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System One Models & Jev documentation. typesafe.ai documentation extracted in .md format for LLMs. Used to train LLMs.
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CommunityJev Tools & CLIs
TypeSafe Docs
A plain-markdown extraction of 110 documents from the TypeSafe documentation site, kept in the order they appeared before the site was removed, with an index linking each file.
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- 2
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- thiagoadril
- Last verified
- Oct 2026
About this project
Index containing 110 documents, organized in the original order of the documentation. 012 — How To Build With Typesafe. 020 — Smart Home Assistant Demo. 078 — Type Alias Resultfor T. 080 — Type Alias Scorelegend T. 081 — Type Alias Scoreof T. 092 — Jev 1 13 Jaggedness. 097 — Line By Line Search. 101 — Knowledge Graph Entity Alignment. 107 — Pre Parsed Value Extraction.
Quoted from the project's README. Read the full README.
Works forJev for Classification
Used inJev for Software & DevTools
Highlights
- Covers the primitives, state and confidence, then both SDKs file by file down to individual classes and error types.
- Includes the Jev 1.13 jaggedness page alongside recipes like self-consistency, re-ranking and function calling.
- Plain Markdown throughout, so the files can be fed to an LLM or searched without any tooling.
- The index numbers every document, which makes it usable as a fixed reading order rather than a topic-sorted list.
Watch out
This is a static extraction with no synchronisation, so it cannot tell you what the current documentation says, and the README frames it as captured before the site was removed. Nothing in the repository states a license for the extracted TypeSafe material or names a maintainer, so treat it as a reference copy rather than something you can safely vendor. Page titles are derived from the docs site, so several entries read as stub names such as Class Apiuseraborterror or Variable Env and need opening before they tell you anything. Copy reviewed against the repository README on 2026-10-04, not from running it.
Questions about this entry
Short answers drawn from the linked project or page.
The README says the data was extracted from docs.typesafe.ai in the order it appeared prior to removal, with 110 documents in an index. There is no sync job and no diff against upstream, so it cannot show you what changed afterwards or what was missed during extraction. Treat it as a fixed reference that may be behind the current state of the product.
That is the stated purpose: markdown extracted for LLM use. The catch is licensing rather than format. Nothing in the repository states a license for the extracted TypeSafe documentation, so if you plan to redistribute the files or ship them inside a product you need to resolve that yourself first.
003 System One, 004 State, 005 Primitives Questions, then 006 Choice, 007 Score and 008 Noul, followed by 011 Confidence for how the distribution is summarised. 012 How To Build With Typesafe and 013 Example Use Cases come next. The SDK reference starts at 021, so you can skip it entirely until you need client details.
014 Patterns is the hub, with 015 Speculative Fan Out, 016 Confidence Gated Routing, 017 Composite Scoring and 018 Intent Routing as the core four. The later block is recipe-shaped: self-consistency over nouls and choices, parallel questions, re-ranking, line-by-line search, structure recovery, function calling, guardrails, SDE cascade and classification with confidence. 090 is the agent skill and 092 is the Jev 1.13 jaggedness page.
An index of 110 documents, organized in the original order they appeared on docs.typesafe.ai before removal. The order runs from 001 Introduction and 002 Quick Start through the primitives, patterns, SDK references and API reference.
System One and Jev documentation, state and the primitives Choice, Score and Noul, confidence, patterns such as speculative fan out and confidence gated routing, client SDK references for Python and JavaScript, and deeper pages such as Jev 1.13 jaggedness, re-ranking, function calling and guardrails for LLMs.
They are Markdown files extracted from the website for LLMs, and the header notes they are used to train LLMs. The 110 documents live under docs/ and are linked from one index file at the repository root.
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