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

In the entrepreneurial world, there’s a well-known principle articulated by Paul Graham: “Do things that don’t scale.” While this advice resonates with many, discussions on how to effectively integrate this principle into the realm of coding are surprisingly few.

After dedicating eight months to developing my AI podcast platform, I’ve discovered a straightforward methodology: any non-scalable hack is granted a lifespan of three months. At the end of this period, it either demonstrates its value and gets a more substantial framework or is discarded.

As developers, we often approach projects with a mentality that prioritizes scalability from the outset. We inundate ourselves with design patterns, microservices, and distributed systems to manage vast numbers of users. However, this is often a misleading perspective at the startup level.

In early-stage ventures, focusing on scalable solutions can lead to procrastination. We tend to optimize for hypothetical users and tackle issues that may never arise. My 3-month rule pushes me to create simple, straightforward, and arguably “bad” code that actually gets deployed, providing real insights into what users need.

Current Infrastructure Hacks: Clever Solutions for Immediate Learning

1. Consolidated Infrastructure on a Single VM

My entire setup—including the database, web server, background jobs, and Redis—runs on a single $40/month virtual machine. While this setup lacks redundancy and relies on manual backups, it has delivered invaluable insights about my resource requirements.

After two months, I’ve realized my “AI-heavy” platform only peaks at 4GB of RAM. The complex Kubernetes configuration I almost set up would have resulted in managing empty containers. Now, when my system crashes (which has happened twice), I gain concrete data about what actually malfunctions—often surprising me.

2. Hardcoded Configurations for Efficiency

In my code, configurations are hardcoded rather than stored in config files or environment variables:

python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this might seem outdated, it allows me to quickly search for any configuration value across the codebase. Each change is tracked in Git, ensuring transparency in adjustments. Creating a dedicated configuration service would take a week, yet I’ve only modified these values three times in three months, making the time investment hardly worth the effort