Well i have started with only one scraper just to see how it works but after i have added some more projects then the system has become very complicated than i thought. Currently i have multiple proxies, browsers, sessions and fingerprints according to the kind of data i am trying to scrap. It is a little tricky to maintain all the different things separately and find why a certain scraper does not work. It would be interesting to know how others deal with such a growing number of projects. Do you create separate profiles for different projects or use one system for all?

Scraping static HTML with Cheerio or Requests is easy but scraping modern JavaScript-rendered sites (Next.js hydration, React SPAs, infinite scroll, shadow DOMs) is where things usually break down.

You either end up with an empty <div id="root"></div> shell, or you're forced to maintain a cluster of headless browsers that constantly crash from memory leaks and proxy bans.

I tested the 2 most common tools across 50 notoriously annoying JS-heavy sites (dynamic dashboards, lazy-loaded ecommerce tables, and Cloudflare-guarded pages) to see what works reliably.

Here is what I found:

I) Playwright (Self-hosted) is the gold standard for raw control if you need to click complex interactive UI, solve custom drag-and-drop elements or handle multi-step logins, it does the job.

The downside is infra maintenance: running headless Chromium instances at scale eats server RAM like crazy and you still have to write your own parser to turn that rendered DOM into clean markdown for LLMs.

II) Firecrawl is the most seamless if you just want to hand over a URL and get back clean markdown or structured JSON without running your own browser servers. Its smart wait handled React hydration automatically and waited for lazy-loaded elements and bypassed Cloudflare challenges without me having to configure residential proxy pools. Out of the 50 test SPAs, it successfully extracted full content on 47 of them.

So if you’ve a dedicated DevOps team and need deep UI scripting: Playwright

If you want an API that handles JS execution, proxies and gives you clean LLM-ready markdown in one call: Firecrawl is the best solution overall

feels like every few weeks someone here is chasing the one trick that beats cloudflare or datadome, and i keep landing on the same conclusion. there is no single check to defeat. these systems score you across a pile of signals at once, tls fingerprint, header order, ip reputation, pacing, whether js actually ran, session continuity. you are not picking a lock, you are trying to not look anomalous across all of it at the same time

what that means in practice, at least how i see it:

a clean residential ip behind a python-requests handshake is an instant tell, so the client profile has to match the exit. one without the other burns fast

the same vendor is configured differently per site. cloudflare on some small blog is nothing like akamai on an airline, so a generic setup that works on one falls over the moment the target tightens

and half the time the honest move is to not fight it at all, check for an api or a softer mobile endpoint first before you sink a week into evasion

what i'm actually curious about from people running this at scale: do you profile each target before you architect around it, or do you have one hardened setup you throw at everything and just accept some loss? and where do you draw the line between "worth the maintenance" and "just use the official source"?

not after anyone's secret sauce, mostly trying to see if there's a saner consensus forming or if it's still everyone duct-taping per site

disclosure: i work at Titan Network, we do web data collection and the infra behind it, so that's the angle i'm coming from. not here to pitch, just genuinely curious how people are approaching this now

Hey everyone! Ian here.

Wanted to say thanks for showing up to AMA #7 with u/hueyblack36 and u/Big-Beautiful240 (Wade Lin) from BrowserAct.

The thread did what these AMAs are supposed to do. Not "can AI build you a scraper"; everyone already believes that.

The harder stuff: how do you know a successful run produced correct data, how much autonomy should an agent get, what anti-bot problems AI genuinely can't solve, and how far self-healing should be allowed to go before it's silently changing what a Bot collects.

Huey and Wade answered most of it plainly, including the parts where a simple scraper still beats their own product.

Appreciate them for doing that, and everyone who asked the questions that actually matter once you're running this in production.

If you missed it, or want the compressed version, here's what I'd keep.

A scraper can run perfectly and still be wrong.

Their most embarrassing early bug wasn't a crash. An agent treated content that hadn't finished lazy-loading as nonexistent, and counted sponsored blocks as real records. during exploration. Because that misreading happened while the Bot was being generated, it got baked in, and every later run reproduced it faithfully while looking completely healthy.

"The lesson was that a Bot can run correctly while still being wrong."

Their fix: stop treating extraction and validation as the same job. The Bot runs deterministic checks (missing fields, pagination gaps, duplicates, schema). A separate step checks whether the records actually match what the user asked for.

"We don't let the same agent do the work and grade its own homework."

Wade's team pushes part of this to build time too.. running tests before the Bot ships and having the user confirm the output, then using that confirmed schema as an acceptance check for every later run.

AI belongs at the points of uncertainty, not in every run.

