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

In the world of startups and technological innovation, the popular mantra from Paul Graham, “Do things that don’t scale,” often comes up. While this advice resonates widely, there’s a distinct lack of discussion on how to integrate it into software development effectively. After eight months of building my own AI podcast platform, I’ve adopted a straightforward yet powerful framework: every unscalable shortcut or hack I employ is granted a trial period of three months. At the end of this period, we either scale it up based on proven value or discontinue it completely.

As developers, we are naturally inclined to create scalable architecture from the outset, focusing on sophisticated patterns like microservices and distributed systems that promise to accommodate large user bases. While these approaches are instrumental for established enterprises, they can often lead to costly delays in a startup environment. My three-month framework encourages me to prioritize simplicity and directness in my code. Doing so not only facilitates quick shipment but also offers genuine insights into user needs.

Current Infrastructure Hacks: Smart Choices With Limited Resources

1. Running Everything on a Single Virtual Machine (VM)
I’m currently operating all core components—database, web server, background jobs, and caching—on a single $40/month VM. While this setup lacks redundancy and relies on manual backups, it has delivered invaluable learning experiences. Within just two months, I gained a clearer understanding of my actual resource usage, revealing that my AI-centric platform generally peaks at 4GB of RAM. The complex Kubernetes infrastructure I nearly implemented would have merely managed idle containers.

When my setup experiences crashes—twice so far—I receive genuine feedback about the underlying issues, which have consistently surprised me.

2. Hardcoded Configuration
Instead of creating configuration files or utilizing environment variables, I’ve opted for hardcoded constants scattered throughout my code:

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

While this approach may seem primitive, it allows me to quickly locate any configuration value across my entire codebase. Each pricing adjustment can be tracked through Git history, and all modifications are subject to my own code review. Configuring a full-fledged configuration service would have consumed a week of development time, but I’ve needed to adjust these values only three times in three months—making the trade