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Building with Jev for MemX App

The MemX app successfully replaced FastText and a multi-stage Gemini pipeline with a single Jev call, achieving approximately 98% accuracy. Explore the full experiment and insights in the linked video.

added by @atripat

Looking to build with Jev or another Decisions API?
For MemX app, I replaced the FastText and multi-stage Gemini pipeline with a single Jev call with ~98% accuracy.

See the full experiment and learnings in this video

https://t.co/kVWGXrNYr4

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Jev Model Evaluation Results

added by @codybrouwers

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.

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AI Agent Integration with Jev by Typesafeai

added by @LennartPrange

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.

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Integration of TypeSafe AI's Jev Model

added by @TaseedMustafa

TypeSafe AI's Jev model has been integrated into a system layer, providing clean, typed decisions from unstructured state in under 500ms. This integration enhances backend processes by scoring and filtering context efficiently.

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Improved Model Routing and Action Review with Jev

added by @FredvanRijswijk

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.

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Jev Decision Model Performance Comparison

added by @r_aravindhan

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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Argument Validator Performance Comparison

added by @JSON_JEFF

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.

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