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Notes on AI, agents, web technologies, and building with LLMs
Technical articles on building with LLMs: agents and agent harnesses, MCP, evaluation, and the web technologies underneath. Worked examples with code, written for working developers.
246 articles · Page 5 of 25
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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.
Consuming Streamed LLM Responses on the Frontend: A Deep Dive into SSE and Fetch
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.
Filling in the Blanks: Teaching AI to Inpaint
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.
Agentic AI: Multi-Agent Systems and Task Handoff
The final article in the Agentic AI series explores multi-agent systems: how specialised agents collaborate through structured handoffs to complete complex user goals.
InteractiveAgentic AIUnderstanding the Orchestrator-Worker Pattern
The orchestrator-worker pattern brings scalable structure to agentic AI workflows by cleanly separating high-level planning from specialised task execution. Through a practical trip planning example, this article demonstrates how LLMs can dynamically coordinate expert agents, grounded in schema-driven logic and real-world data.
InteractiveAgentic AIBuilding with Reflection: A Practical Agentic AI Workflow
This article explores how to implement a reflection loop-an agentic AI pattern where a model generates, critiques, and iteratively improves its output - using image captioning as a practical example.
InteractiveAgentic AI