{"id":47021448,"title":"Show HN: PlanOpticon – Extract structured knowledge from video recordings","points":2,"user":"ragelink","time":1771135831,"time_ago":"11 hours ago","type":"link","content":"

We built PlanOpticon to solve a problem we kept hitting: hours of recorded meetings, training sessions, and presentations that nobody rewatches. It extracts structured knowledge from video — transcripts, diagrams, action items, key points, and a knowledge graph — into browsable outputs (Markdown, HTML,\n PDF).

How it works:

  - Extracts frames using change detection (not just every Nth frame), with periodic capture for slow-evolving content like screen shares\n  - Filters out webcam/people-only frames automatically via face detection\n  - Transcribes audio (OpenAI Whisper API or local Whisper — no API needed)\n  - Sends frames to vision models to identify and recreate diagrams as Mermaid code\n  - Builds a knowledge graph (entities + relationships) from the transcript\n  - Extracts key points, action items, and cross-references between visual and spoken content\n  - Generates a structured report with everything linked together\n

\nSupports OpenAI, Anthropic, and Gemini as providers — auto-discovers available models and routes each task to the best one. Checkpoint/resume so long analyses survive failures.

  pip install planopticon\n  planopticon analyze -i meeting.mp4 -o ./output\n

\nAlso supports batch processing of entire folders and pulling videos from Google Drive or Dropbox.

Example: We ran it on a 90-minute training session: 122 frames extracted (from thousands of candidates), 6 diagrams recreated, full transcript with speaker diarization, 540-node knowledge graph, and a comprehensive report — all in about 25 minutes.

Python 3.10+, MIT licensed. Docs at https://planopticon.dev.","url":"https://github.com/ConflictHQ/PlanOpticon","domain":"github.com","comments":[],"comments_count":0}