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Notes on AI, agents, web technologies, and building with LLMs

Technical articles on AI integration, agents, agent harnesses, MCP, web technologies, and emerging tools. Working notes from a Google Developer Expert with 25+ years of building and teaching.

225 articles · Page 3 of 23

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  1. 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.

    AI LLM NLP Transformers.js JavaScript

  2. 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.

    DevRel Thought Leadership

  3. 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.

    AI LLM Function Calling

  4. 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.

    AI MCP Developer Tools Large Language Models

  5. 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.

    AI MCP Developer Tools Large Language Models

  6. 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.

    Web Development JavaScript LLM Frontend API

  7. 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.

    LLM AI

  8. 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.

    Agentic AI

  9. Understanding 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.

    Agentic AI

  10. Building 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.

    Agentic AI