I used Jev to classify 1,018 AI research papers.
The result: $0.08 total cost and 256ms median end-to-end latency per paper. The pipeline was: 1. Summarize each paper with DeepSeek V4 Flash 2. Send the title + summary + 24 possible topics to Jev 3. Use Jev to classify each
Apple just showcased the ultimate setup for running AI models locally.
These are 4 M5 Ultra Mac Studios running Kimi K2.7 Code locally to fix a bug in a 3D rendered scene on XCode. Each has 256GB RAM, so thats 1TB combined memory. Enough to run the biggest open AI models
Deepseek has just killed the entire coding agent industry
It's called deepseek-harness It's the most complete framework for creating code agents Open source. Claude's most complete plan costs $200 a month. This is FREE And it comes with a brutal idea Everything is a plugin
Been testing Jev on real grocery catalog data.
92% accuracy on a separate test set, and ~14x lower median latency than my DeepSeek setup in a small 10-case test. The interesting part to me is how naturally it fits into code: one state, several decisions. What would you build?
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