Introduction Last year, I spent some time studying Oracle Banking Microservices Architecture (OBMA), together with enterprise schedulers and orchestration platforms such as Control-M . Part of the work involved understanding how to convert traditional Control-M jobs into Airflow DAGs. During this process, I started to observe an important architectural distinction: Not all workflows are the same. While studying OBMA, I noticed that Netflix Conductor was used as the workflow engine inside the architecture.

Introduction When working with AMD GPUs and ROCm-based AI workloads, one common issue appears when using uv for Python dependency management. The problem becomes especially visible when setting up projects like ComfyUI on Linux with ROCm-enabled PyTorch builds. Although ROCm-specific wheels are manually installed, running commands such as uv add or dependency synchronization may silently replace ROCm-enabled packages with standard PyPI versions that only support CUDA. This leads to broken GPU acceleration and unexpected runtime failures.

Introduction Spec-driven development is becoming increasingly popular in the era of AI-assisted software engineering. Instead of starting directly from implementation, teams define specifications, domain rules, contracts, and architectural intentions first, allowing Large Language Models (LLMs) and automation tools to generate significant parts of the system. This approach can dramatically improve development speed, documentation quality, and alignment between business and engineering. However, one important challenge emerges quickly: Architecture drift.

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).

Introduction Recently, I’ve been experimenting with a variety of LLM tooling ecosystems, including: Claude Code Codex via OpenRouter Ollama LM Studio vLLM LiteLLM My goal is to better understand the underlying technologies and explore how to operate these tools in air-gapped or controlled environments. In many enterprise settings, developers are allowed to use GitHub Copilot, but not Claude Code.

I first learnt the idea of the Ralph Loop around January, while following developments in LLMs and AI through multiple channels — video feeds, X, newsletters, GitHub repositories, news, and research papers. That sparked a question: What happens if we combine the Ralph Loop with spec-driven design to generate real, working applications? This blog is a reflection of that exploration. My Background with Spec-Kit Since December last year, I have been using Spec-Kit in both:

I have been using GitHub Copilot and Copilot CLI for about one year. During that time, I also experimented with several developer tools and AI-assisted coding technologies. Recently I became interested in exploring Claude Code and related tools such as Claude Cowork. However, I did not want to immediately commit to a subscription before understanding whether Claude Code actually fits my workflow. So I spent some time experimenting with different ways to run Claude Code without paying for a subscription first.

Things are changing incredibly fast, and AI is undeniably the hottest topic right now. There’s no real option to stand still—the only sensible choice is to embrace the change and learn by doing. Recently, OpenClaw has stood out as one of the most exciting personal AI assistant projects. After following the project for several days and spending time reading through its documentation—especially around skills and agents—I decided to try it out myself.