python, webdev, tutorial, automation
Every scraping tutorial I've read online ends the same way.
"Here's how to grab a title with BeautifulSoup." ...
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the "80% is delivery, 20% is scraping" insight is exactly what separates hobby scripts from production engineering.
most tutorials stop at getting the html. but delivering a clean utf-8 csv with a data quality report and an audit trail is what actually builds trust. i see this exact same principle in ai data pipelines: if the ingestion layer doesn't validate and clean the data, the downstream llm just hallucinates on garbage.
your automated audit step (fingerprinting the cms and checking robots.txt before even writing a selector) is brilliant. it saves so much wasted dev time on doomed projects.
curious about one edge case in your workflow: when playwright gets flagged by advanced anti-bot systems (like cloudflare turnstile or datadome), do you rely on stealth plugins, or do you have a proxy rotation strategy baked into the pipeline to handle the blocks gracefully?
massive respect for open-sourcing the framework with 70+ automated tests. that's how you build tools that actually survive in the wild. 🐯
Thank you,that means a lot coming from someone working in AI data pipelines.
You're right about the ingestion layer. Any downstream consumer of scraped data is only as good as the input. That's why the QA report and audit trail were the first things I built, not the last.
On your question about Cloudflare Turnstile and DataDome-here's my honest approach:
Static request first. If it works, I never open a browser.
Playwright headless with a stealth script that hides navigator.webdriver.
Playwright visible. Oddly effective-the JS challenge runs and the fingerprint looks human.
Proxy rotation. Simple round-robin right now, not enterprise-grade yet.
What I don't do is use CAPTCHA-solving services. Two reasons. First, it crosses an ethical line I'm not comfortable with. Second, clients who need that usually have a deeper problem than tooling can fix.
When I hit a real wall, I tell the client honestly. Sometimes the answer is "use their public API" or "run this manually twice a week." Turning down the wrong project has saved me more time than any single tool.
Curious about your side do you validate at ingestion, or do you let the model flag garbage and filter downstream?
the ethical line on captcha solvers is a massive flex. turning down the wrong project instead of forcing a bad technical solution is the ultimate senior dev move. (and the "visible playwright" trick is brilliantly pragmatic—sometimes the best human fingerprint is just being visible).
to answer your question: i do both, but i heavily lean on ingestion validation to save tokens and latency on the edge.
pre-flight (ingestion): before the llm even sees the request, i run a lightweight constitutional ai check and basic structural validation. if the input is obviously malformed, too large, or violates safety rules, it gets rejected immediately. this prevents wasting compute on garbage.
post-flight (downstream): the llm's generated code goes into a secure, isolated js sandbox (iframe). if the code throws a runtime error, the sandbox catches it and feeds the error trace back to the llm for self-correction, rather than just showing the broken code to the user. so the "downstream filter" is actually an automated, silent retry loop.
it's not perfect, but catching errors at the sandbox level before the user sees them has drastically improved the perceived reliability of the app.
massive respect for the honest breakdown, kabir. this is exactly the kind of real-world engineering talk the community needs. 🐯
Harun,this is the reply I was hoping for.
The constitutional AI pre-flight check is a pattern I hadn't seen framed that way. Filtering at ingestion to save tokens and latency makes total sense you're treating the LLM as the expensive resource, not the compute. That's the right mental model.
The iframe sandbox with a silent retry loop is genuinely clever. Feeding runtime error traces back to the model instead of showing broken code to the user that's the difference between a demo and something people trust. Most AI code tools I've seen fail exactly at that step. The user sees an error and loses confidence in the whole product, even if the model was 95% right.
I'm curious about one thing. When the sandbox retries, do you cap the number of attempts? I've noticed that with LLM self-correction loops, the model often fixes the first issue and introduces a new one. Curious whether you draw a hard line or let it keep going.
On my side, I don't have that kind of pipeline yet. My extraction validation is all rule-based schema checks, price format parsing, deduplication. It's nowhere near a constitutional check, but it catches the obvious stuff before anything downstream sees it.
Thanks for the detailed reply. This is the kind of conversation that's hard to have on Twitter or Reddit-glad dev.to still has this.
Kabir
you hit the nail on the head with the oscillation problem. that is exactly why a hard cap is non-negotiable.
i cap the silent retry loop at exactly 2 attempts.
here is the logic: if the llm fixes the syntax error but introduces a logical bug on retry #1, retry #2 is the last chance to self-correct. if it fails a third time, the loop breaks.
when it hits the cap, it doesn't spin forever or burn tokens. it gracefully fails, logs the final error trace, and falls back to presenting the user with the last known working state (or a clear, human-readable error explaining what went wrong). it's better to admit defeat than to confidently serve broken code or rack up latency on a mobile connection.
also, don't undersell your rule-based schema checks! for structured data extraction (like prices and formats), regex and strict schema validation are actually superior to llm checks. they are deterministic, zero-cost, and 100% reliable. llms should be reserved for the unstructured reasoning, while rule-based checks handle the strict boundaries.
glad to have this conversation here too. this is what dev.to is for. 🐯
Harun,the "gracefully fail and fall back to last known working state" pattern is the piece I hadn't seen articulated before.
