X post · by 生ビール
A Jev-style model running locally, with room to get faster.
生ビール
@wmoto_ai
ローカルTypesafe Jev、できたんやないか？ もうちょい速度は改善余地ありそう
Sep 17, 2026 · 388 likesOpen on X
生ビール (@wmoto_ai) shows a Jev-style model running on their own machine, and notes that its speed can still improve. The post is in Japanese.
Open the source
Ian Nuttall
@iannuttall
Cloudflare Workers has Jev now so I'm putting it to the test on keep.md - 7x faster search rerank compared to the current hybrid - 50x faster tagging of content vs GLM 4.7 Flash with no failures
Albiona Hoti
@albicodes
I built a visual reference finder with Jev One single prompt → 100 images from Cosmos, NASA, and The Met my new rabbit hole for creative work 🌻
Hassan
@nutlope
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 paper 4. Visualize everything on http://1kpapers.com The summaries cost $3.99 on @togethercompute. The classifications cost $0.08 on @typesafeai. So for just over $4 of inference, I ended up with a pretty useful way to explore the top AI research papers from the past year. I think this is where things are heading: different models for different parts of the workflow, instead of using one model for everything. I’m running evals on the Jev classifications before replacing the current ones, but the site is already live: http://1kpapers.com
