The 3-Month Framework: A Pragmatic Approach to Non-Scalable Solutions in Development
In the world of startups and innovative projects, the mantra often heard is Paul Graham’s wise advice: “Do things that don’t scale.” However, it’s one thing to embrace this philosophy and another to effectively put it into practice within your coding endeavors.
After dedicating the last eight months to developing my AI podcast platform, I’ve devised a straightforward framework to tackle this challenge. Each non-scalable solution is allotted a lifespan of three months. At the end of this period, it either demonstrates its value and transitions into a proper build, or it is phased out entirely.
The Paradox of Scalability
As engineers, we are frequently conditioned to focus on scalable solutions right from the start, crafting intricate design patterns and deploying extensive microservices architecture intended to support millions of users. Yet, for a startup, chasing after scalability can often be a costly form of procrastination—optimizing for hypothetical user bases and addressing issues that may never arise. My three-month rule compels me to produce straightforward, albeit imperfect, code that enables rapid deployment and reveals genuine user needs.
Insights from My Current Setup
While my approach may appear unorthodox, it has proved incredibly beneficial for development. Here’s a look at some of my current infrastructure hacks and the rationale behind them:
1. Centralized on a Single VM
All components—database, web server, background jobs, and Redis—operate on a single $40/month virtual machine. There’s no redundancy and backups occur manually on my local device.
This setup has allowed me to quickly glean insights about my resource requirements, which would have otherwise taken extensive planning documents. Surprisingly, my platform’s peak resource usage cap of 4GB RAM has been very manageable. The complex Kubernetes framework I nearly implemented would have merely been managing inactive containers. Each crash (which has happened twice) has also provided valuable learning moments regarding what actually fails—often not what I initially anticipated.
2. Hardcoded Configuration
I’ve opted for hardcoded constants such as:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Rather than utilizing configuration files or environment variables, this straightforward method simplifies tracking and modifies parameters with a quick redeployment. In three months, I’ve adjusted these values only three
One Comment
Great insights on embracing rapid experimentation through the 3-month rule! Your emphasis on “doing things that don’t scale” resonates strongly, especially in the early stages of startup development where agility trumps perfection. I particularly appreciate your pragmatic approach to infrastructure—starting with a single VM and hardcoded configs—to prioritize validation over complexity.
This method not only accelerates feedback loops but also reduces unnecessary overhead and decision paralysis. It’s interesting to see how these “suboptimal” setups can serve as valuable learning tools, guiding more informed scaling decisions later.
One point to consider for future iterations: ensuring your manual backups and local data management don’t become bottlenecks as user base grows. Automating backup routines or integrating lightweight cloud storage could be simple steps that preserve your development speed while safeguarding data.
Overall, your framework underscores that speed and simplicity often outperform intricate architectures in the early phases—especially when the primary goal is to validate assumptions and learn quickly. Looking forward to seeing how this approach evolves as your platform matures!