Pre-Scraping Analysis: Avoid Common Web Scraping Fails

Most web scrapers fail because they skip the reconnaissance phase. I've seen engineers spend 3 days debugging a scraper that could've been designed correctly in 3 hours. The mistake? Writing code before understanding the website's architecture. Here's the reconnaissance framework I follow before writing any scraper: 1. Network Tab First Watch XHR/Fetch requests. Often, the data you need is already in JSON format from an internal API. No need to parse HTML. 2. Inspect Authentication Flows Check if the site uses cookies, tokens, or session-based auth. Missing this means your scraper works locally but fails in production. 3. Map the DOM Structure Identify stable selectors. Look for data attributes or unique IDs. Class names change frequently during frontend deployments. 4. Test Pagination and Infinite Scroll Understand how data loads. Is it URL-based pagination or JavaScript-triggered? This changes your entire scraping strategy. 5. Check Anti-Scraping Signals Rate limits, CAPTCHAs, user-agent checks, IP blocks. Know what you're dealing with upfront. 6. Validate Data Consistency Scrape the same page multiple times. Does the structure change? Are there A/B tests affecting layout? This reconnaissance phase saves you from writing fragile code that breaks every week. Good scraping isn't about clever code. It's about understanding the system you're extracting data from. What's the most overlooked step when you build scrapers? #WebScraping #Python #Automation #DataEngineering #SoftwareTesting #QAEngineering

Most web scraping projects fail during planning, not execution. I've debugged dozens of broken scrapers that had perfect XPath selectors but scraped nothing. The issue? No one mapped the website structure first. Before you write a single line of Selenium or BeautifulSoup, spend 30 minutes understanding what you're scraping: Open DevTools Network tab and reload the page. Check if content loads via XHR/Fetch requests. If yes, you might not need a browser at all. Disable JavaScript and refresh. If critical content disappears, you need dynamic rendering. If it stays, static parsing works. Inspect pagination and infinite scroll patterns. Many sites load data in chunks through API endpoints that are easier to call directly. Check for anti-bot signals: rate limiting, CAPTCHAs, session tokens, fingerprinting scripts. Identify the data source hierarchy. Is it embedded JSON in script tags? Shadow DOM? Lazy-loaded iframes? This upfront analysis tells you whether you need Playwright, Requests, or a hybrid approach. It reveals whether you're solving a scraping problem or a reverse-engineering problem. Most engineers skip this step and waste days fighting the wrong architecture. The best scrapers are built after you understand the system, not during. What's one website structure pattern that caught you off guard while scraping? #WebScraping #Python #Automation #SoftwareEngineering #DataEngineering #QA

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

The past few years have been quite distracting with numerous AI advancements, which may have caused you to overlook this important update in JavaScript. ES2025 introduces practical and helpful improvements that JavaScript developers will truly appreciate. While it doesn't feature flashy new syntax like optional chaining or async/await this year, the emphasis is on enhancing ergonomics, security, and performance, making your code cleaner, safer, and more efficient. Some highlights include: - Iterator Helpers: Easily chain methods like .filter(), .map(), .take(), .drop() directly on iterators. Lazy evaluation means no unnecessary intermediate arrays, perfect for large datasets or infinite streams. - Secure JSON Imports: Native support for importing JSON modules using the syntax { type: 'json' }, which improves static analysis and helps prevent MIME sniffing attacks. - RegExp.escape(): Safely escape user input or dynamic strings for regex, eliminating manual escaping headaches and security risks. - Inline Regex Modifiers: Gain precise control with (?i:...) and (?-i:...) inside your patterns. - Promise.try(): A neat way to handle functions that might throw synchronously or return a promise—great for error handling. - Float16 support: Includes new Float16Array, Math.f16round(), and DataView methods, ideal for memory-efficient graphics, ML, and more. If you're eager to stay up-to-date with modern JavaScript without waiting for major updates, these features are definitely worth exploring. Read more here: https://lnkd.in/e53SpdgY Which new ES2025 feature excites you the most, or which one are you most looking forward to trying in your projects? #JavaScript #ES2025 #ECMAScript #WebDevelopment #Frontend #Coding

Most scraping failures happen before you write the first line of code. I've debugged countless scrapers that broke within days of deployment. The common pattern? Engineers jumped straight into Selenium or BeautifulSoup without understanding the target website's architecture. Before you scrape, map the system. Spend 30 minutes analyzing: Inspect the DOM hierarchy. Identify stable selectors vs dynamically generated IDs. Class names change, but semantic HTML structure rarely does. Monitor network traffic. Check if data loads via initial HTML or async API calls. XHR requests often return clean JSON instead of messy HTML parsing. Test authentication flows. Session tokens, cookies, headers. Know what persists and what expires. A scraper that can't maintain session is worthless. Observe rate limiting patterns. Track response times across multiple requests. Understand the threshold before you trigger blocks. Document pagination logic. Infinite scroll vs numbered pages vs load-more buttons. Each requires a different crawling strategy. This upfront analysis isn't overhead. It's the foundation. A well-architected scraper built on solid understanding of the target site will outlast a hastily coded script by months. The best scrapers aren't written fast. They're written right. What's your first step before building a new scraper? #WebScraping #Python #DataEngineering #Automation #SoftwareEngineering #QA

