Embracing Imperfection: The 3-Month Rule for Scaling Your Startup
In the entrepreneurial world, Paul Graham’s advice rings true: “Do things that don’t scale.” Yet, the implementation of this principle in the realm of coding is often overlooked. After eight months of building an AI podcast platform, I stumbled upon a straightforward yet effective framework: every unscalable solution is granted a lifespan of just three months. At the end of this period, it either proves its worth and is refined or it’s phased out completely.
As engineers, we are frequently conditioned to develop solutions that accommodate growth from the outset. We become enamored with complex design patterns, microservices, and expansive architecture capable of serving millions. However, this mindset may lead us astray in the startup scene, where designing for scale can often transform into an expensive form of procrastination. Too much optimization for potential users isn’t just a waste of resources; it stifles real learning.
My Current Infrastructure Hacks: Why They Make Sense
1. Consolidated Operations on a Single VM
All components—from the database to the web server and background jobs—reside on one $40/month VM. There’s no redundancy and local backups happen manually. Why is this a smart move? It has allowed me to gain invaluable insights into my resource needs in just two months, far beyond what any capacity plan could offer. It turns out that my “AI-rich” platform only requires 4GB of RAM at peak usage. The Kubernetes setup I nearly committed to would have resulted in managing idle containers instead.
Each time the system crashes (which has happened twice), I receive authentic data about the failures—often unexpected insights.
2. Hardcoded Configurations: The Hidden Advantages
Instead of using configuration files or environment variables, I incorporate values directly in the code:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While it may seem counterintuitive, this approach allows for rapid tracking and modification. I can locate any configuration within seconds across my codebase. In the past three months, I’ve modified these constants merely three times, resulting in a mere 15 minutes of redeployment instead of extensive engineering efforts.
3. Leveraging SQLite for Production
Yes, you read that right—my multi-user web application runs on SQLite. With a total database
One Comment
This post offers a refreshingly pragmatic approach to early-stage startup engineering. The 3-month rule is a powerful mindset shift—focusing on rapid iteration and “disposable” solutions that inform future decisions. I especially resonate with the emphasis on **learning through direct experience** rather than over-engineering from the start.
Using a single VM to better understand resource needs is a great way to avoid assumptions and unnecessary complexity early on. It reminds me of the value in **”getting your hands dirty”**—sometimes, the fastest way to learn is by doing, even if that means embracing some imperfections.
The choice to hardcode configurations also stands out as a bold but practical move. It allows for quick adjustments without the overhead of managing multiple environments, which is ideal during the initial validation phase. Of course, as the project scales, you’d want to shift toward more robust configuration management, but this approach keeps momentum going.
Lastly, running SQLite in production is a classic example of **”doing less to do more”**—it’s simple, fast, and effective for small-scale but multi-user apps. This aligns perfectly with the idea of prioritizing speed and learning over premature investment in infrastructure.
Overall, your framework champions the ethos that **short-term sacrifices often lead to better longer-term understanding and growth**. Looking forward to seeing how these insights evolve as your platform scales!