Embracing the 3-Month Rule: A Pragmatic Approach to Building Non-Scalable Solutions
In the world of startups, there’s a well-known piece of advice from venture capitalist Paul Graham: “Do things that don’t scale.” While many of us have heard this mantra, the challenge lies in effectively applying it to the realm of coding. After dedicating eight months to developing my AI podcast platform, I’ve implemented a straightforward yet powerful framework: every unscalable workaround is given a lifespan of just three months. This approach ensures that after this period, each solution either demonstrates its value and is elevated to a more robust form or is discarded altogether.
A Shift in Mindset for Startup Engineers
As engineers, we are often conditioned to think in terms of scalability from the outset. We envision intricate architectures involving microservices, distributed systems, and fault-tolerant designs, which cater to a user base in the millions. Yet, in a startup environment, scaling prematurely can become an expensive form of procrastination. We find ourselves crafting solutions for users who haven’t even signed up yet and tackling potential issues that may never arise. My 3-month rule compels me to prioritize simplicity and speed over complexity, allowing me to produce “imperfect” code that facilitates genuine user learning.
Current Infrastructure Hacks: Smart Choices from My Journey
1. Consolidated Operations on a Single VM
At the core of my platform is a humble $40/month virtual machine that hosts my database, web server, background jobs, and Redis. This setup lacks redundancy and relies on manual backups to my local storage. The brilliance of this approach is that I’ve gained invaluable insights into my resource requirements within just two months, far beyond what traditional capacity planning documents could provide. I’ve learned my platform’s peak demand is merely 4GB of RAM. The elaborate Kubernetes infrastructure I contemplated would have been an exercise in managing empty resources.
When the server crashes (and it has twice), I receive real feedback about failures—and they’re rarely what I expect.
2. Hardcoded Configuration Values
plaintext
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
My configurations are hardcoded throughout the codebase, and there are no environment variables involved. While this may seem unorthodox, it offers a unique advantage: I can instantly search my entire code for
One Comment
This post offers a refreshing perspective on the importance of pragmatic decision-making in startup engineering. The 3-month rule elegantly balances the need for experimentation with a disciplined approach to avoid over-engineering. I particularly appreciate the emphasis on learning from real-world usage—your consolidated VM setup providing immediate feedback and resource insights highlights the value of simplicity.
While shortcuts like hardcoded configurations can seem risky, their strategic use for rapid iteration makes sense in early stages, especially when agility and quick feedback loops are paramount. As you observe, these temporary solutions serve as experiments rather than final architectures, allowing teams to validate assumptions without costly overhead.
This approach aligns well with the Lean Startup methodology, emphasizing validated learning over perfect initial design. It’s a great reminder that engineering for startups isn’t about building for scale from day one but about building just enough to learn, adapt, and evolve efficiently. Thanks for sharing these actionable insights!