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Notes on AI, agents, web technologies, and building with LLMs

Technical articles on AI integration, agents, agent harnesses, MCP, web technologies, and emerging tools. Working notes from a Google Developer Expert with 25+ years of building and teaching.

228 articles · Page 2 of 23

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  1. The Single Agent Pattern

    A look at the simplest agentic AI pattern: one model, one tool, zero orchestration. Illustrated with a tiny TypeScript agent built with the Google Agent Development Kit (ADK) that uses Gemini and Google Maps to review any place by name.

    AI Agentic AI Gemini ADK

  2. Watch the Past Move: Animating Historic Photos with Gemini and Veo

    What if you could take a dusty old black-and-white photograph and watch it come to life? In this post, I walk through a Node.js pipeline that colorises historic photos with Gemini and then animates them into video using Veo 3.1.

    AI Gemini Veo

  3. Building a Historical Time Machine with Gemini and Google Maps

    Have you ever wondered what your favourite landmark looked like a hundred years ago? In this post, I walk you through a Node.js application that generates historically accurate photographs of any real-world location at any point in time, and even checks its own work for anachronisms.

    AI Gemini

  4. Why How You Split Your Documents Matters More Than You Think

    Before you reach for a more powerful embedding model or a larger context window, look at what you're actually feeding into a RAG pipeline. Sometimes the highest-leverage improvement isn't a better model but rather it's a better split.

    AI RAG

  5. Filesystem as Context: Building an AI Detective with bash-tool

    Instead of stuffing documents into prompts, give your AI agent a filesystem and let it retrieve its own context. Here's how, using a murder mystery detective as the demo.

    AI Agents Vercel AI SDK

  6. The Mind's Eye - Engineering a CLI for Intelligent AI Interaction

    This article concludes the series by showing how a deliberately designed CLI becomes a powerful interaction layer, giving users precise control over an AI system's conversational context, short-term memory, and long-term semantic knowledge.

    AI Memory LLM

  7. The Autonomous Brain - Engineering AI for Continuous Learning and Memory Enrichment

    This piece introduces background processors as autonomous AI agents that summarise conversations and extract critical facts to continuously enrich Long-Term Semantic Memory. By running asynchronously and optimising token usage, these processors enable a self-improving, increasingly personalised AI system that learns from every interaction.

    AI Memory LLM

  8. The 'Aha!' Moment - Engineering the Perfect Prompt for Truly Contextual AI

    In this article, we show how dynamic prompt engineering—via a `SessionManager` that intelligently layers short-term context, long-term semantic memory, and system instructions turns stateless LLM calls into genuinely contextual and personalised conversations.

    AI Memory LLM

  9. Semantic Horizons - Engineering an AI's Enduring Long-Term Memory

    In this article, we explain how Long-Term Semantic Memory uses vector embeddings and semantic search to give AI meaningful, persistent memory across conversations.

    AI Memory LLM

  10. Building AI Agents with Google ADK: A Practical Guide

    Learn how to build multi-agent systems with vector search, tool orchestration, and semantic understanding using Google's Agent Development Kit (JS/TS version).

    AI Google ADK Agents