Embracing Imperfection: The 3-Month Rule for Startups
In the entrepreneurial world, we often hear Paul Graham’s timeless advice: “Do things that don’t scale.” Yet, there’s a less-discussed aspect of this strategy—how to effectively implement it in your coding practices.
Having spent the last eight months developing my AI podcast platform, I’ve formulated a practical approach that I call the “3-Month Rule.” According to this framework, any unscalable hack can exist for just three months. If it proves valuable, I’ll invest the effort to refine it; if not, it will be discarded.
A Common Pitfall for Engineers
As software engineers, we are typically conditioned to construct scalable solutions from the outset. We delve into design patterns, microservices, and distributed systems—all the advanced architecture meant to serve millions. However, this mindset is more suited to established companies than to startups.
In a startup, chasing scalability can turn into a costly form of procrastination. Why invest time optimizing for future users when the ones you have are still just hypothetical? My 3-month rule encourages writing straightforward, and yes, sometimes slightly inferior code that can be deployed quickly, allowing me to understand better what my users need.
Key Infrastructure Hacks that Work Wonders
1. Consolidating to a Single VM
Currently, my setup runs on a single $40/month virtual machine that hosts the database, web server, background processes, and Redis—all in one place. This might seem imprudent due to the lack of redundancy, but here’s the advantage: I’ve gained invaluable insights into my actual resource requirements in just two months—far beyond what any capacity planning document could convey.
Surprisingly, my “AI-heavy” platform only peaks at 4GB of RAM. The complex Kubernetes environment I almost implemented would have resulted in managing empty containers. When issues arise—and they have twice so far—I uncover genuine insights about system failures. Spoiler alert: they’re rarely what I initially expected.
2. Hardcoded Configurations
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
My configurations are simply hardcoded constants scattered across files, with no external config files or environment variables involved. While changing any of these values necessitates a redeployment, there’s a surprising benefit: I
One Comment
This is a fantastic approach to balancing agility and practicality in early-stage development. The 3-Month Rule effectively encourages founders and engineers to avoid over-engineering for future scalability before real-world validation. I especially appreciate the emphasis on gaining hands-on insights through simple, unoptimized setups—like running everything on a single VM. It’s often the real-world constraints and failures that reveal what genuinely matters, rather than theoretical capacity planning. Additionally, embracing straightforward practices such as hardcoded configs can save time initially, allowing for rapid iteration and testing. As you say, these hacks might be imperfect, but they’re invaluable for learning and validation. Once your product proves its value, you’ll have a solid foundation to optimize further. Thanks for sharing such a practical and thoughtful framework!