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.
Most scrapers fight the rendered HTML. This goes upstream to where the data actually comes from, am I understanding that right? That's quite interesting.
What gets me most is the stability angle. Anything built on CSS selectors or DOM structure breaks the moment a site redesigns its front-end. If you're anchored to the underlying API calls instead, that problem should mostly disappear.
I'm building an AI platform that pulls structured data into its pipeline, so this is genuinely relevant to me. The edge case I keep running into with this type of approach: sites that sign their internal API requests dynamically, session tokens, HMAC signatures, that kind of thing. How does SCRAPR handle those? That's usually where it gets complicated in my experience.
@joao_seabra Yes, you’re understanding it correctly. The main idea is to focus on where the data actually comes from instead of relying on fragile DOM selectors, which is where a lot of traditional scrapers break when the UI changes.
For cases like signed requests, session tokens, or other protections, those are definitely some of the harder scenarios. SCRAPR doesn’t rely on a single rigid method there — it adapts to how the site normally serves its data and works within that flow.
The goal is not to bypass a site’s logic, but to make data extraction more stable and reliable compared to approaches that depend purely on the rendered HTML.
Also really cool to hear you’re building an AI platform around structured data pipelines — that’s exactly the kind of use case we’re seeing more of.
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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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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.
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@vouchy@vemulasukrit Does SCRAPR have a fallback when the fetch or GraphQL endpoints behind a page change or disappear? Going to the site's real data source feels much cleaner than chasing selectors, but that recovery path is what makes it production-safe.
@piroune_balachandran Good question. Yes — there is a fallback. If the underlying endpoints change or disappear, SCRAPR can fall back to extracting the content directly from the page structure instead.
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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?
@paxhumana Yeah that’s actually a great way to think about it 😄 it’s basically automating the kind of discovery people usually do manually in DevTools.
Under the hood it analyzes how modern sites load their data and figures out the clean data sources from there. It’s not tied to specific selectors or layouts, which helps it stay stable even when sites change their UI.
The goal is that you can throw pretty much any modern site at it and it will still find the structured data without needing manual setup. There are always edge cases of course, but it works reliably across a wide range of sites.
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The interception approach is clever, way faster than spinning up a headless browser for every request. Have you thought about a batch endpoint where you can throw a list of URLs at it in one call? Anytime I've built a scraping pipeline for a project, the single-URL-at-a-time loop is where things get slow and annoying to manage.
@juelz Thanks Julian, really appreciate that!
And yes — that’s a great point. Running things one URL at a time can definitely become slow when you’re building pipelines.
There’s already support for batch-style requests where you can pass multiple URLs in one call, and I’m planning to expand that further so it works better for larger data pipelines.
Really smart approach to web scraping. Focusing on where data actually comes from rather than relying on DOM selectors is a much more resilient strategy. Most scraping tools break the moment a site updates its frontend, so anchoring to underlying API calls makes a lot of sense.
Curious about how you handle rate limiting and sites that aggressively block automated access. Either way, congrats on the launch!
@handuo Thanks, really appreciate that!
For things like rate limiting or stricter access controls, it really depends on how the specific site handles requests. SCRAPR focuses on keeping requests lightweight and behaving like a normal client rather than relying on heavy browser automation.
SCRAPR
BrandingStudio.ai
Most scrapers fight the rendered HTML. This goes upstream to where the data actually comes from, am I understanding that right? That's quite interesting.
What gets me most is the stability angle. Anything built on CSS selectors or DOM structure breaks the moment a site redesigns its front-end. If you're anchored to the underlying API calls instead, that problem should mostly disappear.
I'm building an AI platform that pulls structured data into its pipeline, so this is genuinely relevant to me. The edge case I keep running into with this type of approach: sites that sign their internal API requests dynamically, session tokens, HMAC signatures, that kind of thing. How does SCRAPR handle those? That's usually where it gets complicated in my experience.
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
SCRAPR
@vouchy @vemulasukrit Does SCRAPR have a fallback when the fetch or GraphQL endpoints behind a page change or disappear? Going to the site's real data source feels much cleaner than chasing selectors, but that recovery path is what makes it production-safe.
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?
SCRAPR
The interception approach is clever, way faster than spinning up a headless browser for every request. Have you thought about a batch endpoint where you can throw a list of URLs at it in one call? Anytime I've built a scraping pipeline for a project, the single-URL-at-a-time loop is where things get slow and annoying to manage.
SCRAPR
Copus
Really smart approach to web scraping. Focusing on where data actually comes from rather than relying on DOM selectors is a much more resilient strategy. Most scraping tools break the moment a site updates its frontend, so anchoring to underlying API calls makes a lot of sense.
Curious about how you handle rate limiting and sites that aggressively block automated access. Either way, congrats on the launch!
SCRAPR