The 3-Month Rule: A Practical Approach to Unscalable Solutions in Software Development
In the entrepreneurial landscape, the advice from Paul Graham to “do things that don’t scale” resonates deeply. However, the challenge lies in how to practically apply this paradigm to software development. Having spent the last eight months building my AI podcast platform, I’ve crafted a straightforward framework I call the “3-Month Rule.” This rule asserts that any unscalable approach is granted a lifespan of just three months. After this period, it must either prove its value by being optimized and formalized or be scrapped.
As engineers, we are often conditioned to prioritize scalable solutions from the outset—design patterns, microservices, distributed systems—the kind of architecture that can handle millions of users. This mindset, however, is more suited to larger companies. In a startup environment, focusing on scalability can lead to costly delays, as we may end up optimizing for nonexistent users and unnecessary problems. My 3-Month Rule compels me to implement simplified, even “suboptimal,” coding practices that can actually be deployed, thus revealing genuine user needs.
Insights from My Current Infrastructure Strategies
1. Consolidated Operations on a Sole VM
Everything, from the database to background jobs and Redis, is running on a single virtual machine, costing just $40 a month. This setup offers no redundancy and requires manual backups to my local device.
Why is this considered a clever strategy? These past two months have provided invaluable insights into my actual resource requirements, more so than any capacity planning document could. It turns out my AI-driven platform peaks at 4GB of RAM, meaning the complex Kubernetes infrastructure I had nearly implemented would have ended up managing idle containers. Each time it crashes—which has happened a couple of times—I gain real data about failure points, often contrary to my initial assumptions.
2. Simplified Hardcoded Configurations
Instead of utilizing config files or environment variables, I rely on hardcoded constants in my code, such as:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This method allows me to rapidly track configuration values across my codebase using simple grep commands. Since I’ve only adjusted these parameters three times in three months, the 15 minutes spent redeploying is well worth the time saved compared to the
One Comment
Thank you for sharing such a practical and enlightening approach with the 3-Month Rule. It highlights an essential aspect often overlooked in the pursuit of scalable architecture: the value of rapid experimentation and real-world data, especially in the early stages of a startup. Your emphasis on using unscalable solutions as a means to validate assumptions before investing in more complex systems is a reminder that simplicity often leads to better insights.
I particularly appreciate your method of consolidating operations on a single VM and relying on hardcoded configurations—it embodies the “lean startup” mentality for engineering. This approach not only shortens feedback loops but also helps uncover actual resource requirements and user behaviors that might be missed with over-engineered setups.
In practice, I think this framework can serve as a mindset shift for many developers and founders: focus on building a minimal, functional version first, validate rapidly, then scale intelligently based on actual data and needs. It’s a disciplined way to avoid premature optimization and keeps the focus on learning what truly matters—user needs and system performance in the wild. Thanks for sharing this valuable perspective!