Embracing the 3-Month Rule: A Pragmatic Approach to Unscalable Coding for Startups
In the entrepreneurial world, the adage “do things that don’t scale,” famously stated by Paul Graham, is often echoed but rarely unpacked, especially in the realm of software development. As a passionate engineer working on my AI podcast platform for the past eight months, I’ve devised a straightforward framework: any hack that isn’t scalable gets a lifespan of just three months. After that period, if it has demonstrated its worth, it will be refined into a robust solution; if not, it will be discontinued.
The startup Mindset Shift
As engineers, we are trained to pursue scalable solutions from the very beginning—think design patterns, microservices, and complex architectures built to support millions of users. However, this mindset can be counterproductive in a startup environment. Often, scalable code can simply turn into a costly delay, focusing on hypothetical users and problems that may not exist yet. My three-month rule nudges me to adopt a simpler, more direct coding approach that may be seen as “bad” but ultimately provides invaluable insights into user needs.
My Current Strategic Hacks: A Demonstration of Effective Simplicity
1. Consolidated Operations on a Single VM
All crucial components—database, web server, background jobs, and caching—reside on a single virtual machine costing $40 per month. While this may sound risky due to the lack of redundancy and reliance on manual backups, it has allowed me to accurately gauge my resource requirements. After just two months, I discovered that my AI-driven platform maxed out at 4GB RAM. The elaborate Kubernetes setup I nearly implemented would have resulted in managing idle resources rather than optimizing performance.
Crash incidents have revealed valuable data about system failures, often contrary to my anticipations, enabling me to learn and adapt quickly.
2. Hardcoded Configuration Files
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Instead of managing multiple configuration files or environment variables, I use hardcoded values throughout my project. The result? A streamlined system where any configuration change or price adjustment is tracked in Git history and requires a quick redeployment. What could take a week to develop in a configuration management service has translated into mere minutes of deployment time, highlighting the efficiency of
