Embracing the 3-Month Rule: A Pragmatic Approach to Development
In the entrepreneurial landscape, one piece of advice resonates deeply: “Do things that don’t scale,” a sentiment often echoed by Paul Graham. Yet, the challenge lies in translating this philosophy into practical, everyday coding practices. After eight months of building my AI podcast platform, I have crafted a straightforward framework: every unscalable solution I implement is given a three-month trial period. At the end of this timeframe, each solution must either validate its worth and be fully developed or be discarded.
The Startup Reality: Scalability vs. Practicality
As developers, we are conditioned to prioritize scalable solutions from the get-go. We learn about design patterns, microservices, and distributed systems—architecture meant to support millions of users. However, such thinking is often more suited to established corporations than to startups.
In a startup environment, aiming for scalability too early can lead to costly procrastination. You may find yourself optimizing for potential users who may never materialize, all while neglecting the immediate needs of existing users. By adhering to my three-month rule, I am compelled to write straightforward, albeit “imperfect” code that can be deployed quickly, allowing me to fully understand user requirements in real-time.
Current Infrastructure Hacks: Smart Decisions for Effective Learning
1. Consolidated Infrastructure on a Single VM
I run my entire platform—database, web server, background tasks, and caching—on a single virtual machine for just $40 a month. With no redundancy and manual backups, some might see this as risky. However, this setup has provided invaluable insights into my actual resource demands. In just two months, I’ve learned that the peak requirement for my “AI-heavy” platform is only 4GB of RAM. The complex Kubernetes infrastructure I contemplated would have involved maintaining idle resources. Crucially, when issues arise (and they have), I gather real data on what fails, which often surprises me.
2. Hardcoded Configurations for Simplicity
Instead of using configuration files or environment variables, I’ve opted for hardcoded constants in my codebase:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this may seem like a poor choice, it’s surprisingly advantageous. I can quickly search through my code for any configuration
One Comment
This is a really compelling approach that highlights the importance of rapid experimentation and learning in early-stage development. The three-month rule acts as a practical safeguard against over-engineering, ensuring you prioritize validated solutions over speculative scalability. I especially appreciate the emphasis on getting real-world data through simple infrastructure hacks—like consolidating everything on a single VM—to inform smarter decision-making down the line.
Hardcoded configs, while generally discouraged in mature systems, make perfect sense in these early phases to reduce complexity and focus on understanding actual user needs. This pragmatic mindset aligns well with the “build, measure, learn” feedback loop that startups thrive on.
Ultimately, your framework reinforces that sometimes, the fastest path to robust, scalable solutions is through iterative, real-world testing, even if that means temporarily sacrificing perfect architecture. Looking forward to seeing how this approach evolves as your platform grows!