u/ian_k93

We're launching ScrapeOps AI Scraper Generator on Product Hunt on Tuesday, May 27, 2026.

What I observed was that.. most scraping tools sell the happy path. The real cost is what happens after that.

Selectors drift. Targets ship new front ends. Anti-bot behavior changes overnight. Someone HAS to debug why yesterdays 96% success rate is now 41%.

We're building around a more production-focused workflow:

Schema-based scraper generation
Python / Node.js stack selection
Live generation progress
Output quality scoring
Prebuilt scraper examples
Developer-first workflows instead of black-box demos

I dont think AI replaces scraper engineers. I think it removes a chunk of repetitive setup and gives teams a faster path to code they can inspect, modify, and ship.

Would be interested in hearing from investors/operators watching the data infra space:

Does AI-generated code meaningfully change the scraping market, or does reliability remain the real moat?

Were collecting pre-launch discussion here: https://www.producthunt.com/p/ai-web-scraper-builder

We just posted a quick demo from the ScrapeOps YouTube channel showing how our Claude Code plugin generates a working web scraping project from a prompt.

The example in the video builds a crawler + product scraper pipeline for a Walmart search page. It generates the project files, schemas, parsers, README, run commands, and JSON/JSONL output. The demo uses Python + BeautifulSoup, but the plugin also supports other languages and scraping libraries like Scrapy, Playwright, Puppeteer, etc.

The part I'm most interested in feedback on is the workflow: instead of using AI to just write a parser snippet, the goal is to generate the full scraping pipeline and then let devs inspect, run, modify, or fix it from there.

Video covers:

installing the Claude Code plugin
adding the ScrapeOps to it
using /generate-scraper, /fix-scraper, and /generate-crawler-scraper
choosing language + library
generating crawler and product parser files
running the scraper and checking the structured output

This is still aimed at developers, not "magic no-code scraping."

https://www.youtube.com/watch?v=qcE5sK0DDus

The generated code still should be reviewed, especially for ToS/robots considerations, and production monitoring. But it's been useful for cutting down the boring scaffold/debug loop.

Would be interested to hear what people here think: useful direction, or does AI-generated scraper code create more maintenance debt than it saves?

Scraping tooling is moving from "data extraction" to reliability infrastructure

We built a Claude Code plugin that generates crawler + scraper projects from a URL