Building Claude Code: Origin, Story, Product Iterations, & What's Next // Siddharth Bidasaria// #342

The Genesis of Claude Code

Claude Code originated from Anthropic’s “labs team,” an internal R&D group focused on prototyping new products. A colleague, Boris, created a nifty prototype in his first week: a simple way to access Claude in the terminal. Initially, it had no tools and was just a wrapper around the API.

The turning point came when two tools were added:

1. Spotify Control: A fun addition to manage music from the terminal.
2. File System Tools: The ability to read and write local files.

The addition of local file tools was a “holy [cow]” moment. It felt incredibly ergonomic and low-friction because it eliminated the need for complex setup or synchronization steps (like creating Docker images or giving Git repository access) that were common in other products. A developer could spin up Claude in any local repository and have it start working immediately, just as a human would explore the files. This seamless, collaborative workflow—where a developer can easily step in and continue the model’s work—was the magic ingredient.

After a few weeks of hacking, the tool was released internally at Anthropic. It spread like wildfire, quickly reaching around 300 daily active users in a company of 600. This strong internal validation led to an external early access program and, eventually, a public launch where it gained significant traction.

Core Philosophy: “Let the Model Cook”

A core philosophy for the Claude Code team is to “unhobble the model” and let it be the centerpiece. The application harness should only provide the necessary tools for the model to work its magic, without being overly prescriptive or building features that will become redundant as models improve. This leads to a strong preference for deleting code and features.

There’s like a a phrase that um it’s like unhobling the model, right? Like it’s like basically it translates to let the model cook, right? Like that that is the the centerpiece and the showpiece here.

An early example was stripping out specific file system tools like ls and replacing them with a single, more general bash tool. The model’s understanding of bash was so innate that it could perform the same tasks more effectively than with custom-built tools.

This philosophy creates a constant tension between providing useful steerability for users now versus building complex abstractions that might be obsolete in a few months. The team leans towards being “leaner rather than more harness heavy,” ensuring the tech stack is agile enough to rip out entire features when a new model version makes them unnecessary.

Model Evolution and Verification

There has been a clear step-change in model capabilities, particularly from Opus 3.5 to 3.7. The complexity of tasks the model can handle and its ability to navigate complex code structures have improved dramatically. Experienced users become attuned to a model’s behavior and can immediately sense a “different smell” when a new version is released. This requires users to constantly “rewire” their brains and update their intuition about what the model is capable of.

Verification—the model’s ability to check its work and course-correct—remains a difficult but advancing problem. It can be broken down into two parts:

1. Model Behavior: The model’s innate ability to know it should check its work. This is improving rapidly.
2. Tooling: Providing the model with the right tools to perform checks. This is more challenging.

While tools like Puppeteer can provide visual feedback for web development, verifying more complex tasks like animations or low-level performance work is still fuzzy. The shortest path to success in verification is seen as a renewed focus on robust testing frameworks.

I think that unit tests and like smaller units of tests that humans pay extreme attention to and make the framework for making unit tests and maybe even end to end tests much easier… that will be probably the shortest path to verification success.

This is leading to a rediscovery of test-driven development (TDD). By creating a comprehensive unit testing framework, developers can better leverage AI tools to write code and trust the output.

Power Users and the Future of Sub-Agents

Power users are constantly pushing the limits of Claude Code in creative ways. One user built a “fleet” of 10-12 Claude instances, each with a specific persona (e.g., backend engineer, frontend engineer), that communicated with each other by reading and writing to the file system. This emergent, multi-agent approach heavily inspired the official sub-agents feature.

The current implementation of sub-agents is “agents as a tool,” where a primary agent calls a sub-agent and waits for a response. However, the future could involve more complex topologies, such as a “swarm” of peer-to-peer agents communicating via message buses without a central parent.

This future direction opens up a can of worms and significant challenges:

• Observability: How do you track what a swarm of agents is doing?
• Permission Management: How do you manage permissions when multiple agents are requesting access to tools simultaneously? A user can’t be expected to constantly approve requests. The only scalable solution is likely a “Sauron-like” agent that is exceptionally good at understanding user intent and managing dynamic permissions.
• Complexity: The team is hesitant to add such complexity until there’s clear evidence that these advanced topologies yield better results, as the goal is to build tools for all users, not just power users.

• Tags:
• AI Agents
• Software Development
• Code Generation
• LLMs
• Feature
• Anthropic
• Claude Code
• Test Driven Development