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.
Blog
Filter by tag ▾
All posts
AI API Agentic AI Agents Angular AngularJS Bash CSharp Cloudinary Conference Core Web Vitals DevFest DevRel Developer Tools Firebase Frontend Function Calling Ghost Google ADK Google chrome GraphQL Image Formats Image Optimisation Jamstack JavaScript LLM Large Language Models Learn To Code MCP MEAN Machine Learning Memory MongoDB MySQL NLP Next.js NoSQL Node.js PHP Performance Polymer Progressive Web App Progressive Web Apps React Recruitment Review RxJS Serverless Service Worker Sponsored Taipei Thought Leadership Tooling Transformers.js TypeScript VMware Vue.js Web Assembly Web Development Web Performance this || that
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).
The Token Economy - Engineering an AI's Working Memory
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.
The Grand Orchestration - Engineering a Dual-Memory AI for Enduring Conversations
This article explains why LLMs often “forget” earlier messages and how naive full-history prompting is costly and inefficient. It introduces a dual-memory architecture: a short-term store for immediate conversation flow and a long-term semantic store for durable knowledge across sessions. Together, these systems let an AI maintain coherent dialogue without overloading the model’s context window or budget.
MCP Workshop Answers for DevFest Taipei 2025
These are the answers to the questions asked via Slido during my workshop on MCP at DevFest Taipei 2025.
Detecting Hallucinations in Language Models with Natural Language Inference
A practical look at why hallucinations occur in modern language models, why current evaluation methods make them persist, and how to detect them using Natural Language Inference in JavaScript.
Making the Invisible Visible: The DevRel Value Model
This article discusses a framework that makes DevRel's impact visible by translating community activities into weighted scores and theoretical ROI for leadership.
The Importance of Precise Function Declarations for LLMs
This article explores how clearly defined functions enable large language models to make accurate tool calls, emphasising the importance of precision and developer intent in the function calling process.
Creating an MCP Client: Connecting LLMs to the Real World
In this article, we walk through the process of building a Model Context Protocol (MCP) client. Learn how to connect to servers, discover tools, and invoke them from your own app or LLM integration.
MCP Servers - The Bridge Between LLMs and Real-World Tools
Model Context Protocol (MCP) servers expose tools, resources, and prompts to LLMs in a unified, structured way. This post explores how they work, how to build one, and why they are a critical part of the future AI stack.