Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions in Software Development
In the startup world, conventional wisdom often champions the mantra presented by Paul Graham: “Do things that don’t scale.” However, the challenge lies in applying this philosophy, especially in software development. After working on my AI podcast platform for the past eight months, I’ve devised a straightforward framework: every unscalable hack is granted a lifespan of three months. Following this period, each hack must either demonstrate its value and be refined into a sustainable solution or be discarded entirely.
As engineers, our training typically leans towards building scalable systems from day one. We gravitate towards employing advanced design patterns, microservices, and distributed architectures—designs that promise to support millions of users. However, this approach often reflects the mindset of larger companies, which may not be practical for startups. In the early stages, focusing on scalable infrastructure can transform into an expensive form of procrastination, as we may be preparing for challenges that don’t yet exist. My 3-month rule not only encourages me to deploy straightforward, even “imperfect,” code but also equips me with real insights into user needs.
My Current Infrastructure Hacks: A Blend of Simplicity and Intelligence
- Running Everything on a Single VM
I’ve consolidated all essential services—database, web server, background jobs, and Redis—onto a single $40/month virtual machine (VM). While this setup lacks redundancy and relies on manual backups, it has proven to be enlightening. Within two months, I’ve gained a clearer understanding of my resource demands than I ever could have from traditional capacity planning documents. For instance, my platform, which was labeled as “AI-heavy,” only peaks at around 4GB of RAM demand. The complex Kubernetes environment I had considered setting up could have left me managing idle containers while missing key insights into system fails—data that emerged during my VM’s inevitable crashes.
- Utilizing Hardcoded Configuration Values
With parameters like pricing tiers and user limits embedded directly in my code, any change necessitates a redeployment. While this may seem inefficient, it minimizes potential complications while providing a significant advantage: I can quickly search through my codebase for any configuration value. Thus far, I’ve only made three configuration changes over three months, which translates to a minimal time investment compared to the hours of engineering required to implement a dedicated configuration service.
- **SQLite in a Multi-User
