Explore / Extension / X Post Type Classifier

X Post Type Classifier

Self-hostBrowser extensionWeb

Label your X feed with Jev and dim selected post categories, including promotions and engagement bait.

by admin · listed 8 hours ago

⚡ What System1 does here

Jev selects a post's primary intent from six categories using its text and author name. The extension handles viewport dwell detection, caching, inline badges and user-selected dimming. The current model request contains no image or video content.

About

X Post Type Classifier ↗

X Post Type Classifier is a third-party Chrome extension by aslamdoctor. It adds a compact category badge beside each post's author, helping readers distinguish tutorials, news, promotions, humor and personal commentary while browsing X. The approximately 59-second demonstration scrolls through a labeled feed, opens the category settings, and selects Bait and Promo for dimming. A later view shows a dimmed post beside normally displayed Insight and News posts. Dimming reduces visual prominence rather than deleting content. The original recording has an English interface and no audio. The implementation waits for a post to remain in view for about 350 milliseconds, then sends its text and author name to the TypeSafe API. Jev chooses among Insight, News, Bait, Promo, Meme and Opinion. Ordinary extension code renders the badge, applies the reader's dimming preferences and caches results in memory and browser-local storage. Images and videos visible in the feed are not included in the current classification request. To use it, configure your own TypeSafe API key, install and build with Bun, then load the local extension directory in Chrome's extension manager. Classification requires the online API and sends post text and author information to the provider. This listing was prepared from the repository documentation, classification and extension-entry code, and the supplied recording. The extension was not installed for a live account/API test, and classification accuracy was not evaluated. Its labels are model judgments about content type, not fact-checking results.

🧪 Community test reports

0 reports · 0 passed on the real model
No one has tested this yet. Be the first.

💬 Discussions

New post →
No discussions yet.