Show HN: PlanOpticon – Extract structured knowledge from video recordings https://ift.tt/DQJUvGF

Show HN: PlanOpticon – Extract structured knowledge from video recordings 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, PDF). How it works: - Extracts frames using change detection (not just every Nth frame), with periodic capture for slow-evolving content like screen shares - Filters out webcam/people-only frames automatically via face detection - Transcribes audio (OpenAI Whisper API or local Whisper — no API needed) - Sends frames to vision models to identify and recreate diagrams as Mermaid code - Builds a knowledge graph (entities + relationships) from the transcript - Extracts key points, action items, and cross-references between visual and spoken content - Generates a structured report with everything linked together Supports 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 planopticon analyze -i meeting.mp4 -o ./output Also 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 . https://ift.tt/zAXTvBZ February 14, 2026 at 10:10PM