Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions
In the dynamic world of start-ups, the conventional wisdom from tech luminary Paul Graham resonates deeply: “Do things that don’t scale.” Yet, the conversation often lacks pragmatic advice on how to apply this tenet effectively, especially when it comes to coding.
Having spent the last eight months developing my AI podcast platform, I’ve crafted a framework that I affectionately call the “3-Month Rule.” This guideline dictates that any non-scalable hack I implement must survive for only three months. After this period, if it hasn’t proven its value, it’s time to let it go.
As developers, we often gravitate toward creating scalable architectures right from the start. We think microservices, distributed systems, and other sophisticated frameworks—all designed for extensive user bases. However, in the early days of a startup, striving for scalability can often mean delaying real progress.
I’ve found that adopting my 3-month rule compels me to write straightforward, unrefined code that is deployable. This approach accelerates my understanding of user needs, rather than deferring it with intricate designs for hypothetical users.
Here’s a Peek at My Current Unconventional Infrastructure Choices:
1. Operating on a Single VM
I’ve consolidated everything—database, web server, background jobs, and caching—onto one $40 monthly virtual machine. My setup carries no redundancy and relies on manual backups.
This might seem careless, but it has proven invaluable. Within just two months, I’ve gained insights into my resource consumption far beyond what any formal planning document could provide. My platform, which I presumed would be resource-intensive, actually peaks at 4GB of RAM. The complex Kubernetes framework I considered would merely have been managing idle containers.
Each time the system crashes (which has occurred twice), I obtain concrete insights into what truly fails, often revealing surprises about my assumptions.
2. Hardcoded Configuration Values
Instead of relying on configuration files or environment variables, I’ve chosen a straightforward method using hardcoded constants:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This setup allows me to quickly locate configuration values across my codebase. Changes in pricing or parameters only require a minute of redeployment, sparing me extensive development time that setting
