Jev beats Gemini 2.5 Flash Lite on classifier eval quality and speed
Malte Ubl ran TypeSafe AI's Jev against an existing classifier eval, where it saturated the eval on quality and was 6x faster than Gemini 2.5 Flash Lite.
Using Jev for classification instead of k-means
The author tried Jev for classification on labeled coordinate-point datasets instead of k-means, achieving high accuracy with errors only on edge cases, and sees potential for zero-shot applications.
大分試すの遅くなったけど、Jevをk-meansクラスタリングの代わりに分類させてみた。 座標点と正解ラベルがペアになった各群30点のデータセットを食わせてみたところ、割と正解率が高い。 サンプルデータに含まれてないエッジや境界部分で誤答してるけど、ゼロショットを考えればいろいろ応用できそう
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