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The 3-Month Rule: My Technical Framework for Doing Things That Don’t Scale Variation 463

Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions

In the world of startups, where agility and innovation are paramount, it’s common to encounter the sage advice from Paul Graham: “Do things that don’t scale.” However, the real challenge lies in effectively applying this concept—particularly in the realm of coding.

After dedicating the past eight months to developing an AI podcast platform, I’ve devised a straightforward strategy that I call the “3-Month Rule.” This framework stipulates that each non-scalable solution is granted three months to determine its viability. At the end of this period, it either demonstrates its worth and is transformed into a robust system or is discarded altogether.

The Challenge of Scalable Thinking

In our training as engineers, we’re often conditioned to focus on building scalable architectures from the outset—leveraging design patterns, microservices, and distributed systems that are meant to handle vast user bases. While appealing, this mindset is often representative of large corporations rather than startups.

At a startup, the pursuit of scalability can quickly become a costly exercise in procrastination. We may find ourselves optimizing for hypothetical users and addressing challenges that may never arise. My 3-month rule compels me to create straightforward, albeit imperfect, code that not only gets delivered but also provides invaluable insights into the real needs of users.

Current Infrastructure Practices: Unpacking the Methodology

Here are some of my current “hacks” and the rationale behind them:

1. Single Virtual Machine (VM) Deployment

I’ve consolidated my database, web server, background jobs, and Redis into a single $40/month VM. While it may lack redundancy, this approach has provided profound insights into my resource consumption. I’ve learned that my resource-intensive platform peaks at just 4GB of RAM, sparing me from overcomplicating my architecture. Crashes, which have occurred twice, have offered real-world data on my system’s weaknesses—data that enhances my understanding far more than theoretical capacity planning could.

2. Hardcoded Configuration

Instead of relying on configuration files or environment variables, I employ hardcoded constants, such as pricing and user limits, throughout my codebase. This tactic might seem inefficient, but it allows me to quickly locate configurations across the entire project and track changes systematically through version control. The time savings from this method have significantly outweighed any drawbacks, demonstrating that effective simplicity often trumps overengineering.

One Comment

  • Thank you for sharing this thoughtful approach. The 3-Month Rule resonates strongly, especially in the context of startups where rapid iteration is key. I appreciate how it balances the urgency of getting tangible results with the discipline to evaluate solutions before overinvesting. Your examples—using a single VM to gather real-world resource data and relying on hardcoded configurations for speed—highlight the value of pragmatic trade-offs. These techniques remind us that sometimes, the best way to learn a system’s needs is by making it work in the real world, even if it’s not perfectly scalable initially. Embracing this mindset can prevent paralysis by analysis and foster a mindset of continuous improvement. Looking forward to seeing how these principles evolve as your platform grows!

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