Embracing the 3-Month Rule: A Technical Approach to Non-Scalable Solutions
In the world of startups, the maxim “Do things that don’t scale,” attributed to Paul Graham, is often cited yet seldom put into practice, particularly within the realm of software development. After dedicating eight months to constructing my AI podcast platform, I’ve devised a pragmatic framework to incorporate this philosophy: every non-scalable workaround I implement is permitted to exist for only three months. At the end of this period, it’s make-or-break: either the solution demonstrates its worth and warrants a proper build-out, or it gets discarded.
As engineers, we are conditioned to pursue scalability from the outset—think sophisticated architectures like microservices and distributed systems designed for vast user bases. This is the mindset of large organizations. However, in a startup setting, focusing on scalable solutions can often lead to costly delays, as you’re investing energy into accommodating users who don’t yet exist and resolving issues that may never arise. My approach compels me to create straightforward, albeit “subpar,” coding practices that not only ship tangible products but also reveal genuine user needs.
My Current Infrastructure Strategies and Their Hidden Advantages:
1. Consolidation on a Single VM
Currently, my database, web server, background jobs, and caching system all operate on a single $40-per-month virtual machine, thriving without redundancy and relying on manual backups.
Why is this smart? The concentrated setup has provided more insights into my resource requirements in just two months than any extensive capacity planning document could offer. Interestingly, my presumptions about building an “AI-heavy” platform revealed that it peaks at a mere 4GB of RAM. The complex Kubernetes infrastructure I nearly implemented would have effectively managed empty containers. Each time it crashes (which has happened twice), I gain valuable insights about the actual flaws—insights I wouldn’t have anticipated.
2. Directly Hardcoded Configuration
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
I’ve opted for hardcoded constants throughout my files rather than using configuration files or environment variables. This lets me redeploy with any changes immediately.
The unexpected strength here lies in my ability to quickly search through my codebase for any configuration value. Each adjustment to pricing is neatly logged in Git history, and every modification undergoes a code
