Embracing Imperfection: The 3-Month Rule for Developing Non-Scalable Solutions
In the entrepreneurial world, one piece of wisdom resonates strongly among founders: “Do things that don’t scale.” This advice, famously shared by Paul Graham, is often easier said than done—especially when it comes to the technical side of building products. After eight months of developing my AI podcast platform, I’ve discovered a practical approach that I call the 3-Month Rule, allowing me to navigate the often murky waters of scalability versus functionality.
A Framework for Implementation
As engineers, our instinct is to craft scalable solutions from the outset. We become enamored with concepts like microservices, complex architecture, and distributed systems—ideal for handling immense user bases. However, in a startup environment, placing too much emphasis on scalability can lead to wasted resources as we attempt to optimize for potential users who may never engage with us.
My 3-month approach provides a compelling solution: allow non-scalable shortcuts to flourish for three months. At the end of this period, these hacks are evaluated for their effectiveness—if they prove valuable, they are integrated into a more robust system; if not, they’re discarded.
Leveraging Simple Infrastructure Hacks
Here are a few of my current strategies that, despite appearing rudimentary, have delivered significant insights and advantages:
1. Consolidation on a Single Virtual Machine
I operate with a single virtual machine (VM) that houses the entire stack—database, web server, background jobs, and Redis—at a cost of just $40 a month. This setup is devoid of redundancy and relies on manual local backups.
Why is this a smart strategy? In just two months, I’ve gained a clear understanding of my resource needs, discovering that my platform’s peak usage requires merely 4GB of RAM. The more intricate Kubernetes setup I initially considered would have resulted in managing idle containers. Each crash has provided invaluable data on actual failure points, which often differ from my assumptions.
2. Hardcoded Configuration Values
I’ve taken the route of hardcoding configuration values, such as:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This means no config files or environment variables—just straightforward constants. While it necessitates redeployment for changes, the trade-off is
