Introducing llm-url-markdown, a new plugin for Simon Willison's llm CLI tool that fetches web content as markdown for use as LLM context fragments.
Explores why software testing becomes more critical with AI-generated code, predicting trends like embedded tests, AI automation, and evolving manual QA roles.
Explains how Sampling and Prompts in the Model Context Protocol (MCP) enable smarter, safer, and more controlled AI agent workflows.
Explains how Tools in the Model Context Protocol (MCP) enable LLMs to execute actions like running commands or calling APIs, moving beyond just reading data.
Explains how the Model Context Protocol (MCP) uses 'Resources' to securely serve structured data from systems like files and databases to LLMs.
Explains the architecture of the Model Context Protocol (MCP), detailing its client-server model, core components, and message flow for connecting AI models to tools and data.
A developer explains how they use GitHub Copilot and other AI tools for design thinking and as a 'second brain' in software development.
Explores how Large Language Models (LLMs) like ChatGPT are diffusing technology bottom-up, empowering individuals more than corporations.
Explores the integration of Java with Generative AI and Large Language Models (LLMs) for building innovative applications like AI chatbots.
Explores three key methods to enhance LLM performance: fine-tuning, prompt engineering, and RAG, detailing their use cases and trade-offs.
Explains how LLMs work by converting words to numerical embeddings, using vector spaces for semantic understanding, and managing context windows.
Explores the evolution of AI from symbolic systems to modern Large Language Models (LLMs), detailing their capabilities and limitations.
An explanation of the Model Context Protocol (MCP), an open standard for connecting LLMs to data and tools, and why it's important for AI development.
An overview of the Model Context Protocol (MCP), an open standard for connecting AI applications to external tools and data sources.
A guide to building a production-ready, vendor-neutral AI agent using IBM watsonx.ai, MatrixHub, and MCP Gateway, focusing on decoupled architecture.
A tutorial on building a ReAct AI agent from scratch using Google's Gemini 2.5 Pro/Flash and the LangGraph framework for complex reasoning and tool use.
Explores the ethics of LLM training data and proposes a technical method to poison AI crawlers using nofollow links.
Using an LLM to label Hacker News titles and train a Ridge regression model for personalized article ranking based on user preferences.
An introduction to reasoning in Large Language Models, covering key concepts like chain-of-thought and methods to improve LLM reasoning abilities.
Explains a technique using AI-generated summaries of SQL queries to improve the accuracy of text-to-SQL systems with LLMs.
