The 3-Month Experiment: A Practical Approach to Non-Scalable Solutions in Tech Development
In the tech startup world, one guiding principle often emphasized is Paul Graham’s advice to “do things that don’t scale.” However, the challenge lies in translating this concept into actionable implementation, especially in the realm of coding. After eight months of developing my AI podcast platform, I found an effective strategy: each non-scalable hack I employ gets a life span of three months. At the end of this period, it must either demonstrate its value and evolve into a robust solution or be eliminated.
As engineers, we frequently find ourselves crafting scalable solutions from the onset. We dive into design patterns, microservices, and distributed architectures aimed at accommodating millions of users—thinking typical of larger companies. However, for startups, pursuing scalability too early can lead to unnecessary resource wastage. My three-month timeframe encourages me to create straightforward, efficient code that achieves tangible results and provides insights into my users’ true needs.
Recent Infrastructure Choices and Their Strategic Benefits
1. Unified Virtual Machine Setup
I operate my entire stack—database, web server, background processes, and Redis—on a single virtual machine costing just $40 per month. While this lack of redundancy may seem unwise, it has provided enlightening data regarding my actual resource requirements. For instance, my initially conceived “AI-heavy” platform has shown that, at peak usage, I only require 4GB of RAM. Instead of bogging myself down with a complex Kubernetes setup, I’ve acquired valuable insights about real pressures and failures.
2. Hardcoded Configuration Values
Instead of implementing configuration files or environmental variables, I’ve opted for hardcoded constants for settings like pricing and user limits. This approach means that altering any value necessitates a redeployment. However, it offers a significant advantage: I can quickly search for configuration values throughout my codebase, and every change is meticulously tracked in version history. Given that I have modified these settings a mere three times in three months, this method has saved me countless engineering hours.
3. Utilizing SQLite in Production
Surprisingly, I have adopted SQLite as the database solution for this multi-user web app, efficiently managing its 47MB size without difficulty. With 95% of my access patterns being read operations, SQLite has turned out to be an appropriate choice. Had I chosen a more complex solution like Postgres from the beginning, I would have been preoccupied with optimizing connection management for
One Comment
This post provides a compelling approach to balancing rapid experimentation with thoughtful infrastructure choices — a challenge many startups face. I particularly appreciate your emphasis on the 3-month time horizon for non-scalable solutions. It creates a disciplined framework that encourages quick iteration without sacrificing long-term viability.
Your example of using a single VM and SQLite highlights the importance of aligning tech decisions with immediate needs and validated user behavior. Early-stage startups often over-engineer, which can lead to unnecessary complexity and costs. By limiting scope and focusing on learning, you’re effectively de-risking the process while maintaining agility.
One thing I’d add for consideration is setting clear criteria at the end of each 3-month cycle for evaluating whether a solution should evolve or be replaced. This could include metrics like user engagement, resource utilization, or development effort. Such structured assessments help ensure that decisions remain data-driven and aligned with your core goals, rather than being reactive.
Overall, this disciplined experimentation approach fosters learning and flexibility, enabling startups to adapt quickly and build more sustainable solutions over time. Thanks for sharing these valuable insights!