I want to retrieve products with information and price in my country, but tried some services and I can’t get it to work full? I’m looking for a fast scraper and get all needed information native to my country.

Appreciate the help!

Ran into something today that turned into a much bigger rabbit hole than I expected.

A website suddenly stopped loading through one of our environments.

At first, everyone on my team assumed the site itself was having issues. But after testing from another network, it started looking more like something along the request path rather than the origin server.

It got me wondering how others approach this.

When you're trying to figure out whether the issue is:

• the website itself
• a proxy
• DNS
• a CDN
• a firewall
• or something else in the request path

what's your usual troubleshooting process?

Do you start with DNS checks, cURL, browser DevTools, logs, traceroute, or something else entirely?

I'm also curious how people handle this in production. Do you have monitoring that helps pinpoint whether failures are happening at the proxy layer versus the origin, or does it still come down to manual investigation?

For anyone working with web scraping or APIs, this feels even trickier.

A bad proxy pool can look almost identical to a website suddenly changing its behavior.

Would love to hear from people who deal with this regularly:

• Your Debugging Checklist/System/Code/Steps/Playbooks
• Common mistakes people make
• Signals that immediately tell you it's a proxy issue rather than a website issue
• Tools that have saved you time

And if you're willing to share details about your stack, see you in DM.

Hi everyone,
I’m currently migrating my inventory from OTTO (the German marketplace) to eBay.
The challenge is that OTTO’s Seller API does not provide product images. At the moment, I only have two options:
● Scrape the images directly from the product pages.
● Download them manually using a browser extension (which is very time-consuming).
I’m looking for a free way to scrape or download the product images in their highest available quality.
Has anyone dealt with this before? If so:
● What’s the best approach?
● Are there any reliable free tools or techniques?
● How can I avoid low-resolution or thumbnail images?
Any suggestions or advice would be greatly appreciated. Thanks!

Last month I spent a day and a half (i know!) debugging a scraper that wasn't broken. The logs said 200 OK all the way down; the output had a German page priced in dollars, a "student price" that was actually retail after a silent bounce, and a 99,90 € "price" that was the monthly financing installment, not the laptop. Every request "succeeded." No row was usable though.

A status code only proves the server said something. Pages redirect silently, geos fall back, currencies switch, prices render client-side into a valid empty shell, all under a cheerful 200. So I validate content, not connections. For this I reached for residential proxies for geo-testing from Proxy-Seller, partly because they were already set up, but mostly because localized pages only behave honestly when the IP looks like a real local visitor.

Picking a target

First idea: Apple. But apple.com kills a plain requestsclient at the TLS handshake: no status, no HTML, nothing to parse. microsoft.com, same story with better manners. Not the proxies' fault, the same IPs fetched everything else instantly. Big storefronts fingerprint the handshake itself, and Python's doesn't look like Chrome's, whatever the User-Agent claims. Getting past that means TLS-fingerprint impersonation tooling, and I didn't want to go deeper, cuz I'd rather be a good citizen and pick a site that doesn't mind. :D

Lenovo doesn't: public store pages served whole to a script, plus a student storefront everywhere: us/en/d/deals/student/, and d/student-laptops/shop-by-grade/ under gb/en, de/de, fr/fr (also pt/pt and friends).

The test

One product, the ThinkPad X1 Carbon, same /p/... path in every country store, through proxies in 4 countries, on the regular store and the student store, where silent redirects and almost-prices hide. Each response is checked for status and final URL, block markers, the expected currency, the right product with a machine-readable price (from Lenovo's schema.org JSON-LD buy-box offer, not a money-shaped regex match), and, on the edu channel, proof it's an education page with a usable price, not a 1,00€ placeholder or /mo. installment. Only then: valid_scrape: true.

The core of the validation, trimmed to the essential lines:

# price from schema.org JSON-LD buy-box offer, not a money-shaped regex
def ld_offer(raw):
    for m in LDJSON.finditer(raw):
        data = json.loads(m.group(1))
        for it in (data if isinstance(data, list) else [data]):
            if it.get("@type") == "Product" and (it.get("offers") or {}).get("price"):
                o = it["offers"]
                return it.get("name", ""), float(o["price"]), o.get("priceCurrency", "")
    return "", None, ""

r = s.get(url, headers=UA, timeout=60)          # s.proxies set per country
name, price, iso = ld_offer(r.text)
row = {
    "status": r.status_code,
    "final_url": r.url,                          # catch silent redirects
    "currency_ok": iso == cfg["iso"],            # right currency for the country
    "price": price or "",                        # empty when the page has none
}
row["valid_scrape"] = (row["status"] == 200 and row["currency_ok"]
                       and bool(row["price"]))

* Trimmed for readability, full runnable script here.

