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.
added by @JoshKuechly
Ranked from stored criteria vectors. No live classification on this page.
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.
A space race game was developed for TypeSafe/Jev, achieving 31,382 points with 180 stars and 18 kills in the final 180 seconds. The replay video showcases the decision-making process and verified outcomes without new API calls.
The Jev model from Typesafe AI was tested on Modemdev's evaluation for agent replies, showing an 8x speed improvement and a 25x cost reduction.
This feed optimizer uses a single call to Jev to classify posts into categories like 'ragebait' and 'useful.' It scored 150 posts in under 10 seconds, achieving a 63% retention rate on unseen bookmarks.
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.
The argument validator in PatchOpsAi, built with Luna, shows performance metrics of 1.1–2.8 seconds, while @typesafeai's Jev took 195–251 ms on the validator and 221–901 ms on the scanner.