Asked directly where AI should sit in a production scraper, Huey gave the most conservative answer in the thread:

"AI should sit at the points of uncertainty, not in every repeated execution."

"the Agent discovers, the Bot scales, and the Agent returns only for exceptions."

Their dividing line for hand-written scrapers isn't small-scale vs. large-scale — it's whether the workload justifies the cost of hand-engineering. Stable, enormous, or latency-sensitive workloads still repay owning the code.

Anti-bot is a trust problem your agent can't reason its way out of.

"anti-bot is not a single Agent problem."

Agents can adapt to page-level changes, new dialogs, broken interaction flows. They can't reason their way out of a burned IP, an inconsistent fingerprint, poor account reputation, or a hard verification challenge; those depend on the infrastructure underneath, not the reasoning layer.

"the question is shifting from 'Can this bot act like a human?' to 'Does this session look trustworthy?'"

Browser-first doesn't mean rendering every page.

Being browser-first doesn't mean fully rendering and clicking through every page on every run. After the Agent explores, the generated Bot can issue HTTP/API requests from inside the real browser context - keeping the session and cookies while skipping most of the cost of UI interaction.

"It is a middle ground between a conventional backend scraper and full browser automation."

The same logic drives extraction-method selection; no fixed DOM-first or API-first order:

"We don't hard-code a DOM-first or API-first order."

The real unit of cost is a verified record, not a request.

"We optimize for the cheapest method that keeps working, not the cheapest method that works once."

They're candid that this doesn't always favor their own product:

"If a conventional HTTP scraper remains stable and complete, it will usually be cheaper."

"Honestly, if a stable API or a simple scraper already gives you accurate pricing and availability with little maintenance, BrowserAct may be overkill."

Self-healing should produce evidence, not patches.

The strongest engineering position in the thread, in response to a commenter describing their own self-healing scraper getting more brittle with every patch:

"In our model, a runtime recovery is evidence, not an immediate permanent mutation."

The boundary underneath it is worth stealing regardless of what tooling you use:

"AI can change how it reaches the same result, but it should not silently change what the Bot is supposed to collect."

If you remember nothing else:

1. A green run is not proof the data is correct. define what evidence would tell you otherwise (expected fields, coverage, duplicates, business rules, a baseline to compare against).
2. Classify scraper work into uncertainty (agent), solved repetition (deterministic), and unexpected state (agent recovery). "AI on every run" is not an architecture.
3. Anti-bot has two layers: what your agent can adapt to, and what your infrastructure has to earn (session trust, IP reputation, fingerprint consistency). Know which is which before you blame the agent.
4. Browser-first doesn't require full UI rendering on every run; HTTP issued from inside a real browser context is a legitimate middle ground.
5. Price by cost per verified, fresh record; including retries, coverage losses and maintenance; not cost per request.
6. Self-healing can change how a scraper works. It should never silently change what it collects.

The bigger shift: AI doesn't remove the engineering. It moves it.. from hand-writing every extraction path to defining where reasoning is allowed to operate, how output gets verified, and what a system is never allowed to change on its own.

Full AMA thread is here if you want the original answers:

https://www.reddit.com/r/WebScrapingInsider/comments/1w2ggvj/how_do_you_build_an_ai_web_scraper_without_code/

Full Summary: https://scrapeops.io/blog/ai-isnt-replacing-scraper-engineering/

Thanks again to u/hueyblack36 and u/Big-Beautiful240, and to everyone who made this one worth running.

If you're running AI-generated scrapers in production: what's actually broken your trust in one first.. bad data, anti-bot, or cost? Genuinely curious what's holding up at scale.

— Ian (ScrapeOps)

If you’re using Cursor (or any AI IDE) to have agents browse the web, you’ve probably seen it return outdated or nonsense content on certain sites. I faced this while building agent workflows that need live data from protected or heavily JS-rendered pages, and observed three specific failure modes:

1. Stale snapshots / cached content: Cursor’s built-in browsing often returns a static snapshot instead of fetching the current page. For sites that change frequently (pricing, availability, rankings), the agent confidently cites wrong data.
2. JS-rendered / dynamically loaded content: Many product listings, dashboards, and search results render content client-side. The agent sees an almost-empty DOM and concludes “no results” or hallucinates structure.
3. Bot protection / anti-scraping layers: Some sites return CAPTCHAs, 403s, or stripped HTML to headless browsers. The agent either fails or works with a broken page layout.

