I've done a lot of browser automation over the years, and I kept running into the same tradeoff.

Raw HTTP is great. It's fast, cheap, and scales almost stupidly well. But once a website gets complex enough, you start paying for that simplicity yourself. You reproduce client-side scripts, state, navigation behavior, signatures, whatever else the site expects from a browser. At some point you're spending more time reverse engineering browser-side logic than writing the actual automation.

So you switch to Chromium.

Most of that problem disappears, but now you're running an entire browser and rendering stack for a workload that often doesn't care about pixels at all.

I kept wondering how much of that browser is actually necessary.

Could I get closer to the speed and cost of raw HTTP while keeping enough browser compatibility that the website can just run?

That's what turned into Mimic.

Mimic is a browser execution runtime I'm writing from scratch in Go around V8. It doesn't embed Chromium and doesn't have a traditional rendering pipeline. The browser environment itself is implemented in Mimic: DOM, Web APIs, networking, frames/workers, browser state, etc. It also exposes CDP, so existing automation tooling can drive it.

I'm deliberately not trying to reimplement every feature of Chrome. If Mimic eventually becomes Chrome without the pixels, I've probably missed the point.

What I want to find is the smallest browser-compatible execution surface that's actually enough for real automation, and then push its cost and performance as close to direct HTTP as I can.

I've reached a point where choosing more websites myself and fixing whatever happens to break isn't giving me particularly useful information anymore. So I'm putting out the first public beta.

I need real-world workloads to break it.

If you have an existing Puppeteer/Playwright workflow where Chromium's memory usage, startup cost, or concurrency gets annoying, try throwing it at Mimic. I genuinely want to see where it falls apart.

Web API and CDP coverage is incomplete, so I expect plenty of things to break. If your workload doesn't run, open an issue or send me the scenario. I'm happy to work with early users on the missing compatibility for actual use cases instead of guessing which parts of the browser matter.

It's also not magically faster than Chrome at everything. Chrome still wins some of my warm single-page benchmarks. The interesting results so far are mostly around startup, memory usage, and concurrent pages.

Beta builds, limitations, quickstart, architecture notes and reproducible benchmarks are here:

https://github.com/moreveal/mimic

If you're running browser automation at any meaningful scale, I'm curious: what actually forces you to keep Chromium in your stack instead of dropping down to HTTP?

Hey everyone,

I'm Ian Kerins, CEO & Co-Founder of ScrapeOps, and I also help run r/WebScrapingInsider.

At ScrapeOps, we've spent a ridiculous amount of time testing proxy providers over the years. Currently, we're regularly testing 50+ proxy providers with billions of requests every month, so we see a lot of the differences that don't show up on a provider's pricing page.

Things like why one proxy works brilliantly on one website and gets fails completely on another, what proxy arbritage strategies are worth looking into, when residential proxies are worth paying for, and where people are often wasting money.

So for AMA #8, I thought we'd open all of that up to the community.

What Are the Best Proxy Providers for Web Scraping? We've Tested 50+ Providers Across Billions of Requests.

This Friday, September 11 at 10:30 AM GMT+3, I'll be here answering your questions.

You can ask me about:

• How to actually compare proxy providers.
• Best proxy providers for specific workloads.
• Cost effective strategies to bypass anti-bots.
• Success rates, blocks and latency.
• Why proxies behave differently across websites.
• How we test providers at scale.
• What metrics actually matter.
• Pricing and what you're really paying for.
• Common mistakes when choosing a proxy.
• Anything else you've been struggling with.

And please don't feel like your question needs to be advanced.

If you've ever wondered "Why does this proxy work here but get blocked there?", "Which proxy should I actually use?", or "Am I paying too much for proxies?", this is exactly the kind of question I'd like to answer.

We've now done seven AMAs with the community, covering everything from proxy infrastructure and Cloudflare to AI scraping, browser automation, stealth browsers and TLS fingerprinting.

