System One Models & Jev: Typed AI Decisions

What's inside

1. Level 1: System One Models: A New Model ClassFree
   Learn what a System One model is — a model that takes unstructured state plus a schema you fix in advance and returns typed values with calibrated confidence instead of generated prose.
2. Level 2: Why Kahneman's System 1 Names the CategoryFree
   Trace the name to Daniel Kahneman's fast, intuitive System 1 thinking and see precisely which properties of that metaphor TypeSafe is claiming and which it is not.
3. Level 3: Anatomy of a Jev Decision
   Walk the full shape of one Jev interaction — state in, one or more typed questions, and a response carrying a typed answer, a probability distribution and a confidence figure.
4. Level 4: What TypeSafe Actually Shipped on 15 Sept 2026
   Establish the verified public facts of Jev's limited early access launch — company, funding, access model, headline latency and price — and separate them from claims nobody outside TypeSafe has reproduced.
5. Level 5: Fit and Jaggedness: When Not To Use Jev
   Build a sober fit test for the decision layer, including the capability gaps TypeSafe itself documents per model version.
6. Level 6: Typed Outputs vs LLM JSON Mode
   Compare Jev's pre-enumerated answer space with LLM structured outputs, function calling and grammar-constrained decoding to see exactly what 'cannot produce a type error' buys you.
7. Level 7: Latency, Price and the Unit Economics
   Quantify the decision layer's cost and speed profile against frontier LLM calls and work out where the two-orders-of-magnitude claims come from and where they break down.
8. Level 8: Hallucination vs Being Wrong
   Separate the failure modes a typed decision layer removes from the ones it keeps, and build a precise vocabulary for each.
9. Level 9: Determinism: What Jev Does Not Promise
   Examine what is actually guaranteed run to run — a valid typed answer — and why that is weaker than the determinism of a rules engine or a regex.
10. Level 10: Jev vs Classifiers, Embeddings and Fine-Tunes
    Position Jev against classical ML options — labelled-data classifiers, embedding-plus-linear-head pipelines and fine-tuned small models — on accuracy, iteration speed and total cost of ownership.
11. Level 11: Locating the Decision Layer in the Stack
    Draw the modern stack — rules engines, classical ML, System One decisions, LLM generation and agents — and place each responsibility where it belongs.
12. Level 12: Non-Autoregressive Parallel Sampling
    Understand the core architectural move: every question is evaluated against the same state in one parallel pass, so no answer becomes context for another.
13. Level 13: The Constrained Answer Space
    Study how declaring the answer set in advance turns inference into a distribution over a finite, typed space, with the concrete cardinality limits that implies.
14. Level 14: RLCD Against RLHF and RLVR
    Compare Reinforcement Learning for Calibrated Decisions with preference-based and verifiable-reward training to see what objective Jev is actually optimised for.
15. Level 15: What Calibration Really Guarantees
    Pin down calibration as an aggregate property — among answers reported at p, roughly p should be right — and learn why it says nothing about any single answer.
16. Level 16: Reading Probabilities and Confidence
    Learn that confidence is a statistic derived from the shape of the returned distribution, and why the primitives expose it differently.
17. Level 17: Engineering Around an Opaque Model
    Inventory what TypeSafe has not disclosed about Jev and adopt practices that keep a production system safe despite that opacity.
18. Level 18: Your First Call: /v1/systemone and the SDKs
    Make a working request against POST https://api.typesafe.ai/v1/systemone using the official Python and Node SDKs, and read the response object correctly.
19. Level 19: Constructing Good State
    Learn how to assemble the state a decision is made from — strings, JSON objects or arrays of text — inside the published token budgets, with only what the judgement needs.
20. Level 20: Designing Choice Questions
    Master the Choice primitive: instructions, optional criteria, and an option set of up to 255 mutually exclusive answers that your code can act on.
21. Level 21: The Score Primitive: Ordered Spectra
    Learn to model graded judgements with Score, whose two-to-ten ordered criteria return a fractional position on a spectrum rather than a discrete label.
22. Level 22: Noul: Yes/No as a Bare Probability
    Master the Noul primitive, which returns a single 0-1 probability of the affirmative and deliberately carries no separate confidence field.
23. Level 23: Batching: Many Questions, One Pass
    Understand how packing multiple typed questions against one shared state into a single request exploits parallel evaluation instead of sequential calls.
24. Level 24: Limits: Cardinality, Context and Modality
    Work within Jev's hard boundaries — 255 options per Choice, 64k and 32k token context rules, and text-only input — and design around them.
25. Level 25: Thresholds and Escalation Logic
    Turn calibrated confidence into per-action decision policy, setting different bars for read-only, reversible and high-stakes actions.
26. Level 26: Speculative Fan-Out in Production
    Adopt the pattern of asking every question you might need up front, since an extra question costs tokens but almost no wall-clock time.
27. Level 27: Composite Scoring From Atomic Dimensions
    Decompose a fuzzy holistic judgement into independent atomic Score questions and recombine them with explicit weights in code.
28. Level 28: Cascades: Jev as Router, Not Replacement
    Place Jev as the cheap fast triage layer that dispatches to deterministic code, a frontier LLM or a human depending on answer and confidence.
29. Level 29: Evaluating Calibration, Not Just Accuracy
    Measure a typed decision layer the way probabilities demand, using reliability curves, expected calibration error and selective risk rather than accuracy alone.
30. Level 30: Drift Monitoring and Real Failure Modes
    Diagnose how a typed decision layer actually fails — flat probability distributions, missing escape hatches, criteria drift and silent state degradation — and monitor for it in production.
31. Level 31: The Open System One Ecosystem
    Survey the open-weights and hosted decision models that appeared alongside Jev and judge honestly when a local model beats the API.
32. Level 32: Trajectory of Typed Decision Layers
    Reason soberly about where System One models are heading as a software building block, separating verified early-access facts from vendor claims and open speculation.
33. Level 33: Final Challenge: Architect the Decision Layer Exam
    Prove total mastery by designing, hardening and defending a complete System One decision layer end to end across everything the course has covered.

Access

The first 2 levels are free with a free account. Every level, the podcast edition and the AI tutor come with All Access at £4.99/month or any Creator plan — see pricing.

Start free →