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DV
@degenveteran.eth
Scalability question ❓ Preferably whatever is the current best practices if rolled out. However, as of now... We'll start with the Rule-Based Matching (Scenario 1) for the MVP. As usage grows, we’ll gradually roll out the Hybrid ML System (Scenario 2). Long-term, we aim to make Web-Hunters the first Fully Decentralized, Federated Talent Protocol (Scenario 3) powered by zkTLS and AI. Unless @naaate or @janicka or anyone else that may be a part of this team if it continues has a better plan. https://warpcast.com/kafenvinshi/0xd68b7f70
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DV
@degenveteran.eth
Easiest Scenario #1 (MVP Phase – Rule-Based Matching with Cached Queries) Approach: In the early stages, we will use deterministic, rule-based algorithms combined with caching strategies to efficiently handle matching without heavy computational overhead. How it works: Every job or task will have structured metadata (skills, location, price range, completion time). Web-Hunters will use simple IF/THEN rules to create ranked lists of the best matches. Cached query results will be stored temporarily to avoid recalculating the same matches. Example: If Job = “Graphic Design” + “Adobe Photoshop” → Show Top 5 profiles with those exact skills first. ✅ Why This Works (Short-Term): Fast, cheap, and highly deterministic. Can easily scale to hundreds of users without significant infrastructure. Easy to implement with Redis + PostgreSQL combo. ❌ Limitations: Doesn't improve over time. Not dynamic — can't predict hidden potential matches. May struggle with complex workflows or nuanced tasks.
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naaate
@naaate
I think it's not usually worth the effort to worry about scalability when at the MVP phase. They key is getting to something people want. It's more important to start with a structure you can learn from and iterate on, even if HIGHLY unscalable. So yeah, this makes sense to me. Playing around with the rules yourself and talking to a lot of people trying to use the service (or not using it, and why they don't want to use it) gives you a lot of learning you can then try to apply to the ML system.
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Janicka
@janicka
finally had the time to go thru it all properly, thanks for keeping me in the loop @degenveteran.eth! I love where this is going 💜
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naaate
@naaate
@paybot 26 $lum
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