The 3-Month Rule: A Practical Framework for Non-Scalable Development
In the world of software engineering, the mantra “do things that don’t scale” is often discussed, particularly in the context of startups. However, there seems to be a lack of concrete strategies for putting this advice into practice, especially when it comes to coding. Drawing from my experience of building an AI podcast platform over the past eight months, I’ve developed a straightforward framework that guides my development process: every non-scalable tactic is given a lifespan of three months. After this period, we evaluate its effectiveness—if it adds value, we upgrade it; if not, it gets the axe.
The challenge as developers tends to be our inclination to design scalable solutions right from the outset. Concepts like design patterns, microservices, and distributed systems—while essential in larger organizations—can often lead to over-engineering in the startup phase. In many cases, focusing on scalability can turn into expensive delay as we chase after potential user bases that may not yet exist. This is where my three-month rule comes in, prompting me to adopt a more immediate, hands-on approach that helps uncover real user needs.
My Practical Infrastructure Hacks: Why They Work
1. Single Virtual Machine Setup
I’m currently running all aspects of my application—database, web server, background jobs, Redis—on a single $40 per month virtual machine. While it might seem risky due to the lack of redundancy, this setup has actually provided invaluable insight into my resource requirements. Within a mere two months, I’ve learned that my AI-driven platform peaks at 4GB of RAM. The complex Kubernetes architecture I nearly implemented would have only served to complicate matters without fulfilling any real need.
Each crash—yes, there have been a couple—has given me direct data on failure points, allowing for adjustments based on actual usage patterns rather than theoretical models.
2. Hardcoded Configuration Values
Instead of utilizing separate configuration files or environment variables, I’ve opted for hardcoded constants in my codebase:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This approach may appear archaic, but its efficiency lies in simplicity. By eliminating the need for a configuration management system—a task that could take an entire week—I can make changes quickly. Over the last three months,
