Embracing the 3-Month Rule: A Pragmatic Approach to Non-Scalable Solutions in Software Development
In the world of startups and software development, there’s a prevailing mantra: “Do things that don’t scale.” While this wisdom, often attributed to Paul Graham, is widely acknowledged, the practical implementation of this philosophy—especially in coding—remains under-discussed.
Over the last eight months, as I developed my AI podcast platform, I devised a straightforward framework that emphasizes the transient nature of unscalable solutions: every such workaround is granted a lifespan of three months. At the end of this period, the hack must either demonstrate its value and evolve into a permanent solution or be phased out entirely.
Rethinking Scalability in Startup Environments
As engineers, we are commonly trained to construct scalable systems from the ground up, often envisioning grand architectures featuring design patterns, microservices, and distributed systems meant to accommodate millions of users. However, this line of thinking may be more suitable for established companies than for startups, where premature optimization can often lead to unnecessary complexity and inefficiency. My three-month rule encourages me to focus on rapid development with straightforward, albeit “imperfect,” code that can be deployed quickly, allowing me to understand user needs more effectively.
Key Infrastructure Strategies and Their Surprising Advantages
Here are some of the current unscalable tactics I’ve employed and the insights they provide:
1. Consolidating Everything on a Single VM
Currently, my setup includes a database, web server, background jobs, and Redis all running on a single $40/month virtual machine. While this approach lacks redundancy and relies on manual backups, it has offered invaluable insights into my actual resource requirements. In just two months, I discovered that my AI platform peaks with only 4GB of RAM. The complex Kubernetes architecture I nearly implemented would have resulted in managing underutilized resources.
Experiencing system crashes—twice so far—has also provided critical data about failure points, none of which have been what I initially anticipated.
2. Using Hardcoded Configuration Settings
Instead of maintaining a set of configuration files or relying on environment variables, I have hardcoded values directly into my code. For instance:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this might seem
One Comment
Thank you for sharing this thoughtful approach. The three-month rule resonates strongly—it’s a pragmatic way to balance rapid experimentation with disciplined iteration. In early-stage development, it’s often tempting to optimize prematurely or aim for scalable architectures from the outset, but your framework reminds us that quick, temporary solutions can provide real insights without excessive overhead.
Your experience consolidating everything on a single VM highlights an important lesson: sometimes, simplicity reveals the clearest picture of resource needs and failure points. Embracing this “impedance mismatch” of unscalable setups can lead to more informed decisions when it’s time to scale.
Similarly, hardcoding configuration values—while generally discouraged in production—serves as a valuable tool for rapid prototyping and testing. It forces focus on core logic without distractions, and your practice of phasing out such shortcuts after three months aligns well with Lean principles: validate assumptions quickly, then iterate towards a more robust solution.
Overall, your framework encourages a healthy balance: leverage unscalable tactics for fast learning, but with a clear end-of-life strategy to prevent technical debt. It’s a practical mindset that startup engineers (and even seasoned developers) can adopt to accelerate growth while managing complexity. Thanks for sharing these valuable insights!