Jev Decision Model Performance Comparison
Jev, a new decision model, demonstrated a routing cost 125 times lower than GPT-5.5 when deciding actions for a coding agent after a failed test.
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AI Agent Integration with Jev by Typesafeai
An experiment integrating Jev into a SaaS AI Agent shows remarkable speed, responding faster than user input. It operates at approximately 100 times lower cost than traditional LLMs and efficiently directs requests needing a full LLM.
Jev Adapter for Prompt Classification
The Jev adapter classifies prompts before they reach the LLM, reducing token consumption and costs. It provides classification and confidence scores, marking prompts for review when confidence is near the threshold.
Jev Model Evaluation Results
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.
Jev: Fast Decision-Making for LLMs
Jev offers a new intelligent decision-making primitive that enhances model routing and classification tasks. It allows for quick and cost-effective decision-making, improving the efficiency of LLM calls in applications.
Space Race Game Development with TypeSafe
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.
Argument Validator Performance Comparison
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.