How to build a lecture notes and flashcard generator from YouTube

The problem it solves

Students rewatch entire lectures to review one concept, and many never make notes at all. Instructors want to provide study materials but don't have time to write them for every session. A generator that works across a full playlist gives every learner consistent, searchable materials.

What to include in your first version

• ■One-click import of a full course playlist
• ■Structured notes per lecture with headings and key terms
• ■Flashcards exportable to Anki or other spaced-repetition apps
• ■Search across every lecture in the course
• ■Links from each note back to the timestamp in the video

How to build it, step by step

1. 01

   Import the playlist

   Call /playlists/resolve with the course playlist URL to get every lecture in order.

2. 02

   Fetch transcripts in bulk

   Send the IDs to /batch with format.paragraphs and format.timestamp.

3. 03

   Generate notes and cards

   For each lecture, ask an LLM for structured notes and question-and-answer flashcards, each tied to a timestamp.

4. 04

   Build the study experience

   Show notes alongside the embedded video, add search across lectures, and export flashcards in Anki-compatible format.

Starter code

This idea is built on the "Recipe 3: Bulk-load a playlist into a knowledge base" pattern. Swap in your own channels, prompts and storage.

import requests

API = "https://www.youtubetranscript.dev/api/v2"
HEADERS = {"Authorization": "Bearer yt_sk_live_YOUR_KEY"}

playlist = requests.post(f"{API}/playlists/resolve", headers=HEADERS,
                         json={"playlist_url": "https://www.youtube.com/playlist?list=PLAYLIST_ID",
                               "limit": 100}).json()

ids = [v["video_id"] for v in playlist["items"]]
batch = requests.post(f"{API}/batch", headers=HEADERS, json={
    "video_ids": ids,
    "format": {"timestamp": True, "paragraphs": True},
}).json()

# Large batches run async: poll GET /batch/{batch_id} until status is "completed"
for r in batch.get("results", []):
    if r["status"] == "completed":
        for p in r["data"]["transcript"].get("paragraphs") or []:
            store(video_id=r["video_id"], start=p["start"], text=p["text"])  # your vector DB

Why it's worth building

Sell to students with a monthly subscription, or to instructors and course platforms as a feature that improves completion rates. Universities may license it for accessibility and learning support.

Mistakes to avoid

• ✕Summarizing too aggressively: students need definitions, examples and steps, not just a summary.
• ✕Losing course order: keep playlist order so notes follow the course structure.
• ✕Flashcards without context: link each card to the moment in the lecture where it's explained.

Questions about this idea