The 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Software Development
In the world of startups and tech entrepreneurship, one piece of advice frequently shared by Paul Graham resonates deeply: “Do things that don’t scale.” Yet, when it comes to implementation—especially in coding and software development—this advice often gets overlooked.
After dedicating eight months to developing my AI podcast platform, I’ve devised a straightforward framework that I call the “3-Month Rule.” This dictates that any non-scalable hack I implement has a lifespan of just three months. At the end of this period, that hack either proves its value and is transitioned into a scalable solution, or it is eliminated altogether.
Why the 3-Month Rule Matters
As engineers, we’re typically taught to focus on scalable solutions from the outset. We immerse ourselves in design patterns, microservices, and distributed systems—constructions meant for handling thousands, if not millions, of users. However, such an approach can be ill-fitting in a startup environment where you’re often developing for a user base that doesn’t yet exist.
My 3-month framework compels me to adopt a more experimental mindset. It encourages me to write straightforward, albeit imperfect code, which allows me to glean valuable insights into what users actually need.
Current Infrastructure Hacks and Their Strategic Value
1. Consolidated Infrastructure on One VM
Currently, my entire stack—including the database, web server, and background jobs—runs on a single $40 monthly virtual machine. This setup, while seemingly fragile due to its lack of redundancy and manual backup processes, has been insightful. In just two months, I’ve garnered more information about my resource needs than any capacity-planning document could provide.
This simplicity highlights a crucial learning: My platform, despite being “AI-heavy,” operates smoothly with only 4GB of RAM. Any sophisticated infrastructure I might have built would have been underutilized.
2. Hardcoded Configurations
In my code, configuration values are hardcoded as constants:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this might seem cumbersome—lack of config files or environment variables means I must redeploy to make any updates—it offers a unique advantage. By being able to quickly search for these values throughout my codebase, I maintain a
