core choice logging and self-improvement readiness Current state To show that mindX is or is not a Gödel machine, we need a single, accurate log of core choices: what was perceived, what options were considered, what was chosen, why, and (when available) outcome. 1. Gödel choice schema and global log 2. Instrument core decision points 3. Ollama-driven self-improvement readiness 4. API and UI (optional) 5. File and dependency summary Area File(s) Change Core directive docs/survive.md […]

Epoch Count: Navigating the Training Iterations The Elusive “Optimal” Settings and the Empirical Nature of Tuning It is paramount to realize that there are no universally “optimal” hyperparameter values applicable across all scenarios. The “best” settings are inherently dataset-dependent, task-dependent, and even model-dependent. Finding optimal hyperparameters is fundamentally an empirical search process. It involves: finetunegem_agent is designed to facilitate this experimentation by providing command-line control over these key hyperparameters, making it easier to explore different […]

Understanding PathRAG and the Future of the Retrieval Augmented Generation Engine Retrieval Augmented Generative Engine (RAGE) has enhanced how we interact with large language models (LLMs). Instead of relying solely on the knowledge baked into the model during training, RAG systems can pull in relevant information from external sources, making them more accurate, up-to-date, and trustworthy. But traditional RAG, often relying on vector databases, has limitations. A new approach, leveraging knowledge graphs, is rapidly evolving, and […]