Embracing the 3-Month Rule: A Pragmatic Approach to startup Development
In the world of startup innovation, the mantra “Do things that don’t scale” made famous by Paul Graham often echoes in the corridors of newly established enterprises. Yet, the question remains: how do we effectively translate this principle into our coding practices? Over the past eight months of developing my AI podcast platform, I’ve crafted a straightforward technical framework that I call the “3-Month Rule.” This method revolves around a critical timeline: any unscalable solution gets a trial period of three months. If it proves its worth, it will be built out comprehensively; if not, it will be discarded.
As software engineers, we are often conditioned to create scalable frameworks right from the beginning. We gravitate toward sophisticated design patterns, microservices, and distributed systems, crafted for handling extensive user bases. However, this mindset frequently belongs to the realm of larger corporations and can represent a form of costly procrastination for startups. Instead of preemptively optimizing for future user bases that may never materialize, my 3-Month Rule allows me to embrace simple, less refined code that can be delivered swiftly. Through this process, I gain invaluable insights into the actual needs of my users.
Practical Examples of My Infrastructure Strategies
1. Everything Run From a Single Virtual Machine (VM)
Currently, I’m operating my entire platform—from the database to the web server—on a solitary $40/month VM. While it lacks redundancy and relies on manual backups, this setup has allowed me to accurately assess my resource requirements in a way that exhaustive capacity planning documents could never achieve. The platform, which I anticipated would require extensive resources, peaks at a mere 4GB of RAM. The complex Kubernetes configuration I nearly implemented would have been unnecessarily burdensome.
When my system has crashed—twice, mind you—the breakdown provided crucial insights into where true vulnerabilities lie, and interestingly, it was never where I had initially predicted.
2. Hardcoded Configuration Values
Utilizing hardcoded configuration values instead of using config files or environment variables has its merits:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this may feel inconvenient, it allows me to quickly access and update values without the need for extensive groundwork. Every change I make is
