Embracing Imperfection: The 3-Month Rule for Building Non-Scalable Solutions
In the tech world, we often hear Paul Graham’s mantra: “Do things that don’t scale.” However, the challenge lies in how to approach this concept, especially from a coding perspective. After dedicating eight months to developing my AI podcast platform, I’ve created a straightforward framework: any unscalable hack is given a lifespan of just three months. Within this period, it must demonstrate its worth or it will be discarded.
As engineers, we are typically trained to aim for scalability from the outset. We delve into design patterns, microservices, and intricate architectures capable of accommodating millions of users. This mindset suits larger companies but often leads to inefficiencies in a startup environment. Here, focusing on scalability can be an expensive form of procrastination, as we preemptively optimize for nonexistent users and address potential issues that may never arise. My three-month rule compels me to write straightforward, albeit imperfect, code that delivers tangible results, allowing me to truly understand user needs.
My Current Infrastructure Strategies and Their Strategic Value
1. Consolidated Operations on a Single VM
I’m currently running everything—database, web server, background jobs, and Redis—on a single $40/month virtual machine. While this setup has zero redundancy and relies on manual local backups, it has proven to be incredibly insightful. In just two months, I’ve gained a clearer understanding of my resource needs than I could have gleaned from any capacity planning document. For instance, despite initial assumptions about needing significant resources for an AI-centric platform, I’ve found that it only peaks at 4GB of RAM. The complex Kubernetes architecture I nearly implemented would have merely managed idle containers.
When the server crashes (which it has done twice so far), I receive valuable data on what truly fails—often contrary to my expectations.
2. Simplified Hardcoded Configurations
My configuration is as straightforward as it gets:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
No configuration files or environment variables—just constants peppered throughout the codebase. Changing a value necessitates a redeployment, but herein lies the hidden advantage: I can quickly search my code for any configuration across the board. Every pricing alteration is documented in Git history, and
