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

In the entrepreneurial world, Paul Graham’s adage, “Do things that don’t scale,” is frequently cited, yet the practical application of this advice in software development is rarely discussed. After dedicating eight months to building my AI podcast platform, I’ve devised a straightforward framework: every unscalable solution has a lifespan of just three months. Within that time frame, it must either demonstrate its worth, leading to a more refined implementation, or it will be discarded.

As engineers, we are often conditioned to craft scalable solutions from the outset, focusing on sophisticated design patterns, microservices, and robust systems that can handle millions of users. However, this mindset is more suited to large corporations than startups. In a startup environment, pursuing scalability too early can lead to unnecessary complexity and wasted effort.

My three-month rule compels me to produce straightforward, albeit imperfect, code that is genuinely functional, allowing me to understand my users’ needs better.

Smart Infrastructure Hacks That Teach Valuable Lessons

1. Consolidated Operations on One Virtual Machine

All components—database, web server, background processes, and caching—operate on a single, affordable $40/month virtual machine. This setup comes without redundancy and relies on manual backups to my local system.

This strategy is not as reckless as it may seem. In just two months, I’ve gained insights into my actual resource usage, surpassing what any capacity planning document could provide. What I’ve learned is that my AI-driven platform typically requires only 4GB of RAM. The extensive Kubernetes configuration I nearly implemented would have meant managing largely unutilized resources.

When the system has crashed—an occurrence I’ve experienced twice—I gained immediate insights into the causes, which were never what I anticipated.

2. Simplistic Hardcoded Configurations

In my code, configurations look like this:

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

There are no separate configuration files or environment variables—only constants spread throughout the code. Adjusting these values necessitates a full redeployment.

The benefit of this approach? It allows for rapid searches through the codebase for configuration values. Each price alteration is logged in the version history, and any configuration updates undergo a personal code review. While designing a configuration