Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions in Software Development
In the realm of startup growth and software engineering, the advice from Paul Graham to “do things that don’t scale” often resonates deeply. However, a crucial aspect that rarely gets discussed is the implementation of this principle, especially in coding practices. After dedicating eight months to the development of my AI podcast platform, I have crafted a unique framework that prioritizes rapid learning through temporary, non-scalable hacks, with a definitive lifespan of three months.
The Value of Impermanence in Development
As engineers, we are conditioned to envision scalable solutions from the outset. High-level concepts like design patterns, microservices, and distributed systems dominate our thinking, driven by a big company mentality. Yet, in the fast-paced world of startups, crafting scalable code too early can often lead to unnecessary expenses and complications. My three-month rule challenges this notion, compelling me to prioritize simplicity and practicality over perfection, ultimately revealing the true needs of my users.
Current Non-Scalable Solutions: A Genius Experimentation Strategy
Here are the non-scalable techniques I’ve adopted, revealing their unexpected advantages:
1. Single VM Infrastructure
I am currently operating all critical components—database, web server, and background jobs—on a single virtual machine with a modest monthly cost of $40. While this approach lacks redundancy and relies on manual local backups, it allows me to gain real-time insights into my resource demands. Within two months, I’ve learned that my platform, billed as “AI-heavy,” only requires 4GB of RAM during peak times. The complex Kubernetes framework I had considered implementing would have merely added overhead for managing empty resources. When the server has crashed—which has happened twice—I gathered valuable insights on actual failure points, rather than hypothetical issues I had anticipated.
2. Simplistic Configuration Management
My configuration setup comprises hardcoded constants without any complex environment variables or config files. This approach may seem rudimentary, yet it grants me the ability to swiftly track changes across my entire codebase. For instance, revising a price tier or user cap involves nothing more than a redeployment process taking a mere fraction of the time it would take to establish a full configuration service. In three months, the frequency of these changes has validated my method.
3. Embracing SQLite for Production Use
Utilizing SQLite for my multi-user application—despite its typical limitations—
