The 3-Month Experiment: A Pragmatic Approach to Non-Scalable Development

In the tech world, we often hear the mantra from Paul Graham: “Do things that don’t scale.” Yet, there’s little guidance on how to actually integrate this advice into development processes. After eight months of actively building my AI podcast platform, I’ve formulated a practical strategy that I like to call the “3-Month Rule.” This framework dictates that any non-scalable hack I implement will only remain in play for three months. At the end of this period, it either justifies its existence and evolves into a robust solution, or it faces elimination.

As engineers, we are conditioned to create scalable solutions from the outset. We obsess over design patterns, microservices, and complex distributed systems, all aimed at accommodating massive user bases. However, this mindset is often counterproductive in a startup environment where scalable code can lead to unnecessary delays—essentially, it’s costly procrastination. We may end up planning for users who aren’t even onboard yet and tackling issues that may never arise. My 3-Month Rule encourages me to produce straightforward, albeit imperfect, code that provides immediate insights into user needs and application performance.

Current Technical Hacks: Understanding Their True Value

1. Single Virtual Machine Deployment

I’ve opted to host my database, web server, and background tasks all on a singular $40/month virtual machine. While this setup lacks redundancy, it has been remarkably instructive. Within just two months, I’ve gained more clarity about my resource needs than any theoretical documentation could provide. My application peaks at 4GB of RAM during high usage, which means the elaborate Kubernetes architecture I had nearly implemented would have merely managed idle containers. Crashes (yes, I’ve seen a couple) have revealed unexpected failures, highlighting bottlenecks I would not have foreseen.

2. Hardcoded Configuration

Instead of using configuration files or environment variables, I have opted for hardcoded constants like:

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

While this might seem inefficient, it has its advantages. The ability to quickly search my codebase for a specific configuration means real-time tracking of changes. Over three months, I’ve adjusted these values a mere three times. This simple system saves me a substantial amount of engineering time