Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Development
In the realm of entrepreneurship, particularly within tech startups, a well-known piece of advice from Paul Graham resonates: “Do things that don’t scale.” While the concept is familiar, the practical implications, especially in software development, often get overlooked. Having spent eight months developing my AI podcast platform, I’ve created a straightforward framework that I call the “3-Month Rule,” which allows me to explore unscalable solutions without fear of diminishing returns.
Understanding the 3-Month Rule
As software engineers, we are naturally inclined to build scalable architectures from the very start. We delve into sophisticated design patterns, microservices, and distributed systems—all aimed at accommodating vast user bases. However, in the context of a startup, investing time in scalable solutions can frequently lead to costly delays. My 3-Month Rule stipulates that any unscalable hack I implement is given a maximum of three months to demonstrate its effectiveness. If it fails to show value, it’s time to move on.
This approach encourages me to embrace simple and sometimes “imperfect” coding practices that prioritize shipping features and gaining user insights over crafting polished code. Here are some of my current infrastructure strategies that may appear unconventional but have proven to be effective:
1. Consolidated Services on One Virtual Machine
All my platform’s components—including the database, web server, and background jobs—are running on a singular $40/month VM. This means no redundancy and manual backups to my local machine. While this setup might seem risky, it has accelerated my understanding of resource utilization. In just two months, I’ve identified that my system peaks at 4GB of RAM, which means extensive preparations for scalability would have been unnecessary.
When issues arise, and they have twice so far, I gain firsthand experience about system failures, educating me on the real vulnerabilities of my application. Surprisingly, the failures often come from unexpected areas.
2. Directly Hardcoded Configurations
Throughout my codebase, I have hardcoded constants for key configuration values, negating the need for external config files or environment variables. While changing these settings requires a redeploy, it simplifies searching for configurations across the code. Each change is thoroughly documented in my Git history, and since I’ve only altered these configurations three times in the past three months, the time saved is substantial—spending mere minutes redeploying instead of dedicating hours
