The Three-Month Method: A Unique Approach to Unscalable Solutions in Software Development
In the realm of startups, advice often circulates on the importance of doing things that don’t scale, as famously discussed by Paul Graham. However, the execution of this advice in technical development can feel a bit murky. After eight months of building my AI podcast platform, I’ve adopted a straightforward framework that I call the “Three-Month Method.” Essentially, I give each unscalable solution three months to prove its worth. If it doesn’t deliver value by then, it’s time to let it go.
Why the Three-Month Rule Matters
As engineers, we typically focus on creating scalable architectures from the start—think design patterns, microservices, and distributed systems. While these concepts are essential in large organizations, they can lead to inefficiencies in startup environments. Often, investing time and resources in scalable solutions means delaying actual product development and optimizing for potential users who may never show up.
The Three-Month Method encourages me to focus on straightforward, albeit imperfect, coding that leads to real product deployments. This way, I gain valuable insights into what my users truly require.
Insights from My Current Approach
Here are a few unconventional strategies I’ve employed, and why I consider them intelligent choices:
1. Consolidating Services on a Single VM
Currently, my entire operation runs on one virtual machine (VM) for a mere $40 per month, encompassing everything from my database to background jobs. This lack of redundancy may sound risky, but it has taught me vital lessons about my resource requirements in just two months. I’ve discovered that my platform peaks at 4GB of RAM—data that a more complex architecture would not have revealed. When system failures occur (which they have twice), I get concrete feedback on what actually falters in my setup, often deviating from my initial expectations.
2. Utilizing Hardcoded Configuration
My configuration settings are hardcoded across various files, meaning any change necessitates a redeployment. While this may not be the most elegant method, it offers a hidden advantage: the ability to quickly search my codebase for any configuration value. With only three configuration changes in three months, I’ve saved countless hours that would have otherwise gone into building a configuration management service.
3. Employing SQLite for Production
I am currently using SQLite for my multi-user web application, and remarkably, it handles up to 50 concurrent
