Pre-scraping analysis: map website architecture before coding

Most web scrapers fail because engineers skip the reconnaissance phase. I've debugged dozens of broken scrapers over the years. The pattern is always the same: someone spent days writing Selenium scripts, only to realize the data loads via a hidden API. Or they scraped static HTML when the content renders client-side. The real work happens before you write code. Here's what I do in the first 30 minutes: Open DevTools Network tab. Load the page and filter XHR/Fetch requests. Most modern sites load data through JSON APIs. If you find one, skip the DOM parsing entirely. Check the page source vs. rendered HTML. View source shows what the server sends. Inspect element shows what JavaScript built. If they differ, you need a headless browser or API extraction. Identify pagination and lazy loading patterns. Infinite scroll? API pagination? Load more buttons? Your scraper architecture depends on this. Look for rate limiting and bot detection. Check response headers. Look for Cloudflare, DataDome, or CAPTCHAs. These define your request strategy. Test with curl or Postman first. If you can get data with a simple HTTP request, don't use Selenium. Save your resources. Understanding structure isn't optional. It's the foundation of every reliable scraper. What's your first step before building a scraper? #WebScraping #Automation #Python #SoftwareEngineering #QA #DataEngineering

Stop Defaulting to Selenium: The 100x Faster Way to Scrape Data When it comes to web scraping, many developers' first instinct is to fire up Selenium or Playwright. But using a heavy headless browser to fetch simple data is like buying a truck just to deliver a postcard—it works, but it's overkill. 💡 My rule of thumb: Check the Network tab first. Modern websites are mostly "thin clients" that fetch data from clean, internal JSON APIs. Hitting these endpoints directly via requests or httpx is: ✅ 100x Faster: No rendering of JS/CSS/Images. ✅ Resource Light: Run 1,000 requests for the cost of one browser instance. ✅ More Robust: Less prone to breakage from UI/DOM changes. Next time you start a project, take 60 seconds to peek behind the curtain. Don't use a tank when a bicycle will do. #WebScraping #SoftwareEngineering #Python #Automation #DataScience

Most web scrapers fail because they skip the analysis phase. I've debugged hundreds of broken scrapers over the years. The pattern is always the same: someone jumps straight into writing Selenium or BeautifulSoup code without understanding how the website actually works. Two weeks later, the scraper breaks. Data is inconsistent. Selectors fail randomly. Here's what I do before writing any scraping code: Inspect the DOM structure and identify stable selectors (data attributes over CSS classes). Analyze network traffic to see if data comes from APIs instead of rendered HTML. Check for JavaScript rendering, lazy loading, or infinite scroll patterns. Identify authentication mechanisms, session handling, and token refresh logic. Look for rate limiting, CAPTCHAs, or bot detection systems. This analysis phase takes 30 minutes. It saves weeks of maintenance. Most engineers treat scraping like a coding challenge. It's actually a reverse engineering problem. You need to understand the system before you automate against it. The best scrapers aren't built on clever code. They're built on deep structural understanding. What's your first step when approaching a new scraping target? #WebScraping #PythonAutomation #DataEngineering #QualityEngineering #TestAutomation #SoftwareTesting

For years I kept a Notion doc called "SDET quick commands" — bash one-liners, regex patterns, random snippets I'd reach for weekly. Nobody else on the team ever opened it. Last year I rebuilt the whole thing as Claude Code slash commands. Now they live in the repo, everyone uses them, and new hires pick them up on day one. Here's the cheat sheet I wish someone had handed me earlier 👇 🔹 Built-in (already in Claude Code) → /init — scans your repo, writes CLAUDE.md so Claude knows your stack → /review — full PR review before you push; catches missing assertions + bad waits → /security-review — run it on any test touching auth or tokens → /clear — reset context between unrelated tasks → /model — swap Opus ↔ Haiku on the fly 🔸 Custom slash commands every SDET should create (Each is a ~10-line Markdown file in .claude/commands/ — write once, reuse forever) → /gen-test — user story in, Playwright test out → /heal — paste a CI failure, get a fixed locator back → /flaky — audit a file for parallel-safety risks → /pom — refactor inline selectors into a Page Object → /api-test — cURL or OpenAPI snippet → request-fixture test → /convert — Selenium / Cypress → Playwright, clean output → /test-plan — Jira ticket → structured plan with edge cases → /parallel-audit — weekly scan for shared-state smells ⌨️ Keyboard power moves → Shift+Tab — plan mode (Claude drafts before acting; essential for refactors) → ! — run shell directly: !npx playwright test --last-failed → @ — drop a file into context: @tests/checkout.spec.ts → # — add a rule to memory: #always use page.getByRole My go-to loop after a red CI run: 1) !npx playwright test --last-failed --reporter=json > failures.json 2) @failures.json /heal 3) /review 4) !git commit -am "fix: heal locator drift" Four lines. What used to be a 30-minute triage loop is now about 3 minutes. The lesson wasn't "AI writes tests for us." It was that 90% of SDET work is repeatable pattern-matching — reading failures, fixing locators, re-checking flakes. Slash commands turn that repetition into muscle memory Claude executes for you. What's the first command you'd build for your team? Drop it in the comments — I'm collecting the best ones for a follow-up post. #Playwright #SDET #ClaudeCode #AIinTesting #TestAutomation #QA #DevProductivity

