u/ian_k93

Posted the latest Web Scraping Insider #8 if anyone here wants the full breakdown:

👉 https://thewebscrapinginsider.beehiiv.com/p/the-web-scraping-insider-8

https://preview.redd.it/073298wqhdah1.png?width=1200&format=png&auto=webp&s=fb13515fdeee641c3e79b23be01e364a5bfdb7d5

Quick summary of what's inside:

⚖️ When "Ethical" Proxies Aren't Ethical

"Ethically sourced" has become the proxy industry's favourite marketing word. Almost no provider will show you which apps their residential IPs actually come from - no public partner list, no audit trail, no independent verification.

The last couple of weeks made that gap impossible to ignore:

• Spur Intelligence scanned 6,038 LG webOS + Samsung Tizen apps - proxy SDKs in 2,058 of them (42.5% on LG, 26.9% on Samsung)
• Bright Data's SDK enrolling always-on smart TVs as exit nodes, with consent buried in TV remote arrow-key navigation
• SuperBox streaming boxes (sold at major US retailers) shipping with dormant Popanet proxy software - routing third-party traffic through home connections with no meaningful consent
• FBI/IC3 now warning consumers that everyday devices are being silently turned into proxy nodes

None of those device owners meaningfully opted in. Yet those same residential IPs feed pools sold as "ethical."

Our take: "ethical" should be a claim you have to prove - published partner list, audit trail, who consented / in which app / when - not a landing-page adjective. My bet is the market moves there within the next year or two.

---

🔮 Proxy Tester: now benchmarks residential proxies too (free for you)

We expanded the ScrapeOps Proxy Tester beyond proxy APIs. It already benchmarks ~15 proxy-API-style providers against your exact target URL. Now it does the same for residential pools, so you can compare both side-by-side.

https://preview.redd.it/bcaokzhthdah1.png?width=1163&format=png&auto=webp&s=e0abb2f36866657f4a0814bf0554d9c95093f661

How it works: submit your URL → real requests through each provider → every config they expose gets tested → ranked by success rate + cost per successful request.

Residential is where marketing fluff runs deepest ("30M+ IPs", "99% success rates"). From what we've seen across billions of requests, CPM rarely correlates with performance on your actual target.

Try it: https://scrapeops.io/proxy-providers/tester/

---

🥊 The browser wars are back: people are rewriting Chromium itself

For a decade, scraping browser innovation meant automation libraries on top of Chrome (Selenium → Puppeteer → Playwright). The browser underneath was treated as a commodity.

That may be shifting. Two forces:

1. Anti-bot reads deeper now - TLS, network stack, process behaviour - so runtime patches (playwright-stealth, undetected-chromedriver) break more often than they hold.
2. Chrome is heavy at scale. Thousands of concurrent browser instances (or long-running AI agents) make a purpose-built engine attractive on cost + startup time.

Projects worth watching:

• CloakBrowser - Chromium fingerprints patched at the C++ source level, not JS injection. Drop-in Playwright/Puppeteer replacement. Claims 30/30 on public bot-detection suites.
• Obscura - Rust headless engine from scratch, CDP-compatible so Playwright still talks to it. Claims ~70 MB binary, ~30 MB RAM, near-instant startup vs Chrome's 200 MB+ / ~2s. (Self-reported, v0.1.0 - treat as experimental.)
• Camoufox - modified Firefox with C++-level fingerprint spoofing. Strongest headless evasion in independent tests we've seen. Proves this isn't only a Chromium story.

Stealth is moving below the automation layer. Most of these are young and several lean on self-reported numbers - don't rip out your production stack overnight - but the direction is worth tracking.

Bottom line: the residential proxy supply chain is getting scrutinised from every angle (smart TVs, factory hardware, federal warnings), the browser layer is getting rebuilt from scratch, and the boring work still wins - benchmark on your targets, measure cost-per-validated-payload, not vendor adjectives.

Happy to discuss specifics here - especially if you've benchmarked

— Ian (ScrapeOps)

Most scraping advice online falls into one of two categories:

1. Things that worked 3 years ago.
2. Things that worked once and became a LinkedIn post.

The reality is that scraping today is an adversarial environment. Anti-bot systems are getting smarter, proxy costs are changing, AI is reshaping workflows, and a lot of common advice simply doesn't survive contact with production traffic.

So this Wednesday at 09:30 AM GMT, I'll be doing an AMA here on r/WebScrapingInsider.

Ask me anything about:

• Web scraping
• Proxies
• Browser automation
• Anti-bot systems
• Scraping startups
• AI and scraping
• Infrastructure
• Reliability
• Monitoring
• Scaling

Or anything else that's been causing pain in your stack.

I dont have all the answers, but I have spent the last few years benchmarking providers, testing tools, breaking things, fixing things, and trying to understand what actually works versus what people claim works.

