Embracing the 3-Month Rule: A Framework for Non-Scalable Solutions

In the world of technology startups, the mantra “do things that don’t scale” is often championed, thanks to influential figures like Paul Graham. However, there’s a noticeable gap in discussions regarding how to apply this principle in the realm of coding. After eight months of developing my AI podcast platform, I’ve devised a straightforward and effective framework: every prototype or non-scalable approach gets a three-month trial. If it demonstrates its worth, we build it out properly; if not, we let it go.

As engineers, our instinct often drives us to create scalable solutions right from the get-go. Concepts like microservices, robust architectures, and distributed systems are compelling and serve companies with huge user bases. Yet, this perspective can be a hindrance for startups, where chasing scalability can lead to unnecessary expenses and missed opportunities to connect with real users. My 3-month rule compels me to write simpler, possibly less elegant code that’s capable of rapid deployment and, crucially, helps me discover what my users genuinely need.

Innovative Infrastructure Hacks: Practical Solutions That Work

1. Consolidated Operations on a Single VM

I run everything—database, web server, background tasks, and caching—on a modest $40/month virtual machine. While this approach lacks redundancy and involves manual backups, it has yielded invaluable insights into my actual resource requirements. Within just two months, I learned that my “AI-heavy” platform rarely needs more than 4GB of RAM. The intricate Kubernetes setup I nearly implemented would have been managing underutilized resources. Each time the system crashes (a few instances so far), I gather credible data on the real points of failure—often surprising me.

2. Hardcoded Settings Throughout the Codebase

Instead of utilizing configuration files or environment variables, I’ve opted for hardcoded constants like:

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

Admittedly, making adjustments means redeploying, but here’s the benefit: a quick search across my codebase allows me to locate any setting in seconds. Tracking changes in Git history keeps my pricing transparent. Creating a configuration service would take significant time, but given that I’ve only modified these values three times in three months, the deployment