Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Tech Development
In the tech world, one piece of advice often stands out among entrepreneurs and developers alike: “Do things that don’t scale.” This wisdom, famously attributed to Paul Graham, emphasizes the importance of initial experiments over perfectly engineered solutions. However, many developers find it challenging to navigate this philosophy effectively, particularly in the coding process.
Over the past eight months of building my AI podcast platform, I’ve created a straightforward framework that embodies this principle: every non-scalable solution is given a lifespan of three months. At the end of this period, the solution must either demonstrate its value and warrant further development or be discarded altogether.
As engineers, we’re often conditioned to design scalable architectures from the start, thinking sophisticatedly about patterns, microservices, and distributed systems. But in the startup context, this can often lead to wasted resources as we optimize for future users who may never exist. My 3-month approach encourages creating simple and pragmatic code that leads to tangible results and insights about user needs.
Innovative Infrastructure Hacks: My Thought Process
1. Unified Resources on a Single VM
Currently, my entire development environment – including the database, web server, background jobs, and Redis – operates off one single virtual machine costing just $40 a month. While this setup lacks redundancy and requires manual backups, it has provided invaluable insights into actual resource utilization. Within just two months, I’ve gained a clearer understanding of my platform’s demands, which is particularly beneficial since the elaborate Kubernetes setup I had initially considered would have been wasted on me.
2. Hardcoded Values for Configuration Management
I’m managing configuration directly within my code rather than using sprawling config files or complex environment variables. By hardcoding values, changes require a simple redeployment. This method, while seemingly inefficient, has allowed me to quickly access any configuration setting in seconds, with version control tracking every modification. The time saved in engineering by foregoing a dedicated configuration service has been significant, allowing me to focus on development rather than overhead.
3. Utilizing SQLite for Production
Surprisingly enough, I’m running SQLite in a multi-user application, which handles user loads seamlessly. My discovery of access patterns—primarily read-heavy rather than write-heavy—has confirmed SQLite’s appropriateness for my needs. If I’d opted for a more complex database solution like Postgres, I’d be bogged down by extraneous optimizations for issues
