The 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Tech Development
In the tech world, one piece of advice often echoed by thought leaders like Paul Graham is to “do things that don’t scale.” While this guidance is widely shared, few delve into the practicalities of implementing it effectively within the realm of coding.
After spending the past eight months developing my AI podcast platform, I have adopted a straightforward framework: each unscalable approach receives a trial period of three months. If it demonstrates value within that timeframe, it gets refined into a scalable solution; if not, it gets phased out.
As software engineers, we’re conditioned to create scalable systems from the outset, focusing on robust architecture that can handle millions of users. However, in the startup world, pursuing scalable solutions too early can lead to unnecessary complications, diverting our attention from the actual needs of real users. My 3-month rule encourages me to write straightforward, even rudimentary code that gets rolled out quickly, allowing me to gather insightful feedback on my users’ preferences.
My Current Infrastructure Hacks: A Thoughtful Strategy
1. Unified VM Deployment
All components of my platform—database, web server, background processing, and caching—run on a single $40/month virtual machine. This setup sacrifices redundancy and relies on manual backups.
Why is this effective? Within just two months, I’ve gained more insight into my actual resource requirements than any elaborate planning document could provide. I discovered that, contrary to my initial thoughts about needing extensive infrastructure, the AI-driven platform peaks at just 4GB of RAM. The complex Kubernetes setup I nearly implemented would have only resulted in managing idle containers.
When crashes occur (and they have happened twice), I gain valuable information about the exact points of failure—surprisingly, they’re rarely where I’d anticipated.
2. Hardcoded Configuration
I utilize hardcoded constants throughout my code, such as:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
By avoiding configuration files and environment variables, I can quickly search through my codebase for any settings. Each price adjustment is tracked in my git history, and any configuration change undergoes a personal code review. Opting for a configuration service would have consumed a week of development time; instead, I’ve made just
