Embracing the 3-Month Rule: A Pragmatic Approach to Rapid Development
In the world of startups, the mantra often resonates: “Do things that don’t scale,” as famously advised by Paul Graham. However, there’s a void in the conversation when it comes to practical strategies for integrating this principle into software development. After dedicating eight months to the creation of my AI podcast platform, I’ve established a straightforward framework: any non-scalable hack I implement gets a lifespan of just three months. At the end of this period, it either proves its worth and gets refined into a sustainable solution, or it gets discarded.
As engineers, we are conditioned to develop scalable architectures from the outset, focusing on design patterns, microservices, and distributed systems capable of managing vast user bases. Yet, in the startup realm, this approach can lead to costly delays. Often, we find ourselves optimizing for potential users who don’t even exist yet or addressing issues that may never arise. My three-month rule compels me to write straightforward, even “imperfect,” code that can be deployed quickly, allowing me to better understand the needs of my users.
My Current Infrastructure Techniques: Why They Make Sense
1. Consolidation on a Single VM
I host my complete stack—including the database, web server, background jobs, and Redis—on a single virtual machine for $40 a month. There’s no redundancy, and backups are done manually to my local drive.
This approach may sound reckless, but it has provided invaluable insights into my actual resource requirements. Within just two months, I’ve learned that my “AI-heavy” platform only requires 4GB of RAM at peak usage. The complex Kubernetes setup I considered would have meant managing inactive containers. When my setup does fail (and it has twice), I gain real insights into what goes wrong—typically surmounting my expectations.
2. Hardcoded Configuration Values
Instead of employing config files and environment variables, I utilize hardcoded constants across my codebase:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Modifying a value requires a redeployment, but there’s a hidden advantage here: I can quickly search my codebase for any configuration item. Each price alteration is documented through my git history, and every change undergoes my personal review.
Creating a configuration
One Comment
This is a compelling approach that highlights the importance of rapid iteration and learning in a startup environment. The 3-Month Rule effectively balances the need for agility with mechanical simplicity, allowing you to test assumptions quickly without wasting resources on overly complex solutions early on.
Your emphasis on deploying “imperfect” code that can be refined over time resonates with the lean startup philosophy—validated learning is more valuable than perfecting initial architecture. I particularly appreciate your insight about resource constraints guiding you to practical, minimal setups like consolidating on a single VM. It’s a reminder that real-world constraints often reveal the true requirements, preventing over-engineering.
Hardcoded values for configuration, while generally discouraged in larger systems, seem perfectly suited here for quick iteration and ease of change—especially since deployment entails minimal overhead for modifications. It’s a bold stance that aligns well with your goal of fast validation.
In the long run, I’d be curious to see how you transition from these initial hacks to more scalable solutions once your concepts prove their worth. Overall, your framework underscores that speed, learning, and adaptability can be just as critical as scalability in the early stages of a startup. Thank you for sharing these practical insights!