Embracing the 3-Month Rule: A Framework for Practical Development

In the startup world, the advice from Paul Graham to “do things that don’t scale” is often echoed, yet the practical application of this guidance in software development is rarely discussed. Having spent the past eight months building my AI podcast platform, I have created a straightforward framework to navigate these unscalable ideas: the 3-Month Rule. Essentially, any hack that doesn’t scale is given three months to demonstrate its viability. If it proves its worth, it is reconstructed into a robust solution; if it falters, it is discarded.

The Challenge of Scalable Solutions

As software engineers, we are often conditioned to prioritize scalability from the outset. We gravitate towards sophisticated design patterns, microservices, and intricate distributed systems intended to support millions of users. However, this mindset can become a roadblock in the startup environment, where the complexities of scalable architecture may simply perpetuate inefficiencies. The 3-Month Rule compels me to write straightforward, albeit imperfect, code that can be deployed quickly and yields practical insights about user needs.

Current Infrastructure Hacks: Smart Choices in Simplicity

1. Unified Virtual Machine Setup

All facets of my platform—from the database to background tasks—are functioning on a single $40/month virtual machine. This setup lacks redundancy, relying instead on manual backups to my local system.

This may seem counterintuitive, but it has been enlightening. In just two months, I have gleaned more about my resource requirements than any thorough capacity planning document might convey. The reality is that my “resource-intensive” platform typically requires only 4GB of RAM. The complex Kubernetes configuration I almost initiated would have merely managed dormant containers. Each time a crash occurs, I collect valuable data on real failure points, which often differ from my expectations.

2. Hardcoded Configuration Values

In my code, configuration values are hardcoded directly within the files—no separate configuration files or environment variables. Changing a parameter necessitates a redeployment.

What’s the advantage? I can easily search through my entire codebase for any configuration in mere moments. Every price adjustment is logged in Git history, and I review changes myself. Although establishing a dedicated configuration service might seem preferable, the minimal changes—just three over three months—illustrate how redundant such a service would be.

3. Utilizing SQLite for Production

For my multi-user web application, I am currently using