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The 3-Month Rule: My Technical Framework for Doing Things That Don’t Scale Variation 873

Embracing the 3-Month Rule: A Strategic Framework for Agile Development

In the world of tech startups, the advice from renowned entrepreneur Paul Graham—”Do things that don’t scale”—often serves as a guiding principle. However, the challenge lies in translating this philosophy into actionable coding practices. After eight months of developing my AI podcast platform, I have crafted a straightforward approach: any temporary, non-scalable solution is given a lifespan of just three months to demonstrate its viability. If it proves beneficial, it gets the attention it deserves; if not, it’s time to let it go.

The typical mindset of software engineers is to pursue scalable solutions right from the outset, focusing on complex architectures designed to support millions of users. While this approach might suit established companies, it can lead startups down the path of expensive procrastination. By implementing my 3-month rule, I encourage myself to produce straightforward, albeit imperfect, code that actually makes it to production, helping me uncover the real needs of my users.

Key Technical Approaches and Their Value

1. Consolidated Infrastructure on One VM

I’ve opted to run my entire stack—including the database, web server, background jobs, and caching—on a single $40/month virtual machine. While this approach eliminates redundancy and relies on manual backups, it has provided invaluable insights. Within just a couple of months, I’ve gained a clearer understanding of my resource needs than I would have from extensive planning documents. For instance, it turns out my platform typically requires only 4GB of RAM. The complex Kubernetes setup I once considered would have been unnecessary overhead.

When outages occur, they provide real, actionable data on failure points, helping me learn where my assumptions were incorrect.

2. Simplified Hardcoded Configurations

With constants such as tiered pricing and user limits directly embedded in my code, I can make swift updates by redeploying. While storing configuration in separate files may be a best practice, the simplicity of my setup allows me to track each change easily through Git. This streamlined method means that minor alterations take mere minutes rather than consuming substantial engineering resources.

3. Using SQLite in a Production Environment

Despite SQLite traditionally being viewed as inadequate for multi-user applications, it has proven itself capable in my case. With a compact database size of 47MB and more than enough room to accommodate concurrent usage, I’ve discovered that the overwhelming majority of my operations are read-heavy. Had I opted for

One Comment

  • This post offers valuable insights into an agile and pragmatic approach for startups balancing rapid iteration with technical debt. The 3-month rule is a compelling concept—it encourages testing solutions quickly without over-investing in scalability from day one, which can indeed save time and resources early on. Your emphasis on real-world testing and learning from outages resonates strongly, as it shifts the focus toward practical understanding rather than theoretical perfection.

    I especially appreciate your approach of consolidating everything into a single VM, which allows for quick experimentation and immediate feedback—this can be a game-changer for early-stage products. While some might worry about simplicity compromising robustness, your experience demonstrates that understanding the actual user needs and bottlenecks can often be achieved more effectively with straightforward setups first.

    Using SQLite in production is a bold choice, but your success highlights that with thoughtful constraints and understanding of workload characteristics, simpler tools can suffice for many startup scenarios.

    Overall, your framework underscores an essential principle: start simple, iterate fast within a defined window, and make informed decisions based on real data, not just assumptions. This approach could serve as a valuable blueprint for early-stage startups aiming to avoid unnecessary complexity and focus on delivering value.

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