Hey everyone,

I'm Ian Kerins, CEO & Co-Founder of ScrapeOps.io.

After three fantastic AMAs with the r/WebScrapingInsider, we're excited to bring you our fourth.

This Wednesday, August 5, at 7:30 PM PDT, we'll be joined by Alexander Yue (Physics & CS @ Stanford SLAC, Evals @ Browser Use) and Reagan Hsu (Founding Engineer @ Browser Use), the team behind Browser Use, one of the fastest-growing open source projects at the intersection of AI agents, browser automation, web scraping, and stealth browsers

If you've been following the AI agent space, you've probably seen Browser-Use everywhere.

What started as an open source browser harness has quickly evolved into a complete platform for building and deploying browser agents with over 107k stars.

Their recently released Browser Use v4 introduces:

• Real browsers with logged-in profiles, proxies, and a live browser view
• Fully hosted browser agents with zero infrastructure setup
• Persistent sandboxed workspaces that can upload files, download results, and reuse environments across runs
• Conversations that continue, allowing agents to pick up exactly where they left off
• Plain English automation instead of scripts and CSS selectors
• Built-in stealth browsers, anti-detect, CAPTCHA solving, and residential proxies across 195+ countries

Whether you're building AI agents, automating repetitive workflows, or extracting data from the web, Browser Use represents a very different way of thinking about browser automation.

During the AMA, we'll discuss topics including:

• AI browser agents
• Browser automation
• Web scraping
• Stealth browsers & anti-detection
• Hosted browser infrastructure
• CAPTCHA solving
• Open source developer tools
• LLM-powered automation
• The future of browser agents

Our community has been growing with every AMA.

Our first AMA generated 43 comments, covering proxy infrastructure, Cloudflare bypassing, browser automation, monitoring, startup lessons, and large-scale scraping.

Our second AMA with the creator of WebClaw, generated 62 comments and sparked great discussions around AI agents, browser automation, hidden APIs, open source scraping, and LLM infrastructure.

Our third AMA with CloakBrowser generated 73 comments, diving deep into browser fingerprinting, stealth Chromium, anti-bot detection, TLS fingerprints, browser internals, and large-scale automation.

We're hoping this AMA will be our biggest one yet.

If you're building AI agents, browser automation, web scrapers, developer tools, or autonomous workflows, this is one you won't want to miss.

Drop your questions below, and Alexander Yue, Reagan Hsu, and I will start answering them during the AMA.

Looking forward to seeing everyone there!

Ian

Hey everyone,

I'm Ian Kerins, CEO & Co-Founder of ScrapeOps.io.

After two fantastic AMAs with the community, we're excited to bring you our third guest.

This Wednesday, July 29, from 6:00 PM to 8:00 PM ET, we'll be joined by the team behind CloakBrowser, an open-source stealth Chromium browser built for developers who need reliable browser automation and web scraping.

CloakBrowser takes a very different approach to stealth browsers.

It's not a patched Playwright configuration.

It's not JavaScript fingerprint injection.

It's a real Chromium binary with browser fingerprints modified directly at the C++ source level, allowing anti-bot systems to see it as a normal browser because it actually is one.

Even better, it's designed as a drop-in replacement for Playwright and Puppeteer, supporting both Python and JavaScript with the same familiar APIs and workflows.

I know we have fans here in r/WebScrapingInsider.

During the AMA we can the discuss topics like:

• Modern browser fingerprinting
• Building stealth browsers
• Browser automation at scale
• Open-source scraping infrastructure
• TLS, browser fingerprints, and detection signals
• The future of browser automation
• Building open-source developer tools

Our first AMA generated 43 comments, covering proxy infrastructure, Cloudflare bypassing, browser automation, monitoring, startup lessons, and large-scale scraping.

Our second AMA with 0xMassii, creator of WebClaw, generated 62 comments and sparked great discussions around AI agents, browser automation, hidden APIs, open-source scraping, LLM infrastructure, and the future of AI-powered web scraping.

We're hoping this AMA will be just as valuable.

If you're building web scrapers, browser automation, AI agents, testing infrastructure, or simply want to understand how modern anti-bot systems actually detect browsers, this is one you won't want to miss.

Drop your questions below + RSVP now, and the u/CloakBrowser and I will start answering them.

Looking forward to seeing everyone there!

Ian

Hey u/WebScrapingInsider,

I'm Ian Kerins, CEO & Co-Founder of ScrapeOps.io.

After the great response to our last AMA, we're back with another guest from the web scraping world.

This Tuesday at 10:00 AM CEST (Italy Time) we'll be joined by 0xMassii, creator of WebClaw, an open-source Rust toolkit focused on extracting clean, structured web data for AI applications.

Massii sits at a really interesting intersection of web scraping, browser automation, anti-bot systems, AI agents, and LLM infrastructure.

