The 3-Month Experiment: A Practical Approach to Non-Scalable Solutions
In the world of startups, we often encounter the sage advice from Paul Graham: “Do things that don’t scale.” While many acknowledge the wisdom behind this statement, the challenge lies in effectively implementing it within the technical realm.
Having spent the last eight months developing my AI podcast platform, I’ve established a straightforward approach: every unscalable tactic is given a lifespan of three months. At the end of that period, it either demonstrates its worth and is transformed into a more robust solution, or it is set aside.
As engineers, our instinct is to pursue scalable solutions from the outset. We think in terms of design patterns, microservices, and distributed systems — a mindset often cultivated by larger enterprises. However, this perspective can lead to costly delays in startup environments, where we may find ourselves preemptively optimizing for issues that may never arise.
My 3-month rule encourages me to embrace simple and sometimes inefficient coding practices that can be quickly deployed. This method not only allows for immediate feedback but also provides insight into user needs.
Current Infrastructure Strategies That Work
1. Unified Virtual Machine Setup
My entire application runs on a single, $40/month virtual machine that hosts the database, web server, background processing tasks, and caching with Redis. While lacking redundancy, this setup has proven invaluable.
By operating in this simplified arrangement, I’ve gained insights into my resource utilization that documentation never could provide. My platform only peaks at 4GB of RAM, revealing that the complex Kubernetes infrastructure I almost implemented would have been unnecessary.
When system failures have occurred—twice thus far—I gained firsthand knowledge of the root causes, which were never what I anticipated.
2. Directly Hardcoded Configurations
In my codebase, you’ll find constants like:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
By avoiding configuration files and environment variables, any adjustments require redeploying the application. The advantage is clear: I can swiftly search my entire code for a specific configuration, and any change is easily tracked in Git history.
Investing a week in building a configuration service would yield limited returns — I’ve modified these values merely three times in the past three months. Hence, 15 minutes of redeployment outweighs 40 hours of development
One Comment
This is a compelling approach that challenges the traditional emphasis on immediate scalability, especially during early-stage development. I particularly appreciate the emphasis on rapid experimentation within a defined timeframe—three months—to validate ideas and iteratively refine solutions. It echoes the principles of lean startup methodology, where quick feedback loops are paramount.
Your use of a simple infrastructure, like a single VM and hardcoded configs, exemplifies how focusing on learning and validation first can prevent over-engineering. It’s a reminder that sometimes “less” really is “more” when trying to understand user needs and system behavior, before investing in complex, scalable architectures.
One thing I’d add is the importance of documenting these experiments and their outcomes, even if they’re unscalable. Such insights can inform decisions on when to transition from simple solutions to more scalable ones, ensuring the team retains valuable context. Also, periodically reassessing whether some of these “unscalable” tactics might serve as long-term solutions in niche cases can be worthwhile.
Overall, this framework underscores that effective engineering isn’t just about building the most scalable system from day one but about learning fast and iterating wisely—a philosophy every startup coder should embrace.