Embracing the 3-Month Rule: A Practical Approach to Unscalable Solutions in Tech Development

In the realm of startups and innovative projects, traditional wisdom often encourages developers to “do things that don’t scale.” However, the challenge lies in effectively implementing this philosophy in real-world coding practices. Having spent eight months developing my AI podcast platform, I’ve devised a straightforward method: each unscalable approach should be given a trial period of three months. At the end of this period, we assess whether it has delivered enough value to warrant further investment, or if it should be phased out.

The startup Dilemma: Scaling Too Soon

As engineers, our training typically inclines us to create solutions that cater to scalability from the very beginning. We immerse ourselves in design patterns, microservices, and complex architectures, all while envisioning systems that can support millions of users. However, this is often the mindset of larger corporations.

In the startup world, prioritizing scalable code at an early stage can become a costly form of procrastination. Too often, we find ourselves optimizing for users who may not even exist, addressing potential issues that likely won’t arise. My 3-month framework compels me to produce straightforward, if imperfect, code that can be launched swiftly, allowing for real-time user feedback on their needs.

Current Infrastructure Choices: The Wisdom in Simplicity

Here are some of the unconventional strategies I’m implementing, which may appear unorthodox but have proven to be quite effective:

1. Consolidating Operations on a Single VM

Instead of relying on a sprawling infrastructure, my entire system—database, web server, and background jobs—operates on a single, $40/month virtual machine. The simplicity comes with risks: there’s no redundancy, and backups rely on manual processes.

However, this straightforward setup has provided invaluable insights into my actual resource requirements. Surprisingly, my AI-focused platform peaks at only 4GB of RAM, revealing that an elaborate Kubernetes deployment would have been overkill, managing mostly idle resources.

2. Utilizing Hardcoded Values

In my code, I’m opting for hardcoded constants for essential configurations—such as pricing tiers and limits—rather than more complex configuration files or environment variables. While this may seem inefficient at first glance, it allows for rapid access and modification. Changing parameters involves a swift redeployment rather than extensive engineering efforts. Over three months, I’ve made only a few adjustments, translating to