Hassan

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Jev を使用して 1,018 件の AI 研究論文を分類しました。結果: 総コストは 0.08 ドル、用紙あたりのエンドツーエンド遅延の中央値は 256 ミリ秒でした。パイプラインは次のとおりです。 1. DeepSeek V4 Flash を使用して各論文を要約します。 2. タイトル +... を送信します。

Original post / EN

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 https://t.co/hs63SlHxjw 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: https://t.co/hs63SlHxjw