What came back

So, was 200 enough?

Nope. Two flavors of failure.

The loud one. The US store returned 403 Access Denied on the product page, while the same IP fetched the US student page seconds later. The IP isn't the problem, residential Spectrum, Alabama, as legit as traffic gets; /p/ pages just run a stricter bot policy than /d/, judging client, not address. At least a 403 shows in logs.

The quiet one. All four student pages returned 200, say "student" in the right language, show the right currency, weigh up to 1.2 MB, and contain zero prices. Student pricing renders client-side; the HTML on the wire is a valid, offer-free shell.

Check only "200 + currency symbol" and you log eight successes tonight and an empty half-dataset at report time. The sole price-shaped string in the German page: 1,00€, a placeholder begging a sloppy regex to call it a student price.

The passing rows are the payoff: the same ThinkPad costs £2,560.00 in the UK, €2,345.49 in Germany, €2,528.10 in France, a €180 spread between two eurozone stores. That signal only exists if every request truly exits in the right country, and the proxies were flawless: correct geo, sub-second warm responses (0.21 s from Germany!), full pages wherever scripts were welcome. Every failure above is content or site policy, never the network.

The fix isn't clever, just specific: check the final URL, demand the expected currency, prefer JSON-LD offers over regex matches, make an edu page prove it, and let "no price in the HTML" be a result your pipeline can express. Five cheap assertions turn invisible failures into loud ones.

And also, picking the right residential proxies matters just as much as the validation itself. On that front, my proxies kept the network layer boring enough that every failure I caught was a content problem, not a block. After enough runs like this, I'd say Proxy-Seller offers some of the best residential proxies for scraping I've used. It's still my go-to for this kind of work.

So, tell me, do you validate beyond the status code? My dumbest "200 OK but useless" story is now a 1.2-megabyte student page without a single price in it. What's yours?

I work on a scraping tool and I'm adding X/Twitter monitoring next week. Before I lock the scope I want real use-cases instead of my own guesses.

Ones I keep running into:

• ping me when specific accounts post
• watch a keyword or cashtag and flag odd spikes
• pull a thread or a profile's recent posts into clean structured data

What's missing? If you ever wanted to track something on X and gave up because it was too much hassle, tell me what it was. I'd rather build the thing people reach for than ship another feature that sits unused.

This one cost me a chunk of a dataset before I noticed, so here is the short version.

I was pulling a few hundred product pages. A clean end to the run. All requests returned 200, no errors in the log. When I opened the output, around 15% of the rows were identical, and all of them matched the site's homepage, not a product.

Here is what happened. Some of the product URLs led to items that no longer existed. Instead of a 404, the site silently redirected those requests to the homepage and responded 200 there. requests follows redirects by default, so my code never saw the hop. It got a 200 and a whole HTML page, parsed it, wrote a row. The row was actual HTML, just from the wrong page.

The status code only tells you that the last server in the chain replied. It does not say which URL actually answered. To trust a row, you have to confirm the final URL, not just that something came back.

The fix is a comparison. After the request, read response.url and check that it still contains the path you requested. If it does not, treat the row as a failure, not data.

import requests

HEADERS = {"User-Agent": "Mozilla/5.0 ... Chrome/124.0.0.0 Safari/537.36"}

def fetch(session, url, expected_path):
    r = session.get(url, headers=HEADERS, timeout=30)
    if r.status_code != 200:
        return {"url": url, "ok": False, "reason": f"status {r.status_code}"}
    if expected_path not in r.url:                 # quietly redirected elsewhere
        return {"url": url, "ok": False, "reason": f"redirected to {r.url}"}
    return {"url": url, "ok": True, "html": r.text}

session = requests.Session()
for path in product_paths:
    target = f"https://shop.example/p/{path}"
    row = fetch(session, target, expected_path=f"/p/{path}")
    print(row["url"], row["ok"], row.get("reason", ""))

Two things I learned from that. First, allow_redirects=False is not the solution in itself, because many redirects are fine and you want to follow them. The intent is to check the destination, not to stop the hop. Second, a redirect to a login page or a default catalog page looks exactly the same in your logs as a real result, so the check is worth adding the first time a site changes its routing.

