Embracing the 3-Month Rule: A Pragmatic Approach to Building Scalable Solutions

In the realm of entrepreneurship, particularly in the tech landscape, we often hear the wise words of Paul Graham: “Do things that don’t scale.” While this advice resonates with many, the implementation of such a strategy—especially within software development—remains a nuanced topic rarely discussed.

After eight months of developing my AI podcast platform, I’ve stumbled upon an effective framework for integrating unscalable solutions into my workflow: the 3-month rule. This principle dictates that every temporary, non-scalable hack gets a limited lifespan of three months. Following this period, it either demonstrates its efficacy through real-world value, leading to further development, or it is retired.

Understanding the Challenge

As engineers, we are often conditioned to create scalable solutions from the outset. Concepts like design patterns, microservices, and distributed architectures—while incredibly powerful—often reflect the mindset of large corporations. However, for startups, building scalable code too early can simply serve as a costly delay. We find ourselves optimizing for future users that may not materialize or addressing issues that don’t yet exist.

My 3-month rule encourages me to craft straightforward, albeit imperfect, solutions that can be deployed quickly. This approach not only fosters rapid iteration but also provides invaluable insights into the actual needs of users.

Current Infrastructure Hacks: A Smart Approach

Here’s a look at some of my current infrastructure solutions, and why I believe they are pragmatically sound:

1. University on One VM
I’ve consolidated my application onto a single $40/month virtual machine, encompassing the database, web server, and background jobs. While this setup lacks redundancy and requires manual backups, it has offered me profound insights into my resource demands. Despite my initial assumptions of needing vast capacities, I’ve discovered that my platform, heavy on AI, peaks at just 4GB of RAM. Instead of setting up an over-complicated Kubernetes cluster, which would have resulted in managing idle containers, I gain actionable data every time the system crashes—information that consistently deviates from my expectations.

2. Hardcoded Configuration
An apparent risk, I hardcode settings directly within the codebase instead of utilizing configuration files or environment variables. For instance, my pricing tiers and user limits are directly embedded in the code. While this means I must redeploy for any changes, the upside is profound: I can swiftly search for configuration