Alpha Python toolkit for offline analysis of Jev-like output distributions, parameter sensitivity, and Fisher geometry, with an optional TypeSafe SDK adapter that sends experiment states and questions to the provider. System-information and Cramér–Rao results require explicit statistical assumptions; its first live smoke test captured responses but did not yield stable Fisher estimates.
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Browser PDF viewer for finding passages when you do not know the exact words: pdf.js extracts lines locally, each line gets one Jev Noul on whether it answers the query (batched with the page text as shared state), and lines above a code-owned threshold light up ranked by probability; only extracted text is sent to TypeSafe, never the PDF, and the API key stays behind a server-side proxy.
Zero-shot named entity recognition for Chinese and English text: code splits the text at punctuation and enumerates candidate windows with exact character offsets, Jev answers Choice questions that nominate, verify, and fix the boundary of each entity, and every result keeps its probability and a decision trace (CLI, Python library, and local web UI; MIT). Its authors report 73.7 average strict F1 on 200-sentence samples of 12 public NER benchmarks with jev-1.13.0, with aggregate results committed; input text is sent to TypeSafe, and an entity that no candidate window covers cannot be found.
Town of 10,000 computed personas that reads a post, listing, product, or headline: one opening request scores the text against about 60 audience attributes, 83 for a listing or a product, plus seven moderation questions, and plans the first wave of 600 readers; batched Choice questions then return each persona's reaction, and the text reaches the next wave only while glad reactions outweigh sorry ones. Also returns the audience by interest, job, age, city, and budget, the question buyers would ask a listing first, and a demand curve over an author-set price ladder. Personas are computed from their id rather than written by a model, and each reaction is sampled from the returned probabilities with a fixed seed, so the result is a simulation of a typed audience and not a forecast of real behaviour; open source (MIT), no sign-in, runs on a TypeSafe or OpenRouter key.
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