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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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DV
@degenveteran.eth
Mid-Term Scenario (Hybrid ML Matching + zkTLS Caching Layer) Approach: A Hybrid Matching System that uses Lightweight ML models (e.g., TensorFlow.js or Scikit-Learn) to recommend matches on-the-fly — but only caches zkTLS-verified candidates to reduce query load. How it works: Every new task triggers real-time AI suggestions based on: Job history Skills Reputation (based on zkTLS proofs) zkTLS verification happens before caching to filter out bad actors. Cached results will expire after 24-48 hours or upon task completion. ✅ Why This Works (Mid-Term): Dynamic ranking improves over time. zkTLS caching creates self-cleaning data pools. Can scale to thousands of users with minimal cost. ❌ Limitations: Requires initial ML training set from early usage patterns. zkTLS verification may slow down first-time users without batching.
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