Articles.
Field notes from the studio on applied AI, typed model judgments, and the tools we use to ship them into production software.
6 min read
OpenAI's Decisions API vs Jev: Same Idea, Different Contracts
OpenAI now ships a decision endpoint too. It asks the same three kinds of question as Jev with a different JSON contract and more than twice the input price. Here is the side-by-side, with an interactive translator.
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Jev 1.14: Images, and a Primitive That Points
Jev can see now. Version 1.14 takes images and adds Box, a fourth question type that answers where something is. One rule makes it safe to use: a Box always returns a box, so ask whether the thing is there first.
Open the article 1 interactive5 min read
Claude Fable 5.2 for Builders: What Changed, and What to Do About It
Anthropic's most capable widely released model got cheaper and stricter. The price fell 20%, effort is now the only dial, and harnesses that edit history will get 400s. A builder's guide with an effort dial and a migration checklist you can tick off.
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Jev Decides, Fable 5.2 Writes: The Two-Model Architecture
The cheapest frontier-model call is the one you never make. Put Jev in front of Claude Fable 5.2, let it decide what each message needs, and send Fable only the work that needs a writer. Run the inbox and watch the bill.
Open the article 1 interactive4 min read
Reading the Numbers: Scores, Confidence and Thresholds
Decision models hand you numbers, and the numbers are easy to misread. Drag a probability distribution until the score lies to you, then find the confidence threshold that costs least. Two labs, one for each mistake.
Open the article 2 interactive7 min read
What is Jev? TypeSafe's System One Decision Model Explained
Jev answers typed questions about your data with calibrated probabilities, and never writes a word of prose. What it is, how it differs from an LLM, and where it fits in real software.
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Choice, Score and Noul: The Three Question Types
Every question you ask Jev is a Choice, a Score or a Noul. Here is what each one returns, how to pick between them, and how to compose them into decisions your code owns.
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Decision Models vs LLMs: When Not to Generate Text
Most AI features need a decision, not a paragraph. How decision models like Jev differ from LLMs, and a practical test for when to stop generating text.
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Confidence vs Probability: When to Let Jev Act and When to Escalate
An AI answer is only half a decision. The other half is knowing when not to trust it. How Jev separates probability from confidence, and how we set the thresholds.
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Speculative Fan-Out: Ask Every Question in One Call
Ask every question you might need, in one request, and let code ignore the rest. Why that is cheaper and faster than branching, and when a second call is genuinely required.
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Jev on OpenRouter: The Decisions API, Pricing and Latency
OpenRouter serves Jev through a separate decisions endpoint, not chat completions. Here is the request shape, what it costs, and how fast it answered for us.
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How We Built a Claude Code Model Router with Jev
Not every message to a coding agent needs the biggest model. How we used Jev to size each request and hand small jobs to Haiku and Sonnet, including the limits we hit.
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Picking the Right AI Agent Skill with Jev
Agents pick skills from a cramped one-line index and often guess wrong. Here is how we put Jev in front of that choice, and what it costs.
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AI Guardrails and Citation Checks with a Decision Model
A guardrail should hand you evidence, not make your policy for you. How we use Jev to screen LLM traffic and catch citations that do not hold up.
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Triage at a Fraction of a Cent: Sorting Emails, Leads and Tickets with Jev
Sorting a pile of text is a decision problem, not a writing problem. Our Jev triage setup, what one email cost, and the rules we use when Jev is unsure.
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