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

In the world of entrepreneurship, Paul Graham’s renowned advice to “do things that don’t scale” often inspires considerable discussion. However, the practical application of this philosophy, especially in the realm of software development, tends to be less explored. Over the past eight months, while constructing my AI podcast platform, I’ve devised a straightforward framework that may benefit fellow developers: every non-scalable hack I implement has a lifespan of exactly three months. After this period, I assess its value and either refine it for scalability or retire it altogether.

Rethinking Scalability in Startups

As engineers, we often feel the pressure to create scalable solutions right out of the gate. We dive into the intricacies of design patterns, microservices, and distributed systems, aiming to construct architectures robust enough to accommodate millions of users. However, this mindset frequently represents a flawed “big company” approach that isn’t always suitable for startups. In truth, focusing too much on scalability at this stage can lead to costly delays, as developers invest time optimizing for future users who may not ever materialize.

My three-month rule compels me to embrace straightforward, albeit “imperfect,” code that can be launched quickly. This approach not only accelerates product delivery but also provides real-world insights into user behaviors and needs.

Key Infrastructure Strategies and Their Strategic Advantages

1. Consolidating Resources on a Single VM

Currently, my entire stack — including the database, web server, background jobs, and Redis — operates off a single $40/month virtual machine (VM), without redundancy. Although this setup may seem risky, it has yielded invaluable insights into my actual resource requirements. Through this experience, I discovered that my platform, which I initially deemed “AI-heavy,” barely taps into 4GB of RAM. In retrospect, a complex Kubernetes architecture would have resulted in the unnecessary management of idle containers. Each time the system crashes (which has happened a couple of times), I receive authentic feedback regarding the failure points — often in unexpected places.

2. Utilizing Hardcoded Configurations

In my codebase, you’ll find essential configurations like pricing tiers and user limits hardcoded into the files directly, eliminating the need for configuration files or environment variables. Making any adjustments requires a redeployment of the code. This might seem primitive, yet it allows me to swiftly locate any configuration value across the entire codebase.