Title: Embracing the 3-Month Rule: A Practical Framework for Scalable Innovation
In the world of technology entrepreneurship, Paul Graham’s adage, “Do things that don’t scale,” has become a cornerstone for fledgling startups. Yet, there’s a notable lack of conversation around how to effectively apply this philosophy, especially in the realm of coding. Over the past eight months, as I’ve been developing my AI podcast platform, I’ve devised a straightforward framework: every unscalable solution receives a three-month trial period. After that, we either elevate it to a more robust version based on proven value or retire it altogether.
As engineers, we often find ourselves inclined to create solutions that can support massive scalability right from the onset. The allure of intricate design patterns, microservices, and distributed systems can be irresistible, especially as they promise to handle millions of users seamlessly. However, this mindset can be an expensive trap for startups, leading to efforts that focus on solving hypothetical problems for nonexistent users. My three-month rule compels me to embrace simplicity, allowing for rapid deployment that directly addresses user needs.
Current Unscalable Strategies and Their Unexpected Wisdom
Here are some of the “hacks” I’ve employed in my infrastructure that might seem primitive but have yielded significant insights:
1. Consolidated Operations on a Single VM
By hosting my database, web server, background jobs, and Redis on a single $40 per month virtual machine, I’ve sacrificed redundancy and automated backups—but gained valuable lessons. This straightforward approach has unveiled my actual resource consumption over two months, revealing that my so-called “AI-heavy” platform only maxes out at 4GB of RAM. The intricate Kubernetes setup I considered would have simply maintained idle containers.
When the server crashes—an occurrence I’ve encountered twice—I gather invaluable information about the points of failure. Surprisingly, these failures are rarely what I anticipated.
2. Hardcoded Configuration Values
For ease, I’ve opted for hardcoded configurations like:
python
PRICE_TIER_1 = 9.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
With constants embedded throughout the code, deployment means a simple redeployment after any change, while providing immediate traceability. Instead of taking a week to create a configuration service, I’ve adjusted these settings merely three times in three months—saving countless hours of engineering effort.
3. SQLite as My Production Database
In a surprising
One Comment
Thank you for sharing this insightful post. I love how you emphasize the importance of rapid experimentation and learning through unscalable solutions. The “3-month rule” acts as an effective discipline—allowing startups to validate ideas quickly without over-engineering from the start.
Your examples, like consolidating infrastructure on a single VM and hardcoding configurations, highlight that simplicity often accelerates feedback cycles and prevents paralysis by overplanning. It also reminds me of the concept of “minimum viable infrastructure”—getting something in front of users fast, then iterating based on actual usage rather than assumptions.
One thought is to consider how to balance these early-stage sacrifices with eventual robustness. For example, once a feature proves its value, gradually introducing modularity and better error handling can prevent technical debt from hampering growth. Your approach shows that pragmatic trade-offs, made in the short term, can inform more sustainable scaling strategies later.
Overall, your framework encourages a mindset of disciplined experimentation—by testing hypotheses quickly, measuring real user engagement, and then scaling confidently. Thanks for sharing this practical perspective!