Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Software Development
In the tech world, Paul Graham’s famous advice to “do things that don’t scale” is often echoed but seldom explored in depth, particularly when it comes to implementation in software development. Over the past eight months, as I have been developing my AI podcast platform, I’ve crafted a pragmatic framework: any non-scalable solution I employ gets a lifespan of exactly three months. After this period, the solution must either demonstrate its worth and be developed into a more robust system, or it will be discarded entirely.
As engineers, we are conditioned to think big right from the outset—focusing on scalable solutions like intricate design patterns, microservices, and distributed systems that can theoretically accommodate millions of users. However, in a startup environment, striving for scalability can often lead to wasted resources and energy, as we find ourselves optimizing for users who may never materialize.
My 3-month rule compels me to adopt a more straightforward approach, writing code that is intentional yet arguably imperfect. In doing so, I learn about user needs in real-time rather than relying on assumptions formed in isolation.
My Non-Scalable Solutions and the Wisdom Behind Them:
1. Single-VM Architecture
I’ve consolidated all components—database, web server, background jobs, and Redis—on a single virtual machine costing $40 per month. While I lack redundancy and perform manual backups, this setup has been incredibly insightful.
In just two months, I’ve gained a clearer understanding of my actual resource demands. My platform’s peak utilization is just 4GB of RAM. The complex Kubernetes environment I considered setting up would have led to increased overhead without proportionate benefits. Each crash reveals critical insights about failure points that I would not have anticipated.
2. Hardcoded Configuration Settings
Configurations are directly embedded in the code:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This approach means every change requires a redeployment, but it comes with advantages. I can quickly search my entire codebase for any configuration value, and every change is meticulously tracked in Git. What would take a week to create as a separate configuration service has only cost me 15 minutes of redeployment time across three months.
**3. SQLite for Production Use
One Comment
Thank you for sharing this practical and insightful approach. The 3-month rule effectively balances the need for quick experimentation with the discipline of evaluating whether a solution warrants scaling. I particularly appreciate the emphasis on learning through real-world constraints—such as using a single VM and hardcoded configs—which often reveal bottlenecks and pain points that more complex, ‘perfect’ architectures might obscure.
Your approach aligns well with the concept of iterative development and validated learning, foundational principles in lean startup methodology. It also reminds me that building scalable systems from the outset can sometimes lead to over-engineering, especially in early stages when understanding true user demand is still evolving.
A potential extension of this method could be formalizing a review process at the end of each 3-month cycle, perhaps using metrics or user feedback to determine whether to evolve the current solution or pivot. This disciplined experimentation can help reduce waste and foster innovation rooted in actual usage data.
Thanks again for sharing; it’s a valuable reminder that sometimes, simplicity and quick iteration are the most effective paths to sustainable growth.