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

In the startup world, the well-known advice from Paul Graham, “Do things that don’t scale,” often leaves engineers wondering how to effectively implement this concept in their coding practices. After spending the past eight months developing my AI podcast platform, I’ve created a straightforward framework: every unconventional workaround has a lifespan of just three months. If it doesn’t prove its worth within that time, it gets phased out.

As engineers, we’re conditioned to focus on creating scalable solutions right from the outset. We immerse ourselves in architectural patterns, microservices, and distributed systems meant to support millions of users. While this approach is valuable for large organizations, it can lead to wasted effort in a startup, where scalable coding can become costly procrastination. Often, we end up optimizing for potential users who aren’t even on our radar, addressing issues that might never arise. My three-month rule compels me to write straightforward, albeit imperfect, code that actually gets deployed and reveals the true needs of my users.

My Current Infrastructure Hacks and the Wisdom Behind Them

1. Consolidated Operations on One Virtual Machine

All components—database, web server, background jobs, Redis—operate on a single $40/month virtual machine. This setup lacks redundancy, with manual backups stored locally.

Why is this approach effective? In just two months, I’ve gained more insights into my actual resource needs than any planning document could ever provide. My platform, which I initially assumed was “AI-heavy,” has only peaked at 4GB of RAM usage. The complex Kubernetes architecture I nearly invested time in would have been managing dormant containers.

When the system crashes—yes, it has happened twice—it offers tangible data on the real points of failure, which are seldom what I anticipated.

2. Hardcoded Configurations

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

In this scenario, I employ constants throughout the code rather than relying on configuration files or environment variables. Adjusting any parameter necessitates a redeployment.

The brilliance of this system lies in its simplicity: I can swiftly search my codebase for configuration values. Changes in pricing are documented in git history, and each update undergoes a code review—albeit done by myself. Building a