Embracing the 3-Month Rule: A Pragmatic Approach to Development for Startups
In the startup world, the adage “Do things that don’t scale” from Paul Graham resonates deeply, yet few delve into how to effectively embody this philosophy in technical execution. After eight months of developing my AI podcast platform, I’ve adopted a framework I call the “3-Month Rule”. This principle dictates that any unscalable hack I implement is given a lifespan of merely three months. Following this period, it will either demonstrate its value and transition into a fully developed solution, or it will be retired.
As engineers, we’re often trained to prioritize scalability right out of the gate. We focus on intricate design patterns and robust architectures capable of accommodating millions of users. However, this approach can often lead to wasted resources, particularly in a startup context where many of those features may never see the light of day. My 3-Month Rule encourages me to write straightforward, albeit “bad”, code that is deployable and reveals what users genuinely need.
Current Infrastructure Hacks: Lessons Learned
1. Simplified Infrastructure: All in One VM
Currently, everything from database management to web hosting runs on a single $40/month virtual machine (VM). This approach lacks redundancy and relies on manual backups to my local machine.
However, this decision has proven insightful. I’ve gained a clearer understanding of my resource requirements in just two months than any extensive planning document could offer. For instance, I discovered that my supposedly “AI-heavy” platform peaks at a mere 4GB of RAM. The Kubernetes environment I once considered would have only served to complicate an already simple setup.
Every time my VM crashes—twice so far—I gather real data about what fails, often revealing surprises contrary to my assumptions.
2. Hardcoded Values: A Surprising Benefit
With simple hardcoded configurations like:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
There’s no need for configuration files or environment variables; everything is laid out clearly in the code. While changing these values requires redeployment, I can easily track modifications through Git history, providing a convenient review mechanism.
Building a dedicated configuration service would have taken significant time, whereas I’ve made only three changes in three months. This simple approach has saved countless hours of development.
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