笔记与实验

The useful part of ChatGPT traffic is not the number itself. It is the proof that a user can discover a product without visiting Google, YouTube, or Twitter first.

That changes the work. The question becomes: which pages, claims, and product names are easy for AI systems to cite accurately?

I track this like a distribution loop: mentions, referrals, signups, paid conversion, and the exact pages that answer engines keep surfacing.

A page that tries to rank for everything usually becomes hard for answer engines to summarize.

The better page has one job: define the product, state who it helps, explain the workflow, and show evidence that can be repeated elsewhere.

That is why I care about simple product pages, clear comparison language, and concrete numbers more than decorative content volume.

The site should not ask people to believe I am building. It should make the work visible.

Products, users, traffic sources, citations, revenue, experiments, failed attempts, and current focus are all better signals than a polished narrative.

The goal is a page that a serious operator can scan in one minute and understand what is real.

Twitter is useful when it is attached to a product loop.

The strongest posts do not just describe an idea. They point to a shipped product, a user behavior, a traffic source, or a lesson from a live experiment.

That makes the account easier to follow and the site easier to trust.

I like small AI products because they shorten the feedback loop.

If a product can attract search traffic, earn citations, convert a few paid users, or expose a repeated workflow, it is already giving useful data.

The point is not to ship many toys. The point is to find signals that justify a deeper build.