Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions in Software Development
Many in the tech community are familiar with Paul Graham’s well-known mantra: “Do things that don’t scale.” While it’s an intriguing concept, implementing it effectively within a coding context is less frequently discussed. After dedicating eight months to building my AI podcast platform, I have honed a straightforward framework that I now call the 3-Month Rule. This rule dictates that each unscalable hack has a lifespan of three months; after which, it either proves its worth and is refined into a robust solution, or it’s eliminated.
Why the 3-Month Rule Matters
As engineers, we are often conditioned to prioritize scalable solutions from the outset. We imagine elaborate design patterns, microservices, and distributed systems capable of accommodating millions of users. However, this mindset is generally more applicable to larger companies than to startups. In the startup world, focusing on scalable architecture too early can lead to costly delays as you try to optimize for needs that may never materialize. This is where my 3-month rule comes into play, compelling me to produce straightforward, if imperfect, solutions that can be deployed quickly. This approach yields valuable insights into actual user needs, which can be obscured when over-engineering is at play.
Insights from My Current Infrastructure Hacks
Here are some of the non-scalable strategies I’ve employed – and why they are, in fact, brilliant.
1. Consolidating Everything on One Virtual Machine
Currently, my entire operation runs on a single $40/month VM housing the database, web server, background processes, and Redis. This setup lacks redundancy and relies on manual backups to my local machine.
What seems like a flaw is actually a strategic advantage: I’ve gained a far clearer understanding of my resource requirements in just two months than I would have through any detailed planning document. Surprisingly, my supposed “AI-heavy” platform only requires 4GB of RAM at peak times. The complex Kubernetes infrastructure I was considering would have meant managing a lot of empty containers.
When this setup has crashed (which has happened twice), I’ve obtained real insights into the specific issues at hand—notably, they were rarely what I anticipated.
2. Utilizing Hardcoded Configuration Settings
Take a look at my configuration strategy:
“`python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19
