DeepThinking AI

Tag

production

Covered in Agents & ProtocolsAI EngineeringModels & Benchmarks, where the background and the sources for this subject live.

Agents & Protocols

What breaks when you put Jev in an agent workflow?

Two shipped integrations put Jev inside an agent loop, from Pydantic AI and LangChain. Both leave three things to your code: a ledger of actions already executed, because Jev may re-propose a tool that ran; a per-question projection of state, because accuracy falls as irrelevant context grows; and the confidence threshold that escalates to a language model.

5 min read

AI Engineering

Which Jev use cases actually work at production scale?

TypeSafe publishes four use cases for Jev and two rate limits: 250,000 tokens per second and 1,200 requests a minute. Below 12,500 tokens per request the request limit binds first, so a small-state workload reaches a fraction of the token ceiling. Map-reducing a petabyte stays out of reach.

4 min read

How does Jev by TypeSafe AI actually work?

Jev posts state and a map of questions to one endpoint and returns typed answers carrying probabilities. TypeSafe claims 0% type errors, which is a guarantee about schema conformance rather than about correctness. A noul of 0.95 is a well-formed number that can still be wrong about the thing it scored.

3 min read

AI Engineering

How do you model a classification problem with Jev?

Jev answers noul, choice and score questions. Choice caps at 255 options and score at 10 levels, and both return a confidence derived from their probability distribution. Noul returns no confidence field at all, because the probability it hands back already describes the uncertainty. Modelling the problem means picking among those three.

4 min read

What is a System One model, and does the framing hold up?

TypeSafe AI launched Jev on 15 September 2026 as a System One model, priced at $0.042 per million input tokens with output free, and reports 70ms to 500ms against 3 to 329 seconds for frontier models. The economics are the strong part of the claim. The psychology metaphor carries less weight.

4 min read

AI EngineeringUpdated Sep 16, 2026

When does prompt caching actually save money?

Prompt caching stores a prefix of your prompt so later requests reuse it instead of reprocessing it. Reads are far cheaper than base input tokens, but writing the cache costs a premium and entries expire. It pays whenever a large stable prefix is reused several times inside the TTL, and loses on one-shot traffic.

6 min read