Embracing the 3-Month Rule: A Practical Approach to Unscalable Solutions in Tech Development
In the world of startups, the advice from Paul Graham to “do things that don’t scale” resonates deeply, yet implementing this concept can be challenging—especially in tech development. As I’ve worked on my AI podcast platform over the last eight months, I’ve devised a straightforward and effective method to tackle unscalable tasks: the 3-Month Rule. This rule dictates that any unscalable solution I implement is given a lifespan of three months. Within this period, it must either demonstrate its practical value and inspire further development or face removal.
The Pitfalls of Scalable Thinking
As engineers, we are often conditioned to pursue scalable solutions right from the outset. We dive into the intricacies of design patterns, microservices, and distributed systems—structuring our code for potential millions of users. While this mindset is valuable for larger corporations, it can become a hindrance in a startup environment, where focusing too heavily on scalability can result in costly delays. The pursuit of premature optimization can lead to over-engineering, addressing challenges that might never arise.
By adhering to the 3-Month Rule, I am encouraged to produce straightforward, even “imperfect” code that actually works and reveals genuine user needs.
Current Infrastructure Hacks: Ingenious Yet Simple
Let’s explore some of my current infrastructure hacks and the rationale behind them:
1. Single VM Strategy
I’m running my entire operation, which includes the database, web server, background tasks, and caching, on a single $40/month virtual machine. This setup is devoid of redundancy and relies on manual backups.
Why is this approach beneficial? In just two months, I’ve gained more insights into my resource needs than any theoretical capacity planning document could provide. I learned that my platform, which I expected to use significant resources, only peaks at 4GB of RAM. The intricate Kubernetes framework I considered would have resulted in managing unused containers.
Each crash offers real-world data about system failures, providing unexpected learning opportunities.
2. Hardcoded Configuration
Configurations are scattered across my code as constants, without relying on configuration files or environment variables. While this may appear inefficient, it allows me to quickly search for any config value in seconds.
The advantage? I’ve made only three configuration changes in three months, significantly reducing the time spent on redeployment.
3. Utilizing SQLite for Production
Yes,
