Embracing the 3-Month Rule: An Effortless Approach to Non-Scalable Solutions

In the realm of tech entrepreneurship, Paul Graham’s edict to “do things that don’t scale” is widely acknowledged. However, the practical application of this advice, particularly in coding, is less often explored. After dedicating eight months to developing my AI podcast platform, I’ve adopted a straightforward methodology: every unscalable hack gets a lifespan of three months. At the end of this period, each solution must either prove its worth and transition into a robust implementation or face elimination.

As engineers, we tend to focus on crafting scalable solutions from the outset. We’re trained to admire intricate architectural frameworks, ranging from design patterns to microservices and distributed systems, all designed to accommodate millions of users. This, however, often translates to large enterprise thinking that may not be applicable in a startup context. Here, attempting to build scalable code too early can hinder progress, effectively turning it into an exercise in procrastination for users who aren’t yet on board. My 3-month rule has encouraged me to embrace simplicity over perfection, enabling me to deploy straightforward, sometimes “messy,” code that teaches me about real user needs.

My Ingenious Infrastructure Hacks

1. Consolidated Operations on a Single VM

I host everything – from the database to the web server and background jobs – on a single $40/month virtual machine. While this setup lacks redundancy and relies on manual backups to my local machine, the learning curve has been invaluable. In just two months, I’ve gained insights into my actual resource requirements that elaborate capacity planning documents could never provide. Surprisingly, my AI platform peaks at only 4GB of RAM, which would have rendered a complex Kubernetes setup meaningless, had I committed to it.

When the server crashes, which has occurred twice thus far, I receive authentic data about what fails—not merely assumptions based on hypothetical situations.

2. Hardcoded Configuration Values

My code is filled with straightforward configuration constants, such as:

python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"

I don’t utilize configuration files or environment variables. Any adjustments necessitate a redeployment, which might sound tedious. Yet, this approach empowers me to quickly search for values throughout my codebase and trace any price modification through version control