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
One Comment
This is a thought-provoking approach that genuinely challenges traditional wisdom around building scalable systems from day one. By implementing a strict three-month window for non-scalable solutions, you’re effectively embracing a mindset that prioritizes rapid validation and learning over premature optimization — which is essential in startup environments.
Your example of consolidating infrastructure into a single VM highlights the importance of hands-on experience and real-world data rather than over-engineering. It reminds me that sometimes, simple solutions paired with quick iteration can reveal critical insights that far outperform complex setups constrained by assumptions.
Moreover, your pragmatic stance on hardcoded configurations underscores the value of speed and flexibility during early development stages. As you noted, these choices might not be ideal long-term, but they allow for rapid experimentation and learning, which ultimately informs more thoughtful scaling decisions later on.
Thanks for sharing this practical framework—it’s a valuable addition to the ongoing conversation about balancing immediate needs with future scalability in tech startups.