The visuals of a video —
not just the transcript.
Every other tool hands your AI a transcript and loses the screen — the diagrams, slides, and code where the meaning actually lives. VidLens reads the screen and builds a persistent timeline you can search and question.
[04:32]“…the request flows from the API gateway through the message queue — as you can see in this diagram.”
Made for the people transcripts leave behind.
The same visual layer, delivered three ways — to your agent, to your notes, and to your channel.
Query what was shown on screen, not just what was said.
Every analyzed video becomes a reusable multimodal timeline: speech, visual state, and on-screen text, all cited to the second.
› Where is the architecture explained? ## On screen [04:32] Architecture diagram: Client → API Gateway → Kafka → Worker → PostgreSQL
Notes that show the whiteboard, not just the words.
Formulas, diagrams, and slides captured as real text and linked to the second they appeared — so you can skim the lecture instead of scrubbing through it again.
## Key moments - [12:04] Bayes' rule on the whiteboard <!-- on screen @ 12:04 --> P(A|B) = P(B|A)·P(A) / P(B)
An llms.txt for your channel — make your videos readable by AI search.
Publish clean, structured documents of your own videos at stable URLs, so answer engines and agents can find, read, and cite what you made.
# Maya Chen — video documents - /v/vector-search Vector Search in Production - /v/rag-eval Evaluating RAG, honestly - /v/chunking Chunking is the real model
Analyze once. Ask forever.
Paste a public YouTube link. Gemini reads the video natively, and VidLens keeps the resulting timeline for future questions.
Paste a YouTube URL
Submit a public video link. No browser extension, local SDK, or frame upload is required.
Gemini builds the timeline
Native video understanding fuses speech, visuals, and OCR into timestamped segments.
Ask from the cached index
Stream grounded answers with citations that jump directly to the relevant moment.
VidLens does not fetch or store video bytes. The public URL is passed to Gemini’s native video input.
One recording in. A document your AI can read out.
Summary
A two-sentence TL;DR of the whole recording.
Chapters
The recording split into sections, each with a one-line gist and a timestamp.
On-screen visuals
Diagrams, slides, and charts described in language your AI can read and search.
Timestamped transcript
Every line, speaker-labeled, linked to the second it was said.
On-screen text & code
Slides and code captured as markdown — copy-paste ready.
# Vector Search in Production > 18:24 · May 28, 2026 · Maya Chen ## Summary A pragmatic walkthrough of why naive cosine search disappoints at scale, and the three fixes that matter. ## On screen [04:32] Architecture diagram: Client → API Gateway → Kafka → Worker → PostgreSQL. ## Transcript **[03:58] Maya Chen:** If you take one line home: chunk on structure, not count. ```python def chunk(doc): for s in doc.sections: yield s.heading + s.body ```
Every video analyzed once.
The timeline is cached, so each follow-up reuses the same grounded visual evidence instead of processing the video again.
Try VidLensGive your AI the whole video.
Not just the transcript — the diagrams, slides, and code on screen, as one AI-ready document your agent can call.