Embracing the 3-Month Rule: A Pragmatic Approach to Building Unscalable Solutions
In the tech world, it’s common to hear the advice from Paul Graham to “do things that don’t scale.” However, there’s often a lack of guidance on how to effectively apply this principle, particularly when it comes to programming. After spending the last eight months developing my AI podcast platform, I’ve devised a straightforward framework that I call the “3-Month Rule.” This idea is simple: any unscalable technique I implement is given a three-month trial period. At the end of this timeframe, I assess whether it has provided value and deserves a permanent place in my architecture, or whether it should be retired.
As developers, we are often conditioned to prioritize scalability from the outset. We aim to build sophisticated design patterns, utilize microservices, and enhance distributed systems—all to accommodate projected millions of users. However, this method of thinking is frequently better suited for larger companies.
In the startup environment, the focus on scalable solutions can sometimes lead to costly delays where we optimize for users who may never arrive, addressing issues that aren’t pressing at the moment. My 3-Month Rule pushes me to produce straightforward, sometimes “bad,” code that can be tested and iterated upon quickly, allowing me to gain insights into users’ actual needs.
My Pragmatic Infrastructure Hacks: Smart Choices for Learning
1. Simplifying with a Single Virtual Machine
Currently, my entire infrastructure—from the database to the web server and background tasks—runs on a single $40/month virtual machine. This setup lacks redundancy and involves manual backups to my local system. While it might seem unwise, this strategy has provided valuable insights into my resource requirements in a matter of weeks. I’ve discovered that, despite my platform’s AI-intensive nature, it only demands 4GB of RAM during peak usage. Had I implemented a complex Kubernetes architecture, I would have been managing empty containers.
Whenever my system crashes— which has happened a couple of times—I gain concrete data regarding its weak points. Interestingly, the failures are never where I anticipated.
2. Hardcoded Configuration for Efficiency
For configuration, I engage in hardcoding values directly into the codebase:
plaintext
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
There are no configuration files
