Embracing Imperfection: The Three-Month Experiment for Non-Scalable Solutions in Tech
In the realm of technology startups, one frequently cited piece of wisdom from Paul Graham is the encouragement to “do things that don’t scale.” However, while many are aware of this advice, few delve into practical strategies for its implementation within coding and development processes.
After dedicating the past eight months to developing my AI podcast platform, I have adopted a straightforward framework: each unscalable solution is granted a lifespan of three months. At the end of this period, it must either demonstrate its worth and transition into a more refined iteration, or it will be phased out.
The reality is that as engineers, we are often conditioned to prioritize scalable solutions from the outset. We dream in terms of design patterns, microservices, and robust architectures capable of handling vast user bases. This mindset is often tailored to larger enterprises, but in the startup environment, such scalability can lead to costly delays. My three-month rule compels me to deploy simple, sometimes ‘messy,’ code that facilitates actual product delivery, providing real insights into user needs.
Innovative Infrastructure Hacks: Why They Work
1. Consolidated Infrastructure on One VM
My infrastructure runs entirely on a single virtual machine, housing the database, web server, background tasks, and caching—all for a mere $40 a month. This approach means zero redundancy and manual backups to my local system.
This setup is far from reckless; in just two months, I’ve gained invaluable insights into my actual resource requirements, far exceeding what any capacity planning model could offer. Contrary to initial assumptions about needing extensive resources, my platform’s peak usage amounts to just 4GB of RAM. The complex Kubernetes architecture I initially considered would have ultimately led to more overhead without any actual user demand.
2. Simplified Hardcoded Configurations
My configuration is straightforward—no external files or environment variables, just constants embedded in the code. Adjusting a value necessitates redeployment, but it provides an unexpected advantage: I can easily search my codebase for any configuration parameter, making updates simple and transparent.
Creating a dedicated configuration service could take a week, yet I have modified these values just three times in three months. This approach saves me extensive engineering hours while still allowing for rapid iteration.
3. Utilizing SQLite in Production
Yes, my application runs on SQLite, serving a multi-user environment with a database size of just 47MB. It
One Comment
This post offers a compelling perspective on embracing imperfection through the 3-month rule, especially in the startup context where rapid iteration is crucial. I appreciate how you highlight the value of implementing simple, non-scalable solutions temporarily to gather real-world insights before investing in more complex architectures.
Your example of consolidating infrastructure on a single VM and using hardcoded configurations resonates with the principle of “doing things that don’t scale”—it allows for quick feedback loops and cost-effective experimentation. The decision to run SQLite in production further underscores the importance of aligning infrastructure choices with current needs rather than potential future demands, avoiding over-engineering from the outset.
This approach reminds me that in early-stage development, speed and learning often trump scalability, and your framework offers a practical method to maintain that balance. Have you considered documenting specific criteria or metrics to decide when a solution is ready to evolve? That could further clarify how to transition from “workable mess” to scalable architecture when the time is right. Thanks for sharing your experience—it’s a valuable reminder for engineers to prioritize learning and iteration over premature optimization.