Embracing the 3-Month Rule: A Pragmatic Approach to Unscalable Solutions

In the landscape of startup development, one guiding principle often emerges: “Do things that don’t scale,” popularized by entrepreneur Paul Graham. However, the challenge lies not in the philosophy itself but in the practical application of it—especially in coding. After eight months of building my AI podcast platform, I have honed my own innovative framework: the 3-Month Rule. This rule stipulates that each unscalable solution has a lifespan of three months. Within this timeframe, it must either demonstrate its worth and be developed further or be discarded.

As engineers, we tend to prioritize scalability from the onset, wrapping ourselves in the allure of sophisticated design patterns, microservices, and distributed systems meant to serve millions. While this mindset is appropriate for larger corporations, in startup environments, premature optimization can become a costly form of procrastination. In practice, my three-month approach encourages me to focus on delivering straightforward, albeit imperfect, code that can teach me invaluable lessons about my users’ real needs.

Current Infrastructure Hacks: Why They Work for Me

Here are some of the key hacks I am implementing, underscoring why they are practical decisions:

1. Consolidating Resources on a Single VM

I’m running my entire infrastructure on a single $40/month virtual machine, which houses the database, web server, background jobs, and Redis. This setup may lack redundancy and relies on manual backups, but it has provided meaningful insights into my resource demands. Within just two months, I’ve discovered that my platform requires only 4GB of RAM at peak usage. Instead of navigating the complexities of Kubernetes, I can focus on optimizing my actual needs, learning through each crash—insight that rarely aligns with my initial assumptions.

2. Hardcoded Configurations for Efficiency

In my codebase, configuration values are hardcoded:

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

This methodology may appear archaic, but it empowers me to quickly search for and track configuration changes through Git history, allowing for rapid deployments with minimal downtime. Considering that I’ve adjusted these values only three times in three months, I’ve saved significant engineering hours that could have been spent building a more complex configuration service.

**3. Employing SQLite for Production