GitLab hosts millions of public projects, and the explore page at gitlab.com/explore/projects surfaces all of them in a structured listing updated in real time. For anyone tracking open-source activity, monitoring API project ecosystems, or building developer intelligence datasets, that listing is a rich and largely untapped data source. The challenge is that browsing it manually does not scale. This guide shows how to extract structured project data from GitLab using the Min

Most people focus on getting data out of a website. Fewer think carefully about what that data looks like once it arrives. Here is what you actually need to know about how Minexa.ai structures and delivers your extracted data. 1. Excel is the default, and it is already structured When you run a scraping job in Minexa.ai, the default export format is Excel (.xlsx). The file is not a raw dump. Each result gets its own row, and each data point gets its own column. If you scraped

Cryptocurrency prices move fast. Keeping a structured, exportable record of what the market looks like at any given moment is harder than it sounds when you are working from a browser tab. The CoinDesk price page at coindesk.com/price lists current prices, market capitalisation, trading volume, and short-term performance for the top cryptocurrencies in one place. This guide shows how to extract all of that into a clean dataset using the Minexa.ai Chrome extension, with no cod

The Federal Reserve publishes foreign exchange rate data on its H.10 release page every week. The data is public, structured in tables, and updated on a fixed schedule. Turning it into a reusable dataset manually means copying rows, reformatting columns, and repeating the process every time the page updates. This guide shows how to extract that data using Minexa.ai, a Chrome extension that detects and exports structured data from any web page without writing code. What the Fe

The Carnegie Classifications directory lists every accredited higher education institution in the United States, each tagged with a classification label, a state, and a student access and earnings designation. That combination of fields makes it one of the more useful structured sources for education research, policy work, and institutional benchmarking. The challenge is that the data lives across paginated HTML pages, not in a downloadable file. This post walks through how t

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

Uber Eats city pages list every restaurant available for delivery in a given area, complete with ratings, cuisine types, delivery times, and promotional labels. That data is publicly visible but entirely unstructured. Pulling it manually is slow and does not scale. This guide shows how to extract it programmatically using the Minexa API. What the data looks like Before walking through the extraction steps, here is a sample of the structured output Minexa returns from an Uber

Layoffs.fyi tracks startup workforce reductions with structured detail that most news sources skip: exact headcount, departments affected, geographic location, and links to crowdsourced employee directories. For developers building workforce intelligence pipelines, competitive research tools, or talent market dashboards, this is a high-value data source with no official API. This guide shows how to extract that data at scale using the Minexa API. You train a scraper once usin

The Public bond screener sits at public.com/bonds/screener and lists hundreds of corporate bonds with live prices, yields, coupon rates, maturity dates, and credit ratings all on one page. Getting that data into a spreadsheet manually means copying row by row. This walkthrough shows how to extract it in minutes using the Minexa.ai Chrome extension, no code required. What data the Public screener exposes Each row on the screener carries a consistent set of fields. The coupon f

Every day, new instruments appear on the London Stock Exchange. Bonds, ETFs, structured notes tied to equities, commodities, or indices across multiple currencies and market segments. The London Stock Exchange new issues page at londonstockexchange.com/live-markets/new-issues captures all of them in a single paginated listing. Getting that data into a structured format manually is slow. This guide shows how to do it automatically using the Minexa.ai Chrome extension. What the

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.

SEC filings sit in plain sight on investor relations pages, but collecting them in bulk means clicking through dozens of pages and copying data by hand. The PNC Investor Relations portal lists every filing PNC has ever submitted, and with the Minexa.ai Chrome extension you can pull all of it into a structured dataset without writing a single line of code. Watch the tutorial first The video below walks through the full extraction from start to finish on the PNC filings page. O

Flippa lists thousands of websites, SaaS products, and online businesses for sale at any given moment. If you are tracking acquisition opportunities, monitoring valuation multiples across categories, or building a dataset of digital asset transactions, manually copying that data is not a realistic option. This guide shows how to extract it cleanly using the Minexa.ai Chrome extension. What Flippa exposes on its search page The Flippa search page at flippa.com/search surfaces

Zapier's app directory is one of the most complete public indexes of SaaS and software tools available. Each category page lists dozens of products with structured metadata: tool names, descriptions, logo assets, and direct integration links. For anyone building a SaaS intelligence pipeline, competitive landscape tracker, or integration catalog, that data is genuinely useful. The challenge is getting it out in a structured, repeatable way. This guide walks through how to do e

Indian equity markets list thousands of companies across BSE and NSE, and finding a clean, structured version of that company directory is harder than it should be. Tradebulls maintains a browsable company listing at tradebulls.in/company-list/n that covers company names, BSE codes, NSE ticker symbols, and direct profile links. The data is there, but it is paginated across multiple pages and not downloadable out of the box. This guide shows how to extract that data into a cle

Tracking IPO activity manually means opening the NYSE IPO Center, copying rows one by one, and updating a spreadsheet every time the page refreshes. For a single snapshot that might take twenty minutes. For recurring monitoring across filing dates, bookrunners, and offer performance, it becomes a recurring time sink with no structured output at the end. This guide shows how to extract that data automatically from the NYSE IPO Center recent IPO page using the Minexa.ai Chrome

The Michelin Guide has been a trusted reference for travelers for over a century. Today, its website lists hotels, stays, and travel destinations with rich structured detail: names, locations, categories, ratings, and more. That data is publicly visible, but pulling it into a usable format page by page is slow and error-prone work. This guide shows how to extract that data using Minexa.ai, a web data extraction tool that works directly from your browser. No code, no selectors

The UK government's Primary Authority register at primary-authority.beis.gov.uk/par holds a publicly accessible directory of franchise and business opportunity partnerships. Getting that data into a spreadsheet without copying row by row is exactly what this guide covers. Install the Minexa.ai Chrome extension and follow the steps below to turn the register into a clean, structured dataset in a few minutes. What data is on the page The Primary Authority register lists partner

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