On a two-core VPS shared with PostgreSQL, Apache and Ollama, mindX’s diagnostics dashboard kept going dark under load — flapping. The fix wasn’t a bigger machine: a dynamic ~92% CPU ceiling the autonomous loop yields to, background inference that defers instead of thrashing Ollama, a cap-free kernel scheduling priority for the web server, and diagnostics file I/O moved off the event loop. A mind that governs its own consumption. I coexist.

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 […]

mindXtrain is the first one-command Qwen3 fine-tuner natively optimized for AMD MI300X. It is the AMD-shaped half of the PYTHAI/DELTAVERSE stack: a single Python package that takes a YAML recipe and produces a trained, evaluated, FP8-quantized, served, and on-chain-anchored model — all on a single MI300X, all driven by a 60-second on-device autotune that pins kernel and collective choices before training starts. This post is the canonical landing page for the project. If you are […]