Embracing Imperfection: The 3-Month Rule for startup Development
In the world of startup development, there’s a well-known piece of advice from Paul Graham: “Do things that don’t scale.” However, there is often a lack of discussion on how to effectively apply this principle, particularly in coding practices. Drawing from my experience of building an AI podcast platform over the past eight months, I’ve implemented an innovative framework that I like to call the “3-Month Rule.” The premise is simple: any unscalable solution I introduce has a lifespan of three months. After that period, it either has to demonstrate its value and be refined for better performance, or be discarded altogether.
The Paradox of Scalability
As developers, we are frequently conditioned to think about scalability from the outset. We envision robust architecture featuring design patterns, microservices, and distributed systems – all designed to accommodate millions of users. While these frameworks are essential for larger companies, in the startup realm, they can often lead to costly delays and premature optimization. The 3-Month Rule compels me to embrace a more straightforward approach, allowing me to produce functional, albeit imperfect, code that elucidates real user needs instead of hypotheticals.
Lessons from Practical Infrastructure Hacks
Let me share some of my unorthodox infrastructure strategies and the logic behind them:
1. Consolidated Resources: One VM for Everything
I host my database, web server, background jobs, and caching—all on a single $40/month virtual machine (VM) without redundancy. While this may seem reckless, it has taught me more about my actual resource consumption in two months than any conventional capacity-planning report ever could. I discovered that my platform, which I anticipated would require extensive resources due to its AI focus, only peaked at 4GB of RAM. This firsthand experience has informed my decisions better than any elaborate setup would.
2. A Hardcoded Configuration Approach
Instead of employing configuration files or environment variables, I use hardcoded constants for pricing and settings:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This strategy might come off as archaic, but it provides a unique advantage: I can search through the entire codebase for any config value almost instantaneously. Since I’ve only modified these values three times over three
