I Built a Job Listing + E-commerce API Dataset in 4 Hours, No Code

The problem with "just build your own API"

If you've ever wanted a personal dataset (job listings, product prices, whatever) you know the real cost isn't the idea, it's the pipeline. Scraping. Cleaning. Storage. Refreshing on a schedule. Serving it over an API. Handling schema drift when the source site redesigns. Every one of those is its own mini-project, and each is its own source of friction.

I wanted a dataset combining Amazon products for developers and job listings scraped from multiple sites. Normally that's a multi-day side project. I built it in about 4 hours, without writing a single line of scraping or backend code.

Here's how.

The magic part: describing data instead of scraping it

I used a tool called Quorel. The workflow was almost embarrassingly simple:

1. I pasted in my source URLs (Amazon product pages, a few job board listing pages).
2. I described the schema and extraction intent in plain English: no selectors, no XPath, no code. Something like "product name, price, rating, whether it's in stock" for the Amazon side, and "job title, company, location, salary range, posted date" for the listings side.
3. I hit run and left it alone for 30–60 minutes.

When I came back, the data was fully structured and ready to query.

From there I asked Claude to build me a dashboard on top of the API so I could actually see and explore the data. That part took the rest of the 4 hours. The result is live here: diffboard.vercel.app.

The part that actually sold me: it's versioned like git

Structuring the data was the easy win. What kept me around is that Quorel treats datasets like a git repo for live web data:

• Nightly refresh (or your own cron schedule): the data doesn't go stale.
• Every refresh is a saved version, nothing overwrites the last run.
• Diffing between versions: I can see exactly what changed, like prices that moved, listings that disappeared, new rows that showed up.
• Time travel: I can pull the dataset as it looked days or months ago.
• Rollback: a bad refresh doesn't mean starting over.
• Forking: you can clone a public dataset the way you'd fork a repo, and extend it for your own use case.

That combination, structure plus history, is the part a plain scraper never gives you. Most tools hand you today's snapshot. This hands you the whole timeline.

Talking to your data instead of querying it

The other piece that stood out: Quorel ships an MCP server with every dataset, on every plan, including free. That means I can point an AI agent at my dataset and just talk to it, like "find listings with price drops this week" or "fix the null prices and publish a new version," and it does the pull, clean, and push cycle for me, still landing in the same versioned system. It's genuinely like chatting with your data pipeline instead of writing scripts for it.

Try it yourself

The datasets I built are public and free to use. You can query them directly, or clone one and extend it to fit your own project:

• Quorel: quorel.vercel.app
• Public datasets: quorel.vercel.app/datasets
• My dashboard: diffboard.vercel.app

I've barely scratched what's possible here. Next I want to enrich the job listings with salary normalization and wire the Amazon dataset into a price-drop alert bot. If you build something on top of this, I'd love to see it.