Research for One-Person Startups Using AI#

Finding AI Business Strategies You Can Execute Alone#

Table of Contents#

1. Introduction: Getting Started

   • The Evolution of AI Technology and the Potential of One-Person Startups

2. Key AI Concepts

   • Key AI Concepts (1): AI (Artificial Intelligence), Machine Learning / Deep Learning, Training Methods in Machine Learning, LLM (Large Language Models)
   • Key AI Concepts (2): Fine-tuning, RAG (Retrieval-Augmented Generation), Function Calling, MCP (Model Context Protocol)
   • Key AI Concepts (3): Prompt Engineering, Context Engineering

3. Key AI Services and Tools

AI Services and Tools (2)#

In this chapter, we briefly introduce the APIs provided by OpenAI, along with short explanations and example code for each. Even if you’re not a developer, if you understand the basic idea of what an API is, this chapter may help you get a rough sense of “what you can do” and set direction when you want to build a service using OpenAI’s APIs.

About the APIs provided by OpenAI#

AI providers such as OpenAI offer consumer-facing services like ChatGPT, but they also provide APIs to third-party developers, expanding the ecosystem. For startups, using an API instead of training your own model can be a very practical way to ship high-quality AI features quickly.

Harness Engineering — A Practical Guide to Safe AI Agent Operations#

2026-04-04

Why I Wrote This#

I actively use AI agents (Cursor, Claude Code, etc.) across multiple projects. At first, having an agent write code was impressive enough on its own. But as I integrated them more deeply into real projects, I kept running into recurring problems.

• Every time I open a new session, the agent forgets the project conventions
• It repeats the same mistakes today that we already solved yesterday
• The quality of agent-generated code fluctuates wildly between sessions
• When managing multiple projects, I have to repeat the same setup for each one

The root cause of these problems wasn’t a lack of agent intelligence — it was that the environment surrounding the agent was not properly set up. As 2026 arrived, this concern spread across the industry and began to be systematized under the name “harness engineering.”

I Decided to Call Them Harness Skills — Breaking the Illusion of Doing Well and Opening Up My Harness#

2026-06-08

Facing Things Without a Name#

When I first saw something called LLM Wiki, and then GStack, the first thing that came to mind was surprisingly: “What should I even call these?”

It was clear that both were means for handling AI agents better. From the perspective of harness engineering — the discipline of designing infrastructure to operate AI agents safely and reliably, which I covered in an earlier piece — these were obviously “tools you reach for when building a harness.”