Implementing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions
When it comes to entrepreneurship and startup development, one piece of advice that often surfaces is Paul Graham’s famous guidance: “Do things that don’t scale.” However, the challenge lies in translating this principle into tangible coding practices.
After spending the past eight months developing my AI podcast platform, I’ve devised a straightforward framework that revolves around one key tenet: every non-scalable solution gets a lifespan of three months. At the end of this period, we either transition it into a scalable, robust solution or phase it out entirely.
As engineers, we are conditioned to seek scalable solutions from the get-go, utilizing sophisticated design patterns and modular architectures. However, this mindset often distracts us from the realities of a startup environment, where scalability can turn into costly delays, solving problems that may never arise. By adhering to my 3-month rule, I’m encouraged to adopt simpler, even “imperfect” coding practices that allow me to deliver quickly and learn what truly benefits my users.
My Innovative Hacks and Their Hidden Benefits
1. Consolidating Everything on a Single Virtual Machine
Currently, my database, web server, and background jobs all operate on one $40/month virtual machine, with no redundancy and manual backups. While this setup may seem reckless, it provides invaluable insights into my resource requirements. I’ve learned that my platform operates optimally with just 4GB of RAM, and the complex Kubernetes architecture I almost implemented would have unnecessarily managed empty containers. Each crash offers data that reveals unforeseen issues, allowing me to adapt rapidly.
2. Embracing Hardcoded Configurations
Instead of relying on configuration files or environment variables, I’ve opted for hardcoded constants throughout my codebase. This might seem inefficient, but I can quickly search for any configuration value, ensuring all changes are tracked in version control. Over the last three months, I made minimal changes, which meant minimal redeployment time—15 minutes versus the potential 40 hours it would have taken to create a configuration service.
3. Running SQLite in a Production Environment
Yes, I am using SQLite for a multi-user application, and my entire database size is just 47MB, managing up to 50 concurrent users seamlessly. My analysis shows that access patterns are 95% read-heavy and only 5% write-heavy, making SQLite an ideal fit. Had I opted for a more
