Deep Domains, Not Wide Network

This approach prioritizes deep, sustained engagement within a limited set of business domains, allowing for the compounding of knowledge, data, and operational efficiencies across products.

What it is

Deep Domains, Not Wide Network means I select 2-3 core domains and build multiple, distinct offerings within each. For example, Total Formula 1 operates within the motorsport domain. Instead of then building a product for cooking or finance, I might build a data analysis tool for F1 fans or a fantasy league helper under that same motorsport umbrella. This is about vertical integration of knowledge, not horizontal spread across many unrelated niches. It ensures that every new product contributes to a growing, specialized understanding of a specific market and its users.

Why I do it this way

Compounding knowledge is the primary driver. Each product in a domain deepens my understanding of the user, the market, and the content. This knowledge directly informs the next product, reducing research time and risk. Understanding the F1 fan base for Total Formula 1 makes it easier to predict needs for a new F1-related tool. This also extends to shared infrastructure: technical components and operational workflows become reusable. A user authentication system built for one product in a domain, often deployed on Vercel or leveraging Firebase, can frequently serve another. My Solo-Founder Portfolio benefits directly from this, as a single Vercel deployment or Firebase project can host multiple related services.

My AI agents, whether using Claude Code for development or Gemini for content generation, become more effective when trained on consistent domain data. They learn the jargon, the user pain points, and the content nuances faster, reducing the need for constant re-prompting or retraining for new contexts. For Pregnancy Power Hour, content agents become experts in prenatal health, allowing for richer, more accurate output. This is a form of Critic Gates for Mass Content applied at the domain level, ensuring quality and relevance.

How it works in practice

I identify domains where I have genuine interest, where there's clear user pain, and where content or data is accessible. Accessible often means public APIs, structured data, or a clear path for agent-driven research. Within a chosen domain, I start with a minimal viable product (MVP). For instance, Inky started as a simple AI-powered summarizer for long-form content. As I learn, I identify adjacent problems or opportunities within that content domain. Instead of splitting resources thinly across many domains, I concentrate my agentic engineering efforts and my own time on deepening the existing ones, which means fewer context switches. I build common services like payment processing (Stripe), email (Resend), or banking (Mercury) once, then integrate them across all products within a domain, and often across domains if applicable. A unified data layer, typically Firebase, allows for easier cross-product analysis and feature development within a domain, crucial for understanding user behavior across a portfolio of related products.

Where this breaks down

If a chosen domain becomes oversaturated or the core problem is solved by a dominant player, all products within that domain can suffer simultaneously. Diversification across domains mitigates this, but sacrifices depth. Even within an interesting domain, repeated focus can lead to a lack of fresh perspective if not managed carefully. I counteract this by ensuring each product solves a distinct problem, even if the domain is shared. Some niche domains have an inherent ceiling on total addressable market. This approach optimizes for profit within that ceiling, not necessarily for the hyper-growth often associated with wider market plays. My focus is on Cash Before Vanity, so this is an acceptable trade-off.