Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Tech Development
In the realm of startup ecosystems, the advice from influential figures like Paul Graham—”Do things that don’t scale”—resonates deeply. However, the challenge often lies in translating this principle into actionable coding practices that yield tangible results. After dedicating eight months to developing my AI podcast platform, I’ve crafted a straightforward framework: I allow each unscalable solution a lifespan of just three months. If it doesn’t demonstrate its worth within that window, it gets retired.
As engineers, we are conditioned to think of scalability right from the outset. We design systems meant to support vast user bases with sophisticated architectures involving design patterns, microservices, and distributed systems. While these techniques are essential for larger organizations, they can be counterproductive for startups where scalable solutions may simply be an expensive form of procrastination. We often find ourselves preemptively tackling challenges that may never arise.
My three-month framework compels me to pursue straightforward, even “imperfect,” coding practices that prioritize real-world deployment. The focus is on understanding user needs rather than getting lost in theoretical optimization.
Current Infrastructure Strategies: Practical Wisdom from Simplicity
1. Single Virtual Machine Operations
Currently, my database, web server, background jobs, and Redis run entirely on one virtual machine priced at $40 a month. While this approach lacks redundancy, it has illuminated my actual resource usage more effectively than any extensive capacity planning document could. I found that my “AI-heavy” application peaks at 4GB of RAM. The complex Kubernetes configuration I considered would have simply managed idle containers.
Through occasional crashes, which have occurred twice, I’ve gained genuine insights into my system’s weak points—none of which were what I had anticipated.
2. Simplified Configuration Management
Instead of using configuration files or environment variables, my setup relies on hardcoded constants:
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PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While one might critique this as outdated, there’s a hidden advantage: I can easily search my codebase for any configuration value. Each pricing adjustment is documented in version control, and all updates undergo a personal review process. Investing time into creating a sophisticated configuration management service would have taken a considerable amount of hours, whereas I’ve made
One Comment
This is a compelling approach that highlights the importance of balancing immediate progress with future scalability considerations. Your 3-month rule effectively encourages experimentation and quick validation, which are crucial in early-stage startups where time and resources are limited. I especially appreciate your emphasis on embracing “imperfect” solutions to gain real-world insights quickly — often, the fastest path to learning is through hands-on deployment rather than over-engineering.
Your example of running everything on a single VM and using hardcoded configurations underscores the value of simplicity and direct control, especially when iterating rapidly. It’s a reminder that the goal of early infrastructure isn’t perfection but learning, adaptation, and delivering value — things that often get lost in over-optimization too early.
This framework also aligns well with the concept of “minimum viable infrastructure,” enabling teams to make data-driven decisions about what truly needs to be scaled or optimized as growth manifests. Have you considered integrating this rule into a semi-automated review process, perhaps every quarter, where persistent solutions are revisited for scalability and robustness? That might help balance quick iteration with strategic planning for future growth.