Embracing Imperfection: The 3-Month Rule for Rapid Development

In the world of programming, the well-known advice from Paul Graham to “do things that don’t scale” often serves as a guiding principle for startups. However, a significant challenge remains: how do you practically apply this concept in your coding process?

Drawing on my experience developing an AI podcast platform over the last eight months, I’ve formulated a straightforward framework I call the “3-Month Rule.” This strategy revolves around allowing each unscalable hack a lifespan of three months. After this period, the hack must either prove its worth and be transformed into a robust solution or face elimination.

As engineers, we often lean toward creating solutions that are inherently scalable from the outset—focusing on intricate design patterns, microservices, and distributed systems meant to accommodate millions of users. This mindset, while effective for larger corporations, can be detrimental in a startup environment. Here, investing time in scalable code can devolve into costly procrastination. Instead of preemptively solving for users that may never join your platform, my 3-month rule encourages me to write straightforward, albeit “messy,” code that is deployed rapidly, helping illuminate the actual needs of my users.

Insightful Infrastructure Hacks: Why They Work

1. Simplicity in Infrastructure: One VM

I operate my entire stack—database, web server, background jobs, and Redis—on a single $40/month virtual machine (VM). There’s no redundancy, and I perform manual backups to my local storage.

This approach has proven valuable. In just two months, I’ve gathered insights about my resource requirements that far exceed anything a capacity planning document could provide. For instance, I discovered that my “AI-heavy” platform rarely exceeds 4GB of RAM. The complicated Kubernetes setup I almost initiated would have resulted in managing empty containers.

When my VM crashes (which has occurred twice), I obtain genuine insights into failure points, which are often unexpected.

2. Hardcoded Configuration: A Streamlined Approach

Consider this streamlined configuration:

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

I’ve opted against config files or environment variables in favor of constants embedded in code. Altering any value necessitates a redeployment, but this simplicity allows me to quickly search and modify my