Embracing the 3-Month Rule: A Pragmatic Approach to Development
In the startup world, the mantra “Do things that don’t scale,” famously advocated by Paul Graham, is often echoed, yet concrete implementation strategies are seldom discussed. As a developer who has dedicated the past eight months to building an AI podcast platform, I’ve found a practical approach that I like to call the “3-Month Rule.” This framework allows me to test unscalable solutions within a defined timeframe, encouraging quick iterations and essential learning.
The 3-Month Experimentation Framework
As engineers, we often feel compelled to construct scalable systems from the outset. We immerse ourselves in design patterns, microservices, and distributed architecture—a methodology fitting for large enterprises. However, for startups, pursuing scalability too early can lead to wasted resources and unnecessary complexities. My 3-month rule helps me focus on crafting straightforward, if imperfect, solutions that yield valuable insights into user behavior and needs.
Current Infrastructure Solutions: My Ingenious Hacks Explained
1. Single Virtual Machine Deployment
Everything from the database to background jobs runs on a single, $40-per-month virtual machine (VM). There’s no redundancy and manual backups are done to my local machine.
Why This Works: Over two months, I’ve gathered more real data about resource requirements than any theoretical documentation could provide. My platform, despite being “AI-heavy,” peaks at 4GB RAM usage. If I had pursued a complex Kubernetes setup, I would have ended up managing idle containers. When the system crashes—even in those instances—I gain immediate insight into the failure points, which often surprise me.
2. Simplistic Configuration Management
The configuration is hardcoded—a few constants scatter through the code. Adjusting anything requires a redeployment process.
Advantages: While this might seem primitive, it allows me to quickly search for configuration values across my codebase. Each pricing update is tracked in the Git history, providing a straightforward way to review changes. Creating a full-fledged configuration service would have taken a week, but with only three adjustments in three months, I’ve saved countless engineering hours.
3. Using SQLite in a Production Environment
My multi-user web application employs SQLite, a choice of just 47MB for the entire database.
Key Takeaway: My usage pattern reveals that the application runs 95% reads and only 5% writes—ideal for SQLite. Starting with a more complex database like
One Comment
This is a fantastic practical perspective on embracing “doing things that don’t scale” early on. I really appreciate how you highlight the value of rapid experimentation and learning—sometimes the best insights come from straightforward, even seemingly primitive setups. Your use of a single VM and SQLite underscores that resource efficiency and simplicity can provide critical feedback loops, which are often overlooked in favor of complex, scalable architectures from the outset.
Additionally, I think your approach aligns well with the concept of “minimum viable infrastructure,” where you prioritize learning and iteration over perfection. It reminds me of the idea that the goal isn’t to build a perfect system initially but to understand user behavior and system limits firsthand.
One suggestion for scaling this philosophy might be to incorporate a simple monitoring setup—perhaps lightweight logs or a basic health check—that can give even more granular insights into system performance during these early phases. As your data accumulates and the product grows, you’ll have a much stronger foundation when transitioning to more scalable solutions, avoiding unnecessary rework. Thanks for sharing your framework—definitely a helpful reminder that sometimes, less is more in the startup phase!