Embracing the 3-Month Rule: A Practical Framework for Rapid Development

In the world of startups, the mantra “Do things that don’t scale,” popularized by Paul Graham, is often thrown around. Yet, there is a notable gap when it comes to actionable strategies for implementing this advice, particularly in the realm of coding. After eight months of developing my AI podcast platform, I have established a unique framework: every temporary, unscalable solution is granted a lifespan of precisely three months. At the end of this period, the solution must either demonstrate its value—at which point it will be tidied up into a more robust version—or it will be eliminated.

The Challenge of Scalability in Startups

As engineers, our training tends to emphasize building scalable solutions from the outset, focusing on architectural aesthetics—think microservices, design patterns, and distributed systems—suitable for larger enterprises. However, in a startup environment, pursuing scalability often leads to what can be described as “expensive procrastination.” Instead of investing time and resources in building for hypothetical users, my three-month rule encourages me to craft straightforward, albeit less polished, code that is deployable and insightful. This practice enables me to better understand the needs of my actual users.

Current Infrastructure Strategies That Work

Here’s a breakdown of my current infrastructure hacks and the smart rationale behind them:

1. Concentrating Resources on a Single VM

I host everything—a database, web server, background jobs, and caching solutions—on a single virtual machine costing $40 per month. Yes, there’s no redundancy, and I perform manual backups to my local device.

Why is this a smart move? In just two months, I’ve gained insights into my genuine resource usage that no amount of theoretical capacity planning could ever provide. My platform, focused on AI, only peaks at 4GB of RAM. The intricate Kubernetes setup I considered would have left me managing empty containers rather than addressing real-world needs. Every time the system crashes (which has happened twice), I receive concrete data regarding the actual causes—often surprising insights I never anticipated.

2. Hardcoded Configurations Across the Board

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

Forget about configuration files or environment variables; constants are strewn throughout my codebase. While adjusting