Embracing the 3-Month Rule: A Pragmatic Approach to Unscalable Development
In the startup world, the wisdom of Paul Graham’s mantra, “Do things that don’t scale,” often resonates. Yet, a pressing question emerges: how do we apply this principle effectively to software development? After eight months dedicated to building my AI podcast platform, I’ve cultivated a pragmatic framework anchored in the idea of giving each unscalable solution a lifecycle of three months. After this period, we assess their value to determine whether they evolve into robust solutions or phase out.
Traditionally, engineers are conditioned to consider scalability from day one. We dive into intricate architectures involving design patterns, microservices, and distributed systems aimed at accommodating potentially millions of users. Unfortunately, this method often aligns more closely with the mindset of large enterprises rather than the nimble, fast-paced nature of startups.
In the realm of startups, emphasis on scalability can quickly become a costly form of procrastination. We often find ourselves optimizing for imagined future users and tackling nonexistent issues. With my 3-month rule, I am compelled to prioritize straightforward, albeit imperfect, code that enables quick deployment and real-time feedback on user needs.
My Practical Infrastructure Strategies: Uncovering Value in Simplicity
1. Single VM Architecture
Currently, my entire tech stack—from the database and web server to background jobs and Redis—operates on a single $40/month virtual machine, with no redundancy and manual backups.
Why is this approach strategic? In just a couple of months, I’ve gained invaluable insights into my actual resource requirements, far surpassing any theoretical capacity planning I could have performed. My platform, heavily focused on AI, peaks at 4GB of RAM, revealing that the ambitious Kubernetes infrastructure I nearly implemented would have involved managing idle containers.
Encountering crashes (which has happened a couple of times) provides tangible data regarding performance issues. Oddly enough, the breakdowns occur in areas I hadn’t anticipated.
2. Hardcoded Configurations Across the Board
Imagine configuration values like:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
In my case, there are no configuration files or environmental variables—just constants peppered throughout the code. Any changes demand redeployment.
The real advantage? I can swiftly search my entire codebase
One Comment
This is a compelling and pragmatic approach that highlights the importance of learning by doing, especially in the early stages of a startup. The 3-month rule fosters rapid iteration and minimizes overengineering—allowing you to validate assumptions, understand actual system demands, and adapt quickly without the sunk costs of complex, scalable architectures that might prove unnecessary.
Your emphasis on simplicity, especially running on a single VM and using hardcoded configs for rapid testing, aligns well with the lean startup philosophy. It’s fascinating how these unscalable, “quick and dirty” solutions serve as invaluable learning tools—providing concrete insights that inform future scalable designs, if needed.
One addition I’d suggest is integrating structured retrospectives at each cycle end—evaluating which components genuinely need scaling and which can remain simple. Over time, this disciplined reflection can help evolve a sustainable, scalable architecture grounded in real-world usage patterns. Thanks for sharing such a thoughtful framework—it’s a reminder that sometimes, embracing simplicity and short feedback loops is the most effective path forward in startup development.