Claude Code's source code was leaked by Claude Code. Read that again. I know you missed it, so here's everything you need to know ☺️: What is Claude Code? Claude Code is an AI-powered coding assistant built by Anthropic. But it's not just LLM. It's an agent. It can: • plan multi-step tasks • run commands • edit files • reason about your codebase Think less "copilot"... More an engineer that can actually execute tasks. Who leaked it? No hacker. No insider. It was leaked through Anthropic's own npm package (yes by Claude Code itself 😂). At approximately 3:00 AM (UTC), a .map file (used for debugging) was accidentally published... ...and it exposed the entire unobfuscated source code. • 500,000+ lines of code • ~1,900 files • internal logic, tools, and workflows All sitting there in plain sight. Why was it leaked? Simple. A packaging mistake. The source map wasn't excluded. And just like that... Half a million lines of code became public. What do we know from the leak? This is where it gets really interesting. Claude Code isn't just "prompt in → code out". It’s a full system built on modern tools. Core Tools & Architecture • File system tools → read/write/edit files • Terminal tools → execute shell commands • Search tools → navigate large codebases • Planning modules → break tasks into steps • Permission layers → control what it’s allowed to do ⚙️ Tech Stack Insights React → powering parts of the interface Ink → React for building CLI UIs Bun runtime → fast execution layer Let that sink in... AI agents today are not just models. They are: UI + CLI + runtime + orchestration systems. What should we learn from this? Most people will focus on the "leak". But the real lesson is deeper. AI is powerful... But it's still just software systems. Systems that: • can fail • can expose secrets • can behave unexpectedly The real lesson? AI doesn't remove responsibility. It amplifies it. I’ve dropped relevant links in the comments if you want to dig deeper into the leak 👇 #ArtificialIntelligence #AIEngineering #SoftwareEngineering #ReactJS #JavaScript #DeveloperTools #SystemDesign #MachineLearning #TechNews

Every time I started a new project, I'd spend 30 minutes re-explaining my stack to Claude. "I'm using Next.js 14 with TypeScript..." "Use Supabase for auth, not NextAuth..." "Always validate with Zod..." I got tired of it. So I built a system to never do that again. It's called a .claude/ folder — a permanent brain that Claude reads at the start of every session. Here's what's inside mine: 📁 agents/ — 3 custom AI agents • code-reviewer → scans every PR for security issues + TypeScript violations • debugger → traces errors from API route → DB → response • test-writer → writes Vitest + Playwright tests automatically 📁 rules/ → scoped guardrails per file path • frontend.md → enforces shadcn/ui, no prop drilling, dark mode first • api.md → forces Zod validation, requireAuth(), rate limiting • database.md → prevents RLS bypass, N+1 queries, raw SQL 📁 hooks/ → shell scripts that auto-run • pre-commit.sh → blocks commits if tsc, lint, or tests fail • lint-on-save.sh → auto-fixes ESLint after every file Claude edits 📁 commands/ → one-word automations • /deploy → 12-step pre-flight + deploy + post-deploy verification • /fix-issue → reads GitHub issue, finds the bug, fixes + tests it Built it once. Pushed to GitHub as a template. Now every new project inherits it in 5 minutes. Template link in comments 👇 Follow if you want more setups like this — I post practical AI dev workflows. #ClaudeCode #AITools #NextJS #TypeScript #Supabase #MongoDB #BuildInPublic

Splitting documentation between human docs and a separate CLAUDE.md sounds reasonable. Every time I've tried it, the two drift and I end up maintaining two versions of the same content. The cut that has worked better for me is by content type, not by audience: 📖 What each module does and why: one README per module, read by both humans and agents ⚙️ Automated checks: lint rules, CI, pre-commit hooks ⚖️ Judgment calls: a short conventions file 📐 Contracts: typed code (TypeScript, Django models, OpenAPI) 🎫 Per-task intent: the ticket 🤖 Agent-only rules: the exception, not the default 📋 Specs: few and deliberately scoped, for features that cross modules and stakeholders Once the content is sorted this way, the audience question mostly disappears. Both readers want the same information. Full post with a NestJS and Next.js monorepo example, and when a cross-cutting spec still earns its place: https://lnkd.in/d5CtJ8Gt

Imagine binding your frontend to your backend with a single command. The idea fascinated me as much as I am fascinated by open source developers—people who build tools that revolutionize tech simply for the love of it. That is why I am building Binder. Available on npm. Although still a "young" CLI tool. v0.1.7 on its way (someday). One command: binder bind ./src/components/Dashboard.tsx Binder scans your mock data shapes, matches them to your API hooks (OpenAPI → Orval, TanStackQuerry), then rewrites your React components using AST surgery. No regex (Tried that one, don't do it; it doesn't work). No broken syntax. The agentic architecture: Heuristic matcher does the heavy lifting (key similarity, CRUD intent detection). LLM Architect steps in only when the matcher is uncertain Surgeon executes deterministic AST changes The persistent memory: Every issue I encountered—weird prop spreads, renamed fields, nested data mismatches— got added to the agent's memory. Binder is essentially fine-tuned to be a master at one task: finding the backend and binding it to your React component. No generic AI. Just a focused tool that learns your project's patterns and stops making the same mistake twice. Still building. Still breaking things. But the path is clear. What can it actually do today? If you follow convention—mock usage (no const imports, just inline mocks)—Binder matches it heuristically, does the swap, tests it against the TypeScript compiler, and delivers 100% working code. and this works on 100% cases. I am testing extreme cases to trigger the LLM Architect. The self-healing works: it goes from 8 errors down to 2. But it never delivers after more than 5 attempts max. Still, it self-heals. It never repeats the same mistake twice. And it has gotten surprisingly good at catching nested prop data. You can configure MCP tools, to give binder "eyes". It is not perfect, but it's getting smarter every time it fails. And I love it ... Fully OpenSource : https://lnkd.in/e-PEpzYv Break it if you want, happy hacking. #OpenSource #TypeScript #React #Agentic #HeuristicMatching #PersistentMemory #DevTools #Binder