How Claude Code Works, from Tokens to Agents (nem035.com)

A new walkthrough detailing the inner workings of Claude Code reveals how AI tools like Claude Code, ChatGPT, and Cursor operate seamlessly behind the scenes, illustrating the complex mechanics that drive these applications. At the core is a language model that processes user input as tokens, generating responses based on probability distributions derived from its training data. This token-by-token generation means the model lacks memory and continuity between interactions, relying solely on the current context provided in each prompt. Such design leads to common issues like "hallucinations," where the AI produces confident, yet incorrect, outputs based on outdated or incorrect assumptions. The significance of this analysis lies in understanding the limitations and functionalities of AI models within the AI/ML community. By highlighting how models interact with user data and their surrounding software, developers can better design applications that mitigate pitfalls such as stale APIs and inaccurate assumptions. Furthermore, the exploration of token compounding and the introduction of new connection models, like OpenAI's WebSocket mode, promise to enhance efficiency by reducing redundant processing, ultimately improving responsiveness in agent applications. This knowledge empowers practitioners to optimize AI behavior and creates a path for future enhancements in reasoning and context management.