Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Software Development

In the realm of startups, there’s a notable piece of advice from Paul Graham that resonates with entrepreneurs: “Do things that don’t scale.” While this principle is widely acknowledged, the practical application in software development often remains elusive. After eight months of developing my AI podcast platform, I’ve crafted a straightforward framework: every unscalable hack is given a life span of just three months. At the end of that period, it must either demonstrate its worth and be transformed into a more robust solution, or it will be discarded.

As developers, we often find ourselves conditioned to prioritize scalable solutions from the outset—methodologies involving intricate design patterns, microservices, and distributed systems that can cater to millions of users. However, this expansive mindset is often better suited to large corporations. In the startup environment, pursuing scalability too early can lead to costly delays while unnecessarily complicating the development process.

My three-month strategy compels me to embrace simpler, more direct (albeit “imperfect”) code that can be launched quickly and genuinely supports learning about user needs. Here’s a deeper look into some of the unscalable yet effective infrastructure choices I’ve made and why they work.

Current Infrastructure Strategies: Harnessing Simplicity

1. Consolidating All Functions on One Virtual Machine

I host my entire operation—including the database, web server, and background jobs—on a single $40/month virtual machine. While this setup lacks redundancy and relies on manual backups to my local drive, it has yielded invaluable insights. Over two months, I’ve gained more understanding of my resource requirements than any extensive capacity-planning document would. For instance, my so-called “AI-heavy” platform only peaks at 4GB of RAM, demonstrating that the complex Kubernetes configuration I nearly implemented would have been a waste managing unused resources. Crashes (which have occurred twice) provide clear data on failures, revealing patterns that defy my expectations.

2. Employing Hardcoded Configurations

In my code, you’ll find constants rather than configuration files or environment variables:

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

While some may view this as a misstep, the benefits are undeniable. I can swiftly search the entire codebase for