most of the test suites are one renamed ID away from a full breakdown. i handed the problem to Claude Opus 4.7 (my complete Selenium + Gherkin framework) and gave it one brief: "fix this. properly." i expected maybe a cleaner locator strategy. what i got was something else entirely. it rebuilt my entire Selenium framework from the ground up — and then kept going. a three-stage self-healing engine → primary locator fails? tries every fallback → all fail? scans the live DOM → scores every element by similarity → picks the winner. test still passes. a MutationObserver wait system → replaced every time.sleep() i had written → actually waits for the DOM to go quiet → no more guessing. no more flaky tests. it built a full real-time frontend. a live WebSocket dashboard that opens in your browser the moment tests start. for someone who has never touched a test suite in their life, they can now watch exactly what's happening. every step. every scenario. every self-heal. live. in a browser. no terminal required. progress ring filling up. i broke it on purpose to show you. renamed 3 locators to IDs that don't exist. watched the dashboard light up in real-time as the framework healed itself. all tests: green. no API calls at runtime. all the intelligence is in the code it wrote. AI isn't just writing code anymore. it's making complex engineering visible to people who couldn't see it before. that feels like a big deal. repo in comment #SoftwareTesting #QAEngineering #Claude #TestAutomation #Selenium #AIEngineering #Python #BDD

Most people automate data exports the hard way. Spin up Selenium or Playwright → open browser → apply filters → click download. Wait for it to load. I've been scraping websites for 6 years and I almost always try this first instead: Right-click the download button. Inspect. Look at the href. Half the time it's something like: reports?type=sales&from=2024-01-01&to=2024-03-31&format=csv That's it. Just swap the dates, call it with requests, done. No browser, no clicking, no waiting for page loads. Not always this simple, but worth checking before spinning up a whole browser automation. #WebScraping #Python #Automation

If you are a QA who likes working with spreadsheets due to flexibility but hates importing test cases into Allure TestOps, this is for you. With Dzmitry Mialeshka, we built a small web tool to handle it: Drag and drop a CSV, preview everything, and import in one click. Anyone on the team can use it 🤝 Allure TestOps Importer is an open-source web tool that lets you: -Drop a CSV exported from Google Sheets directly into a browser UI. -Preview every test case before a single API call is made. -Run a dry run to catch issues without touching Allure. -Auto-detect duplicate test cases and skip them. -Choose which feature/story folder the cases land in. Built with Python, Flask, and a lot of iteration. Hosted on Railway: Check it out: https://lnkd.in/e9zb8Mmk GitHub: https://lnkd.in/eWB3bhiH #QA #SoftwareTesting

Recently, I was building an image extraction tool and ran into a challenge that many of us face with modern websites The Problem Today’s websites rely heavily on JavaScript, so a lot of content loads dynamically. Because of that, the usual scraping methods (like simple HTTP requests + HTML parsing) often miss the actual data. What I Did To handle this, I started using Selenium to simulate a real browser. This way, the page loads just like it would for a user, and I could access the actual content. But that was only part of the solution. Once I had the data, there was a lot of noise icons, placeholders, UI elements things I didn’t really need. So I improved the filtering logic and focused on specific URL patterns to extract only useful, high-quality images. The Result • Cleaner and more relevant image data • Better handling of dynamic content • A more reliable extraction process Would love to hear from you: How do you handle scraping from dynamic websites or dealing with protected media? #WebScraping #Automation #Python #DataEngineering

Code passes QA but functionally fails? This can be a problem for solo builders looking to move beyond prototypes.  tl;dr We had a monolithic Python script we wanted to run on our new website. We used Claude Code to plan out the port and -- to make sure it got everything right -- we wrote a data-contract specification and gave it references to the existing script plus other context in detail. The intent was to test the agent-swarm capability. The end result was code that worked but didn't produce the desired / intended output. Case study: what we found and how we found it. What we changed (so that the Orchestration Agent Claude Code has structured information to iterate upon). 1. Added a layer of transparency into the structured JSON being returned in all LLM calls asking Claude to justify its decisions and describe what information it was using to make them ie asking for the "Why" 2. Mandated separation of the QA and Build Agents. Included a functional layer of QA that focuses on outcomes and not code fidelity. 3. Mandated agent-swarms follow the spec-driven-build / ai-dev-tasks process. https://lnkd.in/gQyfHz_r <- Link to case study. Stress and Allostasis Tracking Screen (https://app.habyt.io/sats) <- Stress has a 'phenotype' - What's Yours?