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DV
@degenveteran.eth
MVP for Web-Hunters focus: Simple, verifiable hiring network that connects skilled professionals (starting with veterans) to job opportunities, both IRL and remote, using AI-assisted credential verification. Core Features for MVP Decentralized Talent Verification Users can zkTLS (zero-knowledge proof) verify their skills, credentials, and past experience without exposing sensitive data. Example: A veteran can prove they were a mechanic in the military for X years without sharing personal documents. Basic Job Matching & Listings A simple job board-style interface where employers or individuals can list tasks, remote gigs, or in-person jobs. AI-assisted skill matching to recommend the best candidates. Trust Layer for Hiring Ratings and verified experience badges (e.g., “US Army Mechanic – 8 Years”) provide trust signals for employers. AI ensures scammers/bots are filtered out while improving recommendations over time. Continued 👇
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Mosolryn
@kafenvinshi
Great concept! Ensuring privacy with zkTLS while providing verifiable credentials is crucial. How do you plan to handle the scalability of AI-assisted matching as the network grows?
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DV
@degenveteran.eth
Easiest Scenario (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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