Embracing the 3-Month Rule: A Technical Approach to Non-Scalable Solutions
In the startup world, the adage “do things that don’t scale,” famously uttered by startup guru Paul Graham, often gets mentioned but rarely discussed in terms of practical implementation, especially when it comes to coding. Having spent the past eight months developing my AI podcast platform, I’ve devised a straightforward framework highlighting how I navigate this challenge: every unscalable hack I implement gets a lifespan of three months. After this period, it either demonstrates its value and receives the investment for proper development, or it’s retired.
In the realm of engineering, we are constantly conditioned to devise scalable solutions from the outset. We think in terms of complex design patterns, microservices, and distributed systems—architecture designed to support vast numbers of users. However, this mindset often suits larger enterprises more than startups. In a small operation, focusing on scalability too early can turn into a costly form of procrastination. You’re effectively optimizing for users who may never materialize and addressing problems that might not even exist yet. My 3-month rule compels me to create simple, direct code—what some may call “bad” code—that ultimately leads to real user feedback and insights.
Current Infrastructure Strategies: Smart Hacks That Work
1. Consolidating Everything on a Single VM
I run my database, web server, background jobs, and Redis all on one $40-per-month virtual machine. There’s minimal redundancy, and backups are manually conducted.
This approach isn’t foolish; it has proven to be insightful. Over two months, I’ve learned more about my actual resource requirements than any detailed capacity planning document could provide. My platform, initially deemed “AI-heavy,” peaks at just 4GB of RAM. An intricate Kubernetes setup I nearly implemented would have led to managing a lot of empty containers.
On the occasions I’ve encountered crashes (twice so far), I’ve gained invaluable data on what truly fails—spoiler alert: it was never what I anticipated.
2. Hardcoded Configuration Across the Board
Parameters like:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
exist as constants scattered throughout my code, with no separate configuration files or environment variables. Adjusting any parameter requires a redeployment.
However, this simplicity
