Most people who need web data for a work project assume the task is either trivially easy or completely out of reach. Neither assumption tends to be accurate, and both lead to the same outcome: hours spent doing manually what a tool could handle in minutes. Here are ten things that commonly trip people up before they even get started. 1. Multi-level navigation means the site cannot be scraped Some sites require you to click through several layers before reaching the actual da

SEC filings are public, structured, and updated regularly. But pulling them into a usable dataset from Eli Lilly's investor portal manually is slow work. This walkthrough shows how to build a repeatable extraction pipeline using the Minexa API. The scenario: monitoring Eli Lilly SEC filings at scale Imagine you need to track every new filing that appears on the Eli Lilly SEC filings page and feed that data into a downstream system automatically. You need filing types, dates,

QuintoAndar publishes thousands of property listings across Brazilian cities, updated continuously. If you are building a dataset of house prices, neighborhood trends, or listing volumes in Rio de Janeiro, pulling that data manually is not realistic at any meaningful scale. This guide walks through how to extract structured listing data from QuintoAndar using the Minexa API — a two-phase workflow where you train a scraper once visually, then call it programmatically as many t

CSR job listings on IIMJobs change frequently. Roles get posted, filled, and replaced within days. If you are building a talent intelligence pipeline, monitoring hiring trends, or tracking which companies are actively recruiting for CSR functions, manually checking the page is not a practical approach at any real scale. This guide shows how to extract structured jobs data from IIMJobs using the Minexa API, covering the full two-phase workflow: train a scraper once using the M

Every week, people post requests asking developers to pull data from public websites. The requests are specific: permit volumes by city, trend breakdowns by region, structured output in CSV or spreadsheet format. The person posting knows exactly what they want. They just do not know how to get it without hiring someone. That gap between knowing what data you need and being able to collect it yourself is where a lot of time and money quietly disappears. And in most cases, the

Most developers who integrate the Minexa API use it the same way: train a scraper, pass some URLs, get structured JSON back. That covers the basics. But there is a wider set of capabilities built into the API that rarely gets used, either because it is not obvious from the docs or because the default setup already works well enough that no one goes looking further. This article covers ten of those capabilities, with enough detail to know when each one is worth reaching for. 1

Most developers get a Minexa.ai API pipeline running quickly. The default request body works for a large share of sites, and the Chrome extension generates ready-to-use Python code in minutes. But a handful of parameters sit quietly in the request schema and go unused, even when they would directly solve a problem the developer is already fighting with. Here are ten parameters worth knowing before you hit your first wall. 1. bypass: anti-bot handling that only activates on se

Stock listing pages are full of structured data. Ticker symbols, last prices, percentage changes, volume, market cap, 52-week ranges. Every number is right there on the page. The problem is that it lives inside a browser, not inside your pipeline. If you trade actively and rely on data to make decisions, copying that data manually is not a real option at scale. And building a custom scraper from scratch means maintaining selectors every time a page updates. There is a better

Wise is best known as a money transfer platform, but it also hosts a publicly accessible stock directory covering thousands of listed companies across global exchanges. Each category page on wise.com lists company names, ticker symbols, and links to individual stock detail pages. If you need that data in a structured format for research, financial analysis, or portfolio tooling, copying it manually is not realistic at any meaningful scale. This guide shows how to extract that