I Tried Building Claude Code From Scratch | Here’s How Far I Got

2026-06-03 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

An experiment recreated a simplified version of "Claude Code" in one weekend, inspired by a source map leak revealing that only 1.6% of the original codebase directly interacts with the AI model. The remaining 98.4% manages critical infrastructure, including safety checks, context window handling, tool call routing, and session recovery. The author's Python-based rebuild, comprising approximately 500 lines, demonstrated core functionalities such as reading and editing files, searching a codebase, executing shell commands, generating a plan before execution, and enforcing a budget limit. This effort underscores that the vast majority of an AI product's complexity and functionality resides in its surrounding operational and safety infrastructure, rather than solely in the large language model interaction itself.

Key takeaway

For AI Engineers building agentic applications, recognize that the core model interaction is a small fraction of the total system. Your development efforts should heavily prioritize robust infrastructure for context management, tool orchestration, safety checks, and error recovery. Allocate significant resources to these non-model components, as they constitute over 98% of a functional AI product's complexity and ensure reliable, safe, and efficient operation beyond the model's capabilities.

Key insights

Most of an AI product's functionality and complexity lies in its surrounding infrastructure, not just the model interaction.

Principles

• AI product value is mostly non-model infrastructure.
• Robust AI systems require extensive operational scaffolding.
• Context management and safety checks are crucial.

Method

Rebuild core model interaction logic to understand necessary surrounding infrastructure, including file operations, search, shell commands, planning, and budget control.

In practice

• Implement robust context window management.
• Prioritize tool call routing and error recovery.
• Integrate safety checks for command execution.

Topics

• Claude Code
• AI Agents
• System Architecture
• Large Language Models
• Tool Use
• Context Management

Best for: AI Engineer, Machine Learning Engineer, Software Engineer

Related on AIssential

• Claude Code: The 2026 Zero to Hero Guide — Claude Code transforms engineering by enabling autonomous, agentic code development and system orchestration.
• Agent Harnessing: The Non-Model Infrastructure That Makes AI Agents Actually Work — Effective AI agents require robust non-model infrastructure beyond just advanced LLMs.
• How to Write Robust Code with Claude Code — Automated robustness in agent-generated code is achievable through structured prompting and verification.
• From Prompts to Production: 2 Hands-On AI Workshops This April — AI system failures often result from inconsistent usage and poor context design, not inherent model flaws.
• Claude Code’s 5-Layer Agent Development Kit: The Architecture Most Engineers Are Missing — Claude Code is an agent development kit with a layered architecture for robust, scalable agent behavior.
• LAI #127: The Infrastructure Layer of AI Is Becoming the Product — Robust AI infrastructure, not just prompts, is crucial for reliable, production-grade agentic systems.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.