Embracing the 3-Month Rule: A Fresh Approach to Unscalable Solutions in Tech
In the world of startups, the conventional wisdom of “doing things that don’t scale” is often recited yet seldom put into practice—particularly in the realm of software development. Paul Graham’s timeless advice speaks to the heart of innovation, but how can we translate that philosophy into actionable steps, especially when it comes to coding?
Over the past eight months, as I’ve developed my AI podcast platform, I’ve established a straightforward framework that I call the “3-Month Rule.” This concept advocates for an experimental approach to unscalable hacks, granting them a limited lifespan of just three months. At the end of this period, each method we’ve employed must either demonstrate tangible value, leading to a permanent and improved solution, or face elimination.
The Contradiction of Scalable Solutions
As engineers, we are often trained to focus on scalability from the outset. Our minds leap to design patterns, microservices, and complex architectures designed to accommodate millions of users. However, in a startup environment, pursuing scalable solutions can sometimes be an exercise in delaying the work that truly needs to be done. Instead of optimizing for hypothetical users, I’ve found that constructing simple, straightforward solutions allows us to focus on delivering real value and understanding user needs.
A Closer Look at My Current Infrastructure Hacks
Let’s delve into some of the unscalable strategies I’ve employed, which might seem counterintuitive, yet have proven to be astute decisions.
1. Consolidated Operations on a Single VM
I’ve opted to run my entire platform—database, web server, background tasks, and caching—on one $40/month virtual machine. This approach sacrifices redundancy for simplicity.
Why is this advantageous? In just two months, I’ve gained insights into my resource requirements that far outstrip what any typical capacity planning document could provide. I’ve confirmed that my AI platform only requires 4GB of RAM at peak times, making a complex Kubernetes setup unnecessary. When the system crashes—an occurrence I’ve experienced—it’s provided me with invaluable data about real failure points.
2. Directly Hardcoded Configurations
My configuration values are hard-coded directly within the codebase. For example:
python
PRICE_TIER_1 = 9.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this may
