Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions
In the startup world, the mantra of “doing things that don’t scale” is commonly attributed to the visionary Paul Graham, yet practical implementation remains less discussed—especially in the realm of coding. After eight months of developing my AI podcast platform, I’ve created a framework that I like to call the “3-Month Rule.” This principle dictates that any unscalable approach I utilize gets a testing period of three months. Following this timeframe, I evaluate its effectiveness: if it proves beneficial, I will invest in a proper solution; if it doesn’t, it’s time to let it go.
As engineers, we often lean towards building scalable solutions from the outset—embracing design patterns, microservices, and distributed systems that cater to millions of users. Such an approach is typically reserved for larger corporations, but in a startup environment, this focus on scalability can often lead to costly delays. I argue that scalable solutions can become a kind of procrastination—shifting our attention to potential future users instead of the pressing needs of our current audience. My 3-Month Rule compels me to write straightforward, albeit imperfect, code that is deployed rapidly, illuminating the actual requirements of my users.
Reassessing My Infrastructure: Simple Hacks with Strategic Value
- Unified Virtual Machine Setup
I operate all components—database, web server, background jobs, and caching—on a single $40/month virtual machine. This setup lacks redundancy and relies on manual backups to my local device.
This choice may seem unwise, but it has provided invaluable insights. In just two months, I’ve gained a clearer understanding of my resource demands than any detailed capacity planning could offer. For example, my platform, which I expected to be resource-intensive, tops out at only 4GB of RAM. The complex Kubernetes architecture I almost implemented would have managed unutilized containers instead of addressing real issues.
- Hardcoded Configuration Values
All configuration settings, from pricing tiers to user max limits, are hardcoded within the application. Any modification necessitates a redeployment.
This method has a surprising advantage: I can quickly search my codebase to locate configuration settings, and each adjustment is documented in git history. I’ve only made three configuration changes in three months, which translates to a mere 15 minutes of redeployment—far less than the 40 hours that would have gone into building a configuration service.
