Embracing the Unscalable: A Practical Approach to Rapid Prototyping in Tech
In the entrepreneurial landscape, one maxim that frequently emerges is Paul Graham’s mantra: “Do things that don’t scale.” However, while many endorse this philosophy, the practical implementation—particularly within the realm of coding—often remains overlooked. After eight months of developing my AI podcast platform, I’ve devised a straightforward technical framework that I call the “3-Month Rule.” This approach allows me to test various unscalable solutions for a defined period, where each hack has three months to prove its worth before either being refined or discarded.
The Reality of Scalable Solutions
As engineers, we’re often conditioned to prioritize scalability from day one—designing intricate systems with microservices, distributed architectures, and more, all aimed at catering to hefty user bases. Such approaches may be suitable for established companies, but in a startup environment, chasing scalability can lead to costly delays and distracting red herrings. My 3-month rule pushes me to focus on pragmatic, minimalistic coding practices that deliver immediate results and genuine insights into user needs.
Insights from My Current Infrastructure Strategies
1. Unified Virtual Machine Setup
I run my entire system—including the database, web server, and job processing—on a single, low-cost virtual machine. While it may lack redundancy and data replication, this setup has taught me invaluable lessons about my resource usage within just two months. With actual peaks reaching only 4GB of RAM, I realized that a complex Kubernetes architecture would have been an over-engineered solution for managing empty containers. Any crashes—two so far—have provided direct data regarding fail points, which were not what I initially anticipated.
2. Directly Hardcoded Configuration
In my code, configuration values are directly hardcoded rather than abstracted into config files or environment variables. This may sound inefficient, but it yields an unexpected advantage: I can quickly find any parameter throughout my codebase. Additionally, deploying changes only takes about fifteen minutes versus potentially wasting days developing a configuration management system. Over three months, I’ve made only a handful of changes, confirming the practical efficiency this method brings.
3. Production SQLite
Surprisingly, I’ve opted to use SQLite for a multi-user web application, resulting in a database size of only 47MB. This choice comfortably accommodates up to 50 concurrent users. My data access patterns showed that most operations are reads instead of writes—
One Comment
Thank you for sharing your practical insights and your “3-Month Rule” approach—it’s a refreshing reminder that in early-stage development, speed and learning often outweigh perfect scalability. I especially appreciate your emphasis on quickly testing unscalable solutions like a single VM setup and hardcoded configs; these tactics can reveal real user behaviors and system bottlenecks without the overhead of overengineering.
One thought to consider as you iterate: while SQLite works well for your current user base, keep an eye on how your data access patterns evolve, especially if read/write ratios change or user count grows. At that point, a lightweight database solution like PostgreSQL in a single-server setup or even a managed database service could offer a smooth transition without sacrificing agility.
Your approach underscores that thoughtfully embracing unscalable solutions for rapid validation can inform smarter scaling decisions down the line. Looking forward to seeing how your infrastructure evolves as your platform grows!