The Rise of AI Command Centers
In the shadowy corridors of Silicon Valley, whispers about Boris Cherny’s revelations have transformed into a roaring manifesto. The creator of Claude Code at Anthropic has unveiled a workflow that turns conventional coding on its head. What started as a casual thread on X has become a blueprint for the future of software development. Cherny’s setup, which resembles a command center from a cyberpunk novel, allows a single developer to wield the power of an entire engineering department. This is not just about writing code; it’s about orchestrating a symphony of AI agents.
Cherny’s method is a paradigm shift from the linear coding of the past. Instead of following the traditional ‘inner loop’ of writing and testing functions, Cherny oversees multiple AI agents like a fleet commander. By running five Claudes in parallel, each tasked with different aspects of development, Cherny has optimized productivity to an unprecedented level. This approach transforms the coding process into a real-time strategy game, where developers command autonomous units rather than typing syntax.
The Power of Slow, Smart Models
In a world obsessed with speed, Cherny’s choice to use Anthropic’s slowest model, Opus 4.5, is counterintuitive yet revolutionary. While the industry chases faster token generation, Cherny’s strategy focuses on minimizing human intervention. Opus 4.5, though slower, requires less steering and excels in tool use, ultimately leading to faster overall project completion. This insight challenges the prevailing narrative, highlighting that the true bottleneck lies in the time spent correcting AI errors.
Cherny’s workflow suggests that investing in a smarter model upfront can eliminate the need for constant corrections. This ‘compute tax’ is a small price to pay compared to the ‘correction tax’ that plagues faster but less intelligent models. For tech leaders, this is a game-changing realization that could redefine AI development strategies. By prioritizing intelligence over speed, Cherny has demonstrated a path to exponential productivity gains.
Transforming Mistakes into Learning Opportunities
A persistent challenge in AI development is the issue of ‘AI amnesia’—the inability of models to remember specific coding styles or architectural decisions. Cherny’s team has ingeniously addressed this by maintaining a shared file, CLAUDE.md, in their git repository. This file acts as a living document, recording every mistake the AI makes and transforming it into a rule for future sessions.
This practice turns the codebase into a self-correcting organism. When a developer spots an error, they don’t just fix it; they update the AI’s instructions. This continuous learning process ensures that the AI becomes smarter over time, reducing the likelihood of repeating past mistakes. As Aakash Gupta noted, ‘Every mistake becomes a rule,’ creating a feedback loop that enhances the AI’s capabilities with each iteration.
The Future of Software Engineering
Cherny’s workflow has sparked a fundamental shift in how developers perceive their craft. The tools to amplify human output by a factor of five are already within reach, requiring only a change in mindset. By treating AI as a workforce rather than an assistant, developers can unlock unprecedented levels of productivity and efficiency.
This transformation signals a new era in software engineering, where AI operates not just as a tool but as an integral part of the development process. The verification loops and automation strategies employed by Cherny demonstrate that AI can do more than generate text—it can verify, test, and ensure the quality of its output. As the industry grapples with these revelations, one thing is clear: those who adapt to this new paradigm will not only thrive but redefine the very nature of coding in the digital age.
Meta Facts
- •💡 Cherny runs five AI agents in parallel to manage multiple work streams.
- •💡 Opus 4.5 is used for its intelligence despite being slower than other models.
- •💡 CLAUDE.md file records AI errors to improve future performance.
- •💡 Verification loops enhance AI-generated code quality by 2-3x.
- •💡 AI is treated as a workforce, not just an assistant, for exponential productivity.