Most retry loops I've studied either spin forever or hard-crash. Your middle path-try twice, then revert to a known-good output-is the kind of decision that only comes from actually shipping to users. I'm going to steal that idea for my scheduled scrapes. If a scrape fails on Monday, the previous week's data is still valid and useful; I can ship it with a "stale" flag instead of nothing.
On the rule-based checks point, you're right, and it's the same reason I use SQLite + regex for price validation rather than asking a model. Deterministic beats probabilistic whenever you have a well-defined answer. LLMs are for the cases where you don't.
Glad our paths crossed. Following your work now. 🚀
Kabir
Your CLI example uses --preset shopify, but the next bullet says WooCommerce. Is that a typo or an auto-detected override? The QA report is the part I would reuse with a BrowserAct Cloud Bot too: check missing prices and duplicate URLs before CSV/JSON goes downstream.
One Playwright gotcha worth knowing if you pay for proxy traffic: Chromium only honours the last --disable-features switch. Playwright already passes one (it includes OptimizationHints, which stops a fresh profile from downloading a ~40 MB model on startup). If you add your own --disable-features in args, Playwright's list is silently dropped and that download comes back - on every fresh profile, through your proxy. Merge your features into one switch instead 🙃
Alexandr,this is exactly the kind of gotcha that only shows up after you're already paying for proxy traffic. Thank you for the detailed breakdown.
I didn't know Chromium drops the earlier
--disable-featureslist when you pass a new one. That's the kind of behavior that silently costs money-a fresh profile pulling a 40 MB model on every launch, through a paid proxy, adds up fast.For anyone reading this and wanting to check their own setup: the way to verify is to look at Playwright's default args (it exposes them in the docs and via
browser_type.launch), then make sure your custom flags merge into a single--disable-features=A,B,Clist rather than replacing it. I'll be updating my own fetcher to merge feature flags instead of overwriting them.Curious what your proxy setup looks like at scale-do you rotate per request, per session, or per domain? The last one is what I've been experimenting with, but I'm always looking for how others handle sticky sessions versus fresh IPs.
Thanks again for the pointer-this is the kind of detail that separates a demo from something you can actually run in production.
Kabir
Mostly per session. One sticky IP for a whole logical flow (login, a cart, paging through one category), fresh one when the flow ends or gets blocked. Per-request rotation looks nice on paper, but to the site it's a "user" whose IP changes every click. Warm-up matters too: on Google a cold exit got results ~20% of the time vs ~87% for one that browsed a bit first (wrote it up here if you want a little more detail: dev.to/alkaznodemaven/a-cold-proxy...). Per-domain is a fine middle ground as long as the IP stays put for the whole flow. And if an IP fails 3-4 times in a row, drop it instead of retrying on it
Really insightful article that bridges hobby scraping scripts and real‑world client‑facing work. Too many online tutorials focus purely on extracting raw HTML, ignoring pre‑assessment, data quality and deliverables. The automated audit workflow is brilliant — spending a few minutes evaluating robots.txt, CMS type and page structure upfront prevents countless hours of debugging later.
Your observation that clients pay for clean output, quality reports and audit trails rather than just raw scraped data resonates a lot. The offline HTML dashboard and proper UTF‑8 CSV handling are small details that make a huge difference for end users. Kudos for open‑sourcing this tool with solid test coverage. I will definitely clone the repo and give it a spin!
Thank you that means a lot coming from someone who clearly understands the gap between tutorials and client delivery.
You're right that the audit step is the biggest time-saver. Five minutes of checking robots.txt and page structure saves entire afternoons of debugging selectors that were never going to work.
The "clients pay for deliverables, not scraping" insight came from watching what actually got a yes. A raw CSV feels like a script; a report with clean columns, a QA log, and a JSON manifest feels like a service. Same data, different framing.
If you do clone the repo, the fastest entry point is:
That gives you a full report in under 10 seconds with no setup beyond
pip install -e ".[dev,cloud,llm]"andplaywright install chromium.If you find anything broken or missing, open an issue. Genuinely curious what breaks first when someone else runs it on a fresh machine that's the best kind of feedback.
Kabir
im going to use this in my OSINT process