Embracing the 3-Month Rule: A Practical Approach to Unscalable Solutions
In the realm of startups and software development, there’s a common piece of wisdom that many entrepreneurs and engineers have heard: “Do things that don’t scale,” an adage attributed to Paul Graham. While this advice is frequently discussed, achieving its implementation in coding practices remains largely unexplored.
Having dedicated the past eight months to building my AI podcast platform, I’ve crafted a straightforward framework I call the “3-Month Rule.” Simply put, any unscalable solution I implement receives a three-month trial period. If it demonstrates its value, it gets a proper build-out; if not, it gets phased out.
Rethinking Our Engineering Mindset
As engineers, we’re often indoctrinated to focus on scalable solutions from the outset. We talk about microservices, distributed systems, and robust architectures that accommodate millions of users. However, this mindset often belongs more to larger organizations than to agile startups.
At a startup, the pursuit of scalability can sometimes lead to unnecessary preemptive measures. Often, we find ourselves optimizing for future users that don’t yet exist and solving problems that may never arise. My 3-month rule encourages me to write straightforward, albeit imperfect, code that actually gets shipped. This hands-on approach reveals important insights about what users truly need.
Current Infrastructure Hacks That Make Sense
1. Consolidated Resources on a Single VM
For just $40 per month, I run my entire application—database, web server, background jobs, and caching—all on a singular, non-redundant virtual machine. While this setup lacks redundancy and relies on manual backups, it has taught me more about my actual resource requirements in two months than any form of capacity planning could.
Interestingly, my platform, which is deemed “AI-heavy,” peaks at about 4GB of RAM. The elaborate Kubernetes architecture I nearly implemented would have been managing empty spaces. Plus, when it crashes (and it has), I receive direct data on what actually fails—insights that often surprise me.
2. Simplistic Configuration Management
Rather than utilizing config files or environment variables, I employ hardcoded constants throughout my code:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This method permits me to
One Comment
Fantastic post! I appreciate the emphasis on pragmatic, hands-on approaches—especially the 3-Month Rule—as a way to stay agile and avoid over-engineering. It reminds me of the value in embracing “minimum viable solutions” initially, then iterating based on real user feedback, rather than preemptively building for scenarios that may never materialize.
Your example of consolidating resources on a single VM resonates with the concept of “building in complexity,” where understanding actual needs often surpasses theoretical planning. Similarly, your choice to hardcode constants highlights the importance of simplicity during early stages; it allows for rapid experimentation and validation before investing time in more flexible configurations.
Overall, your framework encourages startups to focus on delivering real value quickly, learning continuously, and resisting the temptation to optimize prematurely. It’s an approach that can save both time and resources—and ultimately lead to a more resilient product that truly meets user needs. Thanks for sharing these practical insights!