Embracing the 3-Month Rule: A Pragmatic Approach to Developing Scalable Solutions
In the startup landscape, there’s a well-known mantra from Paul Graham: “Do things that don’t scale.” While this advice has undoubtedly resonated with many entrepreneurs, the challenge lies in understanding how to apply it effectively within the context of coding.
Over the past eight months, while developing my AI podcast platform, I’ve adopted a straightforward yet impactful framework: any non-scalable solution gets a trial period of three months. After this period, each approach must either demonstrate its usefulness and be refined into a robust solution or be phased out.
As engineers, we’re often trained to prioritize scalable architectures right from the outset—think microservices, distributed systems, and complex design patterns aimed at accommodating millions of users. However, this mindset can lead to unnecessary complications, particularly in a startup environment where the scale of real user engagement is still uncertain. My 3-month rule compels me to focus on simplicity and effectiveness, allowing me to produce working code that reveals genuine user needs rather than hypothetical scenarios.
Innovative Infrastructure Hacks: Learning Through Simplicity
Here are some current strategies I’ve implemented that facilitate learning and growth, while ensuring that I remain agile in my development approach:
1. Unified VM Usage
All aspects of my platform—database, web server, background jobs—operate on a single virtual machine costing just $40 per month. This setup lacks redundancy and relies on manual backups to my local system.
Why this is a wise choice: I’ve gained fresh insights into my actual resource requirements. In just two months, I learned that my platform, which is designed around AI, rarely exceeds 4GB of RAM usage. The complex Kubernetes architecture I initially considered would have been overkill, managing idle containers rather than addressing real, pressing needs.
When the system does crash (which has occurred twice), I receive immediate feedback on the true points of failure—often surprising me.
2. Hardcoded Configuration
In my codebase, configurations such as pricing tiers and user limits are hardcoded directly into the files. There are no external configuration files or environment variables to manage—any changes necessitate a redeployment.
The benefit of this approach lies in its simplicity. I can quickly search my entire codebase for specific values, track revisions in git history, and ensure that all configuration updates go through a review process, albeit a self-review. What would have taken a
One Comment
This framework of a 3-month trial period for non-scalable solutions is a compelling approach that emphasizes learning and practical validation over premature engineering complexity. I appreciate how it encourages focusing on delivering value and understanding user needs before over-engineering—especially crucial in startup environments where agility is paramount.
Your emphasis on simplicity, such as using a unified VM and hardcoded configurations, echoes the principle that “good enough” often surpasses overly complex solutions in early stages. It’s fascinating how the real-world feedback from crashes and resource usage informed your architectural choices, preventing the pitfall of building solutions ahead of actual demand.
This pragmatic approach reminds us that embracing failure, learning quickly, and iterating within a limited timeframe can lead to more sustainable, scalable solutions in the long run. Thanks for sharing these actionable insights—definitely inspiring for engineers aiming to balance simplicity and growth.