Fraud Detection Pipeline Using Jev and Kimi K3
Jev classified 100 emails in 1.42 seconds, achieving 96% accuracy by routing uncertain cases to Kimi K3. The total inference cost was approximately $0.07.
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Evaluation of Jev Model for SEC Filings and Fact Extraction
The Jev model was tested on SEC filings and fact extraction tasks, showing strengths in narrow yes-or-no questions but weaknesses in understanding document context. It performed well as a first-pass filter but lagged behind production models in accuracy.
Improved Model Routing and Action Review with Jev
The integration of Jev from @typesafe_ai replaced three steps in Prio, achieving 100% accuracy in model routing and significantly reducing action review time from 5 seconds to 0.25 seconds for clear cases.
Flutter Game Automation with Marionette
Marionette was used to automate a Flutter game featuring five puzzles. It efficiently read the code and interacted with the game in 9.7 seconds at a cost of $0.0012.
AI Feedback System Test Results with Jev
Jev was tested in an AI customer feedback system using 1,040 GitHub issues, proving to be 98% cheaper and 84% faster than Claude Sonnet 4.6 while maintaining higher accuracy.
Cost-effective AI agent society
A society of 100 agents was built using the Jev model, enabling 100 decisions per request in approximately 250ms. This setup allows for 20,000 decisions at a cost of just 11 cents, making it 240 times cheaper than a frontier model.
Feed Optimizer for Relevant Posts
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.