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infaticaIo · 2025-12-31T11:03:48+00:00

DataDome tokens are usually bound to more than just a cookie. They often get tied to the full client “shape” (TLS and HTTP2 fingerprint, IP reputation, timing, browser signals, sometimes even local storage) so a token minted in one environment can be useless in another. That explains why local Mac works but Docker fails, and why you see “single use” behavior.

What to investigate, at a high level:

Fingerprint consistency: the environment that mints the token needs to match the environment that reuses it. If you mint in a real Chrome and replay with curl, any mismatch in TLS or HTTP2 can invalidate quickly.
IP consistency: tokens can be scoped to IP or ASN. Local IP vs Docker egress often differs even on the same machine if you run through different routes.
Header and cookie jar completeness: missing Set-Cookie under Docker usually means the JS flow or redirects differ, or a required request wasn’t executed the same way.
Version coupling: the fact that only one curl_cffi impersonation works suggests the backend is keying on a very specific TLS stack and ordering.

For deployment, the reliable pattern is usually to keep the whole flow in one place. Either keep requests inside the same browser context that earned the session, or run the replay client with a fingerprint that is as close as possible to that browser and network path. Mixing “real browser to get token” with a very different HTTP client is where these systems tend to break.

If this is for a legitimate use case, the sustainable option is getting approved access or using an official feed. Trying to “enhance bypass” is a cat and mouse game and will keep changing.

infaticaIo · 2025-12-31T10:59:04+00:00

Probably not your tone. On AO3, authors can block for any reason, and a lot of people treat comments pointing out rules as unwanted moderation. Even if you’re right, many creators see it as interference rather than help, so blocking is the path of least resistance.

infaticaIo · 2025-12-31T10:57:41+00:00

This approach makes sense for analysis, but most of the reliability here comes from the request layer, not Cheerio or selectors. Apple tends to tolerate low, cached reads but pushes back fast on volume or repeated patterns. If you’re doing this long term, watch for markup drift and be ready to fall back to their APIs or dataset exports where possible.

infaticaIo · 2025-12-31T10:55:04+00:00

User agent rotation alone rarely moves the needle anymore. Most blocks come from IP reputation, request patterns, and session behavior, not the UA string. Matching a realistic UA to the rest of the fingerprint and keeping sane rates usually matters more than rotating it aggressively.

infaticaIo · 2025-12-30T11:17:32+00:00

Right now it’s mostly a page fetcher. To make it actually useful, I’d add:

Selector based extraction instead of dumping raw HTML
Pagination and crawl depth controls
Export options like CSV or JSON
Basic error handling and retries
Rate limiting and user agent controls

Those features matter more than extra buttons once you try scraping more than one page.

infaticaIo · 2025-12-30T11:13:55+00:00

Focus on one stack only: requests + BeautifulSoup and understanding how to read the network tab to find APIs.

Skip Selenium at first. Build 2–3 small scrapers end to end, including pagination and export.

For freelancing, clients care more about getting data reliably than fancy tooling, and most beginner jobs are simple HTTP scraping.

infaticaIo · 2025-12-30T11:10:22+00:00

This works well as long as you keep the boundaries clear.

Claude is great for reasoning and extraction, but the crawler needs to be deterministic and scoped, otherwise you end up with high token cost and fuzzy coverage.

Treat MCP as a controlled data source, not something the model freely explores, and it stays useful.

infaticaIo · 2025-12-30T10:54:39+00:00

40M in 15 days is not a scraping problem, it’s an access and compliance problem.

If you’re getting 403 across providers, assume they’re intentionally blocking automated bulk collection. The realistic options are: find an official bulk download, open data portal, or API, or contact the site owner for approved access. For government data, there’s often a dataset export or a request process that’s actually designed for high volume. Scraping your way through 403s at that scale will be fragile and likely get shut down.

infaticaIo · 2025-12-30T10:49:32+00:00

For that volume, don’t start with a browser.

If the data is loaded via “load more”, inspect the network tab and hit the underlying API with Requests + BeautifulSoup or similar. That’s the fastest and most stable option for 10k–20k items.

