Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions
In the ever-evolving landscape of tech startups, one piece of advice frequently reiterated is Paul Graham’s directive: “Do things that don’t scale.” However, translating this wisdom into actionable strategies within the realm of software development can be less discussed.
After dedicating eight months to the creation of my AI podcast platform, I’ve established a straightforward framework that I like to call the “3-Month Rule.” The idea is simple—any unscalable solution is given a trial period of three months. After this time, it’s either validated and expanded into a robust system or it’s discarded.
As engineers, we often find ourselves focused on constructing “scalable” systems from the outset. We dive headfirst into design patterns, microservices, and distributed systems, all tailored to support potentially millions of users. However, this mindset is typically rooted in a corporate context. For startups, prioritizing scalability from day one can lead to costly delays in addressing immediate needs.
The essence of my 3-Month Rule lies in the necessity for simple, straightforward coding solutions that prioritize functionality over perfection, enabling us to grasp our users’ true requirements through experimentation.
My Current Infrastructure Hacks and Their Strategic Value
1. Utilizing a Single Virtual Machine for All Operations
I’ve consolidated my database, web server, background jobs, and Redis into a solitary $40/month virtual machine. While this approach lacks redundancy and relies on manual backups, it’s ultimately enlightening.
From this minimalist setup, I’ve gained invaluable insights regarding actual resource usage—in just two months, I’ve discovered that my “AI-heavy” platform rarely demands more than 4GB of RAM. The complex Kubernetes architecture I nearly implemented would have simply managed idle containers. Through occasional system crashes (which have happened twice), I’ve identified precise failure points that provide unexpected learning opportunities.
2. Embracing Hardcoded Values
My configuration includes hardcoded constants rather than complex config files or environment variables. For example:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this may seem retrograde, it offers remarkable efficiencies. I can quickly search my codebase for configuration values and maintain a clear history of any changes through Git. With only three changes in three months, the time
