Fascinating: Singapore’s Foreign Minister just published his personal AI architecture. He’s running his AI second brain on a Raspberry Pi, while most CEOs are still asking ChatGPT to summarize PDFs. Vivian Balakrishnan shared the full setup behind NanoClaw, his self-hosted AI assistant for diplomatic work. It sits on a Raspberry Pi 5 and connects his notes, speeches, briefings, voice memos, research, and messaging workflows into a persistent memory system. Most people still use AI like a vending machine: Ask question. Get answer. Forget everything. Repeat tomorrow. Balakrishnan is using it like infrastructure 🧠 A system that remembers. Synthesizes. Recalls context. Compounds over time. The stack is surprisingly practical: → Raspberry Pi 5 for local hosting → NanoClaw for agent orchestration → Obsidian for human-readable knowledge → SQLite knowledge graph for structured memory → Local embeddings through Ollama → whisper.cpp for on-device voice transcription → Docker containers for isolation → Claude Code to assemble the whole thing The key thing here is that he did not build this by becoming a full-time software engineer. He described it as “tool assembly.” And that may be the new executive skill. ↳ Not writing every line of code. ↳ Not outsourcing every workflow to vendors. ↳ But knowing what to combine, what to keep private, and how to make your knowledge system improve every week. This is where AI at work is heading. → From chatbots to second brains. → From prompts to workflows. → From software subscriptions to personal infrastructure. My takeaway here is this: The next productivity gap will not be between people who use AI and people who don’t. It will be between people using rented intelligence and people building owned memory. Because when everyone has access to the same models, the real moat is the context & the data only you control.