Embracing the 3-Month Rule: A Practical Guide to Non-Scalable Hacks in Development
In the realm of technology startups, the mantra “do things that don’t scale,” championed by Paul Graham, is well-known. However, the nuance of applying this wisdom within the coding process remains underexplored. After months of building my AI podcast platform, I’ve developed a straightforward yet effective framework: any unscalable approach gets a lifespan of three months. At the end of this period, each hack either demonstrates its worth and is fully developed or is discarded.
Why the 3-Month Rule Works
As engineers, we often gravitate towards scalable solutions right from the start. Concepts like microservices, distributed systems, and intricate design patterns fill our toolkits, crafted for an audience of millions. However, this mindset can hinder progress in a startup environment, where focusing on scalability may lead to wasted resources and energy, particularly when catering to a user base that isn’t yet a reality.
This is where my 3-month rule becomes a game-changer. It compels me to produce straightforward, albeit unrefined, code that can be deployed quickly and allows me to gain essential insights on user needs.
Current Hacks and Their Strategic Benefits
Here’s a glimpse into my current infrastructure hacks, each selected for its utility in a learning framework rather than its perfection.
1. Single Virtual Machine Utilization
My entire application—database, web server, background jobs, and caching—resides on a single $40/month virtual machine. While this setup may lack redundancy, it has been invaluable for understanding my actual resource requirements. Over two months, I’ve discovered that my AI-driven platform peaks at just 4GB of RAM—far from the complex Kubernetes architecture I nearly built, which would have been managing idle containers.
Additionally, when my system crashes (yes, it has twice), I gather critical data on failure points, often revealing surprises.
2. Hardcoded Configuration
Constants like pricing tiers and user limits are hardcoded throughout my codebase without any configuration files or environment variables needing to be managed. This may seem primitive, but it allows me to easily search for settings with a simple command and to track changes through version control. In three months, I have only needed to update these values three times, making a quick redeployment far more efficient than developing an entire configuration service.
3. Using SQLite in Production
Running SQLite for my
