A custom Jev harness uses context-sensitive hints and chained decisions to pronounce Chinese characters.
A custom Jev harness uses context-sensitive hints and chained decisions to pronounce Chinese characters. An autoresearch loop cut errors on an unseen 20-character set from 97 to 11 and beat DeepSeek at one-seventh the cost and one-thirtieth the latency.
流行のJevをMiniMax H3に組み込んで、動画生成を高速化してみた！Attention処理のスパース化にJevを使用。 ・層ごとにJevが重要度を判定（4step 49層が対象） ・Jevがスパース率1%, 3%, 5%, 10%を選択 RTX4070で6分7秒→3分34秒で41.7%短縮！動画生成中にJevクラウドに問合せしているのに速い！
@sep_is_heim · Research & data
@sep_is_heim
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I made Jev talk and wrote a paper on it. To publish it on arXiv, I need an endorser for csCL fom someone who has 3+ arXiv papers in any cs category, submitted between 3 months and 5 years ago. It's one click after I DM you the code and the paper. Or maybe someone you know. A
@xucian_ · Research & data
@xucian_
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I asked Jev to predict its own future. My last post (below) got 300k+ impressions, 400+ comments, 101 reposts. I used Jev to categorize every comment and quote to capture public X opinion on Jev’s future. Here’s results: ———— *Note: I created five prediction buckets,
@JoshKuechly · Research & data
@JoshKuechly
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I built an ultra-fast medical simulation with Jev and Grok 4.7 Grok 4.7 and I co-doctored a cancer patient across 20+ possible futures in just minutes. Grok proposed the moves. Jev ran the high-speed state decisions < 0.3 secs Patientic visualized the branched outcomes. 🧵👇
@chris_not_busy · Research & data
@chris_not_busy
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