Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions in Tech Development
In the world of startups, conventional wisdom often strays from the advice of influential thought leaders. Paul Graham famously encourages entrepreneurs to “do things that don’t scale,” yet the practical application of this principle in coding remains elusive for many. After dedicating eight months to developing my AI podcast platform, I’ve crafted a straightforward methodology that I’ve dubbed the “3-Month Rule.” This framework allows any unscalable hack to exist for a limited time—three months, to be precise. If an approach proves its worth within that timeframe, it either evolves into a robust solution or is discarded.
As engineers, we are frequently trained to prioritize scalable solutions from the outset. We delve into design patterns, microservices, and sprawling distributed systems—all of which are designed to accommodate extensive user bases. However, this mentality often reflects the mindset of larger organizations rather than the realities faced by startups. In many cases, investing time and effort into scalable code equates to procrastination, focused on hypothetical future users and nonexistent challenges. My 3-month rule has compelled me to produce straightforward, albeit imperfect code, allowing me to learn firsthand what users actually require.
Current Infrastructure Hacks: A Strategic Approach
1. Consolidation on a Single Virtual Machine (VM)
With my database, web server, background jobs, and Redis functioning on a single $40/month VM, I’ve embraced an environment devoid of redundancy and rely on manual backups to my local machine.
This strategy has proven invaluable. I’ve gained deeper insights into my genuine resource requirements—and learned that my platform peaks at 4GB RAM. The complex Kubernetes architecture I nearly implemented would have led to managing empty containers. Each crash (which has happened twice) has offered genuine data on failure points, providing insights I never anticipated.
2. Hardcoded Configuration Values
In my codebase, essential configuration values such as pricing tiers and user limits are hardcoded. While this could be seen as a flaw, it has enabled me to rapidly locate any parameter and track changes via git history. Despite the time it would take to build a dedicated configuration service, I’ve only needed to modify these values thrice in three months. This results in minimal updating time rather than significant engineering hours.
3. Using SQLite for Production
I’m currently utilizing SQLite in a multi-user environment, managing a modest 47MB database adeptly handling 50 concurrent
