specific dataset performance because numbers from a post are just a starting point, not a config. Every uncertain answer needs a defined escalation path because treating low confidence as normal is how bad decisions ship. Candidate options must be built in code before being presented to the model for selection; it should never invent options itself. Arithmetic and hard rules remain in the code while Jev judges meaning, ensuring your program does the math. Model versions should be pinned to ensure stability after threshold tuning, using jev-latest to track stable releases or jev-1.13.0 once tuned. Logging must include model ID, probabilities, confidence, routing logic, and outcomes for review to catch false positives and retune. Integration should place the model within the decision loop for routing, tool gating, and output judging rather than next to it. The five money workflows include universal verifier, support triage, lead scoring, model routing, and giant dataset jobs. Setup requires joining the waitlist at typesafe.ai before reading the guide on building fast AI agent systems.

@ts_floydTypeSafe just shipped the first model built for machines, not for people. Jev doesn't write text like an LLM; it decides based on input state and outputs typed results. The system relies on three primitives: Choice picks from a list, Score places items on a rubric, and Noul returns a probability between 0 and 1. The architecture splits roles clearly: an LLM generates content, Jev makes decisions, code controls execution, and humans handle uncertain cases. Effective usage demands stating facts with one atomic question and exact criteria while skipping personas or preambles entirely. Descriptive criteria beat simple labels, so defining outcomes like technical failures as broken behavior or integration failure works better than vague tags. Each query must focus on a single judgment; complex multi-part questions like asking if something is valuable, urgent, and likely to buy need to be combined in code rather than asked separately. The model never provides explanations; it returns only decisions, probabilities, and confidence levels, which forms the contract. State management must stay clean, containing only the object, relevant context, and changing facts without extra noise. While the model supports a 64k token limit, accuracy shifts as state size increases, making smaller useful states an upgrade. Queries can be batched to judge multiple dimensions in parallel with minimal latency impact, allowing one call to assess thirteen dimensions at once. Users should include all potential questions in requests since output tokens are free. Decision gating should rely on confidence thresholds rather than the answer itself, acting above 0.85, escalating between 0.55 and 0.85, and routing below 0.55 to humans. These thresholds require tuning based on specific dataset performance because numbers from a post are just a starting point, not a config. Every uncertain answer needs a defined escalation path because treating low confidence as normal is how bad decisions ship. Candidate options must be built in code before being presented to the model for selection; it should never invent options itself. Arithmetic and hard rules remain in the code while Jev judges meaning, ensuring your program does the math. Model versions should be pinned to ensure stability after threshold tuning, using jev-latest to track stable releases or jev-1.13.0 once tuned. Logging must include model ID, probabilities, confidence, routing logic, and outcomes for review to catch false positives and retune. Integration should place the model within the decision loop for routing, tool gating, and output judging rather than next to it. The five money workflows include universal verifier, support triage, lead scoring, model routing, and giant dataset jobs. Setup requires joining the waitlist at typesafe.ai before reading the guide on building fast AI agent systems.