I was curious about how the creator economy actually works, not the influencer marketing pitch decks, but the actual structure.
- Who mentions who in their videos?
- Which brands keep showing up across the same creators?
So I built a pipeline that analyzes YouTube videos at scale, extracts every person mentioned and every brand sponsor, and builds a relationship graph from it. No manual data entry. Everything is extracted from what creators actually say and show.
Current data: 32K creators, 55K relationship edges, 2,700 brands. Started deep in fitness and gaming YouTube.
Things to try out:
- Search "Gymshark". See their sponsored creators, which categories they target, and which brands compete for the same roster
- Search "MrBeast". See his connection network
- Click a category tab on the homepage to filter both creators and brands
The hardest technical problem was entity resolution. The same person gets mentioned as "Jeff Nippard", "Jeff", "Nippard" across thousands of videos. Getting that right is what makes the graph useful.
Sadly can't seem to filter or even see the location or if it's remote or not, as someone not from the US it's almost worthless if I have to click in every single one and read the description for that information.
No like proving they are your kids and not somebody else’s. It would also allow for more trusted adoption procedures. And let parents buy/sell children more securely without government intervention.
With around 12 years in tech coding & managing, I'm your hands-on tech leader, specializing in infrastructure and backend development. I've steered teams through startup hustle, growth vibes, and even the occasional merger chaos. Actively involved in crafting systems from the architecture to the coding. Open to management or hands-on coding roles (backend, DevOps, Infra, Platform, etc.)
If you don't want your financial information shared with anyone, don't put it on the internet. Even with a strong privacy policy, do think anyone can guarantee that your data will always remain safe?
I don't put it on the Internet at all. I was just wondering how so many people were praising the site and its features and how this application has so many users with nobody even asking the question about a privacy policy or the developer not thinking about it. Since this is a paid product, a privacy policy is a set of promises that the vendor makes to you as a customer, and is part of the contract with you (which you can enforce or claim compensation for when breached, depending on the jurisdiction).
While nobody can guarantee anything in life, we still expect reasonable policies and protections to be available. Otherwise why would anyone even worry or talk about the Cambridge Analytica scandal? Take this unrealistic example: would you not feel you're being scammed if you bought a flight ticket but the airline had no policy about baggage loss or delays (essentially anything that could affect you, the paying customer, negatively)? It's quite obvious that the airline cannot guarantee that your bags won't get lost or that there won't be delays in the flight times.
The same principle applies with the airline. If you really don't want to deal with the hassle of having your bags lost, don't fly.
Of course, that takes the whole mentality to an extreme.
It's not often, even with a good privacy policy, or lost baggage policy, that it offers any true protection. It is more for peace of mind.
- Who mentions who in their videos?
- Which brands keep showing up across the same creators?
So I built a pipeline that analyzes YouTube videos at scale, extracts every person mentioned and every brand sponsor, and builds a relationship graph from it. No manual data entry. Everything is extracted from what creators actually say and show.
Current data: 32K creators, 55K relationship edges, 2,700 brands. Started deep in fitness and gaming YouTube.
Things to try out:
- Search "Gymshark". See their sponsored creators, which categories they target, and which brands compete for the same roster
- Search "MrBeast". See his connection network
- Click a category tab on the homepage to filter both creators and brands
The hardest technical problem was entity resolution. The same person gets mentioned as "Jeff Nippard", "Jeff", "Nippard" across thousands of videos. Getting that right is what makes the graph useful.
Stack: FastAPI, Next.js, PostgreSQL, Fly.io. Gemini 2.0 Flash for structured extraction.
No signup, no paywall.