Embracing the 3-Month Rule: A Strategic Approach to Non-Scalable Solutions in Tech Development
In the realm of entrepreneurship, particularly in technology, there’s a well-known piece of advice from Paul Graham: “Do things that don’t scale.” While this wisdom resonates throughout startup culture, the practical implementation of this concept, especially in coding, is often overlooked.
After eight months of developing my AI podcast platform, I’ve formulated a straightforward framework: each unscalable solution is given a lifespan of three months. At the end of this period, I assess whether it has proven its value and deserves to be developed further, or if it should be discarded.
The reality is that as engineers, we are trained to focus on scalability right from the beginning. We become enamored with sophisticated architectures, invoking microservices, distributed systems, and the like—appealing structures designed for a hypothetical future of millions of users. However, in a startup environment, aiming for scalable solutions can often turn into a costly form of procrastination. We tend to optimize for a user base that may not yet exist, focusing on challenges that might never materialize. My three-month strategy compels me to craft straightforward, even “imperfect” code that gets deployed and reveals genuine user needs.
My Unconventional Infrastructure Strategies—and Why They Work
1. A Single Virtual Machine for All Operations
At the heart of my operations lies a single virtual machine (VM) costing only $40 a month, hosting my database, web server, background jobs, and even Redis. This setup lacks redundancy and relies on manual backups to my local system.
Why is this a wise choice, rather than a reckless one? Over the past two months, I’ve gained invaluable insights into my actual resource requirements, something no theoretical capacity planning document could provide. I’ve discovered my AI-centric platform peaks at just 4GB of RAM. The elaborate Kubernetes cluster I nearly implemented? It would have resulted in managing unutilized containers.
Moreover, when the VM has crashed (which it has twice), I’ve collected valuable data about the actual points of failure—predictably not where I anticipated.
2. Hardcoded Configurations for Simplicity
My configuration is captured through hardcoded constants:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
There are no complex configuration
