Embracing the 3-Month Rule: A Practical Approach to Unscalable Solutions in Development
In the startup realm, following Paul Graham’s famous mantra to “do things that don’t scale” can be a game-changer. However, translating this advice into effective coding practices is often overlooked. After dedicating eight months to developing my AI podcast platform, I’ve devised a streamlined framework: each unscalable workaround is given a lifespan of three months. If it demonstrates its worth during this time, it will be refined and optimized; if not, it will be discarded.
As software engineers, we’re frequently conditioned to prioritize scalable solutions from the outset—utilizing design patterns, microservices, and distributed systems intended to accommodate millions of users. However, this mindset often overlooks the realities faced by startups. In this context, pursuing scalability too early can lead to costly delays, as we focus on potential users who may never materialize or issues that might never arise. My three-month rule compels me to implement simpler, more direct solutions that can be executed swiftly, allowing me to better understand my users’ actual needs.
My Approach: Current Infrastructure Strategies That Work
1. Consolidated Operations on a Single VM
Running every service—from the database to background jobs—on one $40/month virtual machine may seem reckless, but it has proved enlightening. Within just two months, I’ve gained more insight into my actual resource requirements than any extensive planning document could provide. For instance, my AI-driven platform consistently requires 4GB RAM at peak times. Had I deployed a complex Kubernetes architecture, I would have spent countless hours managing idle containers instead.
Each crash (I’ve experienced two so far) offers valuable learning opportunities. It’s illuminating to discover that the failures seldom align with my initial predictions.
2. Hardcoded Configuration
I opt for straightforward hardcoded values across my codebase—no configuration files, no environment variables, just constants like:
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This approach allows me to quickly search for any configuration using grep, and each change is captured in my git history. Instead of investing a week in building a configuration service, I’ve executed three value updates in three months, amounting to just 15 minutes of deployment time versus 40 hours of development.
**3. Using SQLite in Production
