The all-caps `clod` file, created via the `init` command, stores project structure and user-defined rules. Unlike temporary in-chat instructions that get lost or degraded as the conversation continues, this file is referenced in every session, ensuring consistent behavior and enforcing project-wide guardrails.

The power of tools like Claude Code comes from giving the AI access to fundamental command-line tools (e.g., `bash`, `grep`). This allows the AI to compose novel solutions and lets product teams define new features using simple English prompts rather than hard-coded logic.

AI platforms using the same base model (e.g., Claude) can produce vastly different results. The key differentiator is the proprietary 'agent' layer built on top, which gives the model specific tools to interact with code (read, write, edit files). A superior agent leads to superior performance.

Claude Code's terminal-based interaction within a specific folder allows it to automatically read and reference local files. This makes "context engineering" drastically faster and more powerful than manually pasting information into a traditional chat interface, as the context is implicitly understood.

The success of tools like Anthropic's Claude Code demonstrates that well-designed harnesses are what transform a powerful AI model from a simple chatbot into a genuinely useful digital assistant. The scaffolding provides the necessary context and structure for the model to perform complex tasks effectively.

The term 'Claude Code' is a misnomer. Advanced users see these tools not just for coding, but as a generalized 'cloud computer.' By giving an agent access to files, terminals, and browsers, it becomes a versatile tool capable of any task, from program management to data analysis.

The terminal-first interface of Claude Code wasn't a deliberate design choice. It emerged organically from prototyping an API client in the terminal, which unexpectedly revealed the power of giving an AI model direct access to the same tools (like bash) that a developer uses.

A key advantage of Claude Cowork is its ability to run locally and access files directly on a user's computer. This provides the AI with vastly more context than is possible with cloud tools that have limited file uploads, enabling complex analysis of large, local datasets like hundreds of documents.

While complex RAG pipelines with vector stores are popular, leading code agents like Anthropic's Claude Code demonstrate that simple "agentic retrieval" using basic file tools can be superior. Providing an agent a manifest file (like `lm.txt`) and a tool to fetch files can outperform pre-indexed semantic search.