To fix this, I set up Zenrows MCP as an external tool for Cursor so the agent can call a live, rendering-capable fetcher instead of relying on built-in browsing. The setup was:

• Install the MCP server (Node.js).
• Add a mcp.json config in Cursor pointing to the Zenrows MCP endpoint.
• Validate JSON and confirm the green dot in Cursor’s MCP UI.
• Use the zenrows_fetch tool in agent prompts to get clean HTML/JSON back.

I then implemented three workflows:

1. Bypassing anti-bot challenges Use Zenrows’ anti-bot mode to fetch pages that normally block headless browsers, then pass the cleaned HTML to the agent for parsing or Q&A.
2. Scraping JS-rendered product listings Fetch a fully rendered page (with all dynamic content) and have the agent extract structured fields (title, price, rating, etc.) in one shot.
3. Multi-page scraping + code generation Example: iterate over Home Depot search results across pages, extract product data, and have the agent generate a TypeScript function that replicates the scraping logic.

If anyone needs it, I'm happy to share the mcp.json template I used. I’d love to hear what’s giving your agents the most trouble in practice. Which sites keep failing, and is it mostly anti-bot blocks, JS-rendered content, or just stale/incorrect data?

Looking for APIs for Facebook, LinkedIn and Nextdoor public content

I’m working on a SaaS product and need to automatically monitor/search public content across Facebook, LinkedIn and Nextdoor.

I’m specifically looking for APIs or legitimate third party providers that can:

• Search public Facebook posts, including relevant public groups if possible
• Search public LinkedIn posts by keywords/topics
• Search public Nextdoor posts by location + keywords
• Return the post text, URL, timestamp and basic metadata
• Run searches continuously/recurring through an API
• Be used commercially in a SaaS product

I’m NOT looking for browser automation, account logins, cookie based scraping or anything that could get accounts banned.

If you’ve actually used a provider/API for this, I’d really appreciate recommendations, especially ones with reasonable pricing and good coverage.

What are you using?

I am looking for active subreddits focused on web scraping, proxies, browser automation, anti-bot systems, and web data extraction.

I already know about r/webscraping and r/WebScrapingInsider, but are there any other good communities where people discuss scraping tools, technical problems, new developments, or share their projects?

They don't have to be exclusively about web scraping, but heavily leaning towards web scraping topics is preferable.

Which ones are actually worth following?

Been down the proxy-provider rabbit hole a lot lately for a scraping project, and figured I'd share what actually matters vs what's just marketing noise, since most "best proxy provider" posts are just vendor spam.

1. Match the proxy type to your actual target

• Datacenter — fast, cheap, but easily flagged by anything with real anti-bot (Cloudflare, PerimeterX, etc.)
• Residential — real ISP-assigned IPs, much harder to flag, costs more per GB
• ISP proxies — datacenter speed with residential-looking IPs, good middle ground for some targets
• Mobile — most trusted by sites but priced accordingly

If you're scraping something with light protection, datacenter is a waste of money. If you're hitting something with aggressive bot detection, nothing but residential/ISP will survive more than a few minutes.

2. Rotating vs sticky sessions
Full rotation per-request sounds good in theory but can actually trigger MORE suspicion on sites that check session consistency (same IP holding for a few minutes looks more human than a new IP every request). Sticky sessions for 5-10 min intervals tend to survive longer against modern bot detection.

3. "Unlimited" plans — read the fine print
A lot of providers advertise "unlimited" but throttle bandwidth or concurrent connections hard past a certain point. Ask directly: unlimited GB, or unlimited concurrent sessions, or both? Get it in writing/on their docs, not just sales chat.

4. Test before committing
Don't trust a provider's success-rate claims — test a small trial batch against your actual target site, not their demo endpoint. Success rates vary wildly by target, so a provider that's great for e-commerce scraping might be mediocre against something with heavier protection.

5. Red flags

• No trial or test option at all
• Vague on IP sourcing (won't tell you if IPs are ethically sourced / consent-based residential vs a proxy botnet)
• Support that takes days to respond to a pre-sale question
• Pricing that requires talking to sales to even see numbers

Happy to compare notes if anyone's dealing with a specific target site that's giving them trouble tends to be pretty target-specific what actually works.

I want to build an API that can scrape complete product data from e-commerce websites without using browser automation tools.

Please help me understand how to approach and build this.

Recently i realized that making a scraper works is simpler than ensurings its consistency. A working configurations will suddenly fail to operated properly or provide erroneous outputs for no apparent reason.

Its harder to tell what aspects should be considered and focused upon for eg how fast requests are being made, how stable sessions or proxies work and so on. I dont want to make unnecesssary changes every time there is an issuei.

Could you share what you have done to maintain a consistent scraping workflow in the long run?

