Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions
In the world of tech startups, instructional wisdom often circulates around the notion of “doing things that don’t scale,” a concept famously championed by Paul Graham. However, the conversation often neglects the practical implementation of this advice, particularly in software development. Over the past eight months spent constructing my AI podcast platform, I’ve devised a straightforward framework: any hack that lacks scalability is given a lifespan of three months. After this period, it’s either validated and developed into a more robust solution or it gets retired.
It’s essential to acknowledge that as engineers, we are conditioned to prioritize scalable solutions from the inception of our projects. We focus on sophisticated architectures—design patterns, microservices, and distributed systems—that can accommodate millions of users. This mindset is well-suited for larger companies but can be less effective for startups, where chasing scalability can serve as a form of costly procrastination. By adopting my three-month rule, I compel myself to create straightforward, albeit imperfect, code that is deployable. This strategy allows me to develop a clearer understanding of my users’ actual needs.
Key Infrastructure Innovations and Their Rational Underpinnings
1. Single VM Architecture
My entire setup runs on a single $40-per-month virtual machine that accommodates the database, web server, background jobs, and Redis without any redundancy. Yes, I’m taking a risk with manual backups, but this decision has yielded invaluable insights regarding my resource needs in a way that no capacity planning document could. For instance, it turns out that my AI-driven platform does not exceed 4GB of RAM during peak use. The intricate Kubernetes configuration I almost implemented would likely have involved managing empty containers rather than solving real problems. When the system crashes (and it has twice), I gain valuable data on what truly fails, which is often unexpected.
2. Hardcoded Configuration
Instead of using config files or environment variables, I integrate constants throughout my codebase:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this approach may seem antiquated, it offers me the ability to quickly search for any configuration value. With every price change stored in my Git history and reviewed during pull requests, I can make updates in a matter of minutes rather than expending weeks
