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.

这是编辑给出的评估,满分 5 分,不是用户打分的平均值。 本页的各方观点由 AI 根据产品信息归纳,不是真实用户的评价。
根据 OpenAITools.app 于 2026-11-11 核对的流量数据,Aquin(aquin.app)是一个收录在 AI 浏览器下的 AI 平台,月访问量为 0,月度环比流量变化为 +0(0%)。其主要地理市场为全球(— 流量份额),采用定价请咨询定价模式。按月流量计算的主要竞争替代方案包括 Dia 浏览器(125.42万次访问/月),Labelbox(76.28万次访问/月),DeepSeek(3.73亿次访问/月)。
Aleph 预测是真的稳,数据科学必备。
可解释性研究做得挺深,文档再详细点就好了。
牛!
门槛有点高,得懂点机器学习。
以下短评基于实时网页探查、定价机制与同类产品基准由 AI 综合模拟生成,旨在呈现不同使用场景下的常见槽点与亮点,非实名真实用户评价。
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.
同一分类下同类 AI 工具的流量并排对比
| 排名 | AI 工具 | 月访问量 | 月度环比增长 | 直接流量占比 | 定价 | 头对头 |
|---|---|---|---|---|---|---|
| ● | Aquin | 0 | 0% | — | 定价请咨询 | 当前产品 |
| 1 | Dia 浏览器 | 125.42万 | +108.4% | 59% 直接流量 | 免费 | 对比 Dia 浏览器 |
| 2 | Labelbox | 76.28万 | -22.8% | 49% 直接流量 | 定价请咨询 | 对比 Labelbox |
| 3 | DeepSeek | 3.73亿 | +4.3% | 78% 直接流量 | 免费增值 | 对比 DeepSeek |
| 4 | Deep Search | 35.9万 | -52.7% | 12% 直接流量 | 付费 | 对比 Deep Search |
| 5 | Adept | 1.23万 | -46.3% | 31% 直接流量 | 定价请咨询 | 对比 Adept |
| 6 | Scalebrowser | 16.2万 | +212.7% | 40% 直接流量 | 免费试用,付费 | 对比 Scalebrowser |
前沿 AI 训练所用的环境,以及负责重复性工作的智能体
免费 / 开源由 DeepSeek-V4 与 R1 驱动的高效开源权重与托管推理 AI,token 成本极低。
基于公开网络数据、每条信息可溯源的人物画像搜索

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免费 / 开源Scalebrowser is a desktop browser platform built specifically for AI agents. It gives each agent its own isolated browser profile with a persistent identity, so automations can stay signed in between runs instead of starting from a blank session every time. Agents connect over MCP, Python and Node SDKs, a REST API or direct CDP, and every tool call is captured as a step with a result and a screenshot.

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