The 3-Month Framework: A Practical Approach to Non-Scalable Solutions
In the realm of entrepreneurship and software development, one piece of advice often echoed by Paul Graham is, “Do things that don’t scale.” However, the challenge many face is figuring out how to apply this wisdom, particularly in coding practices.
After dedicating the last eight months to building my AI podcast platform, I have arrived at a pragmatic framework: every unscalable solution gets a trial period of three months. Within this timeframe, I evaluate its performance and applicability. If a solution proves its worth, I invest in a more robust implementation; otherwise, it gets retired.
The Dilemma of Scalability in Startups
As engineers, we are conditioned to prioritize scalability from the outset. We often delve into complex design patterns, microservices, and distributed systems—architectures designed to support millions of users. However, this “big company” mindset can be a hindrance in a startup environment.
At startups, spending too much time on scalable code can often be a costly form of procrastination, focusing our efforts on future problems that may never materialize. My three-month rule compels me to adopt straightforward coding practices—yes, even “bad” code—fostering genuine learning about user needs and platform requirements.
Current Infrastructure Strategies That Work
Let me outline some of my current infrastructure decisions that may seem unconventional but have proven to be astute choices:
1. One VM for Everything
My platform’s database, web server, background jobs, and caching all run on a single $40/month virtual machine with no redundancy. Although it sounds risky, I’ve garnered valuable insights into my resource requirements much faster than any planning document could provide. For instance, my platform only utilizes a peak of 4GB RAM—far less than what I might have assumed with a complicated setup like Kubernetes.
Every time the system experiences an outage (which has happened twice), I walk away with concrete data on failure points, which has often been surprising.
2. Hardcoded Configuration
My configuration parameters, such as pricing tiers and user limits, are hardcoded throughout the codebase. This means making adjustments requires redeploying the application, but this process has its advantages. I can quickly search and review configuration changes in my git history, streamlining updates that occur infrequently (only three times in three months). This is a trivial trade-off compared to the extensive time that would have
