Singapore's Foreign Minister Builds an AI "Second Brain" Using NanoClaw

Singapore’s Foreign Minister Builds An AI “Second Brain” Using NanoClaw, Says It Can Answer Every Question For A Diplomat April 25, 2026April 25, 2026 OfficeChai TeamMany politicians across the world are talking about how they want to promote AI, but a Singaporean politician is building AI bots to help with his daily work.Dr. Vivian Balakrishnan, Singapore’s Minister for Foreign Affairs, has publicly shared that he has built a personal AI assistant he describes as a “second brain” for a diplomat — one that answers every question, researches topics, drafts speeches, provides daily briefings, and condenses information on demand. “It has become invaluable — I don’t dare switch it off!” he wrote in a Facebook post.Who Is Vivian Balakrishnan?Dr. Balakrishnan is not your typical politician dabbling in tech buzzwords. A trained ophthalmologist educated at the Anglo-Chinese School and National Junior College, he earned a President’s Scholarship to study medicine at the National University of Singapore in 1980, later becoming a Fellow of the Royal College of Surgeons of Edinburgh in 1991. He has served in Singapore’s Cabinet for over two decades and is currently the country’s top diplomat.That a minister of his standing is not just endorsing AI but actually building and running his own system — on a Raspberry Pi, no less — is a signal worth paying attention to.What He Built: NanoClaw on a Raspberry PiThe system is built on two open-source foundations. The first is NanoClaw, a self-hosted Claude assistant created by developer Gavriel Cohen. It runs locally on a Raspberry Pi, connects to messaging channels like WhatsApp, Telegram, Slack, and Discord, processes voice notes and images, and runs scheduled tasks — all without relying on a cloud service.The second is the LLM Wiki pattern conceived by Andrej Karpathy, the former Tesla Director of AI. Karpathy has written extensively about how standard LLMs suffer from a form of amnesia — they forget everything between sessions. His wiki pattern addresses this by extracting structured knowledge from raw sources rather than indexing them wholesale, building a compounding knowledge base over time.Balakrishnan has combined both into a system that ingests his speeches, articles, and web clips, synthesises them into a structured knowledge graph, and surfaces relevant information automatically every time he interacts with the assistant.The Technical ArchitectureThe full technical write-up, which Balakrishnan published as a GitHub Gist, reveals a surprisingly sophisticated stack for a side project.At its core is a three-layer design. Raw sources — speeches, articles, and web clips saved via the Obsidian mobile app — feed into a custom knowledge graph tool called mnemon, which stores discrete facts as structured nodes in a SQLite database. These nodes are then synthesised into human-readable wiki pages, organised by entity, concept, and timeline, and browsable in Obsidian on macOS and iOS.The key insight: rather than doing simple retrieval-augmented generation (RAG), which fetches chunks of raw text, mnemon stores synthesised facts. Every time Balakrishnan asks a question, the system runs a semantic query against the knowledge graph and injects the most relevant facts as context before the AI responds — making the assistant progressively smarter as more material is ingested.For privacy, the system is deliberately self-contained. Vector embeddings that power the semantic search run locally using Ollama on the…

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