Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions in Tech
The tech community often reverberates with advice from industry luminaries like Paul Graham, who famously said, “Do things that don’t scale.” Yet, the challenge of translating this philosophy into practical coding strategies rarely gets the attention it deserves.
After eight months of developing my AI podcast platform, I’ve established a straightforward principle: every non-scalable solution I implement gets a life expectancy of three months. At the end of that period, I analyze its effectiveness—if it shows promise, it receives a proper foundation; if not, it gets phased out.
The Conventional Mindset vs. startup Realities
In the engineering world, there’s a strong emphasis on creating scalable solutions right from the outset. Concepts such as design patterns, microservices, and distributed systems all cater to the needs of large enterprises. However, this focus can be a hindrance in a startup atmosphere.
At a startup, pursuing scalable code from day one often leads to costly delays, as it focuses on hypothetical users rather than real ones. My three-month framework encourages me to prioritize simplicity and immediacy over theoretical constructs, allowing for rapid iterations and genuine insights into user needs.
My Current Infrastructure Innovations and Their Impact
1. Centralized Operations on a Single VM
Everything, from the database to the web server and background jobs, is hosted on a single $40/month virtual machine—with no redundancy and manual backups.
This setup has proven to be surprisingly effective. In just two months, I’ve gained more valuable insights about my actual resource requirements than I would have from an extensive planning document. For instance, my platform reaches a peak usage of only 4GB of RAM. The complex Kubernetes architecture I nearly committed to would have only served to manage idle containers. Each crash (there have been two) has provided real-world data on failure points, which often defy my initial expectations.
2. Static Configuration Throughout the Codebase
Hardcoding configuration values like pricing tiers and user limits directly into my codebase allows for rapid changes, albeit with the trade-off of needing to redeploy.
The advantage here is the simplicity and traceability. I can quickly search for any configuration value across my entire codebase. Changes are captured in git history, ensuring that every adjustment is accounted for and reviewed, albeit by myself. The effort to create a full-fledged configuration service would have consumed a week, whereas I’ve
