Embracing the Non-Scalable: My Three-Month Framework for Technical Growth
In the realm of startups and innovation, the advice from Paul Graham to “do things that don’t scale” resonates deeply. Yet, the conversation rarely dives into how this principle can be applied within the technical aspects of product development. Drawing from my experience while building an AI podcast platform over the past eight months, I’ve crafted a straightforward yet effective framework: each non-scalable solution is granted a three-month trial period. After this timeframe, we assess whether it’s proven its worth and deserves a permanent place in our infrastructure or if it should be retired.
The Startup Mindset: Rethinking Scalability
As engineers, we are often ingrained with the notion that scalable systems are imperative from the outset. Our training emphasizes design patterns, microservices, and complex architectures capable of handling millions of users. However, this approach can lead to unnecessary complexity in a startup environment. Instead of preparing for users who don’t yet exist, we can focus on immediate needs without over-engineering. My three-month rule compels me to adopt straightforward, albeit “imperfect,” solutions that can be delivered quickly and provide real insights into user requirements.
Current Hacks and Their Hidden Wisdom
1. Unified Virtual Machine Approach
I host my entire infrastructure—database, web server, background jobs, and caching—on a single low-cost VM. While there is no redundancy and backups are manual, this strategy has enabled me to understand my actual resource demands within two months. The results were enlightening: instead of needing a complex Kubernetes setup, my “AI-heavy” platform only requires 4GB of RAM. This simplistic approach has provided vital information about failure points, which often aren’t what I initially anticipated.
2. Simple Hardcoded Configuration
My configuration settings are hardcoded directly into the codebase, which means every change requires a redeployment. While it may seem inefficient, the advantage is clear: any configuration value can be quickly located through a straightforward grep command, and every modification is meticulously logged in version control. In just three months, I’ve made very few changes, validating the time saved by avoiding the complexity of a dedicated config management system.
3. Utilizing SQLite in Production
Surprisingly, my multi-user application runs on SQLite with a mere 47MB database size, comfortably accommodating up to 50 simultaneous users. This has fundamentally changed my understanding of data access patterns, revealing that most interactions are read-heavy. Starting
One Comment
This is a compelling approach—embracing the “do things that don’t scale” philosophy early on with a clear, time-bound evaluation framework. I particularly appreciate how the three-month rule provides a disciplined yet flexible way to test assumptions without over-engineering. Your examples—using a single VM, hardcoded configs, and SQLite—highlight how simplicity can accelerate learning and reduce unnecessary complexity.
One point worth emphasizing is that this approach also fosters a mindset of continuous learning and iteration, where each hack serves as a learning experiment rather than a final solution. This can help teams avoid the trap of prematurely optimizing or investing in scalable architectures before truly understanding user needs and system behavior.
Have you considered documenting these experiments and their outcomes? That could create a valuable knowledge base for others facing similar scaling decisions early in their startup journey. Thanks for sharing such practical insights!