Expand Fetch 发布网页抓取与爬取 API，含 Iris 与 X-Ray 两个模型
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Tim Suchanek 介绍 Expand Fetch，一种新的网页抓取与爬取方式，训练了 Iris 视觉模型和 X-Ray JSON 模型两个 System one 模型，分别保留表格、图标与布局，以及提取页面背后的隐藏数据。每次抓取附带 Wayback Machine，每行内容可回溯来源。在覆盖 326 个网页、1094 个问题的基准上，使用 Expand 的智能体正确率为 74%，次优 API 为 55%，定价为每 1000 次抓取 1 美元，今日可用。
查看原帖Introducing Expand Fetch, a new way to scrape and crawl the web. Your agent has never seen the web. No wonder it gets basic questions wrong. Traditional Markdown APIs lose critical details. So we trained two System one models to change that: Iris, a vision model that sees pages like you do, preserving tables, icons, and layouts. X-Ray, a JSON model that…
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Introducing Expand Fetch, a new way to scrape and crawl the web. Your agent has never seen the web. No wonder it gets basic questions wrong. Traditional Markdown APIs lose critical details. So we trained two System one models to change that: Iris, a vision model that sees pages like you do, preserving tables, icons, and layouts. X-Ray, a JSON model that uncovers the hidden data behind every page. Every fetch also comes with a Wayback Machine. Every line links back to its source, so you can verify exactly what your agent saw. Three products. One API. On our benchmark of 1,094 questions across 326 challenging web pages, agents using Expand got 74% right. The next best API got 55%. Available today at $1 per 1,000 fetches. Try it at https://t.co/x4SDYsUX25
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