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bilibili-digest

A browser extension that transforms Bilibili videos into structured learning resources using AI.

free(Free tier)156 github stars

About bilibili-digest

bilibili-digest is a Chrome and Edge browser extension designed to enhance language learning and knowledge retention from Bilibili video content. It adds a sidebar to Bilibili’s web player interface, enabling users to read subtitles, view bilingual translations, generate AI-powered chapter overviews, get instant explanations for selected words, and create time-stamped notes.

The extension draws architectural inspiration and prompt engineering foundations from the youtube-digest project (MIT licensed), adapting it specifically for Bilibili’s platform and Chinese-language context. It targets learners who consume educational or technical content on Bilibili and want deeper engagement with video material.

It operates entirely client-side during browsing — no backend or account required — and integrates directly into the Bilibili webpage without requiring video downloads or external processing.

Key Features

  • Displays synchronized subtitles in the browser sidebar while playing Bilibili videos
  • Shows bilingual (Chinese–English) subtitle alignment for language learning
  • Generates AI-powered chapter summaries based on video transcript structure
  • Provides on-demand word-level explanations when users highlight terms in subtitles
  • Allows creation of notes tied to precise video timestamps
  • Compatible with both Chrome and Edge browsers
  • No sign-up or account needed — works immediately after installation

Pros & Cons

Pros

  • • Free and open-source architecture inspired by MIT-licensed upstream project
  • • Designed specifically for Bilibili’s ecosystem and Chinese-language learning needs
  • • Zero-friction workflow: all features accessible directly on the video page

Cons

  • • No official website or documentation beyond GitHub README
  • • Lack of explicit license information makes reuse and redistribution uncertain
  • • AI features depend on local or third-party LLM integration not detailed in available materials