Study notes from lecture videos
Lectures, courses and tutorials on YouTube become notes, flashcards and practice questions once you have the transcript. The sample is a real transcript of the first lecture of MIT’s Introduction to Algorithms.
In the sample · recorded October 21, 2026
- Video length
- at least 46 min
- Words in the transcript
- 5,756
- Timed segments
- 373
- Chapters
- 11
Counted from the 1 result of one real run below. They describe this sample, not the whole market.
Study notes
Why it works.
Rewatching a 50-minute lecture to find one explanation is slow. With the transcript, you search the text, jump to the right minute, and ask your AI to explain the part you did not get.
Notes built from the transcript stay close to what the lecturer actually said, which matters when that lecture is what the exam is based on.
Who uses it
- Students working through recorded lectures
- Teachers building materials from their own videos
- Learning apps that add notes and quizzes to video courses
Real sample
What the tool returned.
Transcript of “1. Algorithms and Computation” by MIT OpenCourseWare.
1. Algorithms and Computation
MIT OpenCourseWare
- 0:00[SQUEAKING]
- 0:01[RUSTLING]
- 0:02[CLICKING]
- 0:05JASON KU: Good morning, everybody.
- 0:14STUDENT: Morning-- JASON KU: My name's Jason Ku.
- 0:18I'm going to be teaching this class in Introduction to Algorithms with two other instructors here--
- 0:25faculty in the department-- Eric Demaine and Justin Solomon.
- 0:31They're excellent people, and so they will be working on teaching this class with me.
Chapters
- 0:00Chapter 1: Introduction
- 0:55Chapter 2: Course Content
- 3:27Chapter 3: What is a Problem
- 8:35Chapter 4: What is an Algorithm
- 14:11Chapter 5: Definition of Function
- 17:16Chapter 6: Inductive Proof
- 25:27Chapter 7: Efficiency
- 38:57Chapter 8: Memory Addresses
- 41:00Chapter 9: Limitations
- 41:37Chapter 10: Operations
- 42:28Chapter 11: Data Structures
Input and raw JSON
The request body we sent. Long texts and lists in the result are shortened for this page.
{
"videos": [
"https://www.youtube.com/watch?v=ZA-tUyM_y7s"
]
}{
"videoId": "ZA-tUyM_y7s",
"url": "https://www.youtube.com/watch?v=ZA-tUyM_y7s",
"title": "1. Algorithms and Computation",
"channelName": "MIT OpenCourseWare",
"channelUrl": "https://www.youtube.com/@mitocw",
"language": null,
"languageMatched": true,
"text": "[SQUEAKING] [RUSTLING] [CLICKING] JASON KU: Good morning, everybody. STUDENT: Morning-- JASON KU: My name's Jason Ku. I'm going to be teaching this class in Introduction to Algorithms with two other instructors here-- faculty in the department-- Eric Demaine and Justin Solomon. They're excellent people, and so they will be working on teaching this class with me. I will be teaching the first lecture, and we'll have each of them teach one of the next two lectures, and then we'll go from there. This is Intro to Algorithms. OK, so we're going to start talking about this course content now. What is this course about? It's about algorithms-- introduction to algorithms. Really what the course is about is teaching you to solve computational problems. But it's more than that. It's not just about te…",
"wordCount": 5756,
"segments": [
{
"start": 0,
"text": "[SQUEAKING]"
},
{
"start": 1,
"text": "[RUSTLING]"
},
{
"start": 2,
"text": "[CLICKING]"
},
{
"start": 5,
"text": "JASON KU: Good morning, everybody."
},
{
"start": 14,
"text": "STUDENT: Morning-- JASON KU: My name's Jason Ku."
},
{
"start": 18,
"text": "I'm going to be teaching this class in Introduction to Algorithms with two other instructors here--"
},
{
"start": 25,
"text": "faculty in the department-- Eric Demaine and Justin Solomon."
},
{
"start": 31,
"text": "They're excellent people, and so they will be working on teaching this class with me."
},
{
"start": 39,
"text": "I will be teaching the first lecture, and we'll have each of them teach one of the next two lectures, and then we'll go from there."
},
{
"start": 47,
"text": "This is Intro to Algorithms."
},
{
"start": 53,
"text": "OK, so we're going to start talking about this course content now. What is this course about? It's about algorithms-- introduction to algorithms."
},
{
"start": 60,
"text": "Really what the course is about is teaching you to solve computational problems. But it's more than that."
}
],
"chapters": [
{
"title": "Chapter 1: Introduction",
"start": 0
},
{
"title": "Chapter 2: Course Content",
"start": 55
},
{
"title": "Chapter 3: What is a Problem",
"start": 207
},
{
"title": "Chapter 4: What is an Algorithm",
"start": 515
},
{
"title": "Chapter 5: Definition of Function",
"start": 851
},
{
"title": "Chapter 6: Inductive Proof",
"start": 1036
},
{
"title": "Chapter 7: Efficiency",
"start": 1527
},
{
"title": "Chapter 8: Memory Addresses",
"start": 2337
},
{
"title": "Chapter 9: Limitations",
"start": 2460
},
{
"title": "Chapter 10: Operations",
"start": 2497
},
{
"title": "Chapter 11: Data Structures",
"start": 2548
}
],
"scrapedAt": "2026-10-21T06:31:40.763Z"
}The fields that matter
What to look at first.
| Field | Why it matters here | In the sample |
|---|---|---|
| Texttext | The whole lecture as text to read and search. | [SQUEAKING] [RUSTLING] [CLICKING] JASON KU: Good morning, everybody.… |
| Segmentssegments | Jump to the exact minute of an explanation. | 373 entries |
| Chapterschapters | The lecture’s structure, if the uploader set chapters. | 11 entries |
| ChannelchannelName | The source, for citing it. | MIT OpenCourseWare |
Free tool
Try it with a lecture of your own
- 01Paste the lecture link and get the transcript.
- 02Ask your AI for notes, flashcards or questions.
- 03Use the timestamps to rewatch only the parts you need.
Press “Run free” and the real result shows up here, exactly what your agent gets back.
Ask your agent
Or just ask.
“Turn this lecture into study notes with headings and a summary of each part.”
“Make 20 flashcards from this lecture, each with the minute it comes from.”
“Write 5 practice questions on this lecture and the answers.”
Connect the tool to Claude, Cursor or any MCP client with your key and ask in plain words; your agent picks the tool by itself. Setup takes a minute: MCP guide. Prefer code? See the YouTube Transcripts API.
What it costs
Pay per result.
1credit for this sample
1 transcript × 1 credit. With the 2,000 free credits you get at sign-up, you could run this exact sample 2,000 times.
- Failed results are free
- One balance for every tool
- No card needed to start
Questions
Good to know.
Does it work for whole playlists?
With an API key you can send many video links in one call, for example every lecture of a course.
What about lectures in other languages?
You get the original transcript, or the language you ask for when the video has it. Your AI can translate the notes.
Are auto-generated captions good enough?
For most lectures, yes. Technical terms and formulas are sometimes wrong in auto captions; the result tells you whether captions were auto-generated.
Related
More ways to use it.
Same goal, other sources
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