Embracing Imperfection: The 3-Month Rule for Startup Development
In the entrepreneurial landscape, you’re likely familiar with Paul Graham’s mantra: “Do things that don’t scale.” However, the pivotal question remains: how can you effectively incorporate this principle into your coding practices?
After eight months of developing an AI podcast platform, I’ve established a unique framework I like to call the “3-Month Rule.” This approach allows each unscalable solution a trial period of just three months, after which it either earns its place through demonstrated value or is phased out.
As software engineers, we are often conditioned to focus on creating scalable solutions from the outset. We dive into elaborate design patterns, microservices, and robust architectures capable of supporting millions of users. Although these strategies are essential for large corporations, they can hinder the agility of startups. In fact, striving for scalability too soon can lead to costly delays, optimizing systems for hypothetical users instead of addressing the needs of current ones. The 3-Month Rule compels me to write straightforward, albeit imperfect code that enables me to directly engage with user requirements.
Insights from My Current Infrastructure Choices:
1. Simplified Operations with a Single VM
I operate all aspects of my platform—database, web server, background tasks, and caching—on a single $40/month virtual machine. While this lacks redundancy and relies on manual backups, it’s a strategic decision. I’ve gleaned more about my actual resource consumption in two months than in any theoretical capacity planning session. Surprisingly, my “AI-intensive” platform peaks at only 4GB of RAM. The complex Kubernetes setup I nearly deployed would have led me to manage idle resources instead of real demands. Each crash (yes, twice) provides invaluable data on what fails, revealing insights I would have otherwise overlooked.
2. Hardcoded Configurations for Clarity
Using constants like PRICE_TIER_1 = 9.99 instead of configuration files allows for rapid adjustments and clear tracking in my version control system. True, this means that any configuration change requires a redeployment, but I’ve only changed these values three times over three months. The time saved from avoiding an elaborate configuration service amounts to a significant engineering efficiency.
3. SQLite: An Unconventional Choice
Running SQLite in a multi-user environment has been a surprisingly effective decision. My database size is merely 47MB, and it effortlessly manages up to 50 concurrent users. Analyzing usage
One Comment
This is a powerful reminder that starting simple and prioritizing immediate value over premature scalability is often the most effective approach for startups and early development phases. Your “3-Month Rule” aligns closely with the lean startup methodology—testing assumptions quickly and iterating based on real user feedback. I particularly appreciate your emphasis on learning through doing, whether it’s managing crashes or gauging resource consumption, rather than over-engineering from the outset.
One insight I’d add is that this approach can also help developers build a more nuanced understanding of their core problem and user needs. By avoiding over-optimization early on, teams can focus on delivering features that really resonate, and only invest in scaling once the product-market fit is validated.
Your examples, especially using a single VM and SQLite, demonstrate how pragmatic infrastructure choices can accelerate learning and reduce unnecessary complexity. Have you considered building in a lightweight plan to revisit and gradually scale certain components as user demand grows, rather than fully committing to scalable architecture upfront? This balanced approach could combine the agility of your current strategy with future-proofing where appropriate.