Embracing the 3-Month Rule: A Pragmatic Approach to Learning Through Unscalable Solutions
In the world of startups, the mantra to “do things that don’t scale” is widely recognized, yet the practical steps to implement this advice—especially in the realm of software development—are less frequently discussed. Through my eight months of crafting an AI podcast platform, I have cultivated a straightforward framework centered around a rule I call the “3-Month Rule.” This principle suggests that every experimental and unscalable approach is given a lifespan of three months. At the end of this period, it either proves its worth and is upgraded or is promptly retired.
As software engineers, we often fall into the trap of striving to design for scalability from the outset. We envision complex systems utilizing design patterns, microservices, and distributed architectures capable of handling countless users. However, this line of thinking often belongs more to established companies than to agile startups.
At a startup, seeking to construct scalable solutions prematurely can lead to expensive detours, as we invest time in features for users that don’t yet exist, or addressing issues that may never arise. My 3-Month Rule encourages me to code in a straightforward manner, allowing me to identify and meet actual user needs rapidly, using what I like to call “imperfect” solutions.
Current Infrastructure Hacks: Smarter Choices in Simplicity
1. Single VM Deployment
All my core components—be it the database, web server, or background processes—run on a singular $40/month virtual machine, leading to no redundancy and manual backups to my local system. This approach has proven to be more insightful than any theoretical capacity planning document could provide. In a mere two months, I’ve determined that my AI-driven platform operates effectively with just 4GB of RAM. The Kubernetes setup I nearly implemented would have only served to manage idle resources.
When issues do arise (which they inevitably do), I gain invaluable insights into the true weaknesses of my program; typically, the failures are not at all what I anticipated.
2. Hardcoded Configuration
Constant values populate my code, such as:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This may appear rudimentary, especially without configuration files or environment variables, but the simplicity allows me to swiftly search through