My bias is simple:
Benchmark everything. Trust nothing.

If a technique improves success rates, lowers cost-per-success, reduces operational overhead, or makes a scraper more reliable, it's worth discussing.

If it's mostly hype, we can discuss that too.

Feel free to drop questions below ahead of time, or join live on Wednesday.

Looking forward to it.

- Ian
ScrapeOps

Most scraping advice online falls into one of two categories:

1. Things that worked 3 years ago.
2. Things that worked once and became a LinkedIn post.

The reality is that scraping today is an adversarial environment. Anti-bot systems are getting smarter, proxy costs are changing, AI is reshaping workflows, and a lot of common advice simply doesn't survive contact with production traffic.

So this Wednesday at 09:30 AM GMT, I'll be doing an AMA here on r/WebScrapingInsider.

Ask me anything about:

• Web scraping
• Proxies
• Browser automation
• Anti-bot systems
• Scraping startups
• AI and scraping
• Infrastructure
• Reliability
• Monitoring
• Scaling

Or anything else that's been causing pain in your stack.

I dont have all the answers, but I have spent the last few years benchmarking providers, testing tools, breaking things, fixing things, and trying to understand what actually works versus what people claim works.

My bias is simple:
Benchmark everything. Trust nothing.

If a technique improves success rates, lowers cost-per-success, reduces operational overhead, or makes a scraper more reliable, it's worth discussing.

If it's mostly hype, we can discuss that too.

Feel free to drop questions below ahead of time, or join live on Wednesday.

Looking forward to it.

- Ian
Founder, ScrapeOps

I spent years doing the usual scraping workflow:

Find elements → write parser → run → debug → fix selectors → repeat.

Recently tested our Claude Code plugin that takes a target URL, generates the scraper, validates the output, and exports structured JSON automatically.

The result eliminated a surprising amount of repetitive setup work.

What interested me most was the workflow:

1. Provide URL
2. Choose language (Python, JS, etc.)
3. Choose framework (BeautifulSoup, Playwright, etc.)
4. Generate scraper
5. Run scraper against real pages

For production teams, I don't think AI replaces observability, retries, QA, or anti-bot handling.

But it might dramatically reduce the time spent scaffolding new parsers.

Curious if anyone here is already using AI-generated scrapers in production.

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

Posted the latest Web Scraping Insider #7 if anyone here wants the full breakdown:

👉 https://thewebscrapinginsider.beehiiv.com/p/the-web-scraping-insider-7

Quick summary of what's inside:

https://preview.redd.it/oyzgun2j893h1.png?width=1200&format=png&auto=webp&s=25c0147e63abd54d8b57ed2728cec21757701968

🔮 Free Proxy API Tester (your URL, not generic demos) We opened our internal Proxy Tester publicly. You submit a target URL and we benchmark ~15 proxy-API-style providers against it, testing the configs each provider exposes (residential routing, JS rendering, anti-bot modes, etc.), then rank by real outcomes:

• Success rates
• Latency
• Pricing
• Cost per successful payload

The point: "works" and "works profitably" are not the same thing. Most proxy marketing still collapses when you ask what you actually pay per validated payload on your target.

Try it: https://scrapeops.io/proxy-providers/tester/

🌐 Distributed browser networks: the "residential proxy moment" for browsers? Interesting thesis from Driver.dev: instead of running stealth browsers in datacenters, distribute real browsers across real consumer devices.

Anti-bot is increasingly fingerprinting full environments (GPU, hardware entropy, OS quirks), not just IP reputation. If that holds, cloud stealth browsers may become the new datacenter proxies.. detectable by default.. and real-device browser infrastructure becomes the next moat.

💸 Stealth Browser API benchmark (April 2026) We re-ran our stealth browser fingerprint test across 7 providers. Top of the board:

• Scrapeless Browser — 90.95
• Bright Data Scraping Browser — 89.05
• Oxylabs Headless Browser — 85.71 (rebuilt since our first test; went from ~33 → 85)

Then there's a cliff. Several providers still leak automation signals (including CDP/framework checks), and once that happens, proxy rotation doesn't save you.

Notable pattern: top performers mostly came from traditional scraping companies with years of production anti-bot experience, not newer agent/browser startups.

☁️ Cloudflare bypass in 2026: 8 popular methods, only 3 had broad coverage We tested 8 common approaches across 20 protected sites. Roughly 60% of the advice stack doesn't hold up at scale.

What actually had coverage:

• Smart Proxy APIs — 100% domain coverage (20/20), ~97% avg success
• TLS impersonation (curl_cffi) — 80% coverage, strong when fingerprinting is the main gate
• Browser APIs — ~60% coverage combined, much lower avg success depending on provider

Solvers, fortified headless browsers, cached pages, and origin IP bypass mostly underperformed or failed outright.

