- Jev is a model that takes natural-language input, rapidly calculates multiple typed judgments together with their probabilities, and makes them available for program decision-making.
- unstructured state in → typed probabilistic decisions out
- A conventional LLM generates strings, so it needs to know the preceding characters before generating the next ones. Because Jev does not generate strings, it can generate structured outputs in parallel.
- RLCD (Reinforcement Learning from Compiler Diagnostics): reinforcement learning that checks whether a model's output matches the required type and gives it a reward
- Ultimately, the model was created to handle judgment problems with clearly defined outputs faster and more cheaply than conventional LLMs.
- For example, the Next.js article How we closed 1,500 GitHub issues in one month describes one agent that uses structured output to make a pass/fail judgment about whether an issue can be closed and another that looks for reasons not to close it and makes its own pass/fail judgment. The issue is closed automatically only when both agents produce the same response, and Jev seems like it could be used effectively here.
- Perhaps Jev's real value is not “replacing LLMs,” but making it possible to use AI extensively like if statements in conventional software.
typesafe.aiIntroducing System One Models & Jev - TypeSafe AI BlogTypeSafe AI is an AI lab building machine-native intelligence infrastructure for automation, designed to make decisions within software. Try our first System One Model, Jev, in early access.nextjs.orgHow we closed 1,500 GitHub issues in one monthHow the Next.js team used an agent to research old reports and work through the issue backlog.