Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Development
In the world of startups and tech innovation, it’s not uncommon to hear Paul Graham’s advice to “do things that don’t scale.” Yet, while many agree with this principle, few delve into its practical application, especially in the realm of coding.
Having spent the past eight months developing my AI podcast platform, I’ve formulated a straightforward framework that I call the “3-Month Rule.” It operates on a simple premise: every unscalable solution is given a lifespan of three months. After this period, it must either demonstrate its value and be enhanced for scale, or it will be phased out.
The Mindset Shift: From Scalable Thinking to Experimental Learning
As engineers, we often instinctively aim for scalable solutions from the outset. We draw on design patterns, microservices architecture, and distributed systems—technologies that excel when managing a vast user base. However, in a startup environment, striving for scale too early can be a costly distraction. Often, we end up optimizing for hypothetical users and addressing issues that may never arise.
This is where my 3-Month Rule proves invaluable: it prompts the creation of straightforward, albeit “imperfect,” code that promotes shipment and, more importantly, informs me of actual user needs.
Insightful Infrastructure Decisions and Their Impact
Here are several key strategies I’ve employed, illustrating how they fostered my learning and growth:
1. Consolidation on One Virtual Machine
All components of my platform—database, web server, background tasks, and caching—are consolidated on a single $40/month virtual machine. This might sound risky, given the lack of redundancy and the manual backup processes, but the insight gained has been invaluable. In just two months, I’ve accurately gauged my resource needs, discovering that my platform peak requires only 4GB of RAM. This knowledge saved me from the complexities of managing a Kubernetes setup that would likely have been underutilized.
2. Hardcoded Configurations
Instead of relying on configuration files or environment variables, I opted for hardcoded constants throughout my code. While this method initially seems primitive, it has allowed me to quickly search and modify configuration values across the codebase. Over the past three months, I’ve modified settings just a handful of times, saving myself potentially 40 hours of engineering effort for mere minutes of redeployment.
