Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions
In the world of startups, the mantra “Do things that don’t scale,” famously put forth by Paul Graham, is often echoed. However, the practical implementation of this principle, particularly in coding, is rarely discussed. After eight months of developing my AI podcast platform, I’ve devised a straightforward framework: each non-scalable solution is granted a lifespan of three months. During this period, it must either demonstrate its value and evolve into a robust solution, or it will be discarded.
As engineers, we tend to prioritize scalable solutions from the outset, focusing on complex architectures like microservices or distributed systems that can cater to millions of users. While this approach is suitable for larger corporations, in a startup environment, pursuing scalability too early can be a costly delay. It means gearing up for users who aren’t even there yet and addressing challenges that may never materialize. My three-month rule compels me to create uncomplicated, albeit imperfect, code that can be deployed quickly and provides valuable insights into user needs.
Innovative Infrastructure Strategies: Why They Work
1. Consolidated Infrastructure on a Single VM
Everything—database, web server, background jobs, and Redis—stems from a single $40/month virtual machine. While some might view this as risky due to the lack of redundancy, it has provided me with invaluable insights regarding my resource demands. Within just two months, I discovered that my AI-centric platform operates efficiently with a usage peak of only 4GB of RAM. Had I opted for a complex Kubernetes setup, I would have ended up managing redundant containers that were ultimately unnecessary.
When this system encounters issues (which it has, on a couple of occasions), I gain practical data about the faults, often revealing surprises about what actually fails.
2. Simplistic Hardcoding of Configuration Settings
All configurations exist as hardcoded constants within the codebase, such as:
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This method may be seen as archaic, but it enables me to quickly locate configuration values through simple searches across the code. Each alteration is tracked in Git, and my self-review process, while not ideal, ensures accountability.
Creating a dedicated configuration service could take a week, yet I’ve modified these parameters only three
