The 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions in Software Development
In the world of startups and software engineering, there’s a widely accepted nugget of wisdom from Paul Graham that encourages entrepreneurs to “do things that don’t scale.” However, the challenge often lies in how to effectively apply this principle, particularly when it comes to coding. Over the past eight months of developing my AI podcast platform, I’ve devised a practical framework that revolves around the 3-Month Rule: any unscalable hack I introduce is given a lifespan of exactly three months. At the end of that period, the solution must either prove its value and transition into a well-structured system or be discarded.
As engineers, we are often conditioned to prioritize scalable solutions right from the outset—utilizing design patterns, microservices, and distributed systems to cater to millions of hypothetical users. This mindset is more suited for large organizations and can lead to inefficient procrastination in a startup environment. The 3-Month Rule has propelled me to focus on crafting straightforward, albeit less-than-ideal, code that is capable of being deployed quickly, allowing me to uncover genuine user needs.
Current Infrastructure: Smart Hacks from My Journey
1. Unified VM Setup
All essential components—database, web server, background jobs, and caching—are hosted on a single virtual machine costing just $40 per month. While this setup lacks redundancy and relies on manual backups, I’ve learned immensely in just two months about my actual resource requirements. My AI-driven platform peaks at only 4GB of RAM, raising the question of whether I really needed an elaborate Kubernetes architecture, which would have involved managing idle resources.
By experiencing crashes—which have happened twice so far—I’ve gathered valuable data on why failures occurred, often revealing unexpected insights.
2. Hardcoded Configuration Values
I’ve opted for hardcoded config values like:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
There are no configuration files or environment variables; instead, constants are dispersed throughout the codebase. This approach allows for swift search and trackability through Git history for any price adjustments made, reducing the time spent on implementation from an estimated 40 hours to just 15 minutes across three changes.
3. Leveraging SQLite
Yes, SQLite is my choice for
One Comment
This is a fantastic illustration of how embracing “non-scalable” solutions with a clear time boundary—like your 3-Month Rule—can drive rapid learning and iteration in early-stage products. I especially appreciate your pragmatic approach to infrastructure, such as starting with a unified VM and hardcoded configs, which prioritize speed and experimentation over perfection. It echoes the sentiment that real user insights often come from working with simple, flexible setups rather than complex architectures prematurely.
Your method reminds me of the importance of establishing a “learning backlog,” where post-3-month evaluations can inform whether to scale infrastructure or refine the core features. It’s a balanced strategy—allowing for agility in the initial phases while maintaining discipline to evolve or discard solutions based on actual usage data. Thanks for sharing this insightful framework—definitely a valuable approach for startups aiming to move fast without getting bogged down in unnecessary complexity too early.