Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions
In the startup world, many of us are well-acquainted with Paul Graham’s famous mantra: “Do things that don’t scale.” However, a common challenge arises—how do we effectively implement this concept in our coding practices?
After dedicating eight months to developing my AI podcast platform, I’ve devised a framework centered around a straightforward principle: each unscalable approach receives a lifespan of just three months. Following that period, we assess whether the solution has proven its worth for a more permanent implementation or if it should be discarded.
Why Temporary Solutions Matter
As software engineers, we’re often encouraged to design scalable solutions right from the outset. We immerse ourselves in architectural designs like microservices and distributed systems, aiming to accommodate thousands, if not millions, of users. But in a startup environment, focusing on so-called “scalable” code can lead to costly delays, as we’re attempting to optimize for users that may not even exist yet.
This is where my 3-month rule comes into play. It compels me to embrace simplicity and prioritize the development of straightforward, albeit imperfect, code that can be launched quickly. The goal is to learn first-hand what users genuinely need.
Key Infrastructure Hacks: My Practical Choices
1. Consolidated Operations on One Virtual Machine
All of my services—including the database, web server, background processing, and caching—operate on a single $40/month virtual machine. While this approach lacks redundancy and relies on my manual backups, it has proven invaluable. Within just two months, I’ve gathered more insights about my resource requirements than any extensive capacity planning document could offer. My so-called “AI-heavy” platform only requires about 4GB of RAM, which means the complex Kubernetes setup I nearly implemented would have been managing unnecessary resources and empty containers.
When system crashes occur (which have happened twice), I gain genuine insights into the causes—surprising revelations that often differ from my initial expectations.
2. Hardcoded Configuration Values
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Forget about complicated configuration files or environment variables; I use simple constants throughout my code. While this means that any changes require redeployment, it also enables me to search my codebase rapidly for specific
One Comment
This post offers a refreshingly pragmatic approach to balancing speed and scalability in early-stage development. The 3-month rule essentially champions rapid iteration and learning over premature optimization—a mindset that can save startups significant time and resources. I appreciate how the concrete examples—like consolidating all services on a single VM and using hardcoded values—highlight the value of simplicity in the initial phases. It’s a reminder that understanding real user needs often comes before sophisticated infrastructure.
One aspect to consider for future iterations is how to gradually evolve from these temporary measures to more robust solutions once the product gains traction. For example, integrating simple configuration management tools or transitioning important components into modular microservices as scaling needs grow.
Overall, the approach underscores the importance of agility and empirical learning in startup engineering. Thanks for sharing these valuable insights!