Embracing the 3-Month Rule: A Pragmatic Approach to Coding in Early Development
In the world of startups, the wisdom imparted by Paul Graham—“Do things that don’t scale”—often resonates deeply. However, what isn’t commonly discussed is how to effectively apply this principle within the realm of coding. After eight months of developing my AI podcast platform, I’ve devised a straightforward methodology: every non-scalable solution is given a lifespan of three months. Following this period, it must either demonstrate its practicality worthy of a more robust implementation or be phased out entirely.
Understanding the startup Mindset
As software engineers, we are conditioned to create “scalable” solutions right from the get-go. Design patterns, microservices, and distributed systems showcase elegant architecture capable of supporting thousands, if not millions, of users. However, this mindset is often more suitable for large-scale companies than for nimble startups. In the startup landscape, prioritizing scalability too early can lead to unnecessary complexity and expenses—essentially, you’re investing time in optimizing for users who don’t yet exist and problems that may never arise. My 3-month rule drives me to focus on writing straightforward, albeit “flawed,” code that can be deployed quickly and provides real insights into user needs.
My Current Infrastructure: Strategic Hacks That Work
- Consolidating Everything on a Single VM
Everything—from the database to the web server—resides on a single $40/month virtual machine, with no redundancy and manual backups made to my local machine.
This approach has proven to be surprisingly beneficial. In just two months, I’ve gained clearer insights into my resource requirements than any formal capacity planning document could provide. I found that my platform, which I thought needed heavy AI resources, only peaks at 4GB of RAM. The elaborate Kubernetes configuration I nearly implemented would have resulted in managing empty containers. When the system does crash—twice so far—I receive concrete data about what actually fails, and it is rarely what I initially anticipated.
- Utilizing Hardcoded Configuration
Constants fill my codebase—like:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
There’s no complex configuration files or environment variables here. While changing any value necessitates a rede