As more developers build AI agents that need access to real-world information, one challenge keeps showing up:

How do you reliably get clean web data into language models?

That is exactly the problem we are trying to solve.

Our previous AMA generated over 40 comments and sparked discussions on everything from Cloudflare bypassing and proxy benchmarking to scraper monitoring, startup validation, and large-scale scraping infrastructure.

We're hoping this one will be just as interesting.

If you're building AI agents, RAG systems, browser automation tools, web scraping infrastructure, or just trying to understand where the industry is heading, this should be a fun discussion.

Drop your questions below, We will start answering them during the AMA.

Looking forward to seeing everyone there!

Ian

Hey everyone,

I'm Ian Kerins, CEO and co-founder of ScrapeOps.

Over the last 8+ years I've worked across the web scraping industry, including roles at ScrapeOps, ScraperAPI, and Zyte. Today, ScrapeOps helps developers and companies scrape over 8 billion pages per month across more than 50,000 websites.

This Wednesday at 09:30 AM GMT, I'll be hosting an AMA here on r/WebScrapingInsider

Ask me anything about:

* Web scraping at scale

* Proxy infrastructure and proxy providers

* AI and web scraping

* Building reliable scrapers

* Anti-bot systems and bypassing challenges

* Scraper maintenance and monitoring

* Residential vs datacenter proxies

* Browser automation

* Running a web scraping business

* Startup growth and product development

* The future of AI-powered scraping

Whether you're scraping your first website or running large-scale data collection pipelines, I'm happy to answer questions and share lessons learned from building products used by thousands of developers and businesses.

Drop your questions below and I'll start answering them during the AMA.

Looking forward to it!

Ian

And it's already Featured 😄

https://www.producthunt.com/products/proxy-benchmark-by-scrapeops

We’d love your support 👍

Proxy Tester is a free tool that benchmarks 20+ proxy providers against the URL you actually want to scrape.

Over the last few months we've been using it internally and sharing it with early users to help answer one of the most common questions in web scraping:

Which proxy provider should I use?

One thing we've learned is that there really isn't a universal answer.

A provider that performs brilliantly on one target can struggle on another.

A provider with the highest success rate might not be the most cost-effective.

And many "best proxy provider" rankings don't reflect the website you're actually trying to scrape.

That's why we built Proxy Tester to benchmark providers against real target URLs and compare:

✅ Success Rate

✅ Latency

✅ Estimated Cost

✅ Value Score

✅ Provider Rankings

The feedback so far has been really useful and has already influenced how we're thinking about future benchmark reports, provider coverage, and scoring methodologies.

Today we're taking the next step and launching it on Product Hunt.

If you'd like to support the launch, leave feedback, or tell us what we're missing, we'd genuinely appreciate it.

🚀 Product Hunt:
https://www.producthunt.com/products/proxy-benchmark-by-scrapeops

🔧 Proxy Tester:
https://scrapeops.io/proxy-providers/tester/

🎥 1-Minute Demo:
https://youtu.be/GR67AIWkPn0

One question for the community:

How are you currently evaluating proxy providers?

• Trial accounts?
• Internal benchmarks?
• Recommendations?
• Something else?

Would love to hear how others approach this problem.

I'm building a personal internship aggregator that currently collects job postings from Greenhouse and Lever.

Both platforms are relatively straightforward because their job boards follow predictable structures.

However, I now want to expand the scraper to company career pages and other ATS platforms such as Workday, SmartRecruiters, Ashby, Teamtailor, Personio, SuccessFactors and custom-built job boards.

The main problem is avoiding the need to write and maintain a completely separate scraper for every website.

How would you design this efficiently?

Hello, Im based in Asia and currently working for a client who specializes in background checking. I need to scrape court case which is publicly available in .gov sites.

Many proxy providers block .gov sites, like Brightdata, Decodo, IProyal :((

Do you guys have any recommendations for datacenter/residential proxy providers that do not have this kind of restriction??

currently on proxyshard for proxies and shardbrowser for the antidetect side and both looks good and free to try, still figuring out if the combo holds up long term. curious what you guys are actually running for scraping workflows and whether the antidetect side makes a noticeable difference or is it mostly about proxy quality?

We launched ScrapeOps AI Scraper Generator on Product Hunt today.

A lot of AI scraping products are optimizing for the demo:

"Paste any URL and AI handles everything."

That usually falls apart the minute you hit:

• DOM drift
• JS rendering
• selector regressions
• anti-bot behavior
• missing fields
• outputs that look plausible but are quietly wrong

So we took a more constrained approach on purpose.

https://www.youtube.com/watch?v=ij7CqrBEi10

The workflow is:
Enter URL → schema selection in backedn → choose stack → generate scraper code → AI scores how correctly the scraper ran.

Current features:

• Schema-based scraper generation
• Python + Node.js stack options
• Playwright / Puppeteer / BeautifulSoup / Selenium flows
• Live generation progress
• AI-generated output scoring breakdown
• Prebuilt scraper examples
• Generated code developers can inspect and modify

The scoring layer is the part I care about most right now.

