The 3-Month Rule: A Strategic Approach to Non-Scalable Solutions in Tech
In the tech world, we often hear the venerable advice from Paul Graham to “do things that don’t scale.” However, implementing this idea effectively, especially in the realm of coding, can be a challenge. After dedicating the past eight months to building my AI podcast platform, I’ve devised a straightforward framework: every temporary, non-scalable solution gets an evaluation period of three months. At the end of this window, we determine whether it has proven valuable enough to be developed further or if it should be discarded.
The Startup Mindset: Why Scaling Early Can Be Counterproductive
As engineers, we are often conditioned to build scalable solutions from the outset. This translates to crafting design patterns, microservices, and distributed systems, all aimed at accommodating potentially millions of users. However, this is a mindset better suited for larger organizations. In a startup environment, aiming for scalability can often lead to expensive delays. We find ourselves optimizing for users who haven’t yet arrived and tackling challenges that may never materialize. My three-month rule encourages me to embrace straightforward, albeit rough, code that delivers functionality and reveals the real needs of my users.
My Current Tactical Approaches and Their Strategic Value
1. Consolidating Resources on a Single Virtual Machine
I’ve opted to run my entire tech stack—a database, web server, background jobs, and Redis—all on a solitary $40/month virtual machine without redundancy and relying on manual backups.
Why is this a smart decision? In just two months, I’ve gained a far clearer understanding of my actual resource requirements than I would have from any capacity management document. For instance, my so-called “AI-heavy” platform has proven to peak at just 4GB of RAM. The complicated Kubernetes architecture I almost implemented would have involved managing empty containers.
When the system has crashed (twice so far), I received valuable insights into the specific breakdowns—more often than not, the issues weren’t what I expected.
2. Utilizing Hardcoded Configuration
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
In lieu of configuration files or environment variables, I’ve scattered constants across my codebase. Implementing any changes requires redeploying, which might seem primitive.
However, this method provides
One Comment
This is a compelling approach that resonates with many startups and early-stage projects. The 3-month evaluation window offers a practical balance between experimentation and resource management, allowing teams to validate assumptions quickly without over-investing upfront. I especially appreciate your emphasis on starting with simple, non-scalable solutions to gain real user insights and avoid unnecessary complexity—something Paul Graham advocates for clearly.
Your example of consolidating resources onto a single VM and using hardcoded configurations highlights the importance of minimizing technical debt early on. It’s often through such pragmatic decisions that organizations learn what aspects truly require scalability and resilience, rather than prematurely optimizing for the worst case.
One thought to extend this idea: as you approach the end of each 3-month cycle, documenting lessons learned can inform more strategic decisions — whether to scale some components, automate backups, or refactor configuration management. Have you considered integrating a formal review process at each interval to refine the framework further?