The Loop Agent Pattern
When one pass is not enough, let agents iterate. A playlist curator built with Google ADK that generates songs, verifies them against MusicBrainz, and refines until every track checks out.
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The Sequential Agent Pattern
Chaining multiple specialised agents into a pipeline where each one builds on the last. Illustrated with a TypeScript CLI that fetches a quote, researches its author, and writes an inspiration card.
The Parallel Agent Pattern
When agents do not depend on each other, run them at the same time. Illustrated with a TypeScript translation pipeline built with Google ADK that translates a phrase into three languages simultaneously, then aggregates the results with Gemini.
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.
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.
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.
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.
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.
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.
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.