Zero Token Design

I'm not saying the product doesn't need AI. What I mean is: an AI product doesn't always need to burn its own tokens at runtime.

Think about how we built AI SaaS products in the past. The logic was simple: every time a user clicks, the product calls the model. Another click, another request. Another request, another token burned.

That made sense in 2023, because back then, most users didn't really have their own AI workspace.

But now it's different.

Codex, Claude Code, OpenCode, Cursor, and other local agent workspaces are getting more and more mature. They are no longer just chatboxes. They can read your directory, read your documents, run bash commands, edit code, run npx or uvx, and handle a whole workflow inside the user's own environment.

So I think the next generation of AI products can be designed differently.

Not every product needs to put a chatbot inside the webpage and pay for all the reasoning costs by itself. A better way might be: let users finish the reasoning inside their own agent workspace, and then push the result back into your product.

And this is different from Bring Your Own Key.

BYOK still asks users to apply for an API key, configure the key, understand billing, and put that key into your product. Honestly, that's just transferring the bill to the customer, but the UX is still heavy.

Zero-token design is not about asking users to configure a key inside my product.

It is about letting my product work with the tools they already use, like Codex, Claude Code, OpenCode, or Cursor.

CV.pro is a zero-token AI product in my understanding.

It is definitely an AI product, because resume parsing, JD tailoring, and content rewriting all need AI. But these things don't have to happen on CV.pro's server.

The more natural way is: the user copies a quick-start prompt, and maybe there is an npx command inside that prompt. Then they paste it into their own agent workspace. The agent runs the command, parses the file, handles the errors, generates the structured schema, and pushes that schema back to CV.pro.

So CV.pro is responsible for the schema, database, URL views, versions, rendering, and distribution.

Basically: Let the user's agent do the work. Let the product handle the result.

And I think every founder building AI products should think about this carefully.

Should your product do all the reasoning by itself?

Or should it become a system that can be operated by agents, capture the result, and distribute it beautifully?

That's the difference.