The 3-Month Rule: A Strategic Approach to Unscalable Solutions in Tech Development
In the entrepreneurial world, especially in tech startups, we often hear the mantra of Paul Graham: “Do things that don’t scale.” Yet, the conversation rarely extends to practical implementation, particularly in the realm of coding. Over the past eight months, while developing my AI podcast platform, I’ve clung to a streamlined framework that has dramatically influenced how I approach development: every unscalable hack is given a lifespan of three months. Within this timeframe, it must either demonstrate its value and evolve into a more robust solution, or it will be discarded.
It’s important to recognize that, as engineers, we are often conditioned to prioritize scalable solutions right from the outset. We dive into design patterns, microservices, and distributed systems, meticulously planning for millions of users. However, this perspective often aligns with the operational mindset of larger companies.
In a startup environment, focusing on scalability from day one can morph into a costly delay. We find ourselves optimizing for users who may not even exist, addressing challenges that we might never encounter. By applying my 3-month rule, I am compelled to code in a straightforward, sometimes “less-than-perfect” manner that actually gets deployed. This approach has proven invaluable in helping me gain insights into true user needs.
Current Infrastructure Hacks: Simple Solutions with Significant Impact
1. One Virtual Machine for All Operations
I run my database, web server, background jobs, and Redis all on a single $40/month virtual machine. While this setup lacks redundancy and relies on manual backups to my local device, it has taught me more about my genuine resource requirements in just a couple of months than any extensive capacity planning document could provide. The results reveal that my “AI-heavy” platform hardly hits 4GB of RAM during peak usage—an elaborate Kubernetes architecture I once considered would have merely managed non-existent resources. Each crash (which has happened twice) has also provided valuable lessons about failure points that were not what I initially anticipated.
2. Hardcoded Configuration Values
Instead of using configuration files or environment variables, I have opted for hardcoded constants throughout my codebase. For example:
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This seemingly simplistic method allows for rapid searching through the entire codebase for any
