The 3-Month Rule: A Strategic Approach to Non-Scalable Development
In the world of startups and fast-paced development, conventional wisdom often emphasizes the importance of building scalable solutions from the very beginning. This is largely influenced by the well-known advice from Paul Graham: “Do things that don’t scale.” However, when it comes to the practical implementation of this philosophy in software development, many find themselves at a loss.
Having spent the last eight months working on my AI podcast platform, I’ve adopted a straightforward framework that has proven effective: every non-scalable method I employ receives a maximum lifespan of just three months. After this period, these techniques will either be validated and robustly developed, or they will be discarded.
A Necessary Departure from Standard Engineering Mindset
As engineers, we are often trained to envision large-scale solutions right from the start—considering design patterns, microservices, and distributed systems. Unfortunately, this big-picture thinking can be a distraction in a startup setting. In reality, focusing exclusively on scalability can result in costly delays, forcing you to contend with user challenges that may never materialize. My three-month rule encourages me to prioritize creating straightforward, albeit imperfect, code that genuinely serves user needs.
My Innovative Infrastructure Hacks and Their Undeniable Benefits
1. Consolidated Operations on a Single Virtual Machine
I run my entire platform—including the database, web server, background jobs, and Redis—on a single, cost-efficient virtual machine that costs just $40 a month. While it lacks redundancy and depends on manual backups, this approach has been surprisingly informative.
In the past two months, I’ve gained deeper insights into my actual resource requirements than any extensive planning documentation could provide. For instance, I’ve discovered that my “AI-heavy” platform peaks at just 4GB of RAM. The complex Kubernetes setup I was considering would have only managed empty containers, which I now recognize would have been a waste of resources. Additionally, whenever the VM crashes (which has occurred twice), I gain real data about actual failure points—revealing patterns that defy my initial expectations.
2. All Configurations Hardcoded
plaintext
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
With no separate configuration files or environment variables, everything is coded directly into the application.
