LLMs are stateless. Agents aren’t. Here’s what sits in between. Table of contents Introduction LLMs are stateless by design. Each API call is independent — the model has no mechanism to remember what happened in a previous request. But somehow, the agents built on top of these models maintain context across long conversations, recall user […]
Read More →Large Language Models (LLMs) all predict text, but they differ a lot in how they follow instructions, use context, handle tools, and optimize for safety, speed, or cost. If you treat them as interchangeable, you’ll ship brittle prompts. If you treat them as different runtimes with different affordances, you’ll get reliable results. This post explains the major differences across […]
Read More →A practical guide for software engineers navigating the evolving landscape of Large Language Models Introduction: Why This Matters As a developer in 2025, you’re likely interacting with Large Language Models (LLMs) daily—whether through coding assistants, chat interfaces, or integrated APIs. But here’s the thing: not all LLMs are created equal, and the way you communicate with […]
Read More →Listen up, engineers. We are past the “wow” phase. You know what an LLM is. You’ve likely integrated an API, generated some boilerplate code, and maybe even built a RAG pipeline. But here is the hard truth: if you are pasting the exact same prompt into GPT, Claude Sonnet, and Llama using the same strategy, […]
Read More →The Model Context Protocol (MCP) is an emerging approach to managing how language models interact with and understand context. In this blog post, I’ll explain what the Model Context Protocol is, why it matters, and how it’s shaping the future of AI interactions. What is the Model Context Protocol?The Model Context Protocol is a standardized […]
Read More →