Ais

Structure is great, but every team has its own “tribal knowledge”—naming conventions, preferred libraries, and security requirements that aren’t always obvious to a general LLM.

In our previous post, we explored Codebase Indexing and how Kilo Code builds a semantic map of your repository. Now, it’s time to use that power effectively.

In our previous post, we explored the different modes of Kilo Code. While these modes are powerful, their effectiveness depends on the quality of the context they can access.

In our previous post, we unlocked massive power by integrating Qwen Code CLI. But with great power comes the need for great management.

In our previous posts, we covered the basics of Kilo Code and how to get it installed on your machine. One of the most powerful features of Kilo Code is its ability to integrate with various AI providers.

In our first post, we introduced the core concepts of Kilo Code and why it’s a game-changer for agentic software development. Now, it’s time to get your hands dirty.

The world of AI coding is moving fast. We’ve seen the rise of simple autocomplete, then the transition to chat-based assistants, and now we are entering the era of Agentic AI.

AI-powered coding assistants have become essential tools for modern developers. In this post, I’ll compare three popular VSCode extensions: GitHub Copilot, Continue.dev, and Kilo Code (kilo.io), helping you choose the right one for your workflow.

Modern AI-powered IDEs have evolved far beyond simple code completion. Today’s tools like Kilo Code, Cursor, Windsurf, and Google Antigravity introduce new paradigms: autonomous agents, reusable skills, markdown rules, semantic context, and structured workflows.

The early days of AI coding were characterized by “vibe coding”—a process where developers would prompt an LLM, hope for a working snippet, and manually fix the hallucinations. While fast for simple tasks, this approach often falls apart in complex, multi-file projects where “context rot” and technical debt accumulate rapidly.