I built SCRAPR after running into the same problem over and over:
getting structured data from websites is still way harder than it should be.
Most scraping tools today fall into two buckets:
• Browser automation (Puppeteer / Selenium) — slow, fragile, and expensive
• Traditional scrapers — break constantly on modern JS-heavy sites
SCRAPR approaches the problem differently.
Instead of rendering the page or parsing messy HTML, SCRAPR intercepts the real network calls websites use internally, then reconstructs clean structured data from them.
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How does this engine handle JavaScript-heavy or dynamic content without a browser, and what mechanisms ensure data accuracy when the source website changes its layout?
@mordrag For JavaScript-heavy sites, the engine doesn’t use a browser. Instead it looks at the page’s code and finds the API requests the site uses to load its data (like fetch, axios, or GraphQL). Then it calls those data endpoints directly and pulls the real content from there. This makes it much faster and lighter than running a full browser.
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Looks cool — but how well does it actually handle hard targets like Cloudflare, JS-heavy sites, proxies, and rate limits in the real world?
@paradox_hash The engine doesn’t rely on browser automation, so for JS-heavy sites it looks for the actual data endpoints the site calls (APIs, GraphQL, fetch requests) and pulls data directly from those. That avoids a lot of the usual scraping issues.
For things like rate limits or protection layers, it behaves more like a normal HTTP client and adapts requests rather than brute-forcing pages.
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Hey Sukrit, that frustration of scraping tools either being slow and fragile or breaking constantly on modern sites is painfully real. Was there a specific project where you watched your scraper break for the tenth time on some JS-heavy page and thought okay, there has to be a completely different approach?
@vouchy Yeah honestly that exact frustration is what started it 😅
I kept hitting sites where traditional scrapers would either break when the layout changed or become super slow because they needed a full browser. After dealing with that enough times, it felt obvious that the approach itself needed to change.
So instead of relying on fragile selectors or browser automation, the engine focuses on understanding page structure and the data sources behind the page. That way it’s much less likely to break when the UI changes.
This approach is super clever — basically doing what I always do manually in Chrome DevTools Network tab (hunting for those fetch/GraphQL calls) but automated 😮
Does the engine just statically analyze the page source to find those internal API requests, or does it use AI/LLM in some way to detect and reconstruct the right endpoints even on tricky sites?
And how well does it handle completely arbitrary URLs — like, throw any random modern site at it and it still finds the clean data source reliably?
SCRAPR
How does this engine handle JavaScript-heavy or dynamic content without a browser, and what mechanisms ensure data accuracy when the source website changes its layout?
SCRAPR
Looks cool — but how well does it actually handle hard targets like Cloudflare, JS-heavy sites, proxies, and rate limits in the real world?
SCRAPR
SCRAPR
AutonomyAI
This is such a clean solution to a problem that's been annoying developers forever. Rooting for you!
SCRAPR
This approach is super clever — basically doing what I always do manually in Chrome DevTools Network tab (hunting for those fetch/GraphQL calls) but automated 😮
Does the engine just statically analyze the page source to find those internal API requests, or does it use AI/LLM in some way to detect and reconstruct the right endpoints even on tricky sites?
And how well does it handle completely arbitrary URLs — like, throw any random modern site at it and it still finds the clean data source reliably?
Sounds cool. Would love to try it out for example on https://www.maxxi.art/events/categories/mostre/