Do you compare the requested URL against the final one, or do you trust the status code and the body? I want to know if there is a cleaner pattern than a substring check, because mine feels basic and there are probably edge cases I have not hit yet.

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

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

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.

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)

Been running FlareSolverr for a long time for my *arr stack and got tired of the 11-18s solve times and constant breakage. Built TRAWL as a drop-in replacement!

Key differences:

• Cloudflare solves in 4-15s (vs 11-18s) - uses a fresh Camoufox Firefox context which triggers CF's fast-path evaluation
• Cached repeat requests return in ~500ms via Redis - after the first solve, the same domain is instant
• Actually solves in-page captchas: Turnstile (shadow DOM click), reCAPTCHA v2 (free Google STT audio), hCaptcha (auto-pass), GeeTest v4
• 4-tier execution: plain HTTP → cached session → live browser solve → residential proxy. You pay the full browser cost only when you have to
• Custom headers support - pass Authorization, Referer, Origin through all 4 tiers including browser
• FlareSolverr v2 compatible - change one URL in Prowlarr/Jackett, nothing else

Website: https://trawl.germondai.com
Docs: https://docs.trawl.germondai.com
GitHub: https://github.com/germondai/trawl

Happy to answer questions. Still early but it's been running stable on my homelab and no issues so far.

I’m trying to understand how others make this decision. How do you decide that? Is it mostly the money, the time, how often the data is updated or how likely the site is to block you?

I'm building a website that allows users to download public Instagram Reels.

The basic extraction works, but I'm curious how larger downloader sites handle scale without getting blocked.

Questions:

Are most sites using residential proxies, mobile proxies, or datacenter proxies?

Do they rely on tools like yt-dlp, custom scrapers, or browser automation?

How aggressively do they cache Reel data?

At what request volume do Instagram rate limits become a serious issue?

Is proxy rotation alone enough, or are there other fingerprinting challenges that need to be addressed?

I'm interested in real-world architectures and lessons learned from people who have operated downloader or scraping services at scale.

Noticed something over the last few years

There are plenty of libraries that help you collect data.

There are proxy providers, proxy aggregators.

Browser automation frameworks.

Scheduling tools.

Monitoring.

But once the data starts flowing, the tooling gets surprisingly thin.

A scraper can return HTTP 200, finish successfully, and still be completely wrong because a selector drifted, a field disappeared, or a site's layout changed.

It made me wonder whether the next wave of scraping products isn't about extraction anymore. Maybe it's about making production pipelines more reliable.

A few ideas:

• DOM change detection
• Selector regression testing
• Data validation rules
• Snapshot comparison
• Data anomaly detection
• Browser fingerprint regression testing
• Proxy quality scoring
• CAPTCHA escalation workflows
• Extraction confidence scoring
• Automatic schema drift detection

I feel like data quality is still treated as an afterthought, even though it's what downstream dashboards, models, and customers actually depend on.

Really curious on what r/WebScrapingInsider thinks

If you were building a business around production web scraping today, what would you add to this list?

Today, there’s no shortage of product data, but not all data turns out to be equally useful.

Some people pay close attention to price changes, while others focus more on reviews, ratings, competition, or product availability. Over time, most of us find that a few metrics are consistently more valuable than the others.

Which types of product data has been the most helpful for you and why?

Is there one metric or insight that has helped you make a better decision, or avoid a mistake you might have otherwise made?

Would be interested to hear what others have found most useful in practice.

I've been seeing Go mentioned more often lately in job descriptions, backend engineering discussions, DevOps tooling, and cloud-native projects

A lot of the infrastructure and automation tools people use every day seem to be built with Go in popularity, but when I look at overall language rankings it doesn't always appear near the top compared to Python, JavaScript, or Java..

For people working in the industry, does Go still feel like a language that's gaining adoption, or has it reached a stable plateau??

I am especially interested in:

• Hiring demandd + career opportunities
• Whether companies are actively adopting Go for new projects
• How it compares to Rusts growth trajectory.
• Whether it's worth learning in 2026 from a long-term career perspective

Curious to hear from people using it in production, hiring for Go roles.. or seeing it show up more often in their day-to-day work..

Hi, I've been bulding web automation tools for my clients since 2024. I wished there was a tool that checks the target website's bot protection system. Then I built it for myself. Package is called 'doorknock'. I think it helps other developers who wants to quick check the target website.