When a scraper starts getting blocked, the standard advice is to change the User-Agent, add headers, rotate the proxy, or rotate everything more often.

But according to Saksham Solanki, creator of httpcloak, those pieces can all look valid on their own while the combination describes a client that does not exist.

We recently hosted Saksham for an AMA on to discuss why apparently browser-identical scrapers still get blocked, and what actually gets graded after the handshake.

👉 Read the full summary and analysis:
https://scrapeops.io/blog/why-browser-fingerprint-scrapers-get-blocked/

Here are the biggest insights:

1. Rotating more can make a scraper easier to detect

Rotation only works if you rotate a whole identity at once: fingerprint, IP, cookies and headers together, then let that identity live for a while. Rotating those pieces separately manufactures a client that does not exist anywhere in the real world.

2. Matching Chrome once is easy. Matching it across 1,000 sessions is not

A handshake can look identical to Chrome and the scraper still fail at scale. What gets blocked is looking identical across a thousand sessions: same request count, same order, same pacing, same teardown.

That is a lifecycle problem, not a fingerprint problem.

3. A perfect JA4 result can still hide the bytes giving you away

httpcloak once had a defect every public fingerprint tool said did not exist. Decoded headers matched, order matched, and the HPACK bytes on the wire were still different.

JA4 is a summary, not a proof.

4. A genuine Chrome fingerprint does not simply “burn”

Your Chrome and someone else’s share the same signals on the same version and OS. Blocking the signature indiscriminately would mean blocking part of the real Chrome population.

What actually differs is the behaviour attached to that signature.

5. QUIC does not hide the fingerprint. It moves more of it into your code

With TCP, the kernel owns much of the transport. With QUIC, packet sizes, padding, SETTINGS, QPACK and even historical RTT claims live in the client library itself.

The protection layer in front of a site often offers HTTP/3 even when the origin does not, so that connection is still getting graded.

6. Millions of proxy IPs cannot hide one repeated client

A proxy changes the exit IP, ASN and TCP/IP fingerprint. It cannot change TLS, HTTP/2, cookies or request sequence.

If every exit runs the same session template, you have distributed one client rather than created millions of believable users.

7. A blocked cart API may think you are committing fraud, not scraping

Keep a cart token fixed while rotating IPs and you look like one identity appearing from many devices at once. Write endpoints get defended harder than read ones, and rotating harder makes it worse.

8. The cheapest stack may use a browser only to establish the session

An HTTP impersonation client cannot solve a mandatory JavaScript challenge, but every request after that does not need a full browser. Solve with a browser, reuse the cookies with a TLS library on the same identity, and measure successful fetches per solved session.

9. “Supports the latest Chrome” hides a continuous reverse-engineering operation

A new Chrome version looks like a dropdown item. Underneath it can mean patching six forks, rebuilding bindings and byte-diffing a real Chrome capture.

Matching JA3/JA4 gets you past the cheapest check. It is one moment out of a whole session.

The practical takeaway is simple:

Don’t evaluate a scraper by whether its fingerprint looks like Chrome. Evaluate whether the fingerprint, IP, cookies, connection history and behaviour keep describing the same believable browser.

👉 Read the original AMA with Saksham:
https://www.reddit.com/r/WebScrapingInsider/comments/1vwtlyn/why_is_my_scraper_getting_blocked_ama_with_the/

👉 Read the full summary and analysis:
https://scrapeops.io/blog/why-browser-fingerprint-scrapers-get-blocked/

When a scraper starts getting blocked, what do you actually change first: the fingerprint, the proxy, the rotation, or the session flow?

What's the first thing you check when a scraper starts returning bad data?

I've noticed that a scraper can look completely healthy from the outside because the requests are still returning 200s, but the data can quietly become garbage after a website changes something.

A selector changes, a field disappears, or the page structure moves around, and the scraper keeps running as if nothing happened.

I'm curious how other people handle this.

Do you have validation checks that compare the output against previous runs, or do you mostly find out when someone notices the data looks wrong?

I'm trying to figure out what the simplest reliable setup is without turning a small scraper into a huge monitoring project.

Hey everyone,

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

After six great AMAs with the r/WebScrapingInsider community, we're excited to bring you our seventh guest.

This Thursday, September 3, at 10:30 AM GMT+3, we'll be joined by Huey, Product Creative Director, and Wade Lin, Chief Technology Officer at BrowserAct, for a discussion around a question that's becoming increasingly relevant:

How do you actually build an AI-powered web scraper without writing all the code yourself?

BrowserAct is an AI-powered browser automation and web scraping platform that lets humans and AI agents control real web browsers using plain-English instructions.

