The 3-Month Experiment: A Pragmatic Approach to Non-Scalable Development
In the world of startups, the advice “Do things that don’t scale” from Paul Graham is widely acknowledged, yet the practical implementation of this philosophy in coding is often overlooked. After dedicating eight months to creating my AI podcast platform, I’ve devised a straightforward framework: every experimental hack gets a lifespan of three months. At the end of this period, it either proves its worth and evolves into a robust solution or is discarded entirely.
As engineers, our instinct is to develop scalable solutions from the outset, focusing on design patterns, microservices, and distributed systems—elements that cater to significant traffic and diverse user bases. However, this mindset can sometimes lead to unnecessary complexity in a startup setting. My three-month rule prioritizes expediency and simplicity, compelling me to write straightforward, if imperfect, code that gets delivered to users, enabling a more profound understanding of their actual needs.
Understanding My Infrastructure Choices
1. Consolidated Operations on a Single VM
I run my entire platform—encompassing the database, web server, background tasks, and caching layers—on one reasonably priced virtual machine. While this setup lacks redundancy and I handle backups manually, it has given me invaluable insights into my resource usage. Within two months, I’ve gathered more data on my needs than any planning document could provide. I’ve realized that the expected high demand for resources was dramatically overstated, which saved me from setting up an unnecessary complex infrastructure like Kubernetes.
2. Utilization of Hardcoded Configuration
Instead of relying on configuration files or environmental variables, I’ve opted for hardcoded constants throughout my codebase. While this approach involves redeploying the app for any changes, it allows me to quickly search and track any configuration alterations across the project. In just three months, I’ve made minor adjustments, proving that a full configuration service would have been an inefficient use of time.
3. SQLite in Production
Surprisingly, I’ve chosen SQLite for my web application, and it’s performing admirably even under load. Understanding my data access patterns—predominantly reads with minimal writes—has helped me realize that a more complex database setup like Postgres is unnecessary for my current needs. This insight ensures I build solutions based on verified behaviors rather than assumptions.
4. Simplified Deployment Process
My deployment method involves a straightforward command that updates my production server directly from Git. This
