The 3-Month Rule: A Practical Approach to Building What Matters in Tech
In the startup world, the mantra “Do things that don’t scale,” famously championed by Paul Graham, is often discussed but seldom translated into actionable strategies, particularly in software development. After eight months of building my AI podcast platform, I discovered a straightforward yet effective approach: the 3-Month Rule. This principle dictates that every temporary and unscalable solution has a three-month lifespan; at the end of this period, I assess its value and determine whether it evolves into a sustainable solution or is discarded.
Understanding the Challenge of Scalability
As engineers, our instinct is to create robust, scalable architectures from the very beginning, incorporating microservices, distributed systems, and sophisticated design patterns meant to accommodate millions of users. However, this kind of forward-thinking can turn into costly delays for fledgling startups. Often, we find ourselves preemptively solving problems for users who haven’t even come onboard yet. The 3-Month Rule compels me to streamline my code, allowing for rapid deployment and genuine feedback on what users truly need.
My Pragmatic Infrastructure Strategies
1. Consolidating to One Virtual Machine
Currently, all of my platform’s components—including the database, web server, and background processes—are hosted on a single, budget-friendly $40/month virtual machine. While this setup lacks redundancy and relies on manual backups, it has taught me more about my resource requirements in two months than any extensive capacity planning could have. My AI platform consistently uses only about 4GB of RAM, revealing that the complex Kubernetes configuration I had envisioned would have been wasted effort on empty component management.
2. Embracing Hardcoded Configurations
By utilizing hardcoded constants for configurations, I simplify the entire process. Changes in pricing tiers or user limits entail a quick redeployment, avoiding the complications of configuration services that would require significant engineering hours. The result? Streamlined updates tracked efficiently in version control, enabling me to optimize time while limiting tedious tasks.
3. Utilizing SQLite for Lightweight Performance
While it may seem unconventional, I’ve opted for SQLite as my database solution, and it has proven surprisingly effective in handling peak loads. This choice allowed me to uncover that my application primarily requires reads over writes, perfectly aligning with SQLite’s strengths. Had I begun with a more complex solution like Postgres, I would have wasted time addressing issues that were not yet relevant.