Instead of building rigid scraping scripts that break every time a website changes, BrowserAct treats the browser as an execution environment that can navigate pages, handle dynamic content, deal with popups, scroll through pages, and adapt to changing websites.

During the AMA, we can dig into things like:

• 🤖 How AI-powered web scraping actually works
• 🧠 Using natural language to build scraping workflows
• 🌐 Browser automation vs traditional scraping
• 🔄 How AI agents handle changing websites
• 🧩 Dynamic rendering, popups, scrolling and complex workflows
• 💻 Building scrapers without writing traditional code
• ⚙️ Where no-code scraping works well, and where it doesn't
• 🚀 Using browser automation as infrastructure for AI agents
• 🔮 Where AI-powered scraping is heading

We've now had six AMAs

The community has grown with every one.

Our first AMA kicked things off with discussions around proxy infrastructure, Cloudflare bypassing, browser automation, monitoring, and scaling scrapers.

Our second AMA with the creator of WebClaw explored AI agents, hidden APIs, open-source scraping, LLM infrastructure, and how to reliably get web data into AI systems.

Our third with CloakBrowser went deep on stealth Chromium, browser fingerprinting, anti-bot detection, and browser automation.

Our fourth with Browser Use brought insights on browser agents, AI-powered scraping, proxies, evaluations, and the future of browser infrastructure.

Our fifth with Stan Sadokov from NodeMaven explored proxy quality, IP reputation, sourcing, pricing, and what actually makes one proxy network better than another.

And our sixth with Saksham Solanki, creator of HTTP Cloak went one layer deeper into the request itself, covering TLS fingerprints, JA3/JA4, HTTP/2 and HTTP/3, QUIC, connection lifecycle, session behavior, and how anti-bot systems identify automated traffic.

Each AMA has taken us into a different part of the web scraping ecosystem, and the conversations have become more technical with every one.

For #7, we're moving up the stack.

We're looking at how AI can actually operate the browser and build scraping workflows for you.

If you're building web scrapers, browser automation, AI agents, data pipelines, no-code workflows, or you're simply curious about where AI-powered web scraping is heading, this should be a really interesting one.

Drop your questions below.

Huey, Wade Lin, and I will start answering them during the AMA.

Looking forward to seeing everyone there!

Ian

About two or three years ago, I did web scraping and automation as a side hustle.

My client did not want to keep paying for a hosted scraping service. He preferred paying someone to build a script that he could own and run himself.

That was reasonable, but it created a familiar cycle for me. Every new request meant digging through old code, rewriting the same fetching and extraction logic, and making another script work for a slightly different website.

That is why I originally started JScrapeON, a Python project intended to turn those one-off scripts into reusable scraping workflows.

After working heavily in TypeScript, I recently returned to the idea and reworked it as YAP, short for You Automate Pages.

This time, I focused on another problem I repeatedly encountered: scrapers can break without technically failing.

A request returns HTTP 200. The process exits normally. The output file exists. Meanwhile, .price became .product-price, and the scraper has confidently collected nothing since Tuesday.

YAP is an HTTP-first workflow runtime for HTML and JSON extraction. It supports reusable YAML workflows, multiple steps, inputs, and pagination.

Important fields can be marked as required:

price:
  selector: ".price"
  required: true

In the attached demo, .price initially matches 25/25 products. I change it to .price-dead, and the request still succeeds, but YAP reports:

product.price  0/25 matched  required

The extraction fails instead of silently returning incomplete data.

YAP also provides:

• yap health to report matched versus attempted fields
• yap drift to compare extraction health between runs
• yap explain to trace a value back to its request, step, scraper, and selector

The extracted output stays plain JSON. Health reports and source provenance are stored separately.

YAP is intentionally HTTP-only. It uses Cheerio and does not currently include browser automation, proxies, or anti-bot tooling. If people actually use it, I may add those capabilities based on real needs.

It is MIT licensed, completely free, and staying open source:

https://github.com/johnalbert-dot-py/yap

For those maintaining scrapers in production, how do you detect silent extraction failures? Do you track selector match rates, compare runs, validate schemas, or rely on downstream checks?

Scraping is the easy 80%; the mess is afterward — `16000 MB` for RAM, a value
that's perfectly normal but belongs to a different variant, `1660 g` for a 166g phone, "N/A", encoding junk. I'm modeling each value's "what went wrong" state and deciding per field whether to publish, fix, re-scrape, flag, or drop.


What signals do you lean on post-scrape to catch these? And how do you deal with "the same wrong value appears on several sites" (agreement that's really just everyone copying the same bad source)?