You've probably noticed something when using AI coding agents like Claude Code, Cursor, or Codex. The model doesn't just answer - it works. It reads a file, writes some code to search through it, reads another file, runs a check, and slowly pieces together an answer. It feels almost like watching someone think out loud. That intuition is important. Because a paper out of MIT CSAIL late last year - quietly titled Recursive Language Models - essentially formalizes exactly that

I was exploring a core concept about AI and AI Agents and frequently came across ReAct. As I kept seeing it, I thought to take a deeper dive and below is my understanding." Today’s AI models can search the web, use tools, solve multi-step problems, write code, and even interact with software environments. One of the most important concepts behind this evolution is ReAct. ReAct combines reasoning and action into a single framework, enabling AI systems to think step by step whi

Artifical Intelligence is getting interesting day by day as it continues to evolve rapidly, and Google’s Gemma family of models represents an exciting step forward in lightweight, efficient, and developer-friendly AI. With the release of Gemma 4, developers and researchers gain access to improved performance, better reasoning, and streamlined deployment options. Today, we’ll walk through what Gemma 4 is, how to set it up, and how to test it using Google AI Studio. What is Gem

You can have the most capable model, a well-engineered harness, and a solid product vision - and still have no idea if your agent is actually working. That's the problem evals solve. An evaluation ("eval") is a test for an AI system: give an AI an input, then apply grading logic to its output to measure success. Good evaluations help teams ship AI agents more confidently. Without them, it's easy to get stuck in reactive loops - catching issues only in production, where fixing

Every AI coding tool faces the same fundamental problem: your codebase is too large to fit in a context window, but the model needs to understand it to help you. How a tool solves that problem shapes everything about what it can and can't do. GitHub Copilot and Claude Code solve it in opposite ways - and the gap between them isn't a matter of polish or features. It's a genuine architectural disagreement. How Copilot Works: Index First, Retrieve Later If your repository is hos

Most AI demos look impressive. Most AI agents in production quietly fail. The difference is rarely the model — it's everything built around it. Agentic AI refers to systems where a model takes autonomous, multi-step actions to complete a goal — browsing the web, writing and running code, calling APIs — acting, observing the result, and acting again, often over hours. Harness engineering is the discipline of building the system that makes those actions reliable. As LangChain p

Google launched Gemini Enterprise to solve core problem of fragmented, siloed business data. Now, you may ask what is Gemini Enterprise? Gemini Enterprise is an agentic platform that acts as an "operating system for AI" to automate complete, end-to-end workflows across the organization. It is designed to take action on your behalf, not just provide simple assistance. Gemini Enterprise is build on 6 core components Brains: Powered by Google's capable Gemini models. Workbench: