Embracing the 3-Month Rule: A Unique Approach to Developing Scalable Solutions
In the world of startup innovation, there’s a well-known mantra by Paul Graham: “Do things that don’t scale.” While this advice is widely accepted, how can it be applied practically, especially in the realm of coding? After eight months of working on my AI podcast platform, I’ve crafted a straightforward approach that has empowered me to harness the benefits of unscalable solutions in a meaningful way. I call it the 3-Month Rule.
The Essence of the 3-Month Rule
As engineers, we often gravitate towards building systems designed for scalability from the outset. We become entangled in sophisticated architectures—microservices, distributed systems, and elegant design patterns—intended to service millions of users. However, this mindset can be a pitfall, particularly for startups where the emphasis should really be on immediate user needs and feedback.
In a startup environment, focusing on scalable solutions can lead to unnecessary complications and delays. My 3-Month Rule challenges me to implement simple, direct, and often “imperfect” code that delivers results quickly. After three months, each experiment either earns its place in the tech stack by demonstrating value or is phased out.
Practical Implementations of the 3-Month Rule
1. Consolidated Resources: One Virtual Machine
For just $40 a month, my entire infrastructure—database, web server, background jobs, and Redis—is on a single virtual machine. While this setup lacks redundancy and relies on manual backups, it has yielded invaluable insights about my actual resource allocation. Instead of building complex systems, I’ve learned that my platform peaks at 4GB of RAM, allowing me to avoid unnecessary overhead.
When crashes occur (and they have), I gain real data about failure points—information that a more sophisticated architecture would have obscured.
2. Simplified Configurations
Instead of complex configuration frameworks, I’m using hardcoded constants for key variables. This approach allows me to track changes effortlessly through version control and ensures that I can quickly locate any configuration values in my code. While creating a configuration management system would be a time-consuming task, I’ve found that my infrequent updates justify the current setup, saving time for more critical development.
3. Utilizing SQLite in Production
Yes, my multi-user web application is powered by SQLite. With a database size of just 47MB, it does remarkably well under concurrent
