The 3-Month Rule: A Strategic Approach to Non-Scalable Solutions in Development

In the realm of startups, the wisdom of Paul Graham’s mantra, “Do things that don’t scale,” often remains under-explored, particularly regarding its practical implementation in the coding world. After eight months of dedicated work on my AI podcast platform, I’ve devised a straightforward framework to make that advice actionable: I allow any non-scalable hack a lifespan of three months. At the end of that period, it must demonstrate its value or face elimination.

As technical professionals, we are frequently conditioned to pursue scalable architecture from the outset. We often think in terms of design patterns, microservices, and distributed systems, envisioning solutions capable of accommodating millions of users. However, this perspective aligns more with big corporations than the nimble environment of a startup.

In many cases, optimizing for scalability too early can simply be a costly delay. It means preparing for users who have yet to arrive and addressing challenges that may never materialize. My 3-month rule pushes me to create simple, direct, and even “imperfect” code that allows me to deliver tangible products while gaining critical insights into user needs.

Current Infrastructure Practices: Practical Decisions for Growth

1. Unified Virtual Machine Approach

I’ve consolidated all components—database, web server, background tasks, and more—onto a single $40/month virtual machine. While there’s a tradeoff in redundancy and backup procedures, the benefits have been remarkable. Over the past two months, I’ve gained invaluable insights into my resource needs—discovering, for instance, that my platform peaks at just 4GB of RAM. Had I set up a complex Kubernetes cluster, I would have wasted time managing idle containers instead of focusing on system performance.

2. Simplified Hardcoded Configurations

Rather than cluttering my project with configuration files and environment variables, I opted for straightforward constant values embedded within the code. For example:

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

This approach allows me to quickly search through my codebase for configuration values, track changes through Git, and ensure that each update is code-reviewed (even if just by me). In just three months, I’ve adjusted these constants three times, a process that took me a mere 15 minutes compared to