Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Development
In the startup world, it’s a widely accepted mantra: “Do things that don’t scale.” While this advice, famously shared by Paul Graham, resonates deeply among founders, the implementation of such a principle in technical development often goes unaddressed.
Over the past eight months, I have been crafting an AI podcast platform, and throughout this journey, I’ve established a straightforward framework for integrating unscalable solutions. My approach is simple: every short-term hack or workaround is permitted to exist for just three months. At the end of this period, the solution either validates its worth and is refined for sustainability, or it is eliminated from the project.
Why This Works
As software engineers, we are often conditioned to prioritize the construction of scalable architectures from day one. We think in terms of microservices, distributed systems, and design patterns—all vital for managing user bases in the millions. However, this approach can become a hindrance, particularly in a startup environment where building scalable solutions might just prolong the inevitable—essentially becoming a costly delay.
When resources are lean and user numbers are still growing, my three-month rule compels me to write straightforward, and arguably “imperfect,” code that is functional instead of trying to architect a flawless solution prematurely. This method not only prompts me to ship efficiently, but it also provides valuable insights into what users genuinely require.
Current Hacks: Simple Yet Effective Approaches
1. Consolidated Resources on a Single VM
For just $40 a month, my entire operation—including the database, web server, background jobs, and caching—runs on a single virtual machine (VM). There’s no redundancy and backups are handled manually.
What seems like a foolish choice has proved to be quite the opposite. In just two months, I gained insights into my resource demands that typical planning strategies could never provide. It turns out that my platform, which I expected to be resource-hungry, only peaks at 4GB of RAM. The complex Kubernetes architecture I nearly deployed would have solely managed unused containers.
When the system has gone down (twice now), the errors have yielded unexpected data about failure points—insights I wouldn’t have gleaned otherwise.
2. Hardcoded Constants
Instead of using configuration files or environment variables, I have embraced hardcoded constants for essential settings like pricing tiers and user limits.
This choice, while seemingly limiting
