Simulation of 100 generations for binary intent classification showed 3600 LLM calls with inconsistent quality. Using Jev, unusable results were eliminated, achieving 100% parseable quality for the next pipeline.
added by @amQnese
Ranked from stored criteria vectors. No live classification on this page.
The author shares insights on integrating a tool for spotting problematic database migrations in their CI/CD pipeline and experimenting with another tool to filter unnecessary jobs based on git diffs.
Introducing docsignals, a document extraction workflow that combines DWS with Jev for precise typed answers and exact citations.
Fine-tuning the open-weight GLiNER 2.5 model on a local CPU improved accuracy by ~18pp, surpassing Jev by ~9pp. The process took about 51 minutes and resulted in an 8-10x speed boost for specific tasks.
Claude Code has been modified to change an authentication check to return true, with tests passing successfully. The jev-preflight tool flags risks and facilitates re-checks.
A benchmark was conducted comparing Jev by TypeSafe and Claude Code agents for filling out web forms. The test involved three real forms, with no submissions made.
This tool flags AI-generated replies on X by analyzing common writing signals. It provides instant probability assessments for each reply through a Chrome extension.