llm-typesafe 0.1a0 plugin adds Jev model to LLM
llm-typesafe 0.1a0
The interesting part is the answer-type interface: Jev is accessed through structured noul, choice and score outputs rather than free-form chat, which suits classification and triage pipelines. Developers already using Willison's LLM CLI can wire it in with two commands and pipe files straight into scoring workflows. Compared with general chat models, this targets routing and labelling tasks where a typed confidence value is the whole output.
Simon Willison released llm-typesafe 0.1a0, a plugin for his LLM command-line tool that adds support for TypeSafe AI's new Jev model.
Users install it with 'llm install llm-typesafe' and set an API key via 'llm keys set typesafe'.The plugin supports three answer types: yes/no 'noul' questions returning a confidence score such as 0.
99, choice questions with JSON-defined criteria, and score questions using graded criteria arrays.The plugin is at version 0.
1a0 and TypeSafe's API key waitlist is reportedly moving quickly.
llm-typesafe 0.1a0
Release: llm-typesafe 0.1a0 I built this new plugin for LLM to add support for TypeSafe AI's new Jev model . Install it like this: llm install llm-typesafe Then set an API key ( get one here , the waitlist seems to move pretty fast): llm keys set typesafe # Paste key And now you can ask yes/no "noul" questions like this: llm -m jev 'Please refund my last payment.' \ -s 'Does this message explicitly request a refund?' Output: {"type": "noul", "noul": 0.99} Or choice questions like this: cat message.txt | llm -m jev \ -s ' Which team should handle this message? If billing and technical issues both occur, choose billing. ' \ -o answer_type choice \ -o criteria ' { "billing":"Charges, invoices, payments, or refunds", "technical":"Problems installing or using the product", "other":"Neither category fits" } ' Or scoring questions like this: cat report.txt | llm -m jev \ -s ' How reproducible is the problem described in this report? ' \ -o answer_type score \ -o criteria ' [ "No reproduction instructions", "Some instructions, but important steps are missing", "Complete steps with expected and actual results" ] ' See the README for more details. Tags: projects , llm , jev
This report is categorized under AITOP's "How-to" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows and US-China foundation model benchmarks and commercialization comparison. Key entities and technical topics under continuous tracking include llm-typesafe, LLM, Jev, TypeSafe AI.
Source attribution: curated from Simon Willison’s Weblog. Cross-source corroboration: 3 independent sources have covered this development (including arXiv: OpenAI, 掘金本周最热), confirming industry-wide signal strength.
