The 3-Month Framework: A Practical Approach to Unscalable Solutions in Software Development
In the world of startups, the well-known advice from Paul Graham to “do things that don’t scale” often gets overlooked when it comes to implementation, especially in the realm of software development. After spending eight months developing my AI podcast platform, I’ve devised a straightforward approach: each unscalable solution is given exactly three months to prove its worth. If it demonstrates value, it evolves into a robust solution; if not, it’s time to phase it out.
As engineers, we tend to focus on crafting scalable solutions right from the start. We delve into design patterns, microservices, and distributed systems, tailormade for handling vast user bases. This big-picture thinking can be a disadvantage in a startup setting where the quest for scalability may divert our attention from immediate needs. My 3-month framework encourages me to embrace simple, straightforward code—code that actually executes and provides insights into what users genuinely require.
My Current Infrastructure Innovations—and Their Merits
1. Consolidated Operations on a Single VM
All essential components—database, web server, background jobs, and Redis—operate on a single $40/month virtual machine, lacking redundancy and relying on manual backups to my personal device.
This may seem unwise at first glance, but it has granted me valuable insights into my resource requirements faster than any elaborate planning could. For instance, I’ve discovered that my “AI-heavy” platform peaks at 4GB of RAM. The Kubernetes setup I almost initiated would have merely managed empty resources. During the instances of downtime (twice so far), I’ve gained firsthand information about what truly falters—insights I couldn’t have predicted.
2. Hardcoded Configurations for Simplicity
Consider the following snippet:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Rather than employing configuration files or environment variables, I utilize hardcoded constants dispersed throughout my codebase. Modifying any element necessitates a redeployment.
This seemingly basic approach allows for rapid searches across my codebase for any configuration value. Every price adjustment is meticulously recorded in Git history, and I conduct thorough code reviews (albeit solo). Constructing a configuration service would have taken a week, yet I’ve made only three changes in three months
One Comment
This is a compelling reflection on the value of embracing unscalable solutions as part of the learning and validation process in early-stage startups. The 3-month rule acts as a practical and time-bound way to test ideas without overinvesting in infrastructure or overly complex systems prematurely. I particularly appreciate your emphasis on simplicity—such as running everything on a single VM and using hardcoded configurations—to gain rapid feedback and insights into user needs and resource requirements.
This approach underscores a critical mindset: sometimes the fastest way to understand what works is to start with straightforward, unembellished implementations. It also highlights the importance of deliberate pruning—if a solution doesn’t demonstrate value within a set timeframe, it’s better to pivot or discard rather than build ornate and scalable solutions too early.
In your experience, have you noticed any patterns in what tends to prove its worth within the three months? And how do you balance the need for quick experimentation with ensuring technical debt doesn’t accumulate? Thanks for sharing this practical framework—it’s a great reminder that initial simplicity can be a powerful driver of learning and iteration.