Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Development
In the tech community, the phrase “Do things that don’t scale” resonates widely, particularly with the wisdom of entrepreneurs like Paul Graham. However, the challenge often lies in translating this notion into practice, especially in the realm of software development. After spending eight months cultivating my AI podcast platform, I’ve formulated a straightforward framework: every non-scalable workaround gets a lifespan of three months. If it proves its worth, it gets refined; if it fails to deliver, it’s discarded.
The Startup Mindset: Prioritize Learning Over Scaling
As developers, we are often conditioned to focus on scalable solutions from the very beginning—think elegant design patterns, microservices, and complex distributed systems, all crafted to accommodate vast user bases. This mentality is more suited for larger corporations, while in a startup environment, investing resources in scalable code can simply be an expensive diversion.
The reality is, we often find ourselves optimizing for users that don’t yet exist, addressing issues that may never arise. My three-month guideline encourages me to adopt a more straightforward approach, encouraging me to produce simple, albeit “imperfect,” code that delivers tangible results, allowing me to better understand user needs.
Insights from My Current Infrastructure Strategies
1. Centralized Resource Management with a Single VM
My entire stack—database, web server, background jobs, and Redis—runs on a single virtual machine (VM) costing just $40 a month. There’s no redundancy and I manually back up my data locally.
This may sound risky, but it has provided invaluable insights into my actual resource consumption. In under two months, I learned my “AI-focused” platform only peaks at 4GB of RAM, contrasting sharply with the elaborate Kubernetes setup I initially considered. Each crash has provided real data, revealing failures in unexpected areas.
2. Simplistic Hardcoded Configurations
Every configuration value—like pricing tiers and user limits—exists as hardcoded constants throughout my codebase. This means adjustments require redeployment, yet it simplifies tracking changes through Git history and makes it easy to search for any variable.
Creating a dedicated configuration service would consume at least a week’s worth of engineering time. Over three months, I’ve made only three changes—each taking a mere 15 minutes to redeploy, as opposed to 40 hours for a more complex approach.
**3. Utilizing SQLite in Production
One Comment
Great insights! I appreciate how you’ve framed the 3-month rule as a practical lens for balancing experimentation and resource management, especially in early-stage projects. Your emphasis on quick iteration and simplicity resonates strongly—you’re essentially reducing the risk of sunk costs in complex infrastructure before validating core hypotheses.
Using a single VM and simple hardcoded configs as your baseline allows for rapid learning cycles, which is crucial when dealing with unvalidated markets or user needs. The move to SQLite in production, while unconventional, cleverly sidesteps unnecessary complexity early on—your focus on data-driven insights from crashes and resource usage is a smart way to guide future scaling decisions.
This approach highlights that in the startup phase, “less is more”: focusing on building just enough to test assumptions, then iterating rapidly based on real user feedback. Keep experimenting and refining—your strategy seems well-suited for sustainable growth without getting bogged down in premature optimization. Thanks for sharing!