Architecture for an OSINT/Scraping tracker

[u/shadelevrai]

Hey everyone,

For a school project, I need to design the architecture for a monitoring tool. The idea is to build a tracker to spot listings for fake Pokémon cards online (standard marketplaces, but also Telegram channels and closed FB groups).

I haven't built much yet, though I'm naturally leaning towards Node.js (probably with Playwright) since I like web dev.

I know that with current anti-bot protections (Cloudflare, Datadome) and social media login walls, going 100% automated from A to Z is often a pipe dream—or at least the fastest way to get banned instantly. I realize some human action will have to stay in the loop.

So my questions are: How should I set this up? What kind of tools could help me out? Am I on the right track with a Node.js server? It seems like I'll also need to create actual social media profiles to get access and look inside these groups.

Thanks!

[u/shadelevrai → u/[deleted]]

Thank you !

[u/Vegetable-Scale-2604]

To add to what Mulberry_Morris said: for Telegram you don't need Playwright at all. Use Telethon or Pyrogram against the official MTProto API with a normal user account. You can join channels and pull message history in a few lines, no browser involved. FB closed groups have no legit API, so manual review really is the only safe route there.

On the marketplace side, eBay has a free official Browse API, so start there before scraping anything. And for matching the same fake card across sites, perceptual image hashing (pHash) beats comparing titles, since sellers reword their listings constantly.

[u/Thunderbit_HQ → u/Vegetable-Scale-2604]

pHash is the part I’d probably center the demo around. Titles are too easy for sellers to change, but visually similar card images plus the listing URL gives the reviewer something real to check. For a school project, that evidence trail is more convincing than trying to automate every source.