Title: The 3-Month Rule: A Pragmatic Approach to Unscalable Solutions in Tech
In the entrepreneurial space, the advice from Paul Graham to “do things that don’t scale” is well-known. However, seldom do we delve into the mechanics of how to effectively apply this principle, especially within the realm of coding.
After eight months of developing my AI podcast platform, I’ve established a straightforward framework I like to call the “3-Month Rule.” Each unscalable tactic I implement is given a lifespan of three months to prove its merit. If it demonstrates value, it will be thoroughly reconstructed; if not, it will be discarded.
The challenge engineers face is rooted in our innate training to craft scalable solutions right from the start. We often get wrapped up in intricate design patterns, microservices, and distributed systems—perfect for handling massive user traffic. Yet, this is a perspective often more suited to large corporations than startups. In a startup environment, pursuing scalability can lead to costly delays, as we’re frequently preemptively solving problems that may never arise. My 3-Month Rule compels me to create straightforward and direct code that not only launches but also provides insights into the actual needs of my users.
Current Infrastructure Insights: Smart Hacks for Real Learning
1. Unified VM Architecture
All components of my application—including the database, web server, background jobs, and Redis—are hosted on a single $40/month virtual machine. While this might sound impractical given the absence of redundancy and the manual backup process I follow, the reality is enlightening. Within just two months, I gained invaluable insights into my resource requirements that any capacity planning document would fail to convey. The data revealed my platform, despite being “AI-heavy,” operates comfortably with just 4GB of RAM. The elaborate Kubernetes setup I nearly embarked upon would have only required managing idle containers.
When the system fails—something that has happened twice—I’m equipped with real observations on what actually fails, which has often surprised me.
2. Hardcoded Configurations
Rather than relying on a complex configuration management system or environment variables, I use hardcoded constants throughout my code. My configuration might look simple:
plaintext
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this may seem retrograde, it comes with a significant advantage
