The 3-Month Experiment: A Practical Approach to Unscalable Solutions in Tech

In the world of startups, agility and adaptability are key, and no one captures this spirit quite like Paul Graham with his famous adage: “Do things that don’t scale.” However, embedding this philosophy into technical development, particularly in coding, remains a challenge. After eight months of working on my AI podcast platform, I’ve created a framework that I call the “3-Month Rule,” which encourages experimentation through unscalable methods.

Embracing Unscalable Techniques

As developers, we often find ourselves drawn to the allure of scalability—the ability to handle large user bases and complex systems using sophisticated architectural designs like microservices and distributed systems. While such approaches are essential for larger firms, they can be a luxury for startups trying to establish their product.

In my experience, an emphasis on building scalable solutions too early can lead to inefficiencies and missed opportunities for real learning. The 3-Month Rule encourages me to experience and implement unscalable hacks that will either demonstrate their worth within three months or be phased out entirely. This method encourages straightforward coding practices that facilitate shipping real products and gathering user feedback early. Here are some of the practical hacks I’ve utilized, which may appear unconventional but have proven their value:

Current Infrastructure Hacks

1. Consolidated Infrastructure on One VM

All my services, from the database to background jobs, run on a single virtual machine (VM) priced at just $40 per month. There’s no redundancy, and I rely on manual backups.

This approach has allowed me to grasp my resource requirements in ways traditional capacity planning never could. I discovered that my platform typically requires only 4GB of RAM. Had I launched into a complex infrastructure setup with Kubernetes, I would have been burdened with managing unnecessary components. When crashes occur— and they have twice now—I gain valuable insights into real system vulnerabilities.

2. Simplified Configuration Management

Configuration details such as pricing tiers and user limits are hardcoded directly in the codebase. Although this means deployments happen whenever a change is needed, it also enhances transparency. I can quickly locate any configuration value across my entire project, and with numerous changes tracked in version control, I maintain a clear history of adjustments made.

Creating a dedicated configuration service would likely take countless hours, and yet, in three months, these values needed changing just three times. The time saved through this simplicity has been enormous