The 3-Month Rule: A Practical Approach to Non-Scalable Solutions
In the tech world, there’s a common piece of advice from entrepreneur Paul Graham: “Do things that don’t scale.” While this wisdom is frequently cited, practical guidance on implementing it, especially in software development, is often overlooked.
After dedicating eight months to creating my AI podcast platform, I’ve established a straightforward framework: each unscalable approach receives precisely three months of trial before its fate is decided. At the end of this period, we evaluate whether it demonstrates enough value to warrant a more robust solution, or if it will be phased out.
As engineers, our education often pushes us toward creating scalable solutions from the outset. We aspire to design intricate architectures featuring microservices, distributed systems, and other innovations that can support millions of users. However, this mentality is often better suited for larger companies.
In a startup environment, investing time in scalable solutions can lead to procrastination. You may find yourself optimizing for hypothetical users and tackling issues that may never arise. My 3-month rule encourages me to produce straightforward, perhaps less refined, code that can be deployed quickly. This approach allows me to gain insights into the actual needs of my users.
Current Infrastructure Hacks: Smart Insights from Simple Solutions
1. Consolidated Operations on a Single VM
My entire system operates on a single virtual machine costing $40 per month. This setup includes the database, web server, background jobs, and Redis, without any redundancy. I manage manual backups to my local machine.
Why is this strategy advantageous? In just two months, I’ve accumulated more insights about my resource requirements than any formal capacity planning could offer. My AI-driven platform peaks at just 4GB of RAM usage. The sophisticated Kubernetes setup I considered would have only led me to manage unutilized container resources.
Whenever the system crashes (which has occurred a couple of times), I receive direct feedback regarding the causes. Interestingly, the issues often differ from my initial expectations.
2. Hardcoded Configuration
Currently, I rely on hardcoded constants, such as:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
There are no external configuration files or environment variables—just values embedded within the codebase. Any change necessitates a redeployment.
The beauty lies in
One Comment
This post provides a compelling perspective on balancing speed and scalability, especially in the early stages of a startup. The 3-month rule is a pragmatic way to embrace rapid experimentation without getting bogged down by over-engineering. I appreciate how the author emphasizes learning through actual usage rather than theoretical planning—often, the most valuable insights come from real-world feedback.
The example of consolidating operations on a single VM highlights that simplicity can be highly effective initially, allowing for quick iterations and a better understanding of resource needs. While hardcoded configurations might seem limiting, they serve a purpose in the short term—saving time and reducing complexity.
As startups scale, these approaches should evolve, but your framework creates a healthy foundation: test boldly, learn fast, and don’t be afraid to pivot away from unproductive solutions after a set period. This mindset fosters agility and ensures that efforts are aligned with real user needs rather than assumptions. Thanks for sharing this practical approach!