Embracing the 3-Month Rule: A Technical Blueprint for startup Success
In the realm of startup development, guidance from industry leaders like Paul Graham often echoes through the corridors of innovation. His mantra, “Do things that don’t scale,” is a powerful reminder of the necessity to prioritize learning over immediate optimization. However, the challenge lies in how to effectively implement this ethos within our coding practices.
After eight months of dedicated work on my AI podcast platform, I’ve crafted a pragmatic strategy: any unscalable hack in my development process receives a lifespan of three months. By the end of this period, it must either demonstrate its value and evolve into a scalable solution, or it simply fades away.
As engineers, we are routinely trained to create solutions that can effortlessly handle vast user bases right from the outset. We delve into design patterns, microservices, and distributed systems, often overlooking the realities of startup dynamics. In early-stage ventures, pursuing scalability can often lead to costly delays, as we optimize for hypothetical future users rather than addressing current needs.
Key Infrastructure Hacks and Their Unexpected Benefits
Here are the vital infrastructure strategies I’m utilizing, which, while seemingly counterintuitive, have proven immensely insightful:
1. Consolidated Operations on a Single VM
I operate my entire platform—database, web server, background tasks, and caching—on a single $40/month virtual machine. While this setup lacks redundancy and relies on manual backups, it has provided rich insights into my resource utilization. In just two months, I’ve pinpointed that my AI-driven platform experiences peaks at around 4GB of RAM, revealing that the complex Kubernetes architecture I nearly deployed would have been unnecessary.
When my server encounters outages (and it has on a couple of occasions), I receive concrete data about the failure points, often surprising me by pointing to issues I hadn’t anticipated.
2. Hardcoded Configurations Throughout
In my codebase, configuration values are static constants, eliminating the overhead of configuration files and environmental variables. Changes trigger a straightforward redeployment rather than extensive engineering work. This approach allows for quick tracking of modifications through Git history, ensuring every adjustment is documented.
While creating a configuration service could take up to a week, I’ve successfully adjusted values a mere three times in three months, bringing to light the efficiency of my current method.
3. SQLite Database for Production Use
Surprisingly, my multi-user app operates on SQLite, which has proven to be effective with a database size of
