The Three-Month Rule: A Practical Approach to Non-Scalable Solutions in Software Development
In the entrepreneurial tech sphere, one piece of advice reigns supreme: “Do things that don’t scale,” as championed by Paul Graham. Yet, while many embrace this mantra, the application of it within coding practices often remains shrouded in ambiguity. After eight months of developing my AI podcast platform, I’ve established a straightforward framework that I refer to as the “Three-Month Rule.” This method allows me to allocate just three months for each non-scalable strategy. If it proves valuable within that timeframe, it gets a proper buildout; otherwise, it’s time to let it go.
As engineers, we are conditioned to prioritize scalable solutions from the outset. Concepts like design patterns, microservices, and distributed architectures come to mind—targeting systems capable of accommodating vast user bases. This mindset, however, can inhibit startups from effectively evolving and responding to immediate user needs. In fact, focusing too heavily on scalability can often lead to costly procrastination, as we devote resources to hypothetical users rather than addressing the needs of our current clientele. By adhering to my three-month rule, I encourage myself to produce simpler, more direct code that yields tangible insights about user behavior.
My Current Infrastructure Strategies: Ingeniously Non-Traditional
1. Consolidation on a Single Virtual Machine
Currently, my entire infrastructure—including the database, web server, background jobs, and cache—is running on a single $40/month virtual machine. This setup lacks redundancy, and I rely on manual backups to my local machine.
Here’s the brilliance in this approach: in merely two months, I have gained a profound understanding of my actual resource requirements—far beyond what any capacity planning document could provide. Surprisingly, my “AI-heavy” platform typically peaks at around 4GB of RAM. The complex Kubernetes architecture that almost materialized would have simply involved managing empty containers. Moreover, when my system crashes (which it has twice), I receive real-time insights on what actually fails, debunking my previous expectations.
2. Hardcoded Configurations
Instead of implementing a comprehensive configuration management system, I’ve opted for direct constants within the code:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this approach might seem outdated, it has its
