Skip to content

Misereru Slide Jev

A Japanese-language repository that continuously researches TypeSafe's Jev and System One models and organises the findings as Markdown slides, a presentation script and a standalone article.

Stars
0
Language
JavaScript
Published by
Community
Author
myokoym
Last verified
Oct 2026

About this project

TypeSafe AI の Jev / System One Models を継続的に調査し、Markdownスライドとして整理するrepositoryです。. 調査更新の基本順序は research.md → slides.md → 必要に応じて presentation-script.md / article.md → build / Pages deploy確認です。詳細な同期条件と禁止事項はAGENTS.mdを正とします。. misereru-slide-writing: slides.md の生成・再構成・推敲。Jev資料は調査・共有を主目的とするため、原則として Reference mode を適用する. このrepositoryでは presentation-script.md を作成済みで、生成される目次を含む全slideを対象にしたcomplete scriptとして管理します。通常buildでは構造整合性を検査し、全slide分のcoverageを明示的に確認する場合は npm run build:script-complete を使います。.

Quoted from the project's README. Read the full README.

Highlights

  • AGENTS.md is the canonical rulebook, and a request to update the slides means updating the existing canonical files, not creating a new set.
  • Update order is research.md, then slides.md, then the script or article as needed, then build and verify the Pages deploy.
  • The research ledger keeps sources, third-party verification and reservations, and holds low-importance findings back from the slides.
  • The article is written to stand alone with its own structure, and is checked to have a single H1 and no raw HTML.

Watch out

This is a documentation and research repository, not code you call — there is no library, no SDK and no API surface to integrate with. Its stated policy is to treat TypeSafe's own documentation and evaluations as primary information while separating vendor performance claims from third-party measurements, which is the right posture but means the slides are downstream of upstream marketing in places the ledger has not yet independently checked. Price, model version, rate limit and access conditions are handled with explicit dates, so any figure you lift from a slide has a shelf life and should be read with the ledger's reference date. The tracked topics list includes items that are open questions rather than findings — jaggedness, SLA, privacy and data retention, regional differences, comparisons against logprobs-based and rule-based approaches — so treat those slides as a research agenda, not conclusions. Publication is automated: a push to slides.md runs GitHub Actions, Marp, then GitHub Pages. The raw presentation-script.md is deliberately not published, only the rendered version, and the deck is built from the misereru template. 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.

More Jev resources like this

23GitHub stars
Live race rig that labels 1,000 real app reviews with Jev typed questions against Gemini JSON output on identical batches, then replays recordings and re-ranks rows locally without extra model calls.
Jev Practices & Patterns#community#jevbest#catalog-import
Recorded experiments on how Jev reports probability rather than whether it predicts well. On a hidden fair die it picked an option with a mean reported probability of 82.9% while accuracy stayed at 19.0% over 400 trials, and Choice lost a stated 45% versus 55% distinction in an input document.
Jev Practices & Patterns#community#jevbest#catalog-import
3GitHub stars
An experiment that composes TypeSafe Jev Choice decisions into free-form text: every next character becomes one typed decision over 98 options, with branch backtracking and a final complete/incomplete check.
Jev Practices & Patterns#community#jevbest#catalog-import
An eval that puts Jev two questions about shadcn-ui/lint: whether each finding is a real rule violation, and whether the linter's message tells you what to change. It runs the real linter over 131 rule test cases and scores both the detections and the messages.
Jev Practices & Patterns#community#jevbest#catalog-import
24GitHub stars
Unofficial laptop study of parallel constrained decoding: prefill once, share the KV cache across fields, and decide all schema fields in one batched forward pass without writing JSON.
Jev Practices & Patterns#community#jevbest#catalog-import
22GitHub stars
Offline evaluator that checks whether classifier confidences match reality and converts mistake costs into a deployable threshold, emitting an HTML report plus machine-readable threshold files.
Jev Practices & Patterns#community#jevbest#catalog-import

From the Jev guides

Original write-ups on what this entry is for.

Most Jev accuracy problems are state problems. What belongs in a state, what is a pre-made judgment to leave out, and why naming the slice you ask about matters more than the wording.
Jev Practices & Patterns#state#design#primitives
Read article
A Noul returns one number and no separate confidence. How to read the value, pin its boundary with criteria, split compound questions, and set a per-action threshold that matches the cost of being wrong.
Jev Practices & Patterns#noul#primitives#confidence
Read article
A Score is a weighted position, not a ruler. Why the number can land between levels, why levels are judged separately, and how to combine several atomic scores in code instead of one crowded rubric.
Jev Practices & Patterns#score#primitives#scoring
Read article
Back to all resources

From the Jev community

Posts from builders shipping with Jev right now.

Follow @typesafeai