Embracing Non-Scaling Solutions: My 3-Month Experimentation Framework
Paul Graham famously advises entrepreneurs to “do things that don’t scale,” yet the conversation rarely includes the practical application of this in the realm of coding. Over the past eight months, as I’ve developed my AI podcast platform, I’ve created a personal framework to implement this philosophy effectively: I allocate three months for each temporary coding hack. Each hack is then evaluated to determine its viability for future development or to be discarded entirely.
As software engineers, we often fall into the trap of aiming for scalability from the outset. We think in terms of intricate design patterns, microservices, and complex architectures that can serve millions of users. However, this mindset is more suited to large corporations than to startups, where scalability often equates to costly delays. By adhering to my three-month rule, I prioritize simple, straightforward coding that can be deployed quickly, allowing me to gain genuine insights into user needs.
Current Infrastructure Strategies: Practical and Insightful
1. Single VM for Everything
I host my entire platform—database, web server, background processes, and caching—on a single $40 per month virtual machine. There’s no redundancy, and I manage backups manually.
What may seem like a reckless choice is actually strategic. In just two months, I’ve gained deeper insights into my actual resource requirements than any theoretical capacity planning document could provide. I discovered that my platform’s peak usage only requires 4GB of RAM. The complex Kubernetes architecture I was considering would have meant managing empty containers. When my VM crashes (which has occurred twice), I obtain real-time data about failure points—truths I might not have anticipated.
2. Hardcoded Configuration
Take a look at my configuration:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
There are no configuration files or environment variables, just constants throughout the code. Any adjustment requires a redeployment, but this approach comes with a significant advantage: I can quickly search for any configuration value across my entire codebase. Every change is documented in the Git history and reviewed by me. Instead of spending an entire week developing a configuration service, I’ve redeployed just three times in three months, saving countless engineering hours.
3. Utilization of SQLite in Production
Yes, I’m
