Explore practical insights, tutorials, and real-world applications of Artificial Intelligence. From ML models to hands-on experiments, this category helps you understand and build AI step by step in a simple and developer-friendly way.
AI coding assistants write code fast. But they often guess what you want, and they forget earlier decisions when a new chat starts. The result is code that works today and breaks the plan tomorrow. The BMAD Method tries to fix this problem. It gives your AI coding tool a set of skills that act … Read more
If you use Claude Code, you’ve probably noticed it forgets everything between sessions. You tell it your test command on Monday, and by Tuesday it’s guessing again. A CLAUDE.md file fixes that. It’s a plain text file in your project that Claude reads every time it starts, like a note on the fridge that says … Read more
Picture this. You ask an AI coding agent to “add a password reset feature.” Ten minutes later you have 600 lines of code. It sends reset links that never expire. It emails the user’s old password in plain text. It even adds a new database table you never asked for. The agent did not fail. … Read more
The CRAP score is one number that tells you how risky a function is to change. It looks at two things: how complex the code is, and how much of it your tests check. Think about a normal day with an AI coding assistant. You ask for a discount function. You get 60 lines in … Read more
In this article we will disscuss abou type safe AI’ first system one model called Jev. What is System One Model? As per TypeSafe AI’s definition – a System One model is an AI model designed to make fast, structured decisions by evaluating a given state and returning predefined, typed answers with associated probabilities that … Read more
The Model Context Protocol (MCP) has quickly become the standard way for AI assistants and applications to communicate with external tools, APIs, databases, and services. It provides a common interface that allows AI models to access capabilities without requiring custom integrations for every application. The latest evolution of MCP introduces stateless communication, a significant architectural … Read more
You type a sentence, hit enter, and a machine writes back something coherent. It feels like there must be a sprawling, mysterious apparatus behind the curtain. There isn’t. The surprising truth about LLM architecture is that nearly every model you’ve heard of the GPT family, Llama, Claude, Gemini, Mistral is built from the same repeating … Read more
Everyone is talking about AI taking jobs. But most of that conversation is about America. What about India – where 1.4 billion people work across farming, construction, IT, banking, healthcare, and government? Where does AI actually threaten livelihoods, and where is the workforce relatively safe? I wanted to see this visually. So I built it. … Read more
In this article we will discuss how to pull and run Gen AI models using Docker Model Runner(DMR). Docker Model Runner (DMR) Docker Model Runner (DMR) is a tool built into Docker Desktop and Docker Engine that makes it easy to pull, run, and serve AI/LLM models locally directly from Docker Hub, any OCI-compliant registry, … Read more
In this article we will understand the concept of AI agent skills. AI agents are evolving rapidly. From simple prompt based bots to autonomous systems that can search, reason, and execute tools, the architecture behind modern agents is becoming more structured. Agent Skills The open Agent Skills standard was introduced by Anthropic. Agent skill is … Read more