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

In the tech startup world, the advice from Paul Graham to “do things that don’t scale” is often echoed but rarely fully understood, particularly when it comes to implementing this mantra in coding practices. After eight months of dedication to building my AI podcast platform, I’ve devised a straightforward yet effective framework: I give each unscalable hack just three months to prove its worth. If it doesn’t demonstrate clear value in that time frame, it’s time to say goodbye.

As engineers, we are typically conditioned to prioritize scalable solutions from the outset. We dream in design patterns, microservices, and distributed systems—architectures envisioned to support millions of users. However, such thinking often belongs in multinational organizations rather than in the dynamic landscape of a startup, where the focus should be on immediate problem-solving rather than preparing for unknown future users. My three-month rule promotes the creation of simple, straightforward, and sometimes “messy” code that prioritizes shipping and enables real learning about user needs.

Current Infrastructure Hacks: Ingenious Yet Simple

1. Consolidating Operations on a Single Virtual Machine

I run my entire platform—database, web server, background jobs, and caching—on a single virtual machine that costs just $40 monthly. There’s no redundancy, and I handle backups manually on my local machine.

The brilliance behind this approach? In just a couple of months, I’ve gained invaluable insights into my actual resource needs, more than any complex capacity planning could provide. My AI-driven platform consistently peaks at 4GB of RAM, yet I almost opted for a convoluted Kubernetes setup that would have led me down the path of managing inactive containers. When things do go wrong (which has happened a couple of times), I receive real data on system failures—often in unexpected areas.

2. Utilizing Hardcoded Configuration Values

In my coding practice, I’ve embraced hardcoded configuration constants like:
python
PRICE_TIER_1 = 9.99
MAX_USERS = 100
AI_MODEL = "gpt-4"

No environment variables or separate config files—just constants throughout my codebase. This might seem primitive, yet it has a significant advantage: I can quickly locate any configuration value across my entire code repository. Tracking changes over time has proven simple, and I can execute a redeployment in 15 minutes instead of