Embracing the 3-Month Rule: A Pragmatic Approach to Unscalable Solutions in Tech
In the world of startups, it’s common to hear the mantra “Do things that don’t scale,” famously advocated by Paul Graham. However, translating this advice into actionable steps within the realm of coding can often feel elusive. Over the past eight months, while building my AI podcast platform, I’ve developed a straightforward yet effective framework: every non-scalable hack is allotted a lifespan of three months. After this period, I evaluate its worth—if it proves beneficial, it earns a more robust solution; if not, it’s time to say goodbye.
Rethinking Scalability in Startups
As engineers, our training often focuses on devising scalable solutions from the get-go—think intricate design patterns, microservices, and distributed systems. This architecture is brilliant for accommodating massive user bases, but might not be ideal for a nascent startup. Sometimes, striving for early scalability is akin to delaying the inevitable while racking up expenses. By adhering to my 3-month guideline, I’m compelled to create straightforward and often imperfect code that might not conform to standard practices but can be launched quickly. This hands-on experience reveals what users genuinely require, surfacing insights that elaborate plans can obscure.
Key Infrastructure Hacks: Practical Examples
1. Unified Virtual Machine Deployment
My current setup operates on a single virtual machine (VM) that houses everything—from the database to background jobs. For just $40 a month, I have zero redundancy and perform manual backups to my local machine.
Here’s the brilliance in this approach: in two months, I’ve gained insights into my genuine resource requirements that no theoretical capacity planning document could elucidate. As it turns out, my platform only requires 4GB of RAM at peak times. The extensive Kubernetes architecture I was on the verge of implementing would have resulted in managing idle containers. Moreover, any crashes provide invaluable data regarding failures, often in areas I least expected.
2. Hardcoded Configuration Values
Configuration values are hardcoded throughout the codebase instead of stored in external files or environment variables. Here’s a peek:
python
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
While this approach lacks flexibility, it allows me to search for any configuration value
One Comment
This is a compelling perspective that challenges the conventional emphasis on immediate scalability, especially for early-stage startups. The 3-month rule acts as a practical compass, encouraging rapid experimentation and learning while avoiding overinvestment in potentially premature infrastructure. I particularly appreciate the emphasis on understanding real-world resource needs through these short-term hacks—often, practical insights gained from simple setups far outweigh theoretical projections.
Your approach aligns well with the concept of “building in phases”—focusing first on delivering value quickly, then iterating towards more robust solutions once genuine user feedback validates the direction. It’s a reminder that early agility and willingness to accept imperfection can lead to more informed, cost-effective scaling decisions later. Additionally, your example of managing configuration values underscores the importance of prioritizing speed and ease of adjustments in initial phases—something many technical founders overlook.
Would love to hear more about how you handle the transition from these quick hacks to scalable solutions once the platform starts gaining traction. Perhaps establishing a structured review process at the end of each 3-month cycle could facilitate smoother evolution towards sustainable architecture.