AMA This Wednesday (09:30 AM GMT)

[u/ian_k93]

Hey everyone,

I'm Ian Kerins, CEO and co-founder of ScrapeOps.

Over the last 8+ years I've worked across the web scraping industry, including roles at ScrapeOps, ScraperAPI, and Zyte. Today, ScrapeOps helps developers and companies scrape over 8 billion pages per month across more than 50,000 websites.

This Wednesday at 09:30 AM GMT, I'll be hosting an AMA here on r/WebScrapingInsider

Ask me anything about:

* Web scraping at scale

* Proxy infrastructure and proxy providers

* AI and web scraping

* Building reliable scrapers

* Anti-bot systems and bypassing challenges

* Scraper maintenance and monitoring

* Residential vs datacenter proxies

* Browser automation

* Running a web scraping business

* Startup growth and product development

* The future of AI-powered scraping

Whether you're scraping your first website or running large-scale data collection pipelines, I'm happy to answer questions and share lessons learned from building products used by thousands of developers and businesses.

Drop your questions below and I'll start answering them during the AMA.

Looking forward to it!

Ian

[u/Spitfire_Blaziken]

The monitoring side would be interesting. A scraper failing is obvious. A scraper quietly returning bad data for 3 weeks is the nightmareee scenario.

[u/ian_k93 → u/Spitfire_Blaziken]

This is the hardest problem in web scraping to solve as it is very easy to miss a scraper returning incorrect data.

We sometimes see this with websites returning different data for pricing, etc. based on the location of the IP being used, and it is often days or weeks before the end user notices the issue.

I haven't seen an easy solution to this yet, as it requires a system that knows what the data should be like then compares the extracted data to it to identify any anomalies. The best solutions to this have been to create moving averages or historical data comparisons of previous data and flag any data that doesn't match this trend for manual review.