Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions in Tech Development
In the world of technology, we often encounter the well-known mantra by Paul Graham: “Do things that don’t scale.” While this advice resonates with many, few address its practical implementation within the realm of coding. After spending eight months developing my AI podcast platform, I’ve crafted a straightforward methodology to navigate this advice: every unscalable workaround enjoys a lifespan of three months. Following this period, it’s either validated for further development or phased out entirely.
As software engineers, we are typically conditioned to focus on creating scalable solutions from the get-go. We often get entrenched in sophisticated architectural designs—think microservices, distributed systems, and elegant design patterns—all aimed at accommodating vast user bases. However, this level of thinking is better suited for established companies, not agile startups.
For startups, prioritizing scalable code can lead to unnecessary complications and wasted resources. It’s like preparing for users who haven’t even arrived yet while grappling with problems that may never present themselves. My three-month rule encourages me to deploy simple, albeit “imperfect,” code that allows me to glean genuine insights into user needs.
Current Infrastructure Strategies: Smart Solutions from Simplified Choices
- Consolidation on a Single Virtual Machine (VM)
My platform runs on a single $40/month VM hosting the database, web server, background jobs, and Redis—enjoying zero redundancy and manual backups to my local drive. This approach may sound rudimentary, but it’s a stroke of genius. Over two months, I’ve extracted more actionable data about resource utilization than any overly complex capacity-planning document ever could. In truth, my “AI-heavy” platform operates comfortably at just 4GB of RAM, emphasizing that the intricate Kubernetes architecture I nearly implemented would have been a waste.
When the system crashes—as it has twice so far—I receive invaluable insights into the actual failure points, which are rarely what I initially anticipated.
- Hardcoded Configuration Values
Instead of relying on external configuration files or environment variables, I utilize hardcoded constants. For example:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Although it might seem impractical, this makes it easy to track
