Embracing the 3-Month Rule: A Pragmatic Approach to Development
In the ever-evolving world of tech startups, the advice from thought leaders like Paul Graham to “do things that don’t scale” often resonates well. However, the practical application of this principle, especially in coding, tends to go unaddressed. After spending the last eight months developing my AI podcast platform, I’ve devised a straightforward framework: every unscalable solution I implement receives a three-month trial period. At the end of this timeframe, we assess whether it has proven its worth and deserves a more permanent solution, or if it should be discarded.
As engineers, we are typically conditioned to aim for scalability from day one. We dive straight into complex architectures—microservices, distributed systems, and other elegant designs tailored for large user bases. Yet, in the world of startups, rushing towards scalable solutions can often lead to delays and unnecessary expenses. The 3-month rule encourages me to write straightforward, albeit imperfect code, that actually gets deployed, providing invaluable insights into user needs.
Innovative Infrastructure Hacks Worth Keeping
1. Centralized Operations on a Single Virtual Machine
For just $40 a month, I have consolidated my database, web server, background jobs, and caching on a single virtual machine. While this setup lacks redundancy and relies on manual backups, it has been a game-changer. In just two months, I’ve gained a clearer understanding of my resource requirements than any capacity-planning document could provide. My platform peaks at 4GB of RAM, revealing that the complex Kubernetes architecture I almost built would have only managed idle containers. Each crash informs me about genuine issue areas—not what I initially anticipated.
2. Hardcoded Variables Throughout the Code
With straightforward constants like:
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 hardcoded values. Making a change means redeploying, but this method has proven effective. I can swiftly locate any configuration value within my codebase, and modifications are logged in Git history. Creating a dedicated configuration service would consume a week of development time, while my minimal changes over three months have only taken 15 minutes in redeployment.
3. Utilizing SQLite for a Multi-User Application
Yes, I presently