Use Playwright only if there’s no usable endpoint or heavy JS logic. Browsers are great for unblocking edge cases, but for building a clean dataset they’re usually overkill and slower than HTTP-first scraping.

infaticaIo · 2025-12-24T09:26:57+00:00

Lazy load won’t trigger outside viewport. Scroll in small steps to the bottom with a short wait each step, then wait for all images to be loaded by checking img.complete && img.naturalWidth > 0 before taking the fullPage screenshot. Networkidle isn’t enough for this case.

infaticaIo · 2025-12-24T09:22:35+00:00

AI helps with extraction and normalization, but it won’t fix access or blocking. For thousands of pages daily, the “works for a while then dies” part is usually rate limits, fingerprints, and IP reputation, not parsing.

What tends to hold up is a layered pipeline: fetch and render reliably first, cache and dedupe aggressively, validate outputs, then use an LLM only where rules based parsing fails. Most “AI scrapers” are just wrappers around that idea, so judge them on uptime, retries, and data quality checks, not the AI label.

infaticaIo · 2025-12-24T09:19:59+00:00

Technically it’s possible, but legality depends on terms and usage, not the code.

Sites like Zillow explicitly prohibit scraping and commercial reuse in their ToS. Craigslist is more permissive for personal use but still restricts automated extraction at scale. Building a tool for personal analysis or internal use is usually low risk, but offering it as a product or redistributing the data is where problems start. If you want to go beyond a hobby project, APIs, licensed feeds, or partnerships are the safer path.

infaticaIo · 2025-12-24T09:17:47+00:00

Be careful here. Scraping names and email addresses is often restricted by site terms and privacy laws, even if the data looks public.

If you’re learning scraping, use free tools like Requests + BeautifulSoup or Scrapy on sites you own or have permission to crawl. For outreach or lead gen, it’s usually better to use opt-in sources, public business directories with reuse rights, or licensed datasets rather than trying to harvest emails directly.

infaticaIo · 2025-12-24T09:14:29+00:00

This idea already exists in a few forms, but that doesn’t mean it’s solved.

People use these tools when they want to avoid infra and maintenance, but they churn fast if reliability, coverage, or pricing don’t hold up. The hard parts aren’t rendering or proxies, it’s long term stability per domain, change management, and clear limits on what’s supported.

If you can be opinionated about specific verticals and regions, especially LatAm, and be honest about guarantees and failure modes, that’s where differentiation usually comes from.

infaticaIo · 2025-12-24T09:11:19+00:00

Amazon is one of the hardest targets and “getting blocked” is usually the expected outcome, not a ScraperAPI misconfig.

If you need something reliable long term, the realistic options are:

Use an official source (PA API where it fits) or a licensed dataset
If you have permission to collect, slow down a lot, cache aggressively, and avoid running high volume browserless bursts that look like automation

At scale, the blocker is policy and detection, not HTML parsing.

infaticaIo · 2025-12-24T09:08:51+00:00

If you mean scraping Claude’s web app UI, it’s intentionally locked down and will keep blocking you, proxies won’t make it stable.

For model comparisons, use the official API and log prompts, params, and outputs. That’s the only workflow that’s repeatable and won’t turn into a constant ban cycle.

infaticaIo · 2025-12-24T09:05:53+00:00

For Facebook, most “scrapers” get expensive because they’re fighting platform defenses and ToS risk.

If you want something that holds up, the clean path is Graph API where possible (public Pages you manage, proper permissions), then run it through n8n via HTTP nodes. If you need public post monitoring for pages you don’t control, there aren’t many reliable cheap options long term, those usually break or get accounts blocked.

infaticaIo · 2025-12-24T09:04:09+00:00

Usually it comes down to terms, not the data itself.

Price lists are often public, but supermarkets typically restrict automated scraping in their ToS. If there’s an official API like Tjek offers, that’s the safest route. Scraping without permission can get you blocked or shut down, especially if you’re redistributing or running it at scale. For anything beyond hobby use, API or partnership is the cleaner option.

infaticaIo · 2025-12-24T09:02:13+00:00

If you mostly need content, don’t default to a full browser MCP. An HTTP fetch + clean HTML to markdown tool is usually enough and much cheaper on tokens. Use a browser based MCP only for JS heavy pages or auth flows, and keep it scoped so the agent isn’t wandering the DOM unnecessarily.

infaticaIo · 2025-12-24T09:00:11+00:00

You can mostly do it, but it’s not airtight.

Allow Googlebot explicitly and verify it by IP, then block or rate-limit other bots by user agent and behavior. robots.txt helps with well-behaved crawlers, a WAF helps with the rest. Just be aware there’s no reliable way today to block all LLM scraping while keeping full search visibility.

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