The 3-Month Experiment: My Approach to Unscalable Solutions in Tech
In the realm of startups and technology innovation, Paul Graham’s adage, “Do things that don’t scale,” is widely acknowledged. Yet, the mechanics of putting this principle into action—especially within coding—remain largely unexplored. My journey over the past eight months, while building an AI podcast platform, has led me to develop a straightforward framework: every unscalable hack is given a trial period of three months. Following this period, each hack must either demonstrate its value and transition into a more robust solution or be discarded.
One of the primary challenges we face as engineers is the instinct to create scalable systems from the outset. We’re trained to envision sophisticated architectures—think microservices and distributed systems—that are capable of supporting massive user bases. However, this mindset can sometimes hinder startups, as the cost of preparing for hypothetical users can lead to procrastination and inefficiency. My three-month rule encourages the creation of straightforward, albeit less elegant, solutions that provide real insights into user needs.
Innovative Yet Practical Infrastructure Hacks
1. Consolidating to a Single Virtual Machine
All facets of my application—from the database to the web server and background jobs—operate seamlessly on a single VM costing just $40 a month. While this means zero redundancy and reliance on manual backups, it has offered invaluable insights into my actual resource consumption over the past two months. My expectation of a “resource-heavy” platform proved inaccurate, as I discovered that peak usage only reached 4GB of RAM. The complex Kubernetes setup I nearly implemented would have been a distraction, requiring management of idle containers instead of focus on what truly matters.
2. Using Hardcoded Configuration
Prices, user limits, and model identifiers are hardcoded directly into my application. Instead of utilizing configuration files or environment variables, I rely on constants distributed throughout the code. Changes necessitate a redeployment, but the benefit is clear: in a matter of seconds, I can search my codebase for any configuration value. The minimal frequency of necessary updates—just three adjustments in three months—allowed me to save countless hours that would have been spent developing a full configuration service.
3. Deploying SQLite in a Production Setting
Despite being unconventional, using SQLite for my multi-user web application has proven effective, with a mere 47MB database accommodating up to 50 concurrent users effortlessly. This choice was born from discovering my access patterns consisted
One Comment
This is a compelling demonstration of how intentionally flawed or simplistic solutions—when given a clear timeframe and purpose—can yield genuine insights and fast feedback. Your three-month rule effectively balances the need for agility with accountability, helping avoid analysis paralysis often caused by overengineering.
The decision to consolidate infrastructure onto a single VM and leverage hardcoded configs underscores the value of minimizing complexity early on—especially when user feedback is king. Similarly, deploying SQLite in a production setting for a controlled user base exemplifies how reevaluating conventional wisdom can lead to effective, resource-efficient results.
Your approach aligns well with the broader startup ethos: prioritize learning and rapid iteration over perfection. It also highlights that “unscalable” solutions aren’t necessarily black holes—they’re tools for discovery, provided we set clear boundaries and review points. Thanks for sharing this insightful framework that many developers and entrepreneurs can adapt to accelerate their MVP development and validation processes!