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

In the world of tech startups, the mantra “Do things that don’t scale” by Paul Graham is often quoted, yet rarely discussed in practical terms. After eight months of developing my AI podcast platform, I’ve crafted a straightforward principle: every temporary and unscalable solution receives a lifespan of just three months. At the end of this period, it must either validate its worth and be properly developed, or it will be discarded.

As engineers, we are conditioned to design scalable architectures from the outset, embracing tools like microservices and distributed systems. This, however, reflects the mindset of larger corporations. In the startup realm, pursuing scalability can be a costly distraction—one that preemptively optimizes for an audience that may not even exist yet. By adhering to my 3-Month Rule, I’m compelled to write straightforward, if imperfect, code that can be quickly deployed, providing invaluable insights into actual user needs.

Here’s What I’ve Learned from My Simplified Infrastructure Hacks:

1. Consolidated Operations on a Single VM
Everything runs on just one virtual machine (VM) for a mere $40 a month, including the database, web server, background jobs, and Redis. The system lacks redundancy and relies on manual backups to my personal device.

This setup is more insightful than it seems. I’ve gained a clearer understanding of my actual resource requirements in just two months—far superior to what any capacity planning document could offer. My “AI-heavy” platform only peaks at 4GB of RAM; the complex Kubernetes architecture I nearly deployed would have simply contained dormant resources. Moreover, when the system crashes—a rare event—I acquire precise data on failure points, often surprising me.

2. Hardcoded Configuration for Simplicity
Instead of utilizing configuration files or environment variables, I’ve hardcoded all configurations as constants within the codebase. This streamlines changes—making any update necessitate a redeployment, but with far less hassle.

The real benefit? I can quickly search through my code for any configuration setting. Every price adjustment is recorded in Git history, and even though I’m the only one reviewing my code, there’s a systematic approach to changes. Instead of spending a week on a dynamic configuration service, I simply redeployed three times in three months, totaling about 15 minutes of effort.

3. Utilizing SQLite in Production
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