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

In the world of startups and tech development, a well-known piece of advice from Paul Graham resonates: “Do things that don’t scale.” Yet, when it comes to implementation—especially in coding—few discuss actionable frameworks. After months of developing my AI podcast platform, I’ve formulated a straightforward method: every unscalable approach receives a mere three months to prove its worth. If it doesn’t deliver results by then, it’s time to move on.

As engineers, we are often conditioned to aim for scalable solutions from the outset. We get caught up in complex design patterns, microservices, and distributed systems—all designed to cater to millions of users. However, this mindset can be counterproductive for startups, where such complexity may act as an expensive form of procrastination. By optimizing for hypothetical users and problems, we risk stalling our actual learning and product evolution. My three-month rule compels me to write simple, direct code that gets shipped, effectively clarifying what users truly require.

My Current Infrastructure Hacks: A Smart Approach to Learning

1. Unified VM Deployment

Currently, all components of my platform—database, web server, background jobs, and Redis—operate on a single $40/month virtual machine (VM). There’s no redundancy, and backups are manually made to my local machine.

This approach has proven invaluable. In just two months, I’ve gained more insights into my resource needs than I ever could through extensive capacity planning. My platform, which I originally deemed “AI-heavy,” peaks at only 4GB of RAM. The complex Kubernetes architecture I nearly set up would have just been a burden, maintaining empty containers. When the server crashes (as it has on two occasions), I receive genuine feedback about what fails—often elements I never anticipated.

2. Hardcoded Configurations

Instead of utilizing extensive config files or environment variables, I keep my configurations simple and direct, such as:

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

This structure allows for rapid value changes through a straightforward redeployment process. With configurations embedded as constants throughout the codebase, I can instantly search for any value and monitor changes via git history. I’ve only adjusted these values three times in