The 3-Month Rule: A Strategic Approach to Unscalable Solutions in Tech Development

In the world of startups and software development, there’s a widely recognized piece of wisdom from Paul Graham: “Do things that don’t scale.” Yet, when it comes to coding, the guidelines for applying this advice often remain unspoken.

After dedicating eight months to creating my AI podcast platform, I’ve crafted a distinct framework: each unscalable approach gets a three-month trial period. At the end of this period, the innovation must either demonstrate its worth and evolve into a robust solution or be discarded altogether.

It’s vital to recognize that, as engineers, our instincts often lead us to pursue “scalable” solutions from the outset. We become preoccupied with design patterns, microservices, and distributed systems—wonderful architectures engineered to serve millions. Yet, this mindset often represents corporate thinking, which can hinder the agility and adaptability essential for startups.

In a fledgling company, prioritizing scalability can be a costly delay. It often means setting up systems for users who aren’t yet on board, resolving challenges that may never arise. My three-month rule encourages me to design straightforward, albeit imperfect, code that not only functions but reveals valuable insights into user needs.

Innovative Infrastructure Choices: Why They Work

1. Single VM Hosting

Everything—from the database to background jobs—is crammed onto one $40/month virtual machine. This setup, devoid of redundancy and relying on manual backups, may seem precarious. However, it has proven immensely beneficial. In just two months, I gained remarkable clarity about my actual resource usage. I discovered that my “AI-heavy” platform only requires 4GB of RAM. The complex Kubernetes system I nearly implemented would have simply been managing unutilized resources.

Moreover, when crashing incidents occurred (twice, to date), I received actionable insights on system failings, which were not what I initially anticipated.

2. Hardcoded Configuration Values

Instead of utilizing external configuration files or environment variables, I’ve opted for hardcoded constants throughout my application:

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

This approach allows me to quickly locate any configuration values within the codebase. Changes echo through git history, and each adjustment undergoes a rudimentary review process —