Embracing the 3-Month Rule: A Pragmatic Approach to Building Unscalable Solutions
In the world of entrepreneurship and technology, the advice often heard is, “Do things that don’t scale,” as popularized by Paul Graham. However, the practical nuances of implementing this strategy in software development are seldom discussed.
After dedicating eight months to developing my AI podcast platform, I’ve stumbled upon a straightforward yet effective framework that I call the “3-Month Rule.” This principle states that any unscalable solution I implement is given a tenure of three months. At the end of this period, it needs to either demonstrate its value and be transitioned into a more robust structure or be phased out entirely.
Mindset Shift: From Scalable to Agile
As engineers, we are often trained to construct scalable systems from the get-go. We emphasize design patterns, microservices, and distributed architectures capable of accommodating millions of users. While these are essential in larger enterprises, they can often lead to unnecessary complexities in a startup environment.
In reality, focusing on scalable solutions too early can delay key learning opportunities. Therefore, my 3-month rule encourages me to produce straightforward, even suboptimal code that can be quickly deployed, enabling me to gather authentic data on user needs and behaviors.
Practical Implementations: My Current Infrastructure Hacks
1. Single Virtual Machine
I run my entire stack—database, web server, background jobs, and cache—on a single $40/month VM, with manual backups stored on my local machine. While it lacks redundancy, this setup has provided invaluable insights. In just two months, I’ve accurately gauged my resource requirements, realizing my “AI-heavy” platform only peaks at 4GB of RAM. Had I invested in a complicated Kubernetes architecture, I would have wasted time managing unnecessary containers.
2. Hardcoded Configuration
My configuration setup is intentionally straightforward, with constants directly embedded in the codebase:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This means every change requires a redeploy, but it’s streamlined. A quick search can reveal all configuration values in seconds, and each alteration is neatly tracked in my version control history. Rather than creating a dedicated configuration service that could take a week, I’ve saved countless hours by keeping it simple.
