Dustin Warren

HtmlDrag is back on Product Hunt today.

Once you have an existing or AI-generated HTML page, there are often a few things left to fix: an image, some text, or part of the layout.

I built HtmlDrag to make that editing pass more direct. Upload or paste your HTML and make visual adjustments. When you want AI s help, select the elements you want to work on, describe the change, and review the result.

Ever copied something, come back to it later, and had no idea what it was? A clipboard history helps, but once it holds a few hundred items it turns into a pile: a screenshot, a hash, a paragraph, another screenshot. The thing you wanted is in there, you just can't tell which one it is.

1.3.2 is about that problem.

Select any item, press Control-Shift-N, type a name and a few tags separated by commas ("wifi, office"), press Return. The name becomes the item's title in the list, so you can tell at a glance what it is. Names and tags are searchable, so "wifi" finds the Wi-Fi QR screenshot even though the image contains no such word. And an item with a name or tags is not removed by the count or age limits, so the things you keep coming back to stay put while the rest of the history keeps churning. Clear the name and tags and the item goes back to normal.

Snippets get the same tags field. If you had a keyword on a snippet, it is now a tag.

DungeonQ now includes a small MCP client you can copy into another Node project. It uses the official MCP SDK and Node built-ins, connects through the public MCP interface, and imports no DungeonQ runtime code.

The example follows a concrete task: read an artificial order and its review policy, save a multiline review, read it back, then issue and use a one-use world ticket. The ticket works inside its synthetic world. Shipping stays pending.

The client receives only an actor endpoint and token. An operator can observe the session and explicitly apply a finite adaptation policy through a separate interface. A "continue" message simply resumes the client; it cannot approve that policy or give the client operator authority.

This is a reusable integration example for DungeonQ's defensive deception runtime: give a designated suspicious session a persistent world to work in while people or authorized defender agents prepare a response. Creating that time and space is the product goal; measured defensive effectiveness remains unproven.