Embracing the 3-Month Rule: A Pragmatic Approach to Rapid Development in Startups
In the world of startups, conventional wisdom often emphasizes the importance of scalability. Paul Graham’s famous advice to “do things that don’t scale” resonates deeply, yet practical strategies for applying this principle in coding remain under-discussed. Having spent the past eight months developing an AI podcast platform, I’ve discovered a valuable method: implementing a “3-Month Rule” for unscalable hacks. This approach allows each temporary solution a designated lifespan of three months to demonstrate its utility before either getting refined into a robust system or being discarded entirely.
Rethinking Scalability
As engineers, we are typically trained to focus on creating scalable architectures from the outset—strategies that include microservices, intricate design patterns, and distributed systems, all aimed at managing high user volumes. However, in a startup context, such an emphasis can often lead to premature optimization and unnecessary complexity for problems that may not even exist yet.
By applying the 3-Month Rule, I prioritize writing straightforward and less-than-perfect code that can be launched quickly. This, in turn, allows me to collect valuable user insights that inform future development.
My Infrastructure Strategies: Simplifying to Learn
Here are some of the unconventional approaches I’ve adopted that not only save time but enhance understanding of my actual needs:
1. Single-VM Architecture
All of my application components—including the web server, database, and background jobs—run on a single $40-per-month virtual machine. While this setup lacks redundancy and relies on manual backups to my local machine, it has provided me with critical insights into my resource requirements. Within just two months, I learned that my platform’s peak RAM usage was only 4GB, which allowed me to avoid over-engineering solutions like Kubernetes, ultimately saving time and effort.
2. Hardcoded Configuration
I simplified configurations by declaring constants directly in the code:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This approach may seem rudimentary, but it enables swift tracking and updating using tools like grep. Instead of spending time creating a configuration service, I’ve updated parameters just three times in three months, translating to minimal redeployment time.
3. SQLite for Production Use
Yes, I’m leveraging SQLite for
