The 3-Month Rule: A Practical Approach to Unscalable Solutions in Software Development
In the fast-paced world of startups, conventional wisdom often emphasizes a focus on scalability. Renowned entrepreneur Paul Graham famously advises, “Do things that don’t scale.” However, translating this principle into actionable steps within the realm of software development often seems overlooked. After eight months of developing my AI podcast platform, I’ve crafted a straightforward framework: I allocate a mere three months for any unscalable solution to prove its worth. If it doesn’t deliver within that timeframe, it gets scrapped.
As engineers, we are generally conditioned to architect scalable systems from the outset. We immerse ourselves in design patterns, microservices, and distributed systems, all aimed at effortlessly supporting millions of users. However, this mindset can lead to premature optimizations that do not address the immediate needs of a startup. My three-month rule compels me to adopt a more straightforward approach that prioritizes real-world users and their genuine needs over hypothetical situations.
Current Infrastructure Hacks: Why Simplicity is Brilliant
- Single Virtual Machine (VM) Setup
My entire stack—database, web server, background jobs, and caching—runs on one modest $40/month VM. This setup has zero redundancy, and I manually back up data to my local machine.
Why is this strategy effective? In just two months, I’ve gained insights into my actual resource requirements that no amount of capacity planning could provide. I discovered that my so-called “AI-intensive” platform typically operates with just 4GB of RAM. The intricate Kubernetes architecture I nearly deployed would have ended up managing empty resources instead.
Crashes, which have happened a couple of times, have provided invaluable data about failure points—surprisingly, they were never what I anticipated.
- Hardcoded Configurations
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
I eschew configuration files and environment variables in favor of hardcoded constants peppered throughout my code base. This simplifies changes, which require a quick redeployment.
The unexpected advantage of this approach is efficiency. I can search for any configuration in my codebase almost instantly, and every change is documented in Git history. In the past three months, I’ve only modified these values three
