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YouTube to Blog Post, Twitter Thread