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

In the realm of startup ecosystems, the advice from influential figures like Paul Graham—”Do things that don’t scale”—resonates deeply. However, the challenge often lies in translating this principle into actionable coding practices that yield tangible results. After dedicating eight months to developing my AI podcast platform, I’ve crafted a straightforward framework: I allow each unscalable solution a lifespan of just three months. If it doesn’t demonstrate its worth within that window, it gets retired.

As engineers, we are conditioned to think of scalability right from the outset. We design systems meant to support vast user bases with sophisticated architectures involving design patterns, microservices, and distributed systems. While these techniques are essential for larger organizations, they can be counterproductive for startups where scalable solutions may simply be an expensive form of procrastination. We often find ourselves preemptively tackling challenges that may never arise.

My three-month framework compels me to pursue straightforward, even “imperfect,” coding practices that prioritize real-world deployment. The focus is on understanding user needs rather than getting lost in theoretical optimization.

Current Infrastructure Strategies: Practical Wisdom from Simplicity

1. Single Virtual Machine Operations

Currently, my database, web server, background jobs, and Redis run entirely on one virtual machine priced at $40 a month. While this approach lacks redundancy, it has illuminated my actual resource usage more effectively than any extensive capacity planning document could. I found that my “AI-heavy” application peaks at 4GB of RAM. The complex Kubernetes configuration I considered would have simply managed idle containers.

Through occasional crashes, which have occurred twice, I’ve gained genuine insights into my system’s weak points—none of which were what I had anticipated.

2. Simplified Configuration Management

Instead of using configuration files or environment variables, my setup relies on hardcoded constants:

plaintext
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"

While one might critique this as outdated, there’s a hidden advantage: I can easily search my codebase for any configuration value. Each pricing adjustment is documented in version control, and all updates undergo a personal review process. Investing time into creating a sophisticated configuration management service would have taken a considerable amount of hours, whereas I’ve made