I want a small, self-hosted application that will patrol both Amazon, Temu, EBay, AliExpress, Alibaba and Walmart e commerce marketplaces several times every day, hunting for counterfeit items that misuse protected names or images—such as, for instance, Marilyn Monroe, Elvis Presley, the University of Georgia, billabong, Moncler, outerwear, and Stone Island, as an example (there are many more) The workflow is straightforward: 1. Search the designated marketplaces programmatically, pulling back the product title, main images and seller details for every match. 2. Run each result through the decision rules I already drafted with ChatGPT (I’ll share them the moment we start). 3. When a listing meets the criteria, push the data straight into my Airtable base using the field mapping I provide. The tool should deal gracefully with rate limits, captchas, and the usual anti-scraping defences, then repeat the cycle automatically throughout the day. A clear log or dashboard that shows the last run, how many items were checked, and which ones were forwarded to Airtable will help me monitor performance. Deliverables • Source code (Python with Playwright/Selenium, or Node.js with Puppeteer—open to your suggestion) • A simple config file where I can add new keywords or swap in an updated Airtable API key • Setup guide that lets my team deploy the scraper on a fresh VPS in under an hour Once everything is running smoothly, I’ll test it with live searches for additional personalities and brands.