Give Your LLM Access to the Filesystem (Custom Tools Explained) скачать в хорошем качестве

Give Your LLM Access to the Filesystem (Custom Tools Explained)

Modern LLMs are incredibly good at reasoning, but on their own they are limited to text. They cannot inspect your system, read files, or take real actions. In this video, we explore how to build custom tooling for an LLM that allows it to safely interact with your local filesystem, turning it from a passive chatbot into an action-taking agent. We walk through the concept of an agentic loop, where the LLM receives an instruction, reasons about the task, decides whether a tool is required, invokes that tool, and then incorporates the result back into its reasoning. Using a simple “list files” example, you’ll see how an LLM can query the host machine, understand its environment, and continue reasoning based on real system state. This pattern is the foundation behind many practical AI systems, including code assistants, DevOps agents, internal developer tools, and autonomous workflows. Once an LLM can call tools, it can move beyond static responses and start performing multi-step tasks that combine reasoning with execution. The video also covers why tooling is the real unlock for agentic AI, and how even small tools can dramatically expand what an LLM is capable of. We discuss how to design clean interfaces between the model and your system, how results flow back into the model, and why this feedback loop is critical for building reliable agents. Because filesystem access is powerful and potentially dangerous, we also touch on important safety considerations. You’ll learn how to keep tool execution constrained, observable, and predictable, so your LLM remains helpful rather than risky. All the code shown in this video is available on GitHub, so you can explore, run, and extend it yourself. You can find the repository here: https://github.com/vipulsodha/cli_cha... This video is intended for software engineers and builders who want to move beyond prompt engineering and start building real LLM-powered systems. If you’re interested in agents, tool calling, or practical AI automation, this will give you a solid mental model and a clear starting point.