Embracing the 3-Month Rule: A Structured Approach to Non-Scalable Solutions in AI Development
When it comes to startup advice, Paul Graham’s famous mantra to “do things that don’t scale” is a cornerstone. However, there’s often a lack of guidance on how to effectively apply this principle in software development. After spending eight months building my AI podcast platform, I’ve adopted a straightforward but effective framework: each non-scalable workaround is allowed a lifespan of three months. At the end of that period, I evaluate its utility. It either earns a place in the final architecture—complete with proper implementation—or it fades away.
As engineers, we instinctively lean towards building scalable systems. We chase after design patterns, microservices, and distributed architectures meant to accommodate vast numbers of users. However, in the startup landscape, such scalable approaches can often lead to costly delays. Rather than focusing on the users who might eventually come, my 3-month rule compels me to develop simpler, more direct solutions that can be deployed quickly. This hands-on experience enables me to better understand the actual needs of my users.
Current Infrastructure Tactics: Why They Make Sense
1. Simplified Architecture on One VM
All components of my application—the database, web server, background jobs, and even Redis—operate seamlessly on a single $40/month virtual machine. This setup lacks redundancy, and I perform manual backups to my local machine.
Why is this approach clever? Within just two months, I gained insights into my real resource requirements that no amount of pre-planning could have provided. I’ve discovered that my AI-centric platform peaks at just 4GB of RAM. The complex Kubernetes environment I once considered would have resulted in managing idle containers.
When issues arise (and they invariably do), I gather valuable data about failures. And the unexpected: it’s never what I anticipated.
2. Hardcoded Configuration: A Pragmatic Choice
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
I utilize hardcoded constants throughout my codebase instead of configuration files or environment variables. Every change mandates a redeployment.
The advantage? I can quickly search my entire codebase for any configuration value. Each price modification is neatly tracked in Git history, and my updates undergo a self-review process. Setting up a full
One Comment
Thank you for sharing this practical and refreshingly honest approach to balancing speed and scalability in startup development. The 3-month rule resonates strongly—it’s a disciplined way to validate assumptions quickly without getting bogged down in unnecessary complexity early on.
I particularly appreciate the focus on rapid, simple infrastructure—using a single VM to gather real-world data before investing in more sophisticated systems. It reminds me that in early-stage projects, understanding actual user needs and system performance often requires direct experience, not just planning.
Additionally, your choice of hardcoded configurations illustrates a pragmatic mindset: prioritizing speed of iteration and traceability over perfect abstractions at an early stage. While this might seem unorthodox in larger-scale environments, for startups and lean teams, it’s a powerful way to iterate rapidly and keep the development cycle tight.
It would be interesting to hear how you decide when to transition from these initial solutions to more scalable, maintainable architectures as your platform grows. Do you have a defined process for scaling or refactoring once a solution shows sustained value beyond the initial three months?