Building a Production-Ready Web Scraping Pipeline with Python

Stop fixing, start scaling. 🚀 We’ve all been there: you build a scraper, it works perfectly, and then—one small website update later—your entire pipeline is broken. It’s a frustrating cycle that holds your data back. It’s time to move away from fragile, "quick-fix" scripts and toward enterprise-grade data infrastructure. We’ve put together a complete guide to help you master web scraping with Python and build systems that actually last. Check out the full guide here: https://lnkd.in/g-NQk3SJ #WebScraping #Python #DataEngineering #BigData #Boundev

Crawling an entire website used to take: A Python script. Playwright or Selenium. Proxy rotation. Rate limiting logic. Error handling. 3 hours of debugging why page 47 returned a 403. Now it's one API call. Every web scraping startup that raised millions to solve this problem just became a single endpoint.

Today I wrote just 3 lines of Python… and it opened a website automatically. Using Selenium, I launched Chrome and navigated to W3Schools without touching my mouse. This is how web automation starts — understanding how to control a browser programmatically. From here, the journey goes to: • Web scraping • Automated testing • Bot development • Data extraction • Process automation Every expert in automation once started with driver.get(). Consistency over complexity. 🚀

agentpad is live. A multi-language runtime for AI agents that actually runs your code — bash, Python, JavaScript, SQL - against a real project directory. Not a sandbox pretending to be one. Most agent runtimes fake the environment. agentpad doesn't. You get full control over what the agent can touch, what it can write, and exactly what happened when it's done. > Overlay mode to stage changes before they stick. > Read-only mode when writes aren't allowed. > Glob allowlists and timeouts so nothing runs wild. > Structured output every time - stdout, stderr, exitCode, files written. > A full session run log so nothing is a black box. > OpenAI tool helpers so you're not rewriting the function-calling loop from scratch. Just run npm install agentpad Or pip install agentpad Website/Docs: https://slaps.dev/agentpad #openSource #aiAgents #developerTools #typescript #python

When I was building agents-js and Slapify, the browser side was only half the story. The other half was always: run this in the repo - a script, a test, a SQL check, a one-off patch - and get back something structured I could log, replay, or hand to the next step. I didn't want another bespoke subprocess wrapper per project. agentpad is that layer. Bash, Python, Node, SQL against a real working tree, with timeouts, glob allowlists, and a clear result object every time. Same idea in TypeScript and Python, because agents don't care which language your stack speaks. The piece I reach for most is overlay mode - full temp copy of the directory, agent edits there, then apply or discard. It made "let the model touch files" feel survivable in production, not reckless. If you're wiring agents, CI, or internal copilots on top of real codebases - try it and tell me what breaks. Github: https://lnkd.in/gi4Ccqb2 Website: https://lnkd.in/grJXTicc #openSource #aiAgents #developerTools #typescript #python

I'm pleased to announce the release of FlameIQ v1.0.0 — an open-source, deterministic performance regression engine for Python teams. Performance regressions are rarely caught in code review. They accumulate silently across hundreds of commits — a few milliseconds of latency here, a small throughput drop there — until they become expensive production incidents. FlameIQ brings the same engineering discipline to performance that type checkers bring to correctness: automated, deterministic, and CI-enforced. 🔹 What FlameIQ does: • Compares benchmark results against a stored baseline on every CI run • Enforces per-metric thresholds with direction-aware logic • Applies optional Mann-Whitney U statistical significance testing • Generates self-contained HTML performance reports • Outputs machine-readable JSON for pipeline integration 👉 Install: pip install flameiq-core 👉 Documentation: https://lnkd.in/dGj5YAPv 👉 PyPI: https://lnkd.in/dtTC47Ps 👉 Source: https://lnkd.in/dRdTr-_s 👉 Demo: https://lnkd.in/dpu74wSu I welcome contributions: star, fork and lets build together. #Python #OpenSource #SoftwareEngineering #DevTools #CI #PerformanceEngineering #Benchmarking

Most backtests are quietly lying to you - and look-ahead bias is why. Built a vectorized SMA crossover backtesting engine in Python that makes results much closer to real trading outcomes. Every crossover signal is shifted by one day, so a signal on day N only enters on day N+1, no trades based on information from the future. The engine uses pandas.rolling() with boolean masking instead of row-by-row iteration, keeping signal generation at O(n) complexity. On AAPL from 2020 to 2024, a simple SMA 20/50 strategy returned 69.08% vs. 180% buy-and-hold, with a Sharpe of ~0.45 and a max drawdown of ~-18%. Most of this came together during long flights to and from South Africa, which turned into surprisingly productive coding time at 35,000 feet. The architecture is split into four modules: data_loader, strategy, backtest, and visualizer, with yfinance as the data layer and CSV caching to cut redundant API calls. The goal is a modular setup where strategies can be swapped without touching the backtesting core. Repo is open source: https://lnkd.in/dgavp8DG #Quant #AlgoTrading #Python #Backtesting #QuantFinance #OpenSource

Do your backtests lie to you? I tested the same strategy using: - Revised data - Point-in-time data Same model. Same logic. Different results. The reason? Lookahead bias. Using revised data gives your model access to information that wasn’t available at the time, making performance look better than it really is. If you want backtests you can trust: 👉 Use point-in-time data 👉 Replicate the real information set Less impressive results — but far more reliable. Comment “Python” to receive the full script 👇 #quant #finance #backtesting #python #macrobond

Great job Karl Philip Nilsson! This is one of those things that looks small but isn’t. We see a lot of really slick Python workflows that fall apart once you strip out revised data. Being able to pull proper vintages through an API is what turns a nice backtest into something you can actually rely on.

Look ahead bias is a common issue for backtests. Maybe even more so for macro models. Point-in-time data is crucial for accurate modeling. This is why Karl Philip Nilsson built a backtesting model in python to showcase that the proof is in the pudding.