hello everyone, im an ai engineer. i want to build a real-time project which can scrape the all the realestate websites. which is useful for my research, one more, i want free source because Iam student. can anyone help me regarding to this. please this will helpful to my carreer
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.
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).
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.
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?
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:
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:
Projects worth watching:
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 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:
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.
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?
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.
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:
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?
I have been running price monitors for a couple years now, nothing fancy, just watching a handful of sites that like to play games with their pricing. Six weeks ago I started noticing something I had not seen before. Soft blocks that did not not look like IP bans, more like a gradual tightening where I would get through maybe 8 requests then hit a friction checkpoint, then 5, then 3. Reset my proxy pool, fresh residential IPs, same pattern after about 20 minutes.
My first assumption was a bad proxy batch. I use rotation per request, different provider ASN ranges, geo mixed across three countries. Egress probes always came back clean on IP reputation, no data center flags, no known proxy ranges. I burned through probably $427 of residential bandwidth chasing that theory before I stepped back and asked what else could be constant.
That was the embarrassing part. I had been treating "rotate proxy" as synonymous with "new identity." I built a simple diff setup: same target endpoint, capture everything that leaves my box on two sessions with different proxies. Not the HTTP layer, I already randomized headers there. I mean the actual bytes on the wire and what the browser surface reported.
The TLS handshake was the first slap. The raw JA3 string I captured was byte identical across every session. Cipher order, extensions order, extension lengths, ALPN string, all of it. Because it was the same client build, the same underlying library version, the same compile flags. My thousand different residential IPs were all wearing the exact same TLS nametag. I had known JA3 was a thing in theory but I genuinely thought it only mattered at scale for bad actors, not for my little polite monitors.
Then I checked canvas and AudioContext. I run headless with some standard hardening, the usual flags people pass around. Generated 20 profiles, each supposedly isolated, each through its own proxy. Canvas hash prefix matched across 17 of them. AudioContext periodic wave rendered to identical values on 19 out of 20. The one that differed was the one where I had manually twiddled a font list weeks ago and forgotten about it. So my "profile rotation" was mostly theater. The proxy changed, the IP changed, the ASN changed, but the rendering surface was screaming same machine to anyone listening.
And the headless signals. navigator.webdriver was false on most checks but leaked true on one specific frame type I had not thought to test. The Permissions.query state for notifications returned "prompt" consistently across all my profiles, but a real browser on the same site had already resolved it to either "granted" or "denied" based on prior user interaction. Nothing dramatic in isolation, but sitting next to that constant JA3 and those canvas clusters, it was basically a signature.
I figured this out around 2 AM after running a probe script since midnight. Watching two requests from different IPs get bucketed together within about 400 milliseconds, same JA3, same canvas prefix, same permission state. The anti bot side was not even being subtle about it.
What actually fixed it was harder than I expected and I am still iterating. I had to move TLS fingerprinting to a different client stack entirely, one that lets me control extension order and grease values per session. Canvas and audio required actual per session randomization of rendering parameters, not just profile folders. The headless cleanup I ended up doing manually because every abstraction I tried leaked somewhere new. I have gone from 20 minute survival to roughly six hours now.
The lesson that cost me the most: proxy rotation is a network layer fix for a problem that stopped being purely network layer years ago. If your fingerprint surfaces are constant across IP changes, you are not rotating identities. The single biggest fix was generating TLS configs at session spawn time instead of reusing a client pool. That one architectural change took four days of untangling assumptions I did not know I had made. I wish I had spent my first $300 on understanding that instead of burning proxy bandwidth proving it wrong.
EDIT: To close the loop on how I actually isolated which surface was bleeding before I started hand rolling fixes, I threw together a small diagnostic harness that runs all the checks in one pass and just tells you what is leaking. It is open source, leakish, github.com/qruiqai/leakish. Runs the TLS fingerprint, canvas, WebGL, AudioContext, automation artifacts, egress data, the whole set I was staring at that Tuesday night at 2 AM. It does not block or patch anything, only surfaces what flags. I built it pairing with Verdent on the scaffolding while I wrestled with the test matrices.
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:
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..
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.
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.
so the thing is, for some sites the actual content (the text/data i want) is already fully there, but the page keeps loading heavy js in the background — ads, trackers, widgets, analytics, whatever. and if i just wait for everything to finish, it's a waste of time, sometimes it takes way too long for stuff i don't even care about.
so what i want is a way to understand the moment the real content is ready, so i can grab it and stop waiting early. but the hard part is i'm scraping all kinds of different sites, so i can't just wait for one specific element every time.
waiting for "network idle" doesn't really work either, because some pages keep firing requests forever and it never goes idle. and a fixed timeout is either too short (i miss content) or too long (slow as hell).
so how do you guys figure out when the content is actually ready? is there any trick for this? i hope you get my point
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!
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.
Most selector tools — including the "ask an LLM" approach — hand you something that looks right and breaks on the next run. Lists are the worst: you find out later that it grabbed 11 of 14 rows.
So I built Selector Forge to do the opposite. You point at an element (or pick two examples for a list), AI proposes candidates, and then the extension tests every one against the live DOM and throws out anything that doesn't resolve to exactly the right set. The browser is the source of truth — the model only proposes and ranks, it never gets the final word.
Full disclosure: I work at Intuned (we do browser automation) and the selector backend runs on our infra — but the extension is standalone, and a self-hostable backend is on the roadmap. Links in the comments.
Genuinely want the hard cases: throw your nastiest target site at it and tell me where it chokes.
