Claude Code: 3-week project done in 2 days, but nearly failed
In a striking demonstration of artificial intelligence’s accelerating impact on software development, Hugh Williams, a distinguished former engineering Vice President at Google and eBay, recently leveraged Anthropic’s Claude Code to condense what was projected as a three-week development cycle into a mere two-day sprint on Amazon Web Services. This remarkable feat, however, was not without its dramatic tension, as the powerful AI assistant simultaneously pushed the boundaries of its operational limits, nearly bringing the ambitious project to a halt.
Williams, a luminary in the tech world with a Ph.D. in Computer Science and a reputation as the inventor of Infinite Scroll, has held senior leadership roles across industry giants like Microsoft and Pivotal. His extensive background, which includes authoring a best-selling programming book and holding numerous patents, positions him as a seasoned evaluator of new technologies. His decision to employ Claude Code for a complex system build on AWS underscores the growing confidence among top-tier engineers in AI-powered development tools.
Anthropic’s Claude Code, an agentic coding tool, operates directly within a developer’s terminal, integrating seamlessly with popular Integrated Development Environments (IDEs) like VS Code and JetBrains. Powered by advanced large language models, specifically Claude Opus 4.1, it is designed to understand entire codebases, generate features from plain English descriptions, debug and fix issues, and even make coordinated changes across multiple files. It can execute commands and create commits, all while maintaining user control by requiring explicit approval for modifications. This suite of capabilities is precisely what allowed Williams to achieve such unprecedented speed in his AWS project. Claude Code’s ability to grasp project structure and existing patterns, then suggest and implement code directly within the development environment, significantly streamlines the traditional coding workflow.
Yet, the very efficiency that propelled Williams’ project to a rapid conclusion also exposed a critical growing pain for AI development platforms. Just weeks before this news, Anthropic quietly began implementing weekly rate limits for its Claude Code Pro and Max subscribers, with these changes set to take full effect by August 28, 2025. This sudden imposition of caps, which reportedly caught many users off guard, was a direct response to what Anthropic described as “excessive usage” and instances of some users running Claude Code “continuously in the background, 24/7” or suspected account sharing.
Developers, including those on the premium Max plan, reported encountering abrupt “Claude usage limit reached” messages, often without prior warning or transparent usage dashboards. This lack of communication and real-time metrics has generated significant frustration and a perception of unreliability within the developer community, prompting some to explore competitor offerings. While Anthropic claims these new limits will affect less than 5% of its subscriber base, the incident highlights a broader industry challenge: the immense computational costs associated with power users of AI coding tools, and the struggle for AI companies to scale their infrastructure reliably amidst rapid adoption.
This episode with Hugh Williams and Claude Code serves as a microcosm of the current state of AI in software development. AI-powered code assistants are undeniably revolutionizing the industry by automating repetitive tasks, boosting productivity, enhancing code quality, and facilitating knowledge sharing across teams. They free developers to focus on higher-value tasks, from designing complex algorithms to architecting scalable systems. However, as these tools become more deeply embedded in critical workflows, the need for transparent usage policies, robust infrastructure, and clear communication from AI providers becomes paramount. The race for speed and innovation continues, but it’s clear that the path to fully integrated AI development will involve navigating not just technical hurdles, but also the economic and operational realities of supporting these powerful, resource-intensive agents.