Embracing the 3-Month Rule: A Pragmatic Approach to Unscalable Solutions in Software Development
In the world of startups and software development, the mantra “Do things that don’t scale” has become a popular piece of advice, famously shared by Paul Graham. However, many developers struggle to translate this concept into actionable strategies in their coding practices. Over the past eight months of building my AI podcast platform, I’ve adopted a particular tactic known as the 3-Month Rule. This framework not only encourages experimentation but also helps me determine the value of “unscalable” solutions in a structured way.
Why the 3-Month Rule Works
The beauty of this approach lies in its simplicity: give any unscalable hack or workaround a lifespan of three months. After this period, it must either demonstrate its utility and be transitioned into a more robust solution or be discarded altogether. While traditional engineering emphasizes creating scalable solutions from inception—focusing on sophisticated design patterns, microservices, and elaborate architectures—such practices are often misguided in the startup context, where optimizing for potential users can lead to wasted time and resources.
My 3-Month Rule forces me to prioritize straightforward, even “bad” code that can be quickly deployed. This way, I not only learn what truly resonates with users but can also pivot as necessary based on real-world feedback.
Strategic Hacks in My Current Infrastructure
1. Consolidation on a Single VM
All of my application’s components—including the database, web server, background processing, and Redis—run on a single virtual machine that costs just $40 per month. Although this setup may lack redundancy and relies on manual backups to my local system, it allows me to gain invaluable insights into my actual resource needs. For instance, I discovered that the platform’s memory requirements peak at a mere 4GB RAM—information that informs better future scalability decisions.
2. Hardcoded Configuration for Flexibility
Using hardcoded constants for configuration purposes is a deliberate choice—values like PRICE_TIER_1 = 9.99 and MAX_USERS = 100 are spread across code files. This approach means that modifying configurations compels a redeployment, but it also enables swift searches and meticulous tracking of changes through version control. Building a dedicated configuration service would require significant engineering effort, while my approach has sufficed for minimal modifications, demonstrating efficiency in time and resources.
3. Utilizing SQLite in Production
Yes
One Comment
This is a compelling and pragmatic approach that echoes many startups’ real-world constraints—particularly the emphasis on rapid experimentation and learning over pristine architecture early on. The 3-Month Rule provides a structured timeline that balances agility with accountability, ensuring unscalable solutions are either validated quickly or responsibly discarded.
Your use of a single VM to understand resource needs is a great example of leveraging simplicity to inform longer-term decisions. It reminds me that early on, gaining concrete insights can prevent over-engineering. Additionally, the choice to hardcode configurations for speed and simplicity is understandable in the initial phases; however, as your platform grows, investing in flexible configuration management might reduce technical debt.
The use of SQLite in production intrigues me—it’s a reminder that sometimes “unusual” choices early on can be more about practicality than convention. As your user base expands, evaluating how these decisions impact scalability and performance will be crucial, but for now, they seem to serve your unique needs well.
Overall, your approach underscores the importance of intentionally unscaling in the short term to achieve fast, validated progress—a mindset essential for startups navigating uncertainty. Thanks for sharing these valuable insights!