WavuKazi AI \| Connecting Hubs, TV & Social Overview: WavuKazi AI is a distributed intelligence network that powers the WavuKazi ecosystem. Unlike a centralized system, each WavuKazi Verified Creator (WVC) operates their own localized AI node at their Wavu-Hub. These nodes manage hub operations, learning workflows, content curation, and project tracking independently. At the ecosystem level, a central WavuKazi AI instance provides analytics, research, and strategic insights but has no public-facing interface. This instance interacts with WVC nodes through Bolt.diy, and integrates with Mistral at the top of the stack to ensure robust research, evaluation, and AI orchestration. \--- Key Features: Distributed Hub Intelligence: Each WVC node runs a localized Phi3 AI stack, tailored to their Wavu-Hub’s students, projects, and community content. This ensures autonomy, resilience, and context-aware learning. Front-Facing Public AI: WavuKazi maintains a public-facing Phi3 AI interface for Wavu-Social and Wavu-TV, providing interactive experiences for users, while local nodes handle the heavy-lifting operationally. Analytics & Research Backbone: The central AI instance collects metrics from all nodes, runs research analyses, and generates insights for ecosystem growth, but does not interfere with individual hub autonomy. Integration Layer: Bolt.diy serves as the middleware between localized Phi3 nodes and Mistral, ensuring secure, efficient data exchange without compromising hub independence. Privacy & Consent: Local nodes handle all student and participant data according to consent protocols, keeping control decentralized while maintaining compliance. \--- Impact on the Ecosystem: Autonomous Hubs: Each Wavu-Hub can operate independently, providing tailored training, project management, and content generation without relying on a central AI. Consistent Standards & Oversight: While nodes are independent, central analytics ensure ecosystem-wide standards, helping hubs align with WavuKazi policies and maintain quality. Scalability & Resilience: The distributed architecture allows rapid expansion to new towns, multiple hubs per city, and varied course offerings without a single point of failure. Enhanced Learning & Engagement: Students, interns, and pre-grads receive contextual AI support locally, while ecosystem insights guide curriculum refinement, content quality, and hub growth strategies. \--- Operational Notes: Each WVC node runs localized Phi3 AI for hub management. Central WavuKazi AI (Phi3 + Mistral + Bolt.diy) aggregates research and analytics, but does not control hubs directly. Public-facing AI interfaces handle social engagement, content queries, and Wavu-TV interactivity. Nodes communicate securely via Bolt.diy, ensuring data flow without central dependency.