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

In the tech world, Paul Graham’s mantra “do things that don’t scale” is widely acknowledged, yet rarely discussed in terms of practical application—particularly in software development. After dedicating eight months to building my AI podcast platform, I’ve developed a straightforward strategy that I call the “3-Month Rule.” This framework allows unscalable solutions to thrive for a limited time—three months, to be exact. At the end of this period, each solution either demonstrates its worth and is integrated into the broader system, or it gets discarded.

Here’s a critical insight: as engineers, we are often conditioned to design systems with scalability at their core right from the start. Concepts like microservices, distributed architectures, and intricate design patterns become our default mindset, focusing on accommodating millions of users. However, in the startup environment, this approach often turns into an expensive form of procrastination. It’s not uncommon to find ourselves optimizing for users who are not yet on the platform or addressing challenges that may never emerge.

The beauty of the 3-month rule is its encouragement to create simple, functional solutions that can be deployed quickly and provide invaluable insights into user needs.

Current Infrastructure Strategies: Smart Hacks in Action

1. Consolidating Services on a Single Server

All my essential services—database, web server, background tasks, and caching—operate on a single, budget-friendly $40/month virtual machine. While this setup lacks redundancy and relies on manual backups, it has provided a wealth of knowledge regarding my actual resource requirements. Over the past two months, I’ve discovered my “AI-centric” platform peaks at 4GB of RAM. The complex Kubernetes framework I nearly implemented would have been an unnecessary burden, and when outages do occur, I gain genuine insights into what fails—not what I predicted.

2. Hardcoded Configuration

In my codebase, configuration is straightforward and direct, with values like:

python
PRICE_TIER_1 = 9.99
MAX_USERS = 100
AI_MODEL = "gpt-4"

No external config files or environment variables—just constants throughout the code. This might sound limiting, but it offers significant advantages: I can quickly locate configuration values via grep and track changes through git history. In just three months, I’ve made a mere three adjustments that required 15 minutes of redeployment rather than the