The 3-Month Rule: A Practical Approach to Building Unscalable Solutions in Tech
When it comes to tech startups, the conventional wisdom often echoes the words of Paul Graham: “Do things that don’t scale.” However, the conversation rarely dives into the practical application of this advice, particularly in the realm of coding. After eight months of developing my AI podcast platform, I’ve devised a straightforward framework that has greatly informed my development process: every unscalable approach I implement is given a lifespan of three months. At the end of this period, if a hack demonstrates its worth, it gets refashioned into a sustainable solution; if not, it is discarded.
As engineers, we’re conditioned to pursue scalable solutions right from the start. We’re drawn to elegant design patterns and advanced architectures capable of handling vast user bases. However, in the startup environment, the pursuit of scalability can be a costly form of procrastination, as we often focus on theoretical users and hypothesized problems that may never arise. My three-month rule compels me to produce simpler, more straightforward code that is designed for immediate deployment, offering invaluable insights into what my actual users require.
Embracing Tactical Hacks: My Innovative Approaches
1. Consolidated Infrastructure on a Single VM
I’ve chosen to run my entire stack—database, web server, background tasks, and caching—on a single $40/month virtual machine. This arrangement lacks redundancy and includes manual backups stored on my local device.
You might wonder why this strategy is favorable. In just two months, I’ve gained more clarity on my actual resource requirements than any detailed capacity planning document could provide. For instance, I discovered that my “AI-heavy” platform peaks at a mere 4GB of RAM, rendering a complex Kubernetes setup unnecessary. Plus, when issues arise (which they have), I receive real-time data about failures, and notably, they’re rarely what I expected.
2. Hardcoded Configuration Throughout
Imagine a codebase with constants like:
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
In this scenario, I’m operating without configuration files or environment variables, making changes require a redeployment.
The beauty of this approach lies in its simplicity. I can quickly search my entire codebase for any configuration value, and every change is meticulously documented in version control. While
