Embracing the 3-Month Rule: A Strategic Approach to Non-Scalable Solutions in Tech Development
In the realm of entrepreneurship, particularly in technology, there’s a well-known piece of advice from Paul Graham: “Do things that don’t scale.” While this wisdom resonates throughout startup culture, the practical implementation of this concept, especially in coding, is often overlooked.
After eight months of developing my AI podcast platform, I’ve formulated a straightforward framework: each unscalable solution is given a lifespan of three months. At the end of this period, I assess whether it has proven its value and deserves to be developed further, or if it should be discarded.
The reality is that as engineers, we are trained to focus on scalability right from the beginning. We become enamored with sophisticated architectures, invoking microservices, distributed systems, and the like—appealing structures designed for a hypothetical future of millions of users. However, in a startup environment, aiming for scalable solutions can often turn into a costly form of procrastination. We tend to optimize for a user base that may not yet exist, focusing on challenges that might never materialize. My three-month strategy compels me to craft straightforward, even “imperfect” code that gets deployed and reveals genuine user needs.
My Unconventional Infrastructure Strategies—and Why They Work
1. A Single Virtual Machine for All Operations
At the heart of my operations lies a single virtual machine (VM) costing only $40 a month, hosting my database, web server, background jobs, and even Redis. This setup lacks redundancy and relies on manual backups to my local system.
Why is this a wise choice, rather than a reckless one? Over the past two months, I’ve gained invaluable insights into my actual resource requirements, something no theoretical capacity planning document could provide. I’ve discovered my AI-centric platform peaks at just 4GB of RAM. The elaborate Kubernetes cluster I nearly implemented? It would have resulted in managing unutilized containers.
Moreover, when the VM has crashed (which it has twice), I’ve collected valuable data about the actual points of failure—predictably not where I anticipated.
2. Hardcoded Configurations for Simplicity
My configuration is captured through hardcoded constants:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
There are no complex configuration
One Comment
This framework you’ve developed—giving unscalable solutions a three-month trial period—strikes me as a practical and disciplined approach to iterating quickly while avoiding premature optimization. It aligns with the idea of “building to learn,” allowing real user data and usage patterns to drive future development rather than assumptions or overly complex architectures early on.
Your emphasis on starting simple—using a single VM, hardcoded configs, and avoiding unnecessary complexity—resonates strongly with the lean startup mentality. It reminds me of the importance of validating core hypotheses with minimal investment before scaling, which ultimately can save both time and resources.
It’s also insightful how you’ve embraced failure points as learning opportunities, such as crashes providing data rather than setbacks. This approach encourages a mindset where “imperfect” solutions aren’t roadblocks—they’re stepping stones toward understanding what truly adds value.
Would be interesting to hear how you plan to evolve your infrastructure once the product’s core is validated. Do you see a path where, after the three-month validation, you gradually introduce more scalability and robustness? Or will you maintain a lightweight infrastructure long-term based on your observed needs? Thanks for sharing such a grounded, real-world perspective—it’s an inspiring reminder that sometimes less is more in the early stages.