Don’t Inherit the Tool. Inherit the System.

Why AI-assisted projects should be built around portable artifacts, not tool-specific workflows.

I recently worked on a project using LLM-assisted development tools.

The goal was to convey a concept in a more interactive way. Rather than explaining the idea only through a static document, I wanted to create something people could explore directly. They could interact with it, change inputs, observe behavior, and build intuition through use.

At one point, someone asked: if a project was initially created with one LLM development tool, does that make it harder to continue the work using another?

For example, what if the first version was developed with OpenAI Codex, but the next person prefers Cursor or Claude Code? Does the original tool become a dependency? Does the project somehow belong to the environment where it started?

This points to a misconception we will see more often as LLM-assisted development becomes normal.

The tool is not the project.

This is not like writing a system in C and handing it to someone who only knows Python. In that case, the programming language, runtime, libraries, and toolchain are real constraints. They shape what the next developer can reasonably do.

But Codex, Cursor, Claude Code, and similar tools are not the underlying language of the project. They are not the runtime. They are not the architecture. They are not the product.

They are collaborators. Interfaces. Different ways of operating on the same underlying system.

The real project lives somewhere else.

• It lives in the codebase.
• It lives in the architecture.
• It lives in the documentation.
• It lives in the tests.
• It lives in the instrumentation.
• It lives in the setup instructions.
• It lives in the product intent, the data contracts, the evaluation criteria, and the decisions that explain why the system behaves the way it does.

If those artifacts are strong, the work is portable.

A project initially developed with Codex can be continued in Cursor. A project explored in Claude Code can be reviewed with Codex. A design discussion in one tool can become an implementation task in another. One assistant can write a first draft, another can critique it, and a third can help test or refactor it.

This is often where these tools become most useful. You can use one tool to reason through a design. You can use another to implement a piece of functionality. You can ask a different one to review the code, challenge assumptions, or look for edge cases. You may prefer one tool for frontend iteration and another for backend structure. The value is not in declaring loyalty to one tool. The value is in using each tool where it is strongest while keeping the project itself coherent.

If the only durable artifact from an LLM-assisted project is a long conversation inside one tool, then the project is fragile. The next person has to reconstruct intent from chat history. They have to infer what decisions were made. They have to guess how to run it, where to change it, what matters, and what might break.

But if the project is built correctly, the conversation is not the handoff. The system is the handoff.

The durable output of LLM-assisted development is not the conversation you had with a tool. It is the legible, runnable, testable system you leave behind.

That means we should be deliberate about the artifacts we create around this kind of work.

• Can someone clone the project and run it?
• Can they understand the main components?
• Can they see how data moves through the system?
• Can they inspect the assumptions?
• Can they test whether the system still works after a change?
• Can they observe what is happening when something goes wrong?
• Can they understand what is finished, what is provisional, and where the next extension should happen?
• Can they bring their own preferred LLM development tool to the project and still be productive?

This is especially important as more teams adopt LLM-assisted development. We should not create a new kind of tool dependency where people feel blocked because they do not have access to the exact assistant or environment that generated the original implementation.

We want to enforce artifact-driven development.

The assistant may accelerate the work, but the project has to stand on its own.

A good handoff should not be: “You need the same LLM tool I used.”

A good handoff should be: “Here is the system. Here is how it works. Here is how to run it. Here is how to evaluate it. Here are the important decisions. Here are the known limitations. Here is where you can safely extend it.”

That is what makes ownership transferable.

As these tools become more capable, it is easy to over-index on the tool itself: which one generated the code, which one has the best interface, which one produced the first version. Those things matter tactically, but they should not define the long-term maintainability of the work.

Ask this question, “Is this project understandable enough for someone else to own?”

If the answer is yes, then the next person can use Codex, Cursor, Claude Code, or whatever tool fits their workflow. The project does not belong to the assistant that helped create it. It belongs to the people who can understand it, validate it, improve it, and carry it forward.

The tool can help you build. But the system you leave behind is what allows others to continue.

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This writing reflects my personal perspectives on product management, AI, and content discovery. It does not represent the official position of my employer or any affiliated organization.