Embracing the 3-Month Rule: A Technical Approach to Non-Scalable Solutions
In the world of startups, the mantra of “doing things that don’t scale” often surfaces, thanks to the wisdom of industry leaders like Paul Graham. However, a gap exists when it comes to translating this advice into tangible coding practices. Over the past eight months, while developing my AI podcast platform, I’ve adopted a unique framework that I like to call the 3-Month Rule. This approach enables me to rigorously test unscalable ideas, allowing them three months to demonstrate their worth before deciding whether to enhance or eliminate them.
The Challenge of Scalability
As engineers, we are frequently programmed to prioritize scalability from the outset. We’re drawn to elegant architecture and advanced patterns, such as microservices and distributed systems, that are well-suited for large-scale applications. However, in the startup environment, the pursuit of scalable solutions can become an expensive form of procrastination—targeting problems and users that may never materialize. The 3-Month Rule compels me to create straightforward—and sometimes even suboptimal—code that can be deployed quickly, enabling me to uncover what users genuinely need.
My Current Infrastructure Hacks: Why They Work
1. Consolidated Operations on a Single VM
I’ve opted to run my entire infrastructure—database, web server, background jobs, and cache—on a single $40/month virtual machine. This setup lacks redundancy and relies on manual backups to my local system.
Why is this method effective? Within two months, I’ve gained insights into my resource requirements that I wouldn’t have dissected through traditional capacity planning. For instance, I discovered that my AI-heavy application only peaks at 4GB of RAM. The complex Kubernetes setup I initially considered would have been a burden, managing empty containers.
When the system does crash—twice so far—it reveals specific failures that I wouldn’t have anticipated, enabling me to learn and adapt.
2. Simplicity in Configuration Management
By using hardcoded constants for configurations like pricing tiers and maximum users, I eliminate the need for configuration files or environment variables. Each change requires a redeployment.
The benefit of this approach is clarity. I can quickly search my entire codebase for any variable, with complete changelogs available via Git history. In the last three months, I’ve adjusted these values only three times—saving significant engineering hours by forgoing complex configuration services.
