The 3-Month Method: A Pragmatic Approach for Building Unscalable Solutions
In the fast-paced world of startups, the mantra “Do things that don’t scale,” attributed to renowned entrepreneur Paul Graham, is often echoed. Yet, the intricacies of applying this advice—particularly in the realm of coding—are frequently overlooked. Over the past eight months, while developing my AI podcast platform, I’ve formulated a straightforward yet effective strategy: any unscalable workaround is granted a trial period of precisely three months. If it proves its worth within that timeframe, it earns the right to be refined; otherwise, it’s discarded.
The startup Mindset
As developers, we are conditioned to create “scalable” solutions right out of the gate. Our training focuses on sophisticated design patterns, microservices, and distributed systems—all vital architecture capable of accommodating millions of users. However, such an approach may lead to unnecessary complexity, especially for early-stage companies. Often, pursuing scalable code at this stage translates to deferring action, focusing on hypothetical users, and potentially wasting time on problems that may never arise.
Applying my 3-month framework compels me to produce straightforward and at times “imperfect” code that can be deployed quickly. This methodology allows me to interact directly with my user base and discern their genuine needs.
Insightful Infrastructure Hacks
1. Unified Virtual Machine Approach
My architecture is centered on a single $40/month virtual machine that houses everything—database, web server, background jobs, and Redis—all without redundancy and solely relying on manual backups. Far from a reckless strategy, this setup has enabled me to accurately assess my resource requirements in just two months, revealing that my platform peaks at 4GB of RAM. I realize now that I would have been pointless managing elaborate containers with Kubernetes when I can obtain real-time data on system performance during a crash.
2. Hardcoded Configuration
Imagine this:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
Forget about configuration files or environment variables; my constants are distributed throughout the code. Though changing these values necessitates redeploying the application, this approach offers a surprising advantage. I can quickly search the entire codebase for any configuration value. In three months, I’ve made minimal adjustments, which translates to mere minutes of redeployment versus the countless hours required to
