The Three-Month Framework: A Pragmatic Approach to Non-Scalable Solutions
In the tech world, we often hear the wisdom of industry veterans like Paul Graham, who famously advised startups to embrace non-scalable activities. However, implementing this guidance, especially in the realm of coding, is rarely discussed.
Having spent the last eight months developing my AI podcast platform, I’ve established a personal methodology: every unscalable workaround is allowed to exist for a duration of three months. After this period, it must demonstrate its value to be refined into a sustainable solution; otherwise, it is discarded.
As developers, we are typically conditioned to pursue scalable solutions from the outset—focusing on architectural complexities like microservices and distributed systems designed for massive user bases. However, this “big company” mentality can hinder startups, where the costly pursuit of scalability can often result in avoided pressing issues and unproven market demand.
The Essence of the Three-Month Rule
My three-month strategy encourages me to create straightforward, albeit imperfect, code that gets deployed. This process not only delivers immediate value but also highlights user needs more effectively than extensive planning ever could.
Current Infrastructure Choices and Their Strategic Benefits:
1. Consolidation on a Single Virtual Machine
All functionalities of my platform—database, web server, background jobs, and Redis run on a single $40/month virtual machine. This setup lacks redundancy and relies on manual local backups.
Why is this a strategic choice? In just two months, I’ve gained crucial insights into my actual resource requirements that extensive capacity planning could not provide. For example, I’ve discovered that my “AI-heavy” platform peaks at just 4GB of RAM. The complex Kubernetes architecture I nearly implemented would have wasted resources on idle containers.
When issues arise (and they have), I receive immediate feedback on the underlying problems—typically, they’re not what I’d anticipated.
2. Hardcoded Configuration Across the Board
With constants directly integrated into my code, any changes require a redeployment. While this may seem inefficient, it offers a remarkable advantage: I can swiftly search my codebase for configuration values and track changes within my version control history.
Creating a dedicated configuration service would have consumed valuable time. Instead, I’ve made just three significant adjustments in the last three months—optimizing my efforts over lengthy development.
3. Using SQLite in Production
Contrary to traditional wisdom, I’m utilizing SQLite for my multi-user application,
