The 3-Month Experiment: A Pragmatic Approach to Non-Scalable Solutions in Tech Development
In the realm of entrepreneurship, Paul Graham’s wisdom rings true: “Do things that don’t scale.” However, translating this philosophy into the technical world, especially in coding, is a topic often overlooked.
Having dedicated the past eight months to building my AI podcast platform, I’ve cultivated a practical framework that I’d like to share: each non-scalable hack is designated a lifespan of three months. Within this period, the solution either demonstrates its worth and transitions to a more robust system or is phased out.
The crux of the matter is that developers are often conditioned to create scalable solutions right from the outset. We delve into design patterns, microservices, and distributed systems—remarkable infrastructures designed to support millions of users. Yet, this mindset is often more applicable to larger companies.
For startups, striving for scalability too soon can lead to unnecessary delays, as we tend to optimize for hypothetical users and address issues that might not ever surface. My three-month rule compels me to embrace straightforward, even “imperfect” code that actually gets deployed, allowing me to understand the genuine needs of my users.
Current Infrastructure Strategies: A Case for Simplicity
1. Consolidating Resources on a Single VM
All essential components—database, web server, background jobs, and Redis—are functioning on a sole virtual machine costing only $40 per month. While there’s no redundancy and backups are manually executed, this approach has proven to be insightful.
In just two months, I’ve gleaned more about my actual resource requirements than I could have through elaborate planning. To my surprise, my “AI-intensive” platform rarely exceeds 4GB of RAM. The complex Kubernetes architecture I had in mind would have resulted in overseeing empty containers.
When the system has crashed (which happened twice), I’ve gained invaluable data about real failure points, most of which were unexpected.
2. Hardcoded Configuration Across the Board
Instead of using configuration files or environment variables, critical values such as pricing and user limits are embedded directly into the code:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This may seem imprudent, but it has its advantages. I can search through my entire codebase in seconds for
