The Three-Month Rule: A Unique Framework for Scalable Coding
In the competitive landscape of startups, the adage “Do things that don’t scale,” famously shared by Paul Graham, often gets thrown around. However, less frequently discussed is how to effectively apply this concept to coding practices. After dedicating eight months to developing my AI podcast platform, I’ve crafted a straightforward methodology that I call the “Three-Month Rule.” Essentially, any non-scalable approach I implement is given a three-month trial period. During this time, if it demonstrates its utility, it transitions into a more robust system; if not, it’s retired.
The Scalability Quandary
As engineers, our education typically emphasizes the creation of scalable solutions from the outset. We often find ourselves engrossed in design patterns, microservices, and intricate distributed systems intended to accommodate millions of potential users. While these considerations are crucial for large enterprises, they can lead to inefficient practices in a startup environment. In fact, focusing too heavily on scalability can result in costly delays, as resources are allocated to address issues that may never arise.
By adhering to my three-month rule, I’ve embraced the value of producing simpler, more direct code that can be deployed rapidly. This approach not only delivers tangible results but also provides invaluable insights into user behaviors and needs.
Current Infrastructure Strategies: A Pragmatic Approach
Here are some of the infrastructure choices I’ve made that may seem unconventional but are, in fact, strategic and insightful.
1. Single Virtual Machine (VM) for Everything
Currently, my entire infrastructure resides on a single $40/month VM, encompassing the database, web server, background jobs, and caching via Redis. Although this setup lacks redundancy and relies on manual backups, it has provided crucial insights into my actual resource consumption in a way that traditional capacity planning might not have. I learned that my “AI-heavy” platform only peaks at 4GB of RAM, thus saving myself from the complexities of a Kubernetes architecture that would have managed unused resources. Interestingly, every crash—of which there have been two—has revealed unexpected vulnerabilities that differ from my initial assumptions.
2. Hardcoded Configuration
Instead of using configuration files or environment variables, I’ve opted for hardcoded constants throughout my codebase. Values such as pricing tiers and maximum user limits are directly embedded in the code. This may sound archaic, but its benefits are clear: I can easily search for configuration values across my entire
One Comment
Thank you for sharing your practical insights on applying the “three-month rule”—it’s a compelling approach that emphasizes agility and real-world validation over theoretical best practices. I appreciate how you’ve highlighted the importance of starting with simple, non-scalable solutions to quickly test assumptions and gather user insights before investing heavily in complex infrastructure.
Your example of deploying everything on a single VM and using hardcoded configurations illustrates a pragmatic mindset: focusing on learning and iteration rather than premature optimization. This aligns well with the concept of “doing things that don’t scale” early on, allowing founders and engineers to pivot effectively as they understand their actual needs.
One thought worth expanding is how this approach might evolve over time—perhaps transitioning from hardcoded configs to environment variables or configuration management tools as scaling becomes necessary. Balancing immediate simplicity with future scalability considerations ensures that the infrastructure can adapt seamlessly without a complete overhaul.
Overall, your methodology underscores that thoughtful experimentation and validating assumptions early can lead to more sustainable growth. Thanks again for sharing these valuable strategies!