Introduction One of the more interesting recent developments in Retrieval-Augmented Generation (RAG) is Knowledge Graph–based RAG. Unlike traditional vector-based retrieval, this approach introduces structured relationships between entities, enabling deeper reasoning and better contextual understanding. This naturally raises a question: Can we build more robust and reliable software systems by combining: Ontologies Domain-specific design models Spec-driven development (SDD) This article explores that idea through a practical comparison.

Overview Running large language models locally is becoming increasingly practical, even for developers without access to massive GPU clusters. In this post, I walk through how to: Run Qwen3.6 35B (A3B, Q4_K_M quantized) locally using Ollama Integrate the model into VS Code Use it as a local coding assistant This setup is especially useful for: Air-gapped environments

Finally, I managed to get vLLM running on my AMD AI MAX+ 395 GPU on Ubuntu 24.04. It was not straightforward — ROCm support on Ryzen AI (gfx1151) is still evolving, and I ran into multiple low-level GPU faults before finding a stable setup. This post documents: - What didn’t work - The errors I encountered - The working configuration Hopefully this saves you a few hours (or days).