Ray Amjad walks through Jev, a new "System 1" model from TypeSafeAI that doesn't generate text at all \u2014 you give it a prompt, some data, and a fixed set of answers, and it returns probabilities in roughly 100ms for a fraction of a cent. He explains its three primitives (bool, score, choice), shows it playing Minecraft under GPT-5.6's strategic direction, then argues the real payoff is pairing it with slow agents like Claude Code: cheap screening across a whole codebase, with only the high-probability hits escalated to an expensive model. Demos include analyzing 150 code comments in 9.3 seconds for about a cent, and a full-codebase code-smell pass estimated at 28 million input tokens for $1.19.
- Jev takes a prompt plus data plus a set of answers you define, and returns probability scores rather than generated text \u2014 effectively a very smart switch statement you can wire into if-statements.
- Three primitives: bool (how true something is), score (a rubric spectrum, max 11 levels so 0\u201310), and choice (multiple choice, up to 255 options); responses typically land in 100\u2013300ms and repeat within 2\u20133% variance.
- The framing is Thinking Fast and Slow: Jev is a System 1 model that can run continuously in the background, while Claude and GPT models are System 2 \u2014 and the interesting architecture is a loop where System 2 reviews System 1's decisions and rewrites its criteria, thresholds and examples.
- A live demo has Jev playing Minecraft moment-to-moment while GPT-5.6 sets strategy and reviews after setbacks; over the course of the video it builds a shelter, gets a diamond pickaxe and reaches the Nether.
- The Jev cookbook reports it can route among 182 installed skills, cutting wrong-skill loads from 17% to 7.3% with Haiku 4.5 \u2014 which would also let you pull ~10,000 tokens of skill descriptions out of the context window.
- A skill-driven demo analyzed 150 code comments in 9.3 seconds for about one cent, projecting 57 cents to grade every comment in the codebase and a shortlist of roughly 1,700 worth rewriting; a code-smell pass was quoted at 28 million input tokens for $1.19.
- Proposed use cases: qualitative linters (\"does this function name describe its side effects?\", \"is this log line leaking a secret or PII?\"), massively parallel adversarial browser testing per PR for pennies, and a Jev-based code reviewer that cuts what a System 2 agent has to read by ~10x.
- An engineer at Sentry reported Jev running one of their security pipelines over 5x cheaper and faster than the small LLMs they already used, with accuracy maintained \u2014 beating models like GPT-OSS 120B on their comparison.
This is the core architectural idea of the video \u2014 not Jev alone, but a fast classifier whose rules a slow model continuously rewrites.
If we started recording all the decisions that we made and the outcome as well, we could then set up a regular process where a System 2 model would be thinking about, hey, what is the strategy we're doing here? Can we improve it? And then it would rewrite any of the criteria, the examples, or the threshold for us.
A concrete, measured cost and latency result that makes the whole-codebase screening argument tangible.
We managed to do 150 comments in 9.3 seconds for about 1 cent. And it said that it can analyze all the comments in the codebase for about 57 cents. And it expects the shortlist to be about 1,700 comments that would need to be rewritten.
One way of thinking about it is that Jev does not generate any text \u2014 you can imagine it as a really smart switch statement.
You will find that your limiting cost is no longer your tokens, because your tokens have become really cheap, and instead your compute.
Imagine a really fast gut-check review from 100 or 1,000 different angles of any PR that you have.