The 3-Month Rule: A Strategic Approach to Rapid Learning in Tech Development
In the tech world, one of the most recognized pieces of wisdom comes from Paul Graham: “Do things that don’t scale.” Yet, how does one practically apply this advice, especially in software development?
After dedicating eight months to building my AI podcast platform, I’ve devised a straightforward framework: any unscalable coding tactic receives just three months to prove its worth. If it fails to demonstrate its value within that timeframe, it’s time for it to go.
Typically, as engineers, we are trained to construct solutions designed for scaling from the outset. We immerse ourselves in elegant architecture—think microservices, distributed systems, and design patterns—all tailored for handling millions of users. However, in the startup sphere, this mindset can translate into costly procrastination. Often, we find ourselves optimizing for future users who haven’t yet arrived and tackling challenges that may never materialize. My three-month guideline encourages me to produce straightforward, albeit “messy,” code that I can deploy quickly, all while gaining valuable insights into what users genuinely need.
Insights From My Current Technical Hacks
1. Consolidating Resources on a Single VM
My entire platform—spanning the database, web server, background processing, and Redis—is hosted on a single virtual machine costing just $40 a month. It’s a minimalist approach with no redundancy and manual backups.
Why is this strategy effective? In a mere two months, I’ve gained more knowledge about my actual resource demands than any extensive capacity planning could have provided. For instance, I’ve discovered that my “AI-heavy” platform peaks at just 4GB of RAM, sparing me from the headache of managing an ornate Kubernetes setup that would have ended up operating primarily empty containers. Every time it crashes, which has happened twice so far, I obtain concrete data about the breakdown—insights that are frequently unexpected.
2. Embracing Hardcoded Configurations
With configuration values hardcoded directly into my code, I can quickly grep through my entire codebase for any setting. Although this means redeploying for any change, it’s proved nifty after three months with only three modifications. The conventional route of establishing a configuration service would have taken upwards of a week, but these minor redeployments have only consumed about 15 minutes of my time.
3. Utilizing SQLite in a Multi-User Environment
Surprisingly, I
One Comment
Great insights! I appreciate how you’ve embraced a Lean, experimental mindset by setting a clear three-month window for evaluating unscalable tactics. This approach reminds me of the importance of rapid iteration and learning, especially in startup environments where time and resources are limited. Your example of consolidating resources onto a single VM highlights the value of “getting your hands dirty” early on—gaining real-world data that guides future scaling decisions rather than overengineering upfront.
Similarly, the willingness to accept “messy” code and hardcoded configs underscores that the primary goal is learning and validation, not perfection. It’s a pragmatic reminder that sometimes, simplicity and speed trump scalability at the beginning stages. Also, your approach to dismissing elaborate capacity planning in favor of real user data exemplifies a practical border between theory and reality.
Looking forward, it’d be interesting to see how these initial experiments inform the transition to more robust architectures when user demand grows. Thanks for sharing your journey—your framework is a valuable reference for any developer testing new ideas under startup constraints!