Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Tech Development
In the world of technology, it is often emphasized that one should “do things that don’t scale,” a piece of advice that resonates widely within the startup community. Yet, the intricacies of how to effectively adopt this principle in coding are rarely discussed. Having spent the past eight months developing my AI podcast platform, I have devised a straightforward framework that I refer to as the “3-Month Rule.” This approach allows each unscalable solution I implement a lifespan of three months, after which it either proves its worth and is transformed into a more robust system, or it is discarded.
The Traditional Developer Mindset
Many developers are conditioned to think in terms of scalability right from the start. They design sophisticated architectures, employing design patterns, microservices, and distributed systems that cater to millions of users. However, this mindset is often more suited for large companies rather than startups. In a startup context, focusing on scalability can morph into an expensive form of procrastination—developing solutions for users who don’t exist yet and addressing problems that may never arise. My 3-month rule encourages me to write simpler, more straightforward code that can be released quickly, facilitating real user feedback and learning what is genuinely needed.
My Current Infrastructure Strategies and Their Benefits
1. Consolidated Operations on a Single Virtual Machine
I’ve centralized my entire infrastructure—database, web server, background tasks, and cache—on a single $40/month virtual machine, with no redundancy and manual backups to my local device. This decision has been incredibly insightful; within two months, I’ve gained a clearer understanding of my actual resource demands rather than relying on theoretical capacity documentation. For instance, my platform’s peak needs hover around 4GB of RAM, validating that an intricate Kubernetes setup would have been an unnecessary complication.
2. Hardcoded Configuration Values
I have opted for hardcoded configuration values spread across files, eliminating the use of complex configuration files or environment variables. This may sound archaic, yet it has streamlined the process: every configuration change requires a quick redeployment, while also allowing me to easily track changes through version control. In just three months, I’ve adjusted these values three times—a simple task against a backdrop of extensive engineering time that would have been spent building a configuration service.
3. SQLite for Production Use
Surprisingly, I am leveraging SQLite for this
