The 3-Month Rule: A Practical Approach to Unscalable Development
In the ever-evolving landscape of startup development, we often hear the sage advice from tech luminary Paul Graham: “Do things that don’t scale.” Yet, amidst all this wisdom, little is said about effectively implementing this philosophy, particularly when it comes to coding. After eight months of building my AI podcast platform, I’ve crafted a straightforward technical framework that has served me well: every unscalable method I employ gets a three-month trial period. If it proves its worth, I’ll invest in optimizing it; if not, it gets retired.
Rethinking Scalability
As engineers, we’re conditioned to create solutions that are meant to scale from the outset. We’re drawn to design patterns, microservices, and robust distributed systems—architectures built for handling millions of users. However, for a startup, this focus can sometimes turn into costly procrastination. Why invest time and resources optimizing for users who may not even be there yet?
My three-month rule encourages me to write straightforward and even “imperfect” code that can actually be deployed. This approach allows me to better understand the needs of my users through real-world testing rather than assumptions.
Innovative Infrastructure Hacks That Yield Results
1. Single VM Strategy
Currently, my entire setup—database, web server, background jobs, and Redis—operates on a single virtual machine costing just $40 per month. There’s no redundancy, and I rely on manual backups.
Why is this effective? In just a couple of months, I gained valuable insights into my true resource requirements. My platform, which I initially thought would require significant resources, only peaks at 4GB of RAM. Instead of spending time building a complex Kubernetes environment, I’m now focused on optimizing what I truly need.
2. Simplified Configuration Management
By avoiding configuration files and environment variables, I’ve opted for hardcoded constants throughout my codebase. Adjusting any setting requires just a quick redeploy.
The brilliance of this approach lies in its simplicity: I can search my entire codebase for any configuration in seconds. Changes are easily traceable through Git history, providing a clear audit trail. The effort required to roll out a complicated configuration service simply isn’t justified by my limited need for adjustments.
3. SQLite as My Production Database
I’m running my application using SQLite, with a database size of just 47MB. It
