Embracing the 3-Month Rule: A Pragmatic Approach to Unscalable Solutions in Software Development
In the world of startups and software development, the wisdom from Paul Graham’s mantra, “Do things that don’t scale,” often goes unaddressed when it comes to practical implementation. After eight months of dedicated work on my AI podcast platform, I have devised a framework that champions this philosophy: every unscalable hack I implement is given a lifespan of three months. At the end of this period, each solution is either validated and developed into a robust application or retired.
As developers, we tend to be conditioned to focus on building scalable solutions right from the outset, operating under the influence of design patterns, microservices, and distributed systems. While these are all well and good for larger organizations, they often represent a form of expensive procrastination in the startup environment. By preemptively optimizing for growth, we may find ourselves solving non-existent problems. My three-month strategy encourages creating straightforward, sometimes imperfect, code that drives actual deployment and provides invaluable insights into user needs.
Current Infrastructure Hacks: Simple Strategies with Smart Outcomes
1. All-in-One Virtual Machine (VM)
I’ve consolidated my database, web server, background jobs, and Redis on a single virtual machine costing just $40 per month, eliminating redundancy and manually backing up data. This choice has been revelatory; within just two months, I’ve gained insights into my resource requirements that no amount of planning could have predicted. The reality is that my AI-driven platform peaks at a mere 4GB of RAM. The complex Kubernetes architecture I almost implemented would have been managing empty containers! Each crash has furnished me with critical data on actual failure points—insights that often defy expectations.
2. Hardcoded Configuration Values
Instead of relying on configuration files or environment variables, I’ve chosen to hardcode constants directly in the codebase, such as pricing tiers and maximum user limits. While this may seem inefficient at first glance, it allows for swift searches across the codebase and precise tracking of changes. Alterations have occurred only a few times in three months, making it far more efficient than creating a dedicated configuration service that would have consumed a significant amount of development time.
3. Utilizing SQLite for Production
To my surprise, SQLite is serving my multi-user web application remarkably well. With a database size of just 47MB and the capacity to handle 50 concurrent users effortlessly, I’ve
One Comment
This post offers a refreshing perspective on balancing pragmatism with agility during early-stage development. The “3-Month Rule” serves as a practical discipline to prioritize rapid learning and iteration over premature scalability, which is often a trap for startups eager to optimize from day one.
Your examples resonate particularly well—consolidating services onto a single VM and using hardcoded configurations highlight how close-to-the-metal hacks can provide immediate insights with minimal overhead. It’s a reminder that in the early phases, building “just enough” infrastructure to test hypotheses can save both time and resources, allowing teams to validate product-market fit before over-engineering.
I also appreciate your use of SQLite—it underscores the importance of choosing tools that align with current needs rather than future-proofing prematurely. Once the product matures, you can revisit these decisions, but initially, they promote speed and flexibility.
This approach aligns with the “fail fast” philosophy—gathering real-world data quickly and iterating accordingly. It would be interesting to explore how you balance this with planning for inevitable growth, perhaps by setting clear thresholds for when these hacks become bottlenecks and need re-evaluation. Thanks for sharing such a pragmatic, experience-based framework!