Embracing the 3-Month Rule: A Pragmatic Approach to Building Non-Scalable Solutions
In the world of technology startup development, one mantra stands out among the rest: “Do things that don’t scale.” While Paul Graham’s wisdom rings true, the challenge lies in knowing how to effectively apply this principle, especially when it comes to coding and engineering.
Having spent the past eight months constructing my AI podcast platform, I’ve devised a straightforward framework that I like to call the 3-Month Rule. This approach stipulates that every unscalable solution I implement is given a lifespan of only three months. After that period, each solution will either be validated as effective enough to warrant a permanent build-out or it will be discarded.
The Startup Dilemma: Overengineering vs. Practicality
As engineers, we are often trained to create scalable infrastructure right from the outset. Concepts like design patterns, microservices, and distributed systems are part of our DNA, all aimed at accommodating vast numbers of users. Yet, in the startup environment, this perspective may lead to unnecessary complexities and delays—what I refer to as “expensive procrastination.”
The 3-Month Rule compels me to prioritize quick execution over perfection, allowing me to focus on writing straightforward, albeit imperfect, code that is pressed into service quickly and reveals users’ actual needs.
Current Infrastructure: Smart Hacks that Actually Work
Below are some of my current hacks, which may seem rudimentary at first glance but have proven to be incredibly insightful and effective:
1. Consolidated Resources on a Single Virtual Machine
I have all essential services—database, web server, background jobs, and caching—running on a single $40/month virtual machine. This setup lacks redundancy and relies on manual backups.
The advantage? In just two months, I have gained invaluable insights into my resource necessities that any capacity planning document would have failed to elucidate. It turns out my “AI-centric” platform only requires around 4GB of RAM during peak usage, informing me that the complicated Kubernetes setup I nearly pursued would have led to wasted resources.
2. Hardcoded Configurations Without External Dependencies
Instead of using configuration files or environment variables, I have opted for hardcoded constants throughout my code:
“`python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL
One Comment
Thank you for sharing such a pragmatic and insightful approach. The 3-Month Rule effectively encourages a balanced mindset—prioritizing rapid validation and learning over premature overengineering. It aligns well with the Lean Startup philosophy, where the goal is to iterate quickly, test assumptions, and scale only what proves valuable. Your example of consolidating resources on a single VM is a great illustration of how initial simplicity can yield clarity and cost-efficiency, ultimately informing more scalable infrastructure decisions down the line. Additionally, embracing “rudimentary” solutions like hardcoded configs for experimentation makes sense when speed is paramount—it allows for faster iteration cycles and real-world insights. Overall, your framework underscores the importance of staying flexible, intentionally limiting upfront complexity, and letting user feedback guide evolution. Looking forward to seeing how your approach helps refine scalable strategies in future iterations!