Technical articles on AI integration, web development, and emerging technologies.
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.
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.
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.
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.
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.
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.
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.
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.
In this article, we explain how Long-Term Semantic Memory uses vector embeddings and semantic search to give AI meaningful, persistent memory across conversations.
Learn how to build multi-agent systems with vector search, tool orchestration, and semantic understanding using Google's Agent Development Kit (JS/TS version).