Media monitoring used to mean hiring analysts to read through dozens of publications every morning and manually log what they found. That approach does not scale, and it does not stay current. When a story breaks across multiple outlets at once, the window to act on it is short. Structured news data changes that equation entirely. With the right extraction setup, a monitoring firm can pull article titles, publication timestamps, author names, section tags, and summary text fr

Most developers who start building a web scraping pipeline on AWS hit the same wall within the first few hours: the architecture that looks clean on paper turns into a collection of moving parts that each require their own configuration, cost management, and failure handling. This article breaks down the actual decision points, so you can make informed choices before committing to an approach. 1. Public cloud IP ranges get flagged immediately This is the first thing most deve

Collecting job listings at scale sounds straightforward until you actually try to do it. The data is right there on the page. Hundreds of fields per listing, thousands of companies, millions of postings updated daily. The problem is not access. The problem is that getting that data into a structured, usable format requires solving a set of technical problems that compound quickly as volume increases. Anyone who has attempted to build a job data pipeline from scratch knows wha

The infrastructure question every scraping project eventually hits At some point in every data collection project, the same question comes up: do you build the scraping infrastructure yourself, or do you hand that layer off to something purpose-built for it? On the surface, building your own stack sounds straightforward. Spin up a few containers, add headless Chrome, wire in a proxy pool, handle retries. In practice, each of those components introduces its own maintenance sur

Most benchmarks of AI web scraping tools are written at demo scale. A few hundred pages, clean targets, no anti-bot pressure, and a pricing calculator that assumes everything works on the first try. Production looks different. This breakdown covers what actually matters when you are evaluating these tools seriously: how the stacks are actually assembled, where costs compound, what breaks under pressure, and which tradeoffs are worth accepting depending on your volume and team

Every developer who has built a scraping pipeline has hit the same wall. It works in the demo. It works the first week. Then something changes and the whole thing falls over quietly, returning empty rows or wrong values with no obvious error. The frustration is not that scraping is hard. It is that it keeps breaking in ways that feel unpredictable. The questions below come directly from the patterns developers run into most often once they move past simple static pages. Why d

App store category pages are sitting on more structured competitive intelligence than most ASO specialists ever actually collect. Rankings, ratings, review counts, update frequencies, developer names, category positions — it is all right there on the page, visible, but almost never extracted at scale. This post walks through how to pull that data systematically using the Minexa Chrome extension, and what ASO specialists can realistically do with it once it is structured. What

You can see the data. It is sitting right there on the page: prices, listings, contact details, job postings, product specs. But getting it out in a usable format means copying row by row, writing brittle selectors that break on the next deploy, or feeding pages into an LLM and hoping the output is consistent. None of these hold up past a few hundred pages. This is the actual problem with web data collection today. It is not that the data is hidden. It is that every path to e