Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions in Technology
In the realm of startups and innovative projects, advice often echoes: “Engage in activities that don’t scale.” While Paul Graham’s wisdom resonates, the practical application of this principle in the tech world is seldom discussed.
Having spent the last eight months developing my AI podcast platform, I’ve come to establish a straightforward framework: every unscalable workaround receives a maximum lifespan of three months. At that point, it either demonstrates its effectiveness and warrants further development, or it gets retired.
As engineers, our training tends to focus on the creation of solutions designed for scalability right from the outset. We become experts in sophisticated architectures that can manage thousands—if not millions—of users. Yet, this is often a mindset more suited for larger organizations, leaving startup environments to grapple with unique challenges where long-term scalability may not yet be necessary.
In many cases, striving for scalable solutions can lead to costly procrastination, as we prepare for users who haven’t even arrived yet. My 3-month rule compels me to produce straightforward, even “imperfect,” code that successfully deploys and reveals what users truly need.
Current Infrastructure Hacks That Actually Make Sense
1. Utilizing a Single Virtual Machine (VM)
My entire infrastructure—database, web server, background jobs, and caching—is consolidated on a $40/month VM, with no redundancy and manual backups stored locally. This approach may seem risky, but it’s a strategic move. Over the past two months, I’ve gleaned valuable insights about my resource usage, more than any detailed capacity planning could provide. It turns out that my platform, which I believed to be resource-intensive, only peaks at 4GB of RAM. The elaborate Kubernetes setup I was considering would have been wasted managing idle containers.
Each time the system crashes (and it has twice), I get genuine insights about failure points—often different from what I anticipated.
2. Hardcoding Configurations
I’ve opted for hardcoded configurations throughout my application:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
By avoiding dedicated configuration files and environment variables, I can quickly search for any parameter across my codebase. Each price adjustment is tracked in my git history, ensuring clarity and accountability. The
