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When Traditional Web Scraping Fails: A Practical AI Approach

DEV Community: webdev·zhongqiyue·3 days ago
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I've been building web scrapers for years. BeautifulSoup, Scrapy, Selenium — I've used them all. But last month I hit a wall. A client needed me to extract product data from a site that changed its HTML structure every few days. One week the price was in a <span class="price"> , the next it was inside a <div> with a random ID. My scraper kept breaking, and I was spending more time fixing selectors than actually getting data. The Problem The site was a dynamic e-commerce platform. It used JavaScript to render content, and the developers seemed to enjoy shuffling class names. I tried the usual suspects: BeautifulSoup + requests : Failed because the content was loaded via JS. Selenium : Worked, but was slow and brittle. Every layout change required updating XPaths. Playwright : Same story, just faster. I needed something that could understand the meaning of the data, not just its position in the DOM. That's when I thought: why not use an AI model to read the page like a human would?…

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