Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Tech Development
In the world of startup innovation, advice from industry leaders like Paul Graham resonates strongly: “Do things that don’t scale.” Yet, the challenge lies in translating this concept into actionable coding practices.
After dedicating eight months to the development of my AI podcast platform, I’ve designed a practical framework that revolves around a simple mantra: every non-scalable shortcut is granted a lifespan of just three months. At the end of this period, it must either demonstrate its worth and evolve into a robust solution or be phased out completely.
As engineers, we often find ourselves fixated on crafting scalable solutions from day one—considering intricate design patterns, microservices, and distributed architectures capable of supporting millions of users. However, that mindset is more suited to large enterprises.
For startups, pursuing scalability can amount to costly delays, as we optimize for hypothetical users and address challenges that may not even arise. My three-month rule encourages me to embrace simpler, more direct coding methods that allow for rapid deployment, ultimately revealing the precise needs of my users.
Current Infrastructure Strategies and Their Logic
1. Consolidated Operations on a Single VM
All elements of my application—including the database, web server, and background jobs—reside on a single $40/month virtual machine. There’s zero redundancy and backups are manually conducted to my local drive.
Why is this a wise choice? Within two months, I’ve gained invaluable insights into my resource requirements that no planning document could provide. My platform, which I expected to be resource-heavy, only peaks at 4GB of RAM. The Kubernetes setup I initially envisioned would have meant managing superfluous containers.
The two crashes I’ve experienced revealed unexpected issues, highlighting areas that truly need attention.
2. Hardcoded Configuration Values
Instead of using configuration files or environment variables, I have constants spread throughout my code:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This seemingly chaotic approach empowers me to quickly search through my codebase for any configuration value. Each price adjustment is tracked via Git history, and every change is carefully reviewed—even if it’s just by me.
Creating a configuration service would consume a week of development time, while I’ve only needed to alter these values three
