Embracing the 3-Month Experiment: A Pragmatic Approach to Development
In the world of software development, a familiar piece of advice echoes from the renowned Paul Graham: “Do things that don’t scale.” While this wisdom is widely accepted, the execution can often be elusive, especially in the realm of coding. After spending the last eight months building my AI podcast platform, I’ve crafted a straightforward framework that I call the 3-Month Rule, which allows me to effectively implement non-scalable solutions.
The 3-Month Rule Explained
The essence of my approach is simple: any non-scalable workaround is given a lifespan of three months. Within that time frame, it must either prove its worth and be transformed into a robust solution or be discarded. This practice aligns perfectly with the realities of startup life, where the primary focus should not be on creating grand scalable architectures but rather on addressing immediate user needs.
As developers, we are often conditioned to focus on long-term scalable solutions involving intricate design patterns and systems capable of managing vast numbers of users. However, at startups, investing time in scalable code can sometimes be an expensive form of procrastination. My 3-month rule encourages me to adopt a more practical and direct coding style—one that might be seen as “bad” by traditional standards but ultimately leads to tangible learning experiences about what my users truly need.
Current Infrastructure Hacks: Insights Gained
1. Deploying on a Single VM
I’ve consolidated everything—database, web server, background processes, and caching—on a single $40/month virtual machine. While this setup lacks redundancy and relies on manual backups, it has provided invaluable insights into my actual resource usage. Initially, I thought my AI platform would demand extensive resources, but I’ve discovered that it tops out at just 4GB of RAM. The complex Kubernetes infrastructure I contemplated building would have resulted in managing mostly inactive containers. Instead, I now have firsthand data on what fails and why when my system crashes—a learning experience that has proven to be far more enlightening than theoretical capacity planning.
2. Hardcoded Configuration Management
My configuration is embedded directly into the code with defined constants like:
python
PRICE_TIER_1 = 9.99
MAX_USERS = 100
Although this may seem unorganized, it has allowed me to track every change effortlessly through version control while minimizing the deployment time needed for updates. Instead of creating a separate configuration service, which would
