Productize Yourself with AI: Turn Your Skills Into Agents That Work While You Sleep

Naval said "productize yourself" in 2018. At the time I read it as "start a company." Then maybe "launch a course." I built neither. What I built instead was a set of agents — and that's probably closer to what he meant.

Here's the difference between the two framings: a company requires overhead. A course requires an audience. An agent requires your knowledge, encoded, and a few API calls. Same outcome — your work runs while you sleep. Much lower entry point.

I've been running five sites, building automation workflows, and iterating on AI tooling for the past two years. The thing I want to share isn't a playbook — it's the mental model that changed how I think about all of it.

Specific knowledge — the thing you already have

Naval's first principle: specific knowledge is what you know that feels like play to you but looks like work to others. It comes from your genuine curiosities, your odd combinations of experience, the lived path no one else has walked the same way. It cannot be outsourced because it is, by definition, yours.

Mine is a strange overlap: growth marketing across 80-plus countries, deep workflow automation, five years of affiliate site experiments, and a technical mindset that leans toward building over planning. No job posting asks for that exact combination. But I can turn it into systems that scale.

The question is not "what do I know?" It's "what do I know that I've never seen properly packaged?" Because that gap — between what exists and what you'd build if you just started — is where the specific knowledge lives. That gap is the product waiting to be encoded.

Leverage — code and media, without permission

Naval breaks leverage into four types: labor, capital, code, and media. Labor and capital require permission — someone has to agree to work for you, or you need money to deploy. Code and media need no one's sign-off. You ship it and it scales.

For a solo operator, those two are the whole game. Media is your writing, documentation, tutorials — content that accumulates and earns attention without extra work. Code, in 2025, increasingly means agents.

The shift I've noticed: you don't need to build a traditional SaaS product to access code leverage. A workflow in n8n that calls Claude, scrapes data, runs logic, and fires output is deployable leverage. It runs at 3am. It scales to a hundred inputs with no extra effort from you. Once tuned, it is a product. It is productized knowledge.

The ecosystem is wider than most people think

The tooling for this has exploded. Here's how I think about it:

Developer & code-first frameworks

• LangChain / LangGraph — the most widely used Python framework for LLM apps and agent graphs; the default starting point for most developer teams
• LlamaIndex — data-focused, strong for RAG pipelines and document ingestion
• AutoGen (Microsoft) — multi-agent conversation framework, good for complex multi-step tasks
• CrewAI — role-based agent orchestration; structured team-of-agents setups
• Pydantic AI — type-safe agent framework growing fast in 2025
• Claude Code — Anthropic's CLI that turns Claude into an autonomous coding agent in your terminal; what I use for building and shipping sites like this one

No-code & workflow-centric platforms

• n8n — self-hostable, extremely flexible; my daily driver for all automation
• Make — visual workflow builder, massive connector library
• Zapier — fastest to start; less powerful but zero friction
• Relevance AI — purpose-built for AI agent workflows, strong no-code UI
• Flowise — open-source LangChain visual builder, self-hostable
• Dify — open-source LLM app platform, fast-growing community
• Gumloop — newer entrant, clean UI, built specifically for AI-native pipelines
• Voiceflow — conversational AI, strong for chatbots and voice agents

Model access & local tooling

• OpenRouter — single API for 100-plus models from every major provider; how I compare models without managing separate keys
• Ollama — run LLMs locally; what I use for private data and offline experiments
• LM Studio — desktop app for local models, cleanest UI for non-developers
• Hugging Face — where most open-source models live before they land in Ollama
• Hermes Agent — autonomous agent runtime I've been testing alongside my n8n and Claude setup

Accountability — put your name on it

The third piece Naval emphasizes: accountability. Put your name on your work. Publish. Build a track record that cannot be faked.

Most people stall here. They build the system but keep it private. They test the workflow but never share the output. They wait until it's clean enough to show.

Eric Ries wrote in The Lean Startup that the goal is to start learning as quickly as possible. James Clear, in Atomic Habits, makes the same point differently — every action is a vote for the identity you're building. Every public build is a vote for "I'm someone who ships." Publishing also forces specificity. You can't handwave implementation details when a reader can run your workflow and find out it doesn't work.

The opportunity window is real. A Goldman Sachs report estimated that AI could automate the equivalent of 300 million full-time jobs globally over the next decade. The Economist called autonomous agents "the most consequential software since the web browser." Whether those projections land exactly or not, the market is moving like they're correct. The window to build a public track record as someone who knows how to work with agents is open right now.

Build in public — the compounding asset

How I'm actually doing this: I publish what I build. I write about what broke in my n8n flows and how I fixed it. I share the Claude prompts I use for content pipelines. I push Claude Code sessions to GitHub so the work is visible and timestamped.

This is not influencer behavior. It is a compounding asset. Every post is a searchable artifact. Every commit is proof of work. Every workflow diagram is something a future client or collaborator can evaluate without a conversation.

The discipline is the same as Zone 2 running. Not every session is fast. Not every post goes viral. But the accumulated mileage gives you the base to race on when it matters. Most people skip the easy miles and wonder why they can't hold pace late in the race.

The actual move

Here is the practical version — specific, not abstract:

1. Name your specific knowledge. Not your job title — the real overlap. The thing you've done that no hiring manager has a template for.
2. Map one repeatable process you do manually. Something weekly that takes 30 to 90 minutes and follows the same pattern every time.
3. Build a workflow around it. Start in Zapier if you need to. Move to n8n when you want real control and self-hosting.
4. Add an LLM call. OpenRouter is the fastest way to test multiple models without vendor lock-in. Claude is where I start for anything reasoning-heavy.
5. Publish what you built. One post, showing what it does and what you learned. That post is the beginning of the track record.

That is the loop. Once it runs, you tune it. Once it's tuned, you build the next one. Agents compound the same way habits do — slowly, then suddenly.

Naval's insight — the one that actually landed for me — is that the goal is not to work hard. It's to own something that works.

The builders who captured code leverage early didn't work more hours. They wrote one program that ran a million times. Your knowledge can do the same thing. Encode it. Ship it. Let it run.

Key takeaways

• Specific knowledge plus AI equals a scalable product only you could have built.
• Code leverage doesn't require SaaS — a workflow that runs while you sleep counts.
• Accountability means publishing. The track record is the product.
• The agent ecosystem is vast; go deep on one framework before going wide.
• Build in public because it forces the reps, not because anyone is watching.

What I'll do next

I'm encoding more of my growth marketing judgment into Claude agents — not to replace my thinking, but to handle the first pass so I can focus on the final 20 percent. The next system automates the research and brief stage for affiliate content. When it works, I'll write about it.