Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions in Tech

In the world of startups, the adage “do things that don’t scale” has become a guiding principle for many entrepreneurs. This wisdom, popularized by Paul Graham, encourages innovators to embrace simplicity and experimentation during the early stages of their ventures. However, practical implementation of this philosophy in the realm of coding remains a less-discussed topic.

After dedicating eight months to building my AI podcast platform, I’ve created a straightforward framework that I like to call the “3-Month Rule.” This approach allows for any unscalable hack to thrive for three months. At the end of this period, each solution must either demonstrate its worthiness for further investment or be retired.

The Challenge of Scalable Solutions

As engineers, we often come from backgrounds that emphasize scalable solutions right from the outset. We are well-versed in using design patterns, microservices, and distributed systems, all crafted to support millions of users. However, this line of thinking is more suited to established institutions rather than agile startups.

In the fast-paced environment of a startup, striving for scalability can feel like a costly form of procrastination. Why build complex solutions for users we don’t yet have? The 3-Month Rule encourages me to focus on straightforward, albeit “imperfect,” code that actually gets deployed. This practice not only drives product development but also reveals crucial insights into user needs.

Current Infrastructure Hacks: Practical Wisdom or Foolhardiness?

1. Everything Runs on One Virtual Machine

All essential services—database, web server, background jobs, and caching—reside on a single $40/month virtual machine. There’s zero redundancy, and backups involve manual transfers to my local machine.

You might wonder how this could be considered smart. The truth is, within two months, I’ve gained more insight into my resource usage than any theoretical capacity planning document could provide. It turns out that my AI-centric platform functions optimally with only 4GB of RAM. The complex Kubernetes architecture I nearly implemented would have only managed empty containers instead of addressing real user needs.

When the server crashes—as it has twice—I gain valuable data about what fails in ways I never anticipated.

2. Hardcoded Configuration for Simplicity

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PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
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