The 3-Month Experiment: A Pragmatic Approach to Unscalable Solutions
In the world of rapid startup development, one piece of wisdom often echoed by angel investors and successful founders alike is Paul Graham’s mantra: “Do things that don’t scale.” While many acknowledge this advice, the true challenge lies in the execution, especially within the realm of coding.
Over the past eight months while developing my AI podcast platform, I’ve crafted a unique methodology to tackle this challenge: the 3-Month Rule. Under this framework, every unscalable solution is granted a trial period of three months. If the approach demonstrates its worth, it evolves into a well-constructed feature; otherwise, it’s phased out.
The startup Mindset
As engineers, our instincts often lead us toward building solutions that can accommodate vast growth from the start. We focus on inclusive architectural patterns, microservices, and systems designed for high traffic—all of which are essential for larger companies. However, for a startup, meticulously preparing for scalability can sometimes equate to delaying present realities and wasting resources.
By adhering to my 3-month rule, I can embrace simpler coding practices that yield immediate insights into what my users genuinely require. Here are some unconventional yet insightful strategies that would typically be deemed “bad” coding practices but have proven to be beneficial for my project.
Current Infrastructure Strategies
- Single Virtual Machine Setup
My entire platform, including the database, web server, background processing, and Redis, operates on a singular $40/month virtual machine—without redundancy and backed up manually to my local device.
Why it works: This setup has revealed my actual resource requirements in just two months, far beyond what any theoretical planning report could have provided. My AI-driven platform peaks at a modest 4GB RAM, meaning an intricate Kubernetes architecture would have simply dealt with idle resources. Each crash (and there have been a few) has delivered unexpected, valuable insights into what truly fails in my system.
- Hardcoded Configuration
Instead of using config files or environment variables, I have implemented constants such as:
python
PRICE_TIER_1 = 9.99
MAX_USERS = 100
The advantage: The simplicity allows me to quickly locate configuration values throughout my codebase using a grep command. In three months, I’ve amended these constants only three times, leading to minimal redeployment time compared to
