Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Development
The mantra “Do things that don’t scale” often echoed by startup guru Paul Graham is a well-known adage in the entrepreneurial world. However, the conversation rarely delves into practical methods for implementing this ideology, especially in the realm of coding. After eight months of developing my AI podcast platform, I’ve refined a straightforward framework: any unscalable hack is given a lifespan of three months. If it proves its worth, it gets the attention it needs; if it doesn’t, it gets the boot.
As engineers, we’re often conditioned to prioritize scalable solutions from the outset. We envision intricate architecture—microservices, distributed systems, and the like—that can accommodate vast user bases. Yet, in the early days of a startup, focusing on scalability can sometimes equate to costly procrastination. Why invest time optimizing for hypothetical users when you can focus on real ones? My three-month rule compels me to create straightforward, albeit imperfect, code that not only launches but also reveals the genuine needs of my users through tangible interactions.
Creative Infrastructure Shortcuts: Learning Lessons
1. All-In-One Virtual Machine
Instead of distributing my workloads across multiple instances, I opted for a single virtual machine (VM) that houses everything—from the database to the web server and background jobs—at a mere $40 per month. While many might argue this approach lacks redundancy, it has allowed me to rapidly assess my actual resource requirements. Within two months, I’ve gleaned insights about memory usage that elaborate capacity planning could never provide. Surprisingly, my “AI-heavy” platform only needs 4GB of RAM at peak usage. The Kubernetes architecture I nearly implemented would likely have been wasted on idle resources.
Moreover, when my system encounters crashes—twice thus far—I gain invaluable feedback on failure points, which often differ from my initial assumptions.
2. Hardcoded Settings for Efficiency
Consider this for a moment:
plaintext
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
With no configuration files or environment variables, everything is hardcoded across my codebase. While it may seem primitive, this method has significant benefits. Searches through my code for any configuration value can be executed in seconds, and every change is logged in the git history. Over three months,