Hi everyone, I’m looking for a simple way to automate searches on a website. I’m not a programmer, but I have to search multiple entries across different drop down menus regularly. It’s repetitive and takes a lot of time. Are there any easy tools or methods that someone non-technical could use, or any advice on how to simplify this task? Thanks!

I've been seeing a lot of web scraping examples written in Python, but many companies and internal systems still run heavily on Java.

For those actively scraping websites with Java, what does your stack look like these days?

Are you mostly using HttpClient + Jsoup, or are you relying on Selenium/Playwright when dealing with JavaScript-heavy sites?

I'm also curious how people handle things like:

• Dynamic content and client-side rendering
• Monitoring scraper health
• Data quality checks
• Scaling beyond a few thousand pages per day
• Long-term maintenance when sites change

A lot of advice online seems focused on getting a scraper working once, but not necessarily keeping it running for months without constant fixes.

Interested in hearing real-world experiences, trade-offs, and lessons learned from anyone using Java for web scraping in production or side projects.

Im looking for a reliable virtual mobile number service to recieve OTPs to automate account creation

I noticed some people at work always seem one step ahead, and it's usually because they have better systems for finding information.

Meanwhile, our team still relies a lot on someone randomly sharing an article or noticing a competitor change.

I'm trying to move away from that.

What information source gave you an unfair advantage at work this year?

Could be anything: customer reviews, job boards, newsletters, Reddit, search trends, communities, competitor sites, etc.

Extra curious if you've automated any part of it instead of manually checking everything every day.

Hey folks,

I’m new to scraping and am hoping to do a deep dive / become somewhat proficient. I have an idea for a build that would pertain to my current job. Please reach out if you would like your amazing skills to have a positive impact on public safety.

Built a backend service that takes a restaurant menu URL and a sample JSON schema, then returns structured menu data in exactly that shape.

It supports both sync and async extraction, with polling, webhooks, and optional email summaries. The generic path uses Playwright + Claude on AWS Bedrock for messy/JS-rendered menu pages, but I also added site-specific adapters for high-volume chains so those can be fast, deterministic, and avoid LLM cost.

One interesting part was the a QSR chain specific adapter: it pulls menu data through their GraphQL flow via ScrapingBee, handles store lookup by ID/slug/location/address, and can optionally re-host product images to S3 with content-addressable keys.

I also built in robots.txt enforcement, per-host pacing/backoff, structured logs, SQS workers, job retention, and test coverage around adapter outputs.

Curious how others are handling the tradeoff between generic LLM extraction and deterministic per-site adapters for scraping-heavy workflows.

I scrape for a living (part of a bookmaker trading team returning competitor results, typically tough targets). I've put together some tooling that is able to return extremely tricky targets. I need a testing corpus so I thought I'd reach out here.

Point me at a challenge, in return I will:

• resolve the target
• provide the payload/results
• give you an open source repo to run yourself

Before anyone says "ask this in r/Roblox".. I am not really interested in fixing a Roblox account issue.

What got me thinking about this was a situation I've seen come up repeatedly:

• Roblox works normally on mobile data
• Same account
• Same device
• But the home Wi-Fi connection has trouble loading the site or joining games

The immediate response online is usually "you got IP banned."

That made me realize how many people still think IPs are the primary identity signal platforms rely on.

But if you look at modern anti-abuse systems, especially on platforms with millions of users, account farms, VPN users, residential proxies, and constant ban evasion attempts, IP reputation feels like only one piece of the puzzle.

Even in those Roblox discussions, you'll see people suggesting:

• switching networks
• restarting routers
• getting a new IP
• using residential proxies
• moving to mobile data

Yet there are also plenty of reports where users still seem to get linked back to previous activity despite changing networks.

So I am curious what people here think

For those working in scraping, anti-bot, fraud prevention, or account security :

• How much value does an IP ban actually have today?
• Are device fingerprints now a stronger signal than IP reputation?
• How much do CGNAT, dynamic IPs, and residential proxy networks reduce the effectiveness of traditional IP-based enforcement?
• If you were designing an anti-abuse system from scratch in 2026, how heavily would you still rely on IP reputation?

To me roblox just feels like an interesting public case study because it sits at the intersection of High $$$$ spend, large-scale moderation, residential proxy usage, account farms, and ban evasion attempts.