Foundit is one of Southeast Asia's most active job platforms, and its Philippine portal at foundit.com.ph lists thousands of accounting, finance, and professional roles updated daily. If you are building a hiring intelligence tool, a salary benchmarking dataset, or a job market tracker, that volume of structured listing data is exactly what you need. The challenge is getting it out in a usable format without writing and maintaining a custom scraper from scratch. This guide sh

Franchise and business opportunity data changes constantly. Listings appear, prices shift, categories expand. If your pipeline depends on that data being current and structured, manually checking Seek Business page by page is not a sustainable approach. This guide walks through how to extract franchise and business listing data from Seek Business (seekbusiness.com.au) using the Minexa API — a two-phase workflow where you train a scraper once using the Minexa Chrome extension,

Car listing pages are packed with structured data. Every search result contains make, model, trim, price, mileage, location, dealer name, fuel type, transmission, and more. But that data sits locked inside HTML, visible on screen and completely inaccessible at scale without a scraping workflow. For automotive marketplaces, that gap is a real operational problem. Manually collecting listing data across dozens of sources is slow, inconsistent, and impossible to maintain. This p

Government funding portals publish structured competition data in plain HTML, yet collecting it at scale means clicking through pages manually or writing brittle scraping scripts that break whenever the layout shifts. The Apply for Innovation Funding service, part of the UK government's service.gov.uk platform, lists open and upcoming Innovate UK competitions with details that matter to grant consultants, R&D teams, and business intelligence analysts. This guide shows how to

OpenRent is one of the UK's largest direct-to-landlord rental platforms. Its West London listings page surfaces rental prices, deposit amounts, availability dates, bedroom counts, furnishing status, and direct property URLs, all in a consistent list structure that maps cleanly to a structured dataset. This guide covers how to extract that data at scale using the Minexa API, a deterministic web extraction platform that replaces manual selector writing with a one-time visual tr

Marketplace listing pages contain exactly the data that price monitoring tools are built around: current price, original price, discount percentage, seller name, availability status, shipping cost, and condition. The problem is not that the data is hidden. It is visible on every page. The problem is extracting it consistently across thousands of listings, across multiple sources, without the extraction layer introducing errors or requiring constant maintenance. This post walk

The Companies House SIC code list is a complete reference of Standard Industrial Classification codes used by UK companies when registering or filing. Every active company on the register is assigned at least one SIC code, making this page a foundational lookup table for anyone building company intelligence pipelines, sector filters, or compliance tooling. This guide walks through how to extract the full SIC code dataset from resources.companieshouse.gov.uk/sic/ using the Min

Non-profits and NGOs tracking federal funding opportunities spend considerable time manually checking Grants.gov for new postings. Building a structured pipeline from simpler.grants.gov/search changes that entirely: every new opportunity, deadline, and award amount becomes queryable data your systems can act on automatically. This guide walks through how to build that pipeline using the Minexa API, starting from scraper training in the Chrome extension through to a running Py

The TIOBE Index is one of the most referenced sources for tracking programming language popularity over time. It aggregates search engine query data across dozens of sources and publishes a ranked list of languages monthly. For developers building tooling dashboards, curriculum platforms, or hiring signal pipelines, having programmatic access to that ranking data is more useful than checking the page manually each month. This guide walks through how to extract structured rank

If you are building a job market intelligence pipeline for the Canadian market, Simply Hired is a solid data source. The simplyhired.ca Toronto listings page surfaces entry-level roles across dozens of industries, updated regularly. Getting that data into a structured format, reliably and at scale, is what this guide covers. The workflow uses the Minexa API: train a scraper once using the Minexa Chrome extension, then call the API programmatically to extract listings whenever

Most Python developers encounter web scraping early, glance at it, and move on. It looks like a niche utility skill, something useful for data analysts or e-commerce teams, not a core part of becoming a stronger developer. That assumption is worth revisiting. The reason web scraping keeps coming up in Python learning discussions is not because it is glamorous. It is because a single scraping project forces you to use more interconnected skills simultaneously than almost any o

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

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

Every SEC filing J.Jill submits is publicly visible on their investor relations page. The problem is that reading it is easy, but extracting it at scale is not. Form types, filing dates, PDF links, XBRL zips, conversion format arrays, and detail page URLs all sit inside a structured HTML table that changes with every new submission. Manually collecting that data is slow. Parsing it with custom selectors breaks whenever the page updates. This guide shows how to use the Minexa

Most data extraction pipelines have the same weak point: the gap between fetching a page and getting usable data out of it. Crawling is solved. Rendering is mostly solved. The part that still costs engineering time is turning raw HTML into a consistent, structured output that downstream systems can actually use. The Minexa API is built specifically for that last step, and it handles more of the pipeline than most developers expect going in. The scraper is the foundation Befor

WorkBC publishes one of the most complete public job boards in British Columbia, covering roles across every sector, employment type, and region. For developers building labour market tools, recruitment pipelines, or regional salary datasets, that listing page is a structured data source waiting to be tapped. This guide shows how to extract that data at scale using the Minexa API. Train a scraper once via the Chrome extension, then call the API programmatically against any vo

Finanzfluss is one of Germany's most widely used personal finance platforms. Its ETF search tool at finanzfluss.de/informer/etf/suche/ lists hundreds of funds with structured attributes including total expense ratios, fund volumes, share class sizes, launch dates, distribution policies, and replication methods. For developers building investment research pipelines, fund comparison tools, or cost-monitoring workflows, this page is a reliable and regularly updated data source.

The USDA Rural Development lender directory at rd.usda.gov/resources/lenders lists hundreds of approved mortgage lenders across the United States, each with a company name and a direct website link. For developers building fintech tools, mortgage research pipelines, or lender qualification workflows, that data is genuinely useful. The problem is that it sits in an HTML table with no public API and no bulk export option. This guide walks through how to extract that lender data

LeCEGEP (lecegep.ca) is the central directory for continuing education and professional development courses offered across Quebec's CEGEP network. Hundreds of institutions list their programs there, covering everything from 3D design and database management to workplace safety and HR certification. The data is public and well-structured, but there is no export button and no official API. If you need this data in a usable format, whether to map the continuing education landsca

Seek is one of the largest job boards in Australia, and its listings page for Perth alone can surface hundreds of roles across industries. If you need that data in a structured format for analysis, hiring intelligence, or market research, copying it manually is not a realistic option. This guide covers how to extract jobs data from Seek using the Minexa API developer workflow: train a scraper once using the Minexa Chrome extension, get a stable scraper_id, then call the API t