Embracing the 3-Month Rule: A Practical Approach to Non-Scalable Solutions in Tech
In the world of technology, we often hear the advice to “do things that don’t scale.” While this wisdom from Paul Graham is widely recognized, the implementation of this concept, particularly in coding, is rarely discussed.
Over the past eight months, while developing my AI podcast platform, I’ve adopted a simple yet effective framework: any non-scalable tactic is given a trial period of three months. After this timeframe, it either demonstrates its worth and is refined into a robust solution, or it╬ô├ç├ûs retired permanently.
As engineers, we are ingrained with the mindset of creating scalable solutions from day one╬ô├ç├╢think microservices, distributed systems, and sophisticated architecture meant to serve millions of users. However, this “big company” mentality doesn’t always apply, especially in the startup world. Here, focusing on scalability too soon can often lead to unnecessary complexity and higher costs, as we’re essentially preparing for users that don╬ô├ç├ût yet exist and problems that may never arise. My three-month rule helps me prioritize writing concise, straightforward code that is functional and reveals the true needs of my users.
Current Non-Scalable Strategies and Their Unexpected Wisdom
1. Operating on a Single Virtual Machine
All componentsΓÇödatabase, web server, background jobs, and cachingΓÇöoperate on a single $40/month virtual machine. ThereΓÇÖs no redundancy and backups are manually done to my local system.
This seemingly reckless approach has proven to be enlightening. Within just two months, I╬ô├ç├ûve gained a deeper understanding of my resource requirements than any formal capacity planning could have provided. Surprisingly, my AI-centric platform only peaks at 4GB of RAM, rendering my intended Kubernetes setup unnecessary and simply a management burden for essentially empty containers. When crashes occur, I’ve gleaned valuable insights into what really goes wrong╬ô├ç├╢often in ways I never anticipated.
2. Hardcoded Configurations
Take a look at my code, and youΓÇÖll find constants like this:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
I donΓÇÖt use configuration files or environment variables. Every change means a redeployment, but the process has its perks. I can quickly search for any configuration value in seconds, everything is tracked in Git history, and changes are subject to my code review (
2 Comments
This is a compelling approach that highlights the value of deliberately resisting premature scalability efforts in early-stage development. The 3-month rule provides a pragmatic framework to test non-scalable solutions for real-world viability before investing in complexity. I particularly appreciate the emphasis on understanding resource needs through simplicityΓÇösuch an iterative approach often uncovers nuanced insights that would be obscured by over-engineering from the start.
Your example of single VM operation and hardcoded configs underscores the importance of direct experimentation and quick feedback loops. It reminds me of the concept that “premature optimization is the root of all evil,” especially in the startup context where agility and learnings take precedence.
In my experience, adopting such a testing mindset enables teams to find a balance between immediate, functional solutions and future scalability considerationsΓÇöonly scaling once genuine demand is proven. Thanks for sharing this practical and thoughtful framework, itΓÇÖs a valuable addition to the ongoing dialogue around building resilient, adaptable systems without falling into the trap of unnecessary complexity early on.
This is an excellent illustration of how intentional non-scalability strategies can provide invaluable insights early on, especially in a startup context. Your three-month rule echoes principles from lean development and “build-measure-learn” cycles, emphasizing rapid experimentation and learning over premature optimization.
By intentionally over-simplifying architecture╬ô├ç├╢like running everything on a single VM╬ô├ç├╢you╬ô├ç├ûre effectively adopting a form of “minimum viable infrastructure” that reduces surface area for bugs and accelerates feedback. This approach aligns with the concept of *progressive refinement*: as learnings accumulate, systems evolve naturally rather than over-engineering from the outset.
Similarly, hardcoded configurations prioritize agility and speed, which are vital in early phases when requirements are still evolving. While these practices might contradict conventional wisdom in large-scale systems, they foster a practical mindsetΓÇöfocused on learning, iteration, and understanding user needs deeply before committing to scalable solutions.
This methodology underscores a broader truth: early-stage development should prioritize learning and adaptability over scalability, which can be addressed later as the product and user base grow. Your framework offers a disciplined way to balance risk and resource investmentΓÇöan inspiring approach for founders and engineers alike.