Few things pretty obvious to a few AI researchers but that most don't want to believe: 1. 90% of the most impactful AI research is already on arxiv, x, or company blog posts 2. q* aka strawberry = STaR (self-taught reasoners) with dynamic self-discover + something like DSPy for RAG optimization 3. State of the art search = GoT(graph of thoughts) + MCTS (monte-carlo tree search) + DSPy / CLIN inspired tooling for "self-play" like optimization at build-time and runtime + graph-based knowledge bases 4. Continuous learning agents is clever graph retrieval inspired by GraphRAG, CLIN, DSPy and dynamic reasoning modules 5. Multiple OOM improvement to be realized when those design patterns are baked into models, fine-tuned on and prompt-optimized at build-time + runtime with human in the loop. Although new foundation models around these patterns may not be needed to achieve significant improvement on existing open-source models... Read for yourself and thank @aidan_mclau and @apples_jimmy GOT: arxiv.org/abs/2308.09687 MCTS: arxiv.org/abs/2406.07394 STaR: arxiv.org/pdf/2203.14465 DSPy: github.com/stanfordnlp/dspy CLIN: arxiv.org/abs/2310.10134 Self Discover: arxiv.org/pdf/2402.03620 GraphRAG: github.com/microsoft/graphra… Path to AGI: situational-awareness.ai/fro… Q* / Strawberry: reuters.com/technology/artif…