bootstrapped-ai

Kevin Nelson @BootstrAppdAI The wave of interest in self-learning from training data (10 new papers in 2 weeks!) is validation for what we've been building at Bootstrapped A.I. For almost a year, our work on dynamic latent space nodegraphs, self-seeding perpetual multi-hop, reward systems, and agentic sub-processes has been ready. Now, the industry is catching on. Bootstrapped A.I. Thought Process Frameworks can transform any LLM into a persistent zero-shot, live-learning, evolving, remembering A.I. #Bootstrapped [Embedded infographic: "Traditional Prompting vs TPF: Static Data to Live Knowledge" — comparing "Traditional Prompting" (Static Data → Single-Path Processing → Limited Response, with stated limitations: Context persistence 20%, Multi-perspective processing 40%, Emergent behavior 15%, Dimensional representation 2D, Adaptation capability Static) against "Thought Process Frameworks" (Static Data → Multi-Pillar Processing → Live Knowledge, with stated advantages: Context persistence 90%, Multi-perspective processing 90%, Emergent behavior 88%, Dimensional representation "12,288+ Dimensions", Adaptation capability Dynamic). Footer: "BOOTSTRAPPED AI RESEARCH"] Last edited 3:04 AM · May 10, 2025 · 1,508 Views [3 retweets, 8 likes, 4 bookmarks]