u/kamililbird

It's easy to assume that if a page loads and the text extracts cleanly, the data is good enough to feed into a model. That's what majority thinks. What I have in mind is duplicate pages with slightly different info, structured data that doesn't match what's rendered on screen, inconsistent formatting across sites that breaks parsing logic downstream. Can relate? What's been the hardest data quality issue to catch once an AI agent started sourcing from the web instead of a clean dataset, and how did you end up dealing with it?

I have been going back and forth on this for the full scraping setup and found something useful. PycURL's multi-handle interface would seem like it is still the way to go if you're doing huge numbers of requests in a minute and need libcurl level control (custom SSL certs, connection reuse tuning, no async runtime dependency). It's a bit wack to set up on macOS though so keep in mind. I think that httpx is the better call once you're doing async work. AsyncClient with gather() on a shared proxy handled 10 concurrent requests without the boiler plate PycURL needs for the same throughput, and HTTP/2 comes free. Rotation without health checks seems to eat up IPs for some reason, sticky sessions per proxy for login/form flows works better for rotation.

What do you do with the data that you scrape? Is it personal project or a regular job?

My main use case is to build small scrapers for various projects, but mainly these are projects. Also do some research on the side as a freelancer for few agencies. Please share your use cases, what you're using scrapers for, whether you built the scrapers yourself or purchased dedicated ones, which ones gives more freedom, insights. Which one is more sustainable in the long run.

This post is written with new users in mind. Write your own suggestions for the most optimal/best ways to bypass modern anti-bot solutions such as Cloudflare/DataDome/Kasada. What worked for you? What was your experience tackling these anti-bot measures, what was successful, what wasn't. Share your ideas. The mic is yours.

Added Chat feature to this sub. Kindly click on three dots and then chat/create. This will help people to create new posts much easier.

Also, added another addon, which will help moderators manage trending posts/comments.

title. Would like to hear opinions and encourage knowledge-sharing among each other. What was the best advice/ opinion about proxies/ scrapers that you have received. Doesn't matter when, if it's relevant, let us know.

https://preview.redd.it/lvon1ku4uzah1.png?width=574&format=png&auto=webp&s=f4a099056b6a54236c4bdb4dc0b827ef70813051

I was inspired by today's news

Hey folks, looking to expand my views and would like to find something new. Particularly interested in free open-source, maintained web scraper, preferably agentic.

If you guys know anything or if you have used these sort of solutions, let me know

Sup y'all, I'm a dev who got tired of the content grind. Instead of hiring someone or giving up, I decided to see how far I could push automation. This is a writeup of what I built and what actually happened.

The architecture: a coordinator agent that delegates to three specialists.

1. The researcher - Continuously monitors for recently trending content, pulls engagement signals and transcripts, then assigns an AI virality score. Think of it as a scout that never sleeps.

2. The producer - Takes the highest-scored references and generates new video content from scratch using an AI video model. Also writes the captions and stores everything in a pipeline database.

3. The publisher - Manages the posting schedule and uploads directly to Instagram. Posts multiple times daily at staggered times to figure out when the audience is actually paying attention.

Four weeks of data:
4 Mil total views.

3.4 Mil one video alone.

5 broke 60k views.

16k profile visits

What I didn't expect: the variance. Some days the output was genuinely good. Other days I was deleting videos before anyone could see them. Roughly a third of what got generated wasn't worth posting, so I'd check in once or twice a day and pull anything embarrassing. A shared spreadsheet acts as the memory layer between agents, not glamorous, but reliable. The only other time I spent was responding to comments as much as I can, (yes, I am fully aware that you can make an automation for the comments, but they will sound generic and have that "AI slang".)

Heyo, story time: Spent the last year running Walmart scrapers in production. Headless browsers (Playwright specifically) are almost always recommended over plain "requests" + BeautifulSoup for JS-heavy sites like Walmart, and that's true, but "use a headless browser" isn't the whole story. Here's what I learned that actually works in practice You may ask why depend on headless at all? Walmart's product pages are JavaScript-rendered. A raw HTTP request returns an HTML shell, prices, titles, and availability are injected by JS after load. BeautifulSoup never sees that data. Now for the headless browser part, it runs a Chromium engine, executes the JS, and lets you query the fully-rendered DOM. That part works well. Even with a headless browser, you'll hit blocks, it's not a holy grail as some of people have praised it over the reddit. Walmart fingerprints more than just your IP, browser canvas signatures, WebGL data, timing patterns, and TLS handshake characteristics are all signals. Vanilla Playwright out of the box is detectable. You need "playwright-stealth" or equivalent patches to mask the most obvious headless tells.

Walmart A/B tests constantly. The "<h1>" for the product title and "<span itemprop="price">`" for pricing, the selectors everyone uses, can and do shift. A scraper that worked Monday can silently return empty strings by Wednesday. You need selector fallbacks and output validation, not just "element.inner_text()". As for the resources, well, each Chromium instance eats ~150–300MB of RAM. If you're running concurrent scrapers, this adds up fast. For small datasets it's fine at scale, you either need careful concurrency limits or a distributed setup. Rotating proxies help with IP bans but don't solve fingerprinting. Worse, misconfigured proxies inside a browser context can cause silent failures, the request goes through but returns a CAPTCHA page that your parser doesn't catch. Always validate that your response actually contains product data before storing it.

Honest suggestions, people:

- ALWAYS USE "playwright-stealth" to patch headless fingerprints

- Add "wait_for_selector()" with a timeout before extracting, don't assume the element is there

- Build in retry logic with exponential backoff on failures

- VALIDATE YOUR OUTPUT: if price is empty string, treat it as a failed scrape and retry

- Rotate User-Agents per session, not per request

- Use residential proxies, not datacenter, Walmart's filters are tuned to spot datacenter ranges, (however, I was running datacenter at first with the help of residential proxies, ditched datacenter after some time).

Headless browsers are the right tool for Walmart, but they're not a reliability silver bullet as some of you praise it. For me particularly, ~85–90% success rate with a well-tuned setup was what I got at most, dropping toward 60–70% if you skip stealth patches and output validation. The remaining failures are mostly CAPTCHAs and transient blocks that retries will catch. For anything production-scale, budget time for maintenance. Walmart's defenses update, and your selectors will break. That's just the reality of scraping a site this sophisticated.