Embracing the 3-Month Rule: A Pragmatic Approach to Unscalable Solutions in Tech Development
In the world of startup engineering, Paul Graham’s advice to “do things that don’t scale” often echoes through the corridors of innovation. However, translating this wisdom into actionable strategies within the realm of coding remains an under-discussed theme. I have spent the last eight months developing an AI podcast platform, and during this journey, I have crafted a unique framework: each unscalable solution is given a three-month lifespan. After this period, it either proves its worth and receives the necessary investment for development, or it’s laid to rest.
As engineers, we are instinctively inclined towards creating scalable solutions from the outset—focusing on design patterns, microservices, and distributed architectures that can cater to millions of users. While these endeavors are critical for established companies, they can often be a misstep for startups. In this environment, pursuing scalable code can transform into costly procrastination, optimizing for hypothetical users and problems that may not even surface. My three-month guideline compels me to produce straightforward, albeit imperfect code, that not only launches quickly but also provides invaluable insights into user needs.
A Glance at My Current Infrastructure Strategies
1. All Services on a Single Virtual Machine
I operate my database, web server, background jobs, and Redis on a single virtual machine costing a mere $40 per month. While this setup lacks redundancy and relies on manual backups to my local system, it has been remarkably enlightening. In just two months, I’ve gained more clarity on my resource needs than traditional capacity planning would’ve offered. Interestingly, my application, which centers on “AI-heavy” functions, peaks at 4GB of RAM. What seemed like a necessity for an advanced Kubernetes framework would have turned into a management task of empty containers.
When the server crashes (which it has—twice), I gather authentic data regarding failures, which contradicts my initial expectations.
2. Hardcoded Configuration
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
I’ve omitted configuration files and environment variables, opting instead for constants directly scattered throughout my codebase. This might seem impractical, but its advantages are impressive: I can swiftly locate config values, and any alterations are recorded in the version control history
One Comment
Thank you for sharing your practical approach to balancing speed and sustainability in early-stage development. The “3-Month Rule” resonates strongly, especially in typical startup scenarios where rapid iteration often trumps immediate scalability. I appreciate how your infrastructure choices—such as running all services on a single VM and hardcoding configurations—reflect a focus on immediate feedback and learning rather than premature optimization.
This reminds me of the concept of “spaghetti code” in its disciplined form: intentionally rough and unrefined, yet intentionally designed to reveal what truly matters in user behavior and core functionality. Limiting unscalable solutions to a fixed timeframe ensures that you’re continually assessing their value without falling into the trap of over-engineering.
It’s fascinating how your approach emphasizes authentic failure data—server crashes, resource usage—bringing real-world learnings into your development process. This kind of empirical testing can often save more in the long run than theoretical capacity planning.
Would be interesting to see how this methodology scales as your platform grows—whether the three-month window remains effective or if some unscalable solutions evolve into scalable ones over time. Thanks again for sharing your insights; they provide a refreshing perspective on pragmatic startup engineering.