Bottom line: there isn't one "best" Cloudflare bypass. Different domains use different protections. Benchmark on your targets, optimize for cost-per-validated-payload, and don't trust vendor adjectives over measured outcomes.

If you're choosing proxy APIs, stealth browsers, or bypass stacks without testing them on your actual URLs, you're probably overpaying for underperformance.

Happy to discuss specifics here.. especially if you've run your own comparisons on hard targets lately.

- Ian

Hey WebScrapingInsiders,

Quick update from the ScrapeOps side.

We're launching ScrapeOps AI Scraper Generator on Product Hunt on May 27, 2026.

This has been in beta with ScrapeOps users, and so far the AI Scraper Generator has generated 3,000+ scrapers.

https://preview.redd.it/a826ag5ve83h1.png?width=1458&format=png&auto=webp&s=931c693dce1810a622911716b48c2b9902bc2aef

That gave us a lot of useful signal on what actually matters:

• which page types people want to scrape
• where generated code needs to be cleaner
• which stacks people prefer
• where schema selection creates friction
• how useful AI-powered output scoring is after the scraper runs

"AI scrapes any URL."? That pitch is too vague and usually breaks the minute you hit real scraping conditions: DOM drift, missing fields, dynamic rendering, selector regressions, and output that looks plausible but is quietly wrong.

The workflow we’re building around is more controlled:

Give a URL → choose your stack → generate scraper code → AI scores how correctly the scraper ran

The generated code is inspectable, and the AI scoring breakdown checks things like:

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

The goal is to remove repetitive setup, generate a solid first version faster, and give developers a clearer signal on whether the output is actually usable.

We're launching it on Product Hunt here:
https://www.producthunt.com/p/ai-web-scraper-builder

Would appreciate feedback from this community, specifically because you'll spot the real problems faster than a general launch audience.

We've just made one of our internal ScrapeOps tools public: a live proxy provider benchmarking dashboard for web scrapers.

The idea is simple: compare proxy providers against real target websites, side by side, using actual performance data.

Right now it tracks:

• Success rates
• Latency
• Pricing
• Provider-by-provider performance on specific domains

The reason we built this internally is because the proxy market is messy. A provider can look great on one domain and completely fall over on another. Another provider might be slower but far cheaper per successful response. Another might market itself as "premium" but lose badly once you measure cost-per-success instead of sticker price.

That's the bit most proxy comparisons miss.

Price per GB or price per request is not the real number. The real number is:

How much does it cost to get a usable response from the target you actually care about?

We originally built this to power the ScrapeOps Proxy Aggregator. For the last few years, it's been part of how we evaluate providers and route traffic across them in production.

Opening it up publicly felt useful because the community needs fewer generic "best proxy provider" lists and more domain-level performance data.

At launch, it supports Proxy APIs. Residential proxy network benchmarking is next.

Feedback welcome. Especially interested in what people would want added next:

• More providers?
• Historical performance charts?
• Cost-per-success rankings?
• Region/device-type filters?
• Exportable benchmark data?

Link is in the comments.

https://preview.redd.it/1vsth5uz1o2h1.jpg?width=1280&format=pjpg&auto=webp&s=f48401a4d15ce122f3e0745e82cabdebecc414ad

https://preview.redd.it/7tzqizuz1o2h1.jpg?width=1280&format=pjpg&auto=webp&s=7a3f2d1e4be6a7b8a2137923ac920ff982f6b3c1

We just posted a quick demo from the ScrapeOps YouTube channel showing how our Claude Code plugin generates a working web scraping project from a prompt.

The example in the video builds a crawler + product scraper pipeline for a Walmart search page. It generates the project files, schemas, parsers, README, run commands, and JSON/JSONL output. The demo uses Python + BeautifulSoup, but the plugin also supports other languages and scraping libraries like Scrapy, Playwright, Puppeteer, etc.

The part I'm most interested in feedback on is the workflow: instead of using AI to just write a parser snippet, the goal is to generate the full scraping pipeline and then let devs inspect, run, modify, or fix it from there.

Video covers:

• installing the Claude Code plugin
• adding the ScrapeOps to it
• using /generate-scraper, /fix-scraper, and /generate-crawler-scraper
• choosing language + library
• generating crawler and product parser files
• running the scraper and checking the structured output

This is still aimed at developers, not "magic no-code scraping."

https://www.youtube.com/watch?v=qcE5sK0DDus

The generated code still should be reviewed, especially for ToS/robots considerations, and production monitoring. But it's been useful for cutting down the boring scaffold/debug loop.

Would be interested to hear what people here think: useful direction, or does AI-generated scraper code create more maintenance debt than it saves?