Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions
In the world of startups and tech innovation, the mantra “Do things that don’t scale,” popularized by Paul Graham, resonates deeply. However, the real challenge lies in how to put this advice into practice, especially when it comes to software development. After dedicating eight months to the creation of my AI podcast platform, I’ve forged a simple yet effective framework: any unscalable solution gets a three-month trial period. If it proves beneficial, it gets developed further; if not, it gets the axe.
The default mindset for many engineers is to craft solutions that are “scalable” from the outset. This often leads to designing complex architectures, such as microservices and distributed systems, all aimed at accommodating millions of users. While these strategies may suit large corporations, in the startup realm, scalable solutions can sometimes translate into costly delays—optimizing for a potential audience that may not even exist yet. My 3-month rule encourages me to embrace simplicity and directness, allowing for rapid deployment and real-time feedback on user needs.
Current Infrastructure Shortcuts and Their Value
1. Consolidated VM for Operations
All components of my platform—including the database, web server, and background jobs—run on a single $40/month virtual machine. There’s no redundancy, and I perform manual backups to my local machine.
Why is this a clever approach? In just two months, I gained invaluable insights into my resource requirements that no capacity planning document could teach. Astonishingly, my “AI-heavy” platform peeks at just 4GB of RAM. The intricate Kubernetes setup I nearly implemented would have involved managing dormant containers.
When crashes occur—and they have—I gather authentic data about failure points, often revealing surprises.
2. Hardcoded Configuration Values
No separate configuration files or environment variables here; I use constants instead:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Modifying anything necessitates a redeployment, which brings an unexpected advantage. I can swiftly search my entire codebase for configuration values, ensuring that any price adjustments are meticulously documented in the git history. The time spent on building a configuration service would have far exceeded the mere minutes I dedicate to occasional redeployments.
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