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TypeSafe AI Playground — markjaquith

A Rust CLI of Jev experiments, led by a PHI screener that judges four separate 45 CFR 160.103 metrics per file and an English part-of-speech tagger that batches only the unresolved words into one request.

Stars
4
Language
Rust
License
MIT
Published by
Community
Author
markjaquith
Last verified
Oct 2026

This is one of several Jev listings for the same project. The canonical entry is TypeSafe AI Playground — BunsDev.

About this project

Just a playground for experiments around Jev, TypeSafe's System One model. The experiments live in a Rust CLI called typesafe-ai, built with usage-rs: PHI detection, code-comment review, parts-of-speech and load-bearing analysis, interactive tone analysis, and business and occupation classification. Alternatively, set TYPESAFEAPIKEY to call TypeSafe directly. If both keys are set, OPENROUTERAPIKEY takes precedence.

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

Highlights

  • PHI detection asks the four independent 45 CFR 160.103 metrics separately — identifiability plus three health nexuses — not one collapsed verdict.
  • Parts-of-speech spends questions only on ambiguous words: articles, conjunctions, subject pronouns and possessives resolve locally, as does punctuation.
  • With --request you can print the exact request JSON with no API call and no credentials, which makes the batching and shared state inspectable.
  • A local viewer on port 8231 parses a passage on submit and can be seeded from a saved analysis file or a pipe, so exploring results costs nothing further.

Watch out

The README calls this a playground, and the shape of it shows why: the PHI command deliberately refuses to produce a legal conclusion. Its four metrics mirror the independent elements of 45 CFR 160.103, and it says in as many words that real PHI status also depends on facts text cannot establish — who created or received the information, whether the holder is a covered entity or business associate, regulatory exclusions, and whether a Safe Harbor or Expert Determination process was completed. The identifiability rubric follows the conjunctive reading in American Hospital Association v. Becerra, requiring identifiability plus at least one actual health nexus. Operationally, a file is sent whole in one evaluation with no truncation, so request limits are your problem; PDFs and other binaries must be converted to text first; empty input is an error; and a directory scan includes hidden files without recursing or following symlinks. The parts-of-speech tagger leans on conventions the model has to interpret: option definitions live once in shared state while short mnemonic codes repeat with null descriptions, which the README admits is interpreted rather than a native reference feature, and the local shortcuts for articles, conjunctions and pronouns assume ordinary usage, so quoted words and unusual names will confuse them. Sentence boundaries are heuristic, not a parser, and the whole input plus question batch has to fit the model's limits. 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.

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