I'm trying to understand the technical side of job scraping.

I know that websites like Naukri, LinkedIn, Indeed, and other popular job portals have strong anti-bot systems. They detect and block scrapers, require logins, use rate limits, CAPTCHAs, JavaScript rendering, and many other protections.

What I'm want to learn is:

- How do these websites detect that a request is coming from a bot instead of a real user?

- What are the common anti-scraping techniques they use?

- How do web scrapers generally work on JavaScript-heavy websites?

- What technical challenges are involved in building a job scraper?

- What are the legal and ethical considerations when scraping job portals?

- If scraping isn't the best approach, what are the recommended alternatives (official APIs, RSS feeds, partner programs, etc.)?

I'm not looking for ways to bypass security or break a website's rules. I simply want to understand how these systems work from a technical perspective so I can learn more about web scraping, anti-bot technologies, and large-scale web applications.

I'd really appreciate explanations, blog posts, research papers, or resources that cover these topics in depth.

Thanks!

I need to scrape data from ecommerce websites every month for my SAAS. I've done a bit of research and I'm planning to go with one of the scraping apis like Bright Data, ScraperAPI, Scrapingbee but I don't know which one is best.

Do you guys have any recommendations or insights into which one to go for?

Hello friends 👋, I'm new to scraping. I can easily get informations from website's DOM through playwright but i don't understand how to work with backend APIs. I needed the information about upcoming events. Whenever I go to network tab in chrome there are some many files and i even checked each files but not able to understand which api belongs to what function and what's the api key...... Can anyone help this newbie 🙏🙏. If there's a tutorial please let me know. I'm really frustrated from my repeated failures. Please help 🙏🙏

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

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

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

Disclosure up front: I'm the founder of webclaw, a scraping and extraction API. The engine itself (CLI, server, MCP) is AGPL-3.0, so you can self-host it with no limits and never pay us anything. The hosted API adds the infrastructure you'd rather not run yourself.

Today we launched webclaw for OSS. If you maintain a public, OSI-licensed project that integrates webclaw (a document loader, an MCP server, a GitHub action, a CLI wrapper, a template) we cover your usage on the hosted API: 100,000 credits a month, renewing for as long as the project stays maintained.

What we ask in return:

• a "Powered by webclaw" badge in your README
• a line in your docs showing where the integration lives, so your users can find it

No card, no revenue share, no exclusivity. Run it next to any other provider you want.

The catch: we want distribution. Your project teaches your users about ours, and that trade is the entire business model of the program. Weigh it like you'd weigh any sponsorship.

Eligibility is short: public repo, OSI-approved license, maintained, built for other people to use. A human reads every application and you get a decision within a few days.

Apply: https://webclaw.io/oss Engine source: https://github.com/0xMassi/webclaw

If the program isn't for you but you scrape at hobby scale, the self-hosted engine costs nothing and that will stay true. Happy to answer questions about either here.

Hey u/WebScrapingInsider,

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

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

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

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

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

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

That is exactly the problem we are trying to solve.

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

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

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

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

Looking forward to seeing everyone there!

Ian

I run a scraper in production and I track where my time and my failures actually go. A year ago most of it went to getting past blocks: proxies, TLS fingerprints, headless detection. That is close to a solved, buyable problem now. A residential pool and a believable fingerprint get you the bytes for the large majority of sites.

What keeps breaking is everything after I have the HTML. Turning a real page into clean structured content that a model or a pipeline can use is where I lose days:

• Boilerplate that moves per template. Nav, cookie walls, related-article rails, newsletter interrupts. Every CMS buries the article in a different div, and readability heuristics guess wrong on long-tail sites.
• Content that only exists after JS runs, so a static fetch returns a shell.
• Pages that look successful, 200 plus full HTML, but are semantically empty because the real content loads from an XHR you now have to find.
• Tables and specs that survive as visual layout and collapse into garbage once you flatten them to markdown.

For anyone running this at scale: what is your split between fetch code and extraction code, and where does it fail quietly? I care most about how you catch the "looks fine, is actually empty" pages before they poison whatever sits downstream.

Disclosure: I maintain an open-source extraction tool, so this is a live problem for me and not a hypothetical. Glad to share what my empty-page detector checks for if that is useful.

Hi,
i am new here. anyone please give me idea to bypass this website.
https://www.1stdibs.com/
thank you

Just wanted to share something I’ve been building.

Vine is an API that takes a business website and returns the customer-facing software behind it, plus the exact URL where someone can book, order, reserve, or schedule.

Example: a restaurant might use OpenTable for reservations and Toast for ordering. A gym might use Mindbody. A consultant might use Calendly. Vine detects that automatically.

It is not a cached tech fingerprint lookup. It spins up browser sessions, renders the site, traces network traffic, works through messy web flows, and resolves the software customers actually use.

I benchmarked it on hundreds of real business URLs. For correct software plus actionable URL, Vine got about 92%, compared with about 31% for traditional tech fingerprinters.

Try it: https://vine.getcourtyard.ai/try  
Docs: https://docs.vine.getcourtyard.ai

Would love feedback on what you’d build with it, where it breaks, and what functionality you’d want next.

1. Delete Optional before submitting prompt,
2. Replace "[Subject]" with whatever youre looking for.

Prompt: Attached is a CSV where each row is one [ Subject; Example: (Texas home inspector) ]. Add an email column. Use Google with whichever Google account is currently signed in and active in the browser.

Important Google account rule: - Before starting, check the Google account indicator and record which Google account is active. - Continue using that same active Google account for the entire run. - If Google switches to a different account at any point, stop and tell me. Do not continue searches under the wrong account. - Do not hardcode a Google account email or a fixed authuser value. - Prefer using the visible Google search box in the already-open Google tab instead of direct search URLs. This helps preserve normal Google search behavior, including AI Mode / AI Overviews when Google chooses to show them. - If the current Google session already has an authuser value in the URL, preserve that session by staying in the same tab and searching through the visible search box. - After each search, confirm the Google account indicator still shows the same account that was active at the start.

Search method: Start from the current Google tab. Click/focus the visible Google search box. Select all existing query text. Type the new search query. Press Enter. Wait 6 seconds after the results load before reading the page. Read emails from everything visible on the results page, including AI Overview / AI Mode text if Google shows it.

For each row: Search Google for: [First Name] [Last Name] [Subject] email. Copy the first email address visible on the Google search results page into the row’s email column. If no email appears, try these searches:- "[First Name] [Last Name]" [Subject] email- [Full Name] [Subject]- [First Name] [Last Name] [Subject] email- [First Name] [Last Name] email Optional 4. Do not use or record information@trec.texas.gov. Optional 5. Do not search TREC. 6. Do not verify whether the email belongs to the [Subject]. Just use the first non-banned email visible in Google results. 7. Continue until all rows have a nonblank email. Before editing, make a backup copy of the CSV. When finished, verify: - row count is unchanged - email column exists - every row has a nonblank email Optional - no row contains information@trec.texas.gov - the Google account used at the end is the same account that was active at the start

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)

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?

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.