Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Software Development
In the world of startups, particularly in the tech industry, the notion of “doing things that don’t scale” often echoes through startup culture. This wisdom, famously articulated by Paul Graham, emphasizes the importance of trial and error, especially when developing new products or services. However, the challenge that many software engineers face is understanding how to effectively implement this advice within their coding practices.
After eight months of building my AI podcast platform, I have adopted a structured, hands-on approach I like to call the “3-Month Rule.” This framework allows me to experiment with unscalable hacks for a limited time: if these hacks prove their worth within three months, they transition to a more robust solution; if not, they’re promptly discarded.
The challenge we often face as engineers is the innate inclination to design scalable systems from the outset. Concepts such as microservices, distributed architectures, and elaborate design patterns are all geared towards building for growth, but in a startup context, this can lead to unnecessary complexity. In many instances, pursuing scalable solutions can feel more like avoiding essential tasks than preparing for future growth.
By adhering to my 3-Month Rule, I have been able to concentrate on creating straightforward, albeit imperfect, solutions that yield valuable insights into user needs.
Infrastructure Hacks that Yield Valuable Insights
Here are some of the unconventional strategies I’ve employed in my current infrastructure, demonstrating their effectiveness:
1. Centralized Operations on a Single VM
I have consolidated my database, web server, and background jobs on a single $40/month virtual machine. While this setup lacks redundancy and relies on manual backups, it has provided me with crucial insights into my resource requirements. In just two months, I discovered that my platform, which I initially perceived to be resource-intensive, only peaks at 4GB of RAM. An overly complex Kubernetes architecture would have required managing empty containers instead of focusing on actual user needs.
When the system does encounter crashes (which has happened twice), I’ve gathered invaluable data regarding failure points—insights that often defy my initial expectations.
2. Simplistic Hardcoded Configurations
My configuration is straightforward, utilizing hardcoded values for various parameters like pricing tiers and user limits. While this approach might seem primitive, it has proven incredibly efficient. Changing a configuration demands a simple redeployment, allowing me to track alterations in Git history easily. This
