Aquin (Aquin Labs) is an AI research organisation working on interpretability and applied prediction. Its core project, Aleph, is positioned as a universal predictor for applied ML, data science and annotation work, producing schema-bound outputs such as probabilities, labels, boxes and scores that programs can consume. Alongside it the lab publishes research on attention circuits, weight editing and self-learning developer agents.

編集部による 5 点満点の評価です。ユーザー評価の平均ではありません。 このページの各視点は、製品情報をもとに AI がまとめたものです。実際の利用者のレビューではありません。
OpenAITools.app のテレメトリ(2026-11-11 検証)によると、Aquin(aquin.app)は AIブラウザに分類される AI プラットフォームで、月間 0 の訪問を生み出し、前月比のトラフィック増減は +0(0%)です。主要な地域市場はグローバル(トラフィックシェア —)で、料金は要問い合わせの料金モデルで運営されています。月間ボリューム別の主な競合代替ツールには Dia ブラウザ(月間 125.42万訪問)、Labelbox(月間 76.28万訪問)、DeepSeek(月間 3.73億訪問)が含まれます。
Alephの予測精度が高くて驚いた。
研究志向で面白いけど導入にコツがいる。
サイト検証・料金体系・同カテゴリ比較に基づき AI が多角的な論点を要約した参考視点であり、実在ユーザーの個別投稿ではありません。
A single base that handles tables, media, annotation and decision tasks, with the option to fine-tune toward your specific data.
Outputs are typed and scored rather than free text, so downstream programs can act on results and escalate low-confidence cases.
Treats attention as a routing map from a single forward pass, isolating a small subgraph aimed at the answer and testing it under ablation.
Agentic weight-editing experiments that localise causal layers, apply rank-one MLP updates and validate then roll back steps when checks fail.
An agent for controlling developer environments and running coding or research loops with recorded lessons and rejected paths.
Aquin (Aquin Labs) is an AI research organisation working on interpretability and applied prediction. Its core project, Aleph, is positioned as a universal predictor for applied ML, data science and annotation work, producing schema-bound outputs such as probabilities, labels, boxes and scores that programs can consume. Alongside it the lab publishes research on attention circuits, weight editing and self-learning developer agents.
Point Aleph at your tables and media assets to produce labelled, scored outputs that a production system can consume.
Use confidence scores to route only the genuinely uncertain cases to a human reviewer, keeping review time focused.
Apply attention-circuit and weight-mechanism findings, and the interpreter tooling, to understand what a model is relying on.
Use the aq developer environment and axi agent to iterate on models and measure changes with structured lessons and rejected paths.
同じ主要カテゴリの競合 AI ツールをテレメトリで並列比較
| 順位 | AI ツール | 月間訪問数 | 前月比成長率 | 直接流入の割合 | 料金 | 直接比較 |
|---|---|---|---|---|---|---|
| ● | Aquin | 0 | 0% | — | 料金は要問い合わせ | 表示中 |
| 1 | Dia ブラウザ | 125.42万 | +108.4% | 直接流入 59% | 無料 | vs Dia ブラウザ |
| 2 | Labelbox | 76.28万 | -22.8% | 直接流入 49% | 料金は要問い合わせ | vs Labelbox |
| 3 | DeepSeek | 3.73億 | +4.3% | 直接流入 78% | フリーミアム | vs DeepSeek |
| 4 | Deep Search | 35.9万 | -52.7% | 直接流入 12% | 有料 | vs Deep Search |
| 5 | Adept | 1.23万 | -46.3% | 直接流入 31% | 料金は要問い合わせ | vs Adept |
| 6 | Scalebrowser | 16.2万 | +212.7% | 直接流入 40% | 無料トライアル、有料 | vs Scalebrowser |
無料・オープンソースDeepSeek-V4 と R1 を中核とし、極めて低いトークンコストで提供される高効率なオープンウェイト/ホスト型推論 AI。
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