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
One Comment
Thank you for sharing your practical application of the 3-Month Rule—it’s a refreshing perspective that emphasizes speed and learning over premature optimization. I particularly appreciate your approach to consolidating services on a single server and using hardcoded configurations for rapid iteration. These tactics clearly allow for more immediate insights and reduce unnecessary complexity early on.
This resonates with the idea that startups often benefit from “failing fast” and validating assumptions quickly, rather than pouring significant resources into scalable architectures that may never be needed. Your framework reminds us that sometimes the most effective way to build scalable solutions is to first understand real user behavior and system requirements through simple, unscalable setups.
Would be interesting to hear how you plan to adapt or evolve this approach if your platform begins to grow beyond that initial three-month window—do you see a point where more scalable infrastructure becomes necessary, or do you anticipate iterating back to simplicity again?