Embracing the 3-Month Rule: A Framework for Unconventional Coding Practices
In the realm of entrepreneurship and tech innovation, Paul Graham’s mantra—“Do things that don’t scale”—rings true as a guiding principle. Yet, the challenge often lies in translating this philosophy into actionable steps, especially when it comes to coding. Over the past eight months while developing my AI podcast platform, I’ve devised a framework that has proven effective: any unscalable solution I implement is given a lifespan of three months. At the end of that period, it either demonstrates its worth and undergoes proper development or it is phased out entirely.
As engineers, we are typically conditioned to prioritize scalable solutions right from the outset. Concepts such as design patterns, microservices, and distributed systems often dominate our thinking—concepts that cater to businesses poised for exponential growth. However, for startups, fixating on scalability can frequently translate to delaying progress and investing resources into unforeseen future challenges. By adhering to my 3-month rule, I focus on creating straightforward, albeit imperfect, code that allows me to ship rapidly and gain genuine insights into user needs.
Current Infrastructure Hacks: A Smart Approach to Learning
1. Consolidated VM Setup
All critical components—database, web server, background jobs, and caching—operate off a single $40/month virtual machine (VM). While many argue this approach lacks redundancy, it has provided invaluable insights. In just two months, I’ve gained a clearer understanding of my resource requirements than any complex planning document could have offered. My findings? The platform’s typical peak usage hovers around 4GB of RAM. Had I committed to an intricate Kubernetes architecture, I would have merely been managing a bunch of idle containers. Moreover, experiencing system crashes twice has equipped me with real data about failure points that I never anticipated.
2. Simplistic Hardcoded Values
With straightforward configurations like:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
I’ve opted for hardcoded values scattered throughout my code base instead of relying on configuration files or environment variables. This method may seem outdated, but its advantage lies in the speed of access. I can instantly search for any configuration using a simple command, and every change is tracked via Git history. Redefining values has only taken me approximately 15