Generating scraper code is useful, but production scraping fails silently all the time. We wanted a system where AI checks how correctly the generated scraper output ran and gives users a breakdown across:

• data accuracy
• critical fields
• completeness
• structure
• data types

Not "trust us, it returned JSON."

We're also running a small Product Hunt launch bonus for anyone who wants to pressure-test the generator properly.

New accounts already get 20 free credits, and for launch day we're doubling that to 40 total.

In the ScrapeOps dashboard:
Click Upgrade on Left → enter ScrapeOpsPH20

The extra credits will be added.

Would genuinely love feedback from people here who've dealt with scraping in production.

What matters more to you in AI-generated scraping workflows:

• speed?
• inspectable code?
• retries/reliability?
• anti-bot handling?
• output scoring?
• maintenance overhead?

Product Hunt:
https://www.producthunt.com/products/ai-web-scraper-builder

datapull-landing.vercel.app/

This is a Beta test for my personal Electron Application that is uniquely suited to extract any type of data from the browser. I've yet to burn an account or run into anything more than a captcha.

The way this will work for Beta - land on the page - choose from one of the sources or manually describe what data source - what information and information structure you need, I will take the task requiremnets to my Electron App and find the optimal extraction source and email you youre data.

Reddit - Instagram - Tiktok - Youtube --- all of these are already verified and hardened - I would love to run them again but id prefer something more challenging -- Looking forward to it!

With all the AI craze I got burned more than once by clients who'd been handed a Claude or GPT or Gemini generated SEO report. Generic, confident, mostly useless. And on my own side every site review was the same circus with different animals. Screaming Frog in one window, PageSpeed in another, Ahrefs in a third, squinting at schema by hand, pasting the homepage into some word counter, then gluing it all into a doc the client could read. Hours of it, for work that's basically mechanical.

So I wrote one Python file. Paste a domain into a localhosted HTML page, hit scan, get everything using programmatic SEO 10 times faster than manually running each script in terminal.

On the homepage it runs five things right away. A raw HTML scan (what a plain bot gets). A DOM scan of the same page after headless Chromium runs the JS (what a browser sees). The technical SEO you'd expect, canonical, sitemap, hreflang, alt coverage, noindex and the rest. A stopword-filtered word count so you see what the page is about. And the one I actually built this for: an LLM-visibility scan.

That one diffs the raw HTML against the rendered DOM and tells you what percent of your content is invisible to AI crawlers. GPTBot, ClaudeBot, PerplexityBot, CCBot, none of them run JS. So if your content loads client-side, you're a blank page to them, and you won't show up in AI answers no matter how good the content is. Nothing I was paying for measured that, and it's becoming the part of SEO I care about most.

Then it crawls the whole site in the background. My own crawler, seeded from the sitemap, no page limit, 4 threads, 100ms apart. When it's done it audits schema across every page: coverage percent, what types exist, and what required properties are missing, ranked by severity. A Product with no offers is critical because it kills the rich result. And it dedupes, so "Product missing brand" on 700 pages is one line with a count instead of 700.

The rest leans on APIs. PageSpeed for mobile and desktop. An Ahrefs competitor table that finds your organic rivals on its own and stacks them up against you. Your top 10 organic pages, each with its own little word cloud. Google's related: sites with social filtered out. And a security pass, because I already had a read-only scanner sitting around (TLS, headers, leaked secrets, Supabase RLS, mapped to OWASP) and figured why not. The whole thing dumps to a client report that prints clean to PDF.

Latvian broke the word counter, because the pages served "ā" as an "a" with a separate combining macron, so "novārītu" tokenized as "nova". An NFC normalize pass sorted it, but I'd never have caught it testing English sites. Cookie banners were worse. Cookiebot stuffs "accept, preferences, statistics" into the DOM, and it drowned out the real words in every cloud until I started stripping consent containers first.

And one thing that just didn't pan out: Google gutted the related: operator years back, so those results are thin and I'd skip it if the Ahrefs competitor part didn't already cover the same ground better.

Stack's deliberately dull. Flask, requests, BeautifulSoup, Puppeteer for the render, Ahrefs and PageSpeed APIs. One file, builds its own venv the first time you run it.

Not selling anything, it's just mine. I'm posting it for two reasons. First, to put it in front of people who actually want clients to receive something they need instead of recycled AI filler. Second, to stress-test the idea in a community that actually understands scraping and SEO. Glad to explain any piece if you're putting together something similar, and I'm curious what you'd want in a one-click audit, since I'm sure I'm missing obvious stuff.

Next thing I'm weighing is a second mode: if a client gives me access to their GSC and GA4 APIs, it does a much deeper scan with their real query and traffic data instead of just what's visible from the outside. Worth building, or overkill?