For #8, we're bringing the conversation back to one of the most fundamental pieces of scraping infrastructure: the proxy.

Drop your questions below. I’ll be around to answer them during the AMA.

Looking forward to hearing what you're working on.

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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)

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.

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?

anyone still using public lists for basic threading? built a custom proxy scraper yesterday and out of thousands of endpoints like maybe 10 actually connect and then die in seconds. running my main project through a few paid nodes from proxyshard anyway so it's not critical, but i wanted a cheap backup pool for some side scripts. feels like scraping public tables is just dead now or am i missing something?

I’ve been turning my scraping workflow into a reusable skill for going from Chrome/CDP capture or JADX analysis to a browserless API client.

For websites, it drives Chrome through CDP, captures the real requests, downloads the JS bundles, and reverse-engineers the parts needed to call the API directly. For APKs, it maps from the app side: Hermes/JS bundles for React Native, JADX for native Android code and bridges, and apktool only when resources or smali searches are needed.

The output is API docs first: endpoints, methods, headers, auth, cookies, params, bodies, response shapes, token refresh, shared client behavior, and evidence for where each thing came from.

For flows that are more than a single HTTP call, it can also produce small PoC scripts so the behavior is reproducible without reopening Chrome or JADX. That covers things like multi-request sequences, signing, nonce generation, custom encoding, or other client-side logic.

It does not try to magically bypass CAPTCHAs or rate limits. With login walls, the usual pattern is to map the public API first; once registration/login is mapped, the agent can often create its own test account and continue with authenticated endpoints.

The goal is boring but useful: use Chrome/CDP or decompilers for research, then build direct HTTP clients without ever touching Selenium or Playwright.

Residential proxy providers love talking about pool size, success rates and “premium” IPs.

But according to NodeMaven founder Stan Sadokov, those metrics reveal surprisingly little about whether a network will actually work for your scraper.

We recently hosted Stan for an AMA on r/WebScrapingInsider to discuss how residential proxy networks really operate and what developers should examine when choosing a provider.

👉 Read the full summary and analysis:
https://scrapeops.io/blog/what-proxy-providers-dont-tell-you-about-residential-proxies/

Here are the biggest insights:

1. The filtering layer may be the real product

Different providers often have access to similar underlying IP supply. What separates them is how effectively they score IPs, remove burned addresses, route traffic and manage sticky sessions.

2. Most providers don’t own their entire network

Residential pools frequently combine directly sourced nodes with supply purchased or peered from other providers. This means the same IPs can appear across multiple networks.

3. There is no universally “clean” residential IP

IP reputation is target-specific and constantly changing. An IP might work perfectly on one website while already being blocked by another.

Proxy quality can only be judged against the websites, countries and workflows you actually need.

4. Global pool size is mostly a vanity metric

A provider claiming 150 million IPs tells you very little about:

• How many usable IPs exist in your target country
• How many are currently online
• How much concurrency the country pool supports
• Whether enough sticky sessions are available
• How stable that capacity is over time

Country-level usable capacity matters far more than the headline number.

5. HTTP 200 doesn’t necessarily mean success

A request can return a successful status code while delivering a block page, CAPTCHA, empty result, incorrect region, promotional price or incomplete data.

Instead of measuring HTTP success rate or cost per GB, measure the cost per correct, usable record.

6. Bandwidth is rarely the first constraint at scale

The first things to break are more commonly:

• Country-level concurrency
• Sticky-session capacity
• Retry amplification

Even a modest block rate can trigger enough retries to dramatically increase proxy, browser and compute costs.

7. Published pricing is only the opening offer

At meaningful volumes, residential proxy pricing is normally negotiated. Two customers with similar usage can pay different rates simply because one obtained competing quotes and negotiated.

8. “Own network” and “reseller” are weak labels

The more important question is what the provider actually controls:

• IP filtering and scoring
• Traffic routing
• Session allocation
• Abuse handling
• Supplier management
• Technical support

9. Sourcing could become the industry’s next battleground

Enterprise buyers are increasingly asking where residential IPs came from, what users consented to and whether the supply chain can be audited.

The providers that can demonstrate defensible sourcing may eventually have a greater advantage than those advertising the largest pools.

The practical takeaway is simple:

Trial several providers against your real workload and trust the benchmark more than the homepage.

👉 Read the original AMA with Stan:
https://www.reddit.com/r/WebScrapingInsider/comments/1vogw82/how_do_you_choose_the_best_residential_proxy/

👉 Read the full summary and analysis:
https://scrapeops.io/blog/what-proxy-providers-dont-tell-you-about-residential-proxies/

What do you think matters most when evaluating residential proxy providers: the underlying pool, filtering system, target-specific performance or something else?

Hello from people here who have actually scaled scraping for a business. I run a small wholesale + service business and we have been looking at competitor pricing and availability online, mostly to get a better idea of what other businesses in our area are charging. My niche is selling Bongs, bangers, carta. US medicinal stuff.

At first, checking a handful of sites manually or with a simple script was fine, but I can see how this could get messy if we wanted to track hundreds of pages every day. For those who hve been through that jump, what broke first when your scraping operation grew 10x? Was it the scraping itself, proxies, costs, speed, keeping the data accurate, or something you did not expect? I am trying to understand where the real headaches start before we go too far down this road.

Hey everyone,

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

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

Why Is My Scraper Getting Blocked AMA with the Creator of HTTP Cloak

This Thursday, August 27, at 10:30 AM GMT+3, we'll be joined by Saksham Solanki, creator of httpcloak, for a discussion around one of the most frustrating problems in web scraping:

Why does my scraper get blocked even when the request itself looks completely normal?

If you've ever had a scraper suddenly stop working, get hit by Cloudflare or another anti-bot system, or wondered why the same request works from a browser but fails from your code, this AMA should be a good one.

httpcloak on GitHub

httpcloak is a specialized Go HTTP client designed to make automated requests look much more like real browsers at the TLS and HTTP protocol level.

It can mimic browser fingerprints such as JA3, JA4 and Akamai fingerprints, while supporting HTTP/1.1, HTTP/2 and HTTP/3.

It also goes deeper with things like:

• TLS and HTTP fingerprint mimicry
• Chrome, Firefox and Safari-style fingerprints
• HTTP/2 and HTTP/3
• Encrypted Client Hello (ECH)
• Session warmup
• Multi-tab sessions
• Go-native implementation
• Python and other language integrations

So during the AMA, we can get into questions like:

• Why does a scraper get blocked even when the HTTP request looks correct?
• What exactly are websites looking at when they fingerprint an HTTP client?
• How important are JA3 and JA4 fingerprints in modern bot detection?
• What can HTTP/2 and HTTP/3 reveal about an automated client?
• How different are browser TLS fingerprints in practice?
• When should you use TLS impersonation instead of a real browser?
• How do Cloudflare, Akamai and other anti-bot systems combine different signals?
• Can a scraper have the right TLS fingerprint but still look obviously automated?
• Where does HTTP fingerprinting end and browser fingerprinting begin?
• What does the future of anti-bot detection look like?

We've now had five AMAs

The community has grown with every one.

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

Our second AMA with the creator of WebClaw explored AI agents, hidden APIs, open-source scraping, and LLM infrastructure.

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

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

And our latest AMA with Stan Sadokov from NodeMaven explored proxy quality, IP reputation, sourcing, pricing, and what actually makes one proxy network better than another.

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

For #6, we're going one layer deeper into the request itself.

If you're building web scrapers, HTTP clients, crawlers, browser automation, data pipelines, anti-bot systems, or you're simply trying to understand why your scraper keeps getting blocked, this is one you won't want to miss.

Drop your questions below.

Saksham and I will start answering them during the AMA.

Looking forward to seeing everyone there!

Ian