Embracing the 3-Month Rule: A Practical Framework for Scalable Development
In the tech world, Paul Graham’s mantra, “Do things that don’t scale,” has gained recognition, but the discussion rarely extends to how developers can effectively apply this wisdom in their coding practices. Over the past eight months, while creating my AI podcast platform, I’ve established a personal framework for implementing unscalable approaches: the 3-Month Rule. This rule dictates that every non-scalable solution gets exactly three months to prove its worth; if it doesn’t, it’s time to retire it.
As software engineers, we’re typically conditioned to prioritize scalable architectures from the outset—think design patterns, microservices, and distributed systems. However, this mindset can lead to misalignment in a startup environment. Often, the pursuit of scalability acts as a form of procrastination, where we optimize for hypothetical users and scenarios that may never materialize. My 3-Month Rule encourages me to embrace simpler, more direct coding methods that focus on immediate learning and user needs.
My Ingenious Infrastructure Strategies
1. Centralized Operations on a Single VM
I run my entire operation—a database, web server, background jobs, and caching—on a single $40/month virtual machine. This setup lacks redundancy and relies on manual backups stored locally.
Why is this approach effective? In just a couple of months, I’ve gleaned valuable insights into my actual resource requirements that no amount of theoretical planning could provide. My platform, which is perceived as “AI-heavy,” only needs 4GB of RAM at peak times. Had I embarked on a Kubernetes infrastructure, I would have wasted time managing empty resources.
When my system has crashed (twice so far), I received first-hand information about the real failure points, which were surprises rather than predictable outcomes.
2. Hardcoded Configuration Across the Board
Instead of utilizing configuration files or environment variables, I’ve opted for hardcoded constants throughout my codebase:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Why is this smart? It allows me to search for and track configuration values throughout my code quickly. Each modification is documented in Git history, and every change, regardless of how minor, undergoes a self-review process. While creating a dedicated configuration service would
