Blog

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).

In this article, we explore how Short-Term Conversational Memory creates the illusion of memory in otherwise stateless LLMs through careful context persistence and structured prompt reconstruction. We also show how token limits, cost, and context degradation are managed using asynchronous, AI-driven summarisation that preserves meaning while keeping conversations efficient and coherent.