The Three-Month Experiment: A Practical Approach to Unscalable Solutions in Development
When it comes to startup advice, Paul Graham’s notion to “do things that don’t scale” is often celebrated. However, the challenge arises in figuring out how to apply this philosophy within the realm of coding. After eight months of developing my AI podcast platform, I’ve devised a straightforward framework: every temporary hack I implement is given a lifespan of three months. After this period, it must either demonstrate its value and warrant further development or face elimination.
As software engineers, we’re ingrained with the idea of crafting scalable solutions from the get-go. Think about it: design patterns, microservices, and distributed systems are the gold standards we often aspire to build. Yet, for startups, an obsession with scalability can often turn into a costly form of procrastination. It’s like preparing for users that don’t yet exist, all while avoiding the immediate necessities of the current user base. My three-month rule has encouraged me to embrace simpler, even “imperfect” coding that is functional, allowing me to gain deeper insights into the actual needs of my users.
Innovative Infrastructure Hacks: Turning Limitations into Learning Opportunities
1. Single VM Setup
My entire setup runs on a single $40-per-month virtual machine hosting the database, web server, background jobs, and Redis—no redundancy and manual backups to my local system.
This setup has proven to be advantageous. Within just two months, I’ve gained invaluable insights into my resource requirements. Surprisingly, my “AI-intensive” platform only peaks at 4GB of RAM. The intricate Kubernetes system I was planning to deploy would have been managing idle containers instead of facilitating actual growth. Whenever the system crashes, which has occurred twice, I receive real data about failures, often revealing surprises about what actually breaks.
2. Hardcoded Configuration
My configuration is as simple as it gets:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
I’ve opted for hardcoded constants over complex configuration files. While this means redeploying for any change, it also enables quick searches across my codebase for any configuration value. Tracking changes in Git history and code reviews, albeit review by just myself, keep the process streamlined. Creating a dedicated configuration service would have taken an entire week, but I’ve only
