Embracing the 3-Month Rule: A Pragmatic Approach to Development

In the startup world, the mantra of “do things that don’t scale,” popularized by Paul Graham, often gets tossed around. Yet, few delve into how this philosophy manifests in practical coding scenarios. After eight months of building my AI podcast platform, I’ve devised a straightforward framework: any non-scalable solution gets a trial period of three months. At the end of this timeframe, it either proves its worth and evolves into a more robust implementation, or it fades away.

As engineers, we are conditioned to seek scalable solutions from the outset—focusing on design patterns, microservices, and distributed systems that can accommodate millions of users. However, this mindset is often counterproductive for startups. Focusing on scalability early on can lead to wasted resources as we preemptively optimize for a user base that doesn’t yet exist. My 3-Month Rule compels me to create lean, straightforward, and sometimes “imperfect” code that can actually be deployed and provides genuine insights into user needs.

My Infrastructure Hacks: Simplicity as a Strategic Advantage

1. Consolidated Operations on a Single VM

I’ve opted to run everything—from the database to the background jobs—on a single $40-per-month virtual machine. This means zero redundancy and manual backups to my local machine.

Why this approach is effective: In just two months, I gained a clearer understanding of my actual resource requirements than any detailed capacity planning document could offer. My AI platform, which I initially considered “resource-heavy,” peaks at just 4GB of RAM. The complex Kubernetes architecture I nearly implemented would have simply managed unused containers. Each time it crashes (twice so far), I gain valuable insights about real points of failure—none of which align with my prior expectations.

2. Simplified Configuration Management

I have opted for hardcoded configurations, such as:

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

With no separate config files or environment variables, each change necessitates redeploying the whole application.

The advantage of this setup: It allows me to quickly search my entire codebase for configuration values. Every price modification can be traced back in git history, and every adjustment undergoes code review—by me