The 3-Month Framework: Navigating Unscalable Solutions in Software Development
In the world of startups, there’s a well-known piece of advice from entrepreneur Paul Graham: “Do things that don’t scale.” While this guidance is often shared, the actual application of it within coding practices remains relatively under-discussed. After eight months of developing my AI podcast platform, I’ve crafted a practical approach: every unscalable solution is allowed a lifespan of three months. At the end of this period, it’s either validated through demonstrated value and subsequently refined, or it gets scrapped.
As engineers, we often focus on scalability from the outset. We’re trained in advanced architectures, from microservices to complex distributed systems designed to manage millions of users. However, this mindset can be a hindrance in a startup environment where anticipating future user demand may lead to ineffective procrastination. Instead of laying the groundwork for future users who may never arrive, I have embraced a three-month timeframe that compels me to create straightforward code that delivers immediate results and reveals users’ actual needs.
My Strategic Hacks: Insights over Complex Architecture
1. Unified Infrastructure on a Single VM
Currently, my entire setup—database, web server, background jobs, and caching—operates on a single $40-per-month virtual machine. While this approach lacks redundancy and relies on manual backups, it has proved to be a learning goldmine. In just two months, I’ve better understood my resource requirements than any theoretical planning document could provide. My platform, defying expectations, peaks at only 4GB of RAM. The elaborate Kubernetes infrastructure I nearly implemented would have been a burden for managing empty containers. Each crash (and there have been two) offers valuable insights into what truly fails—often unanticipated components.
2. Simplified Configuration Management
Instead of utilizing configuration files or environment variables, I’ve opted for hardcoded constants throughout my codebase:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
This method, while unorthodox, allows for rapid changes, as I can quickly grep my code for any configuration value. Since these constants have only changed three times over three months, the time commitment for redeployment has been minimal—merely 15 minutes compared to the 40 hours required for a more formal configuration system.
One Comment
This post offers a compelling perspective on emphasizing rapid experimentation over premature optimization—particularly in the early stages of a startup. The three-month rule not only encourages agility but also aligns well with the lean startup philosophy of validated learning. Your choice to operate on a single VM and hardcoded configurations exemplifies a pragmatic approach that prioritizes speed and real-world feedback over theoretical perfection.
One insight I’d add is that this methodology can foster a healthy culture of continuous iteration—where failures are seen as valuable learning opportunities rather than setbacks. As your platform evolves, you can gradually introduce more scalable elements once you’ve validated core hypotheses and identified genuine user needs. Additionally, adopting automated monitoring during this period could provide even richer data on what’s working and what’s not, helping to inform more deliberate scaling decisions later on.
Ultimately, your framework underscores the importance of balancing code simplicity and system architecture with practicality—especially when resources and time are limited. It’s a strategic mindset that startup teams can benefit from, reminding us that sometimes, doing unscalable things with discipline is the fastest route to sustainable growth.