Embracing the 3-Month Rule: A Framework for Developing Non-Scalable Solutions
In the startup ecosystem, a common piece of wisdom attributed to Paul Graham urges creators to “do things that don’t scale.” While this advice resonates deeply, particularly when it comes to initial product development, practical implementation can often be overlooked—especially in coding practices.
Having spent the past eight months developing my AI podcast platform, I’ve established a straightforward approach: any non-scalable hack I implement is given a lifespan of just three months. At the end of this period, I assess its impact—if it proves valuable, I invest time and resources to enhance it; if not, it gets discarded.
Rethinking Scalability in Early Stages
As engineers, we’re often conditioned to prioritize scalable solutions from the very beginning. We’re drawn to sophisticated architectures like design patterns, microservices, and distributed systems designed to support high user volumes. However, this mindset can be detrimental in a startup environment, where focusing on scalability can equate to wasting precious time and resources on potential user demands that may never materialize.
My 3-month rule compels me to produce straightforward and often imperfect code that actually gets released. Through this process, I learn what my users genuinely require—an invaluable insight that is difficult to glean from theoretical planning alone.
Current Infrastructure Hacks: Smart Decisions for Learning
Here are some of my current strategies, which might seem unorthodox but have proven effective in my development journey:
1. Consolidated Resources on a Single VM
I run my entire infrastructure—database, web server, background jobs, and Redis—on a single virtual machine costing just $40 per month. While this might appear risky due to a lack of redundancy, it has offered significant learning opportunities. Within two months, I’ve gained critical insights into my resource needs—discovering that my “AI-heavy” platform only peaks at 4GB of RAM. With this knowledge, I can avoid the complexities of a Kubernetes setup that would have required management of empty containers.
2. Hardcoded Configuration Values
Instead of using external configuration files or environment variables, I utilize constants directly in my codebase, such as:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This strategy allows me to swiftly track changes through Git and enables
One Comment
Thank you for sharing this practical and thoughtful approach to balancing experimentation with learning in the early stages of product development. The 3-month rule is a compelling way to systematically evaluate rapid prototypes without getting bogged down by unnecessary complexity or over-optimization. I particularly appreciate how you highlight the importance of focusing on immediate learning—by deploying simple, potentially imperfect solutions, you gain real-world insights that often surpass what theoretical planning can provide.
Your examples of infrastructure hacks, like consolidating resources on a single VM and using hardcoded configuration values, reinforce the value of simplicity and agility in early development. These approaches enable quick iterations and cost-effective experimentation—crucial elements for startups operating with limited resources.
One consideration—while these hacks are excellent for learning, it’s also beneficial to establish some boundaries to prevent accruing technical debt that might hinder future scalability. Regular reviews, like your 3-month assessments, are a great practice for maintaining that balance. Looking forward, integrating a gradual transition plan from these initial hacks to more scalable infrastructures as user demand grows would ensure sustained growth without compromising the lessons learned along the way.