Technical articles on AI integration, web development, and emerging technologies.
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.
These are the answers to the questions asked via Slido during my workshop on MCP at DevFest Taipei 2025.
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.
This article discusses a framework that makes DevRel's impact visible by translating community activities into weighted scores and theoretical ROI for leadership.
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.
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.
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.
Learn how to build a responsive, real-time user experience by consuming streamed Large Language Model responses on your frontend. This article provides a comprehensive guide to using both Server-Sent Events (SSE) and the Fetch API with Readable Streams, complete with code examples and a detailed comparison.
A hands-on guide for exploring how to train a simple AI model using TensorFlow.js to inpaint missing parts of images - without needing large datasets or prior machine learning experience.
The final article in the Agentic AI series explores multi-agent systems: how specialised agents collaborate through structured handoffs to complete complex user goals.