Embracing the 3-Month Rule: A Technical Framework for Innovating Without Scale

In the startup world, the popular mantra of Paul Graham resonates strongly: “Do things that don’t scale.” However, the conversation rarely delves into practical implementations, particularly from a coding standpoint.

Over the past eight months, while building my AI podcast platform, I’ve adopted a thoughtful approach to experimentation: every unscalable solution is given a lifespan of just three months. At the conclusion of this period, we assess its effectiveness—either it evolves into a robust system or it’s phased out.

Rethinking Engineering Mindsets

As engineers, we are often conditioned to create scalable solutions right from the start—embracing advanced design patterns, microservices, and distributed architectures that can accommodate millions of users. However, this approach can also lead to complex infrastructure and costly decisions that may not align with the realities of a startup.

In my experience, striving for premature scalability can often translate to unwarranted procrastination, where you might find yourself optimizing for future users that don’t yet exist. My three-month rule encourages me to build straightforward, albeit imperfect code that can be deployed quickly, allowing me to gather invaluable feedback on user needs.

My Current Strategies and Their Strategic Value

1. Unified Hosting on a Single Virtual Machine

I’ve consolidated all components—database, web server, background jobs, and caching—onto one $40/month virtual machine (VM). There’s zero redundancy and I rely on manual backups.

This approach has proven to be advantageous. In just two months, I’ve gained critical insights into my resource usage, learning that my platform’s peak requirement is just 4GB of RAM. The complexities of Kubernetes that I initially considered would have led to unnecessary management of inactive containers. Each crash (which has occurred twice) has provided real data about actual points of failure, debunking many of my earlier assumptions.

2. Plain Hardcoded Configurations

Unlike conventional practices, I’ve opted to hard-code configuration values directly within the codebase, such as pricing tiers and user limits. Although this might seem impractical, it offers unexpected benefits, like easily searching for any value across the entire project in mere seconds.

This method has led to only three configuration changes in three months—taking about 15 minutes to redeploy instead of the 40 hours it would have taken to develop a sophisticated configuration service.

3. Using SQLite for Production

Against conventional