The 3-Month Rule: A Practical Guide to Implementing Non-Scalable Solutions
In the world of startups, the advice from Paul Graham to “do things that don’t scale” resonates deeply, yet few address how to translate this philosophy into effective coding practices. After eight months of developing my AI podcast platform, I’ve created a straightforward framework that I call the “3-Month Rule.” This guideline allows me to test unscalable hacks for a limited time: if they prove effective, they receive a more robust implementation; if not, they are simply discarded.
Traditionally, as engineers, we’re conditioned to prioritize scalable solutions from day one, implementing architectural designs suitable for thousands, if not millions, of users. However, this mindset often leads to wasted efforts and over-engineering at the startup level. The 3-Month Rule compels me to focus on straightforward and expedient coding practices that deliver real results, allowing me to understand the needs of my users more accurately.
Practical Insights: My Current Infrastructure Strategies
1. One Virtual Machine (VM) to Rule Them All
I operate all components of my platform—a web server, database, background jobs, and caching—on a single $40/month VM without redundancy or automated backups. While this approach may initially seem reckless, it has provided me with insights that traditional capacity planning wouldn’t have revealed. In just two months, I’ve discovered that my platform peaks at 4GB RAM usage. The complex Kubernetes setup I considered would have ended up managing empty containers, while real-world crashes have shown me exactly what breaks in my infrastructure.
2. Hardcoded Configuration for Simplification
In my project, configuration is embedded directly within the code:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This method allows for quick changes and ease of tracking through version control. Building a dedicated configuration service would be time-consuming, but I’ve only needed to update these values thrice in three months, saving countless hours of development.
3. SQLite as My Production Database
Running SQLite for a web application might raise eyebrows, but my database is merely 47MB and handles up to 50 concurrent users effortlessly. Monitoring access patterns indicated that 95% of my operations are reads, making SQLite an ideal fit. Had I initiated my project with PostgreSQL, I’d be
