Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Development
In the world of software development, the mantra “Do things that don’t scale,” famously coined by Paul Graham, resonates deeply among entrepreneurs and engineers alike. However, a pertinent question often arises: how do you effectively put this principle into practice, especially when coding?
After spending eight months developing an AI podcast platform, I’ve distilled my approach into a straightforward framework: any hack that isn’t scalable is given a dedicated lifespan of three months. At the end of this period, it must either demonstrate tangible value and be properly implemented or be eliminated.
The Scalable Mindset vs. startup Reality
As developers, we are typically conditioned to create scalable solutions from the onset. We become engrossed in elegant architectural designs—microservices, distributed systems, and other sophisticated patterns intended to support millions of users. However, this mindset is often more fitting for established companies, not startups.
In a startup environment, chasing scalability can lead to wasted resources and unnecessary delays. Why invest in optimizing code for users who haven’t arrived yet or tackling issues that may never materialize? My three-month rule compels me to write straightforward, even “subpar” code that goes live and, crucially, reveals the real needs of my users.
Current Infrastructure Strategies: What I’ve Learned
1. One Virtual Machine for Everything
I’ve consolidated my database, web server, background jobs, and caching into a single $40/month virtual machine. This means zero redundancy and backups done manually to my local machine.
While this may seem reckless, the reality is that I’ve quickly learned about my resource requirements within a mere two months—as opposed to what a capacity planning document might reveal. My initial assumption of needing a robust, Kubernetes-based setup turned out to be unnecessary; my platform’s peak usage only tapped 4GB of RAM. When my system experienced failures (twice), I obtained valuable insights that weren’t tied to my preconceived notions.
2. Hardcoded Configurations for Quick Adjustments
With settings like:
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
I’ve opted for hardcoded constants spread throughout my codebase instead of separate configuration files or environment variables. While this decision makes altering configurations require a redeployment, it offers surprising
