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What is capability overhang?

Capability overhang is the gap between what AI can already help people do and what people commonly use it for. A model may be able to help build an interactive lesson, a working prototype, or a workflow across several tools while many of us still use it mainly to write and summarize.

The useful question is: What could I do now that I still assume is too difficult, expensive, or time-consuming?

A related idea, product overhang, is the gap between those capabilities and the products or interfaces that make them easy to use. A better prompt, a connected tool, or a simpler interface can help close that gap.

Make the most of your collection

Bookmark examples you want to return to. Use Notes to capture what caught your eye, a question, or a possible application. Your bookmarks, notes, personal tags, and saved drafts belong to your signed-in account. AI tags are shared editorial suggestions; your personal tags are private.

Ways to use AI groups approaches you could borrow. Compare creations that use the same idea: a game, a teaching tool, and a business dashboard might all turn a static explanation into something you can explore. Relevance depends on your task.

Try in my work copies a prompt to paste into your own AI. It asks that AI to use the context it actually has about you, suggest relevant applications, and start a useful first draft. When you know what to make, explore the build guides for prompts, tools, setup, and a small result you can inspect.

Prepare to share helps you edit and copy a caption with the original public post link. It does not send messages for you. Verified creator resources remain linked beneath each example.

How the examples are selected

The daily search runs at midnight Eastern. Owner-configured searches cover AI models, tools and workflows for building software, explaining ideas, making communication assets, and automating research or analysis. Each search has a minimum-like threshold. Up to 20 demonstrations are selected per edition; an attributed AI-assisted creation and a verified playable video are required. Historical editions retain their original search rules, including the earlier Astra and Fable 5.1 scope.

Checking the creation and its source means looking for an actual creation in the video and evidence connecting it to the named model, tool or workflow. We follow shared clips to their original source where possible and retain creator credits and relevant links. A creator's claim about build time or autonomy remains a claim unless separately checked.

Evidence and reliability appears beside each example. It distinguishes the video evidence, creator-reported claims, available instructions and any tests See Then Build actually ran. A tested starter is a small adaptation; creator source checks do not verify the original AI production process. Each build guide states its scope and limits.

A partial edition means the search may have missed qualifying posts. Xpoz, the service used to search X, has sometimes returned posts outside the requested dates or conflicting result counts. We check timestamps ourselves, but cannot infer a complete search from an inconsistent result list. Each day's collection note explains the known limits.

A striking demonstration is a reason to investigate. It does not by itself show that the result is reliable, that the model did everything, or that the same approach will work in every setting.

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