Embracing the 3-Month Rule: A Strategic Approach to Non-Scalable Solutions
In the entrepreneurial realm, there’s a well-known mantra from Paul Graham: “Do things that don’t scale.” However, the challenge lies in how to authentically apply this wisdom to the world of coding. After dedicating eight months to developing my AI podcast platform, I’ve created a straightforward framework to tackle this—every unscalable workaround is allotted exactly three months. At the end of this period, the solution is either validated and refined, or it’s discarded.
Understanding the Startup Mindset
As software engineers, we are often groomed to prioritize scalable architectures and solutions. Concepts like design patterns, microservices, and distributed systems dominate our thinking, leading us to create sophisticated frameworks that can handle vast user bases. Yet, this mindset can be a setback for startups. Focusing on scalability too soon can result in costly delays, as you find yourself optimizing for future users who may never materialize, while neglecting the immediate needs of current users.
My 3-month initiative encourages me to craft straightforward, albeit imperfect, code that can be deployed quickly. This approach not only expedites learning about user needs but also provides valuable insights into product direction.
Key Infrastructure Strategies That Are Surprisingly Effective
1. Consolidating Services on One Virtual Machine
Currently, my database, web server, background processes, and caching system all operate on a single virtual machine, costing just $40 a month. While this setup lacks redundancy and relies on manual backups to my local machine, it has significantly sharpened my understanding of actual resource requirements. I quickly learned, for instance, that my AI platform peaks at 4GB of RAM, revealing that an elaborate Kubernetes configuration would have been excessive, managing empty containers instead of addressing real issues.
When the system crashes, which has happened twice so far, I receive concrete data on what fails, consistently challenging my assumptions.
2. Embracing Hardcoded Configuration
My current approach features constants scattered throughout the codebase—think pricing tiers and user limits—with changes necessitating a complete redeployment. While it may seem inefficient, this method affords me quick searches across the entire codebase, simplifying tracking and code reviews. With changes made only three times over three months, I’ve saved time and effort by cutting down on lengthy configuration service setups.
3. Using SQLite for Production
Running SQLite for a multi-user web application has proven
One Comment
This post highlights a crucial balance many founders and engineers grapple with: the tension between building scalable architectures and executing quick, learnable solutions early on. I really appreciate the “3-month rule” framework—setting a clear timeframe for non-scalable work helps prevent endless optimization traps and keeps momentum alive.
Your example of consolidating services on a single VM resonates strongly, as it emphasizes the value of real-world data over theoretical best practices. Sometimes, simplicity not only accelerates development but also offers invaluable insights into actual bottlenecks and resource needs.
I also find your acceptance of hardcoded configurations insightful—for early-stage projects, agility often outweighs perfection. As the startup evolves, transitioning to more flexible solutions can be phased in strategically.
Overall, this approach champions iterative learning and pragmatism over premature optimization, which can be a game-changer for early product validation. Thanks for sharing these practical strategies—definitely food for thought for anyone navigating the chaos of early startup tech development!