Embracing the 3-Month Rule: A Practical Approach to Building Unscalable Solutions
In the tech world, many of us have heard Paul Graham’s sage advice: “Do things that don’t scale.” However, the implementation of this concept, especially within the coding realm, is seldom discussed. After spending eight months developing my AI-driven podcast platform, I’ve established a framework that I call the “3-Month Rule.” The essence is simple: every unscalable approach I adopt is given three months to prove its worth. At the end of this period, it either gets the investment it needs for enhancement or gets phased out.
As engineers, we’re often conditioned to create scalable solutions from the very beginning. We gravitate toward complex design patterns, microservices architectures, and distributed systems that are crafted to support millions of users. However, such thinking is more suited to larger enterprises rather than startups. In many cases, focusing on scalability from day one can lead to expensive delays—especially when you’re trying to address issues that may not even exist yet. My 3-month rule compels me to produce straightforward, if somewhat “messy,” code that can be released quickly, providing valuable insights into user needs.
Ingenious Infrastructure Hacks Worth Sharing
1. Centralized Operations on a Single VM
My entire setup—database, web server, background jobs, and Redis—runs on a single virtual machine costing just $40 per month. This means no redundancy and manual backups to my local storage.
This approach is far from foolish; it has afforded me deeper insights into my actual resource requirements over the last two months than any theoretical capacity analysis could have provided. Interestingly, my “resource-heavy” platform only requires about 4GB of RAM at peak usage, rendering a complex Kubernetes setup unnecessary.
Each time the system crashes—twice so far—I gain real knowledge about the actual points of failure, which are rarely what I initially anticipated.
2. Embracing Hardcoded Configuration
My code features hardcoded constants such as:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
No config files or environment variables here; just variables spread across the codebase, making changes require a simple redeployment.
The benefit? I can quickly search my entire code for any specific configuration. Each price adjustment is meticulously documented in
