Embracing the 3-Month Experiment: A Pragmatic Approach to Unscalable Solutions
In the tech world, the mantra often heard is Paul Graham’s “Do things that don’t scale.” However, there’s a distinct lack of discussion on how to practically apply this philosophy, especially in coding. After eight months of developing my AI podcast platform, I’ve discovered a straightforward approach: I give every unscalable solution a three-month trial period. If it proves its worth by then, it gets the investment it needs to become a robust feature; if not, it’s time to bid farewell.
As developers, we’re often conditioned to pursue scalable solutions right from the start—design patterns, microservices, distributed architectures—all aimed at accommodating large user bases. Yet, in a startup environment, such scalability can often lead to costly delays and irrelevant optimizations. My three-month framework encourages the use of straightforward, perhaps flawed code that can be deployed quickly, providing valuable insights into user needs.
Here’s a Look at My Current Infrastructure Hacks and Their Unexpected Wisdom
1. Consolidation on One Virtual Machine
I’ve opted to run everything—from my database to the web server and background jobs—on a single virtual machine, costing me just $40 a month. This decision means I sacrifice redundancy for simplicity, manually backing up data to my local machine.
This strategy has proven insightful; I’ve gained a clear understanding of my actual requirements within two months, far exceeding what any planning document could offer. My platform’s peak memory usage caps at 4GB. The complex Kubernetes setup I nearly implemented would have meant juggling empty containers instead of addressing real user needs.
When my setup does crash—a couple of times so far—it provides genuine insights into failure points, which are often unexpected.
2. Hardcoding Configuration Values
Here’s a peek at how I manage configuration:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
With constants scattered throughout my code instead of complex configuration files, adjustments necessitate redeployment, but this simplicity holds a key advantage: quick access. I can easily search my codebase for any value and keep all changes tracked within Git history. In just three months, I’ve updated configurations a mere three times—spending 15 minutes redeploying instead of engaging in 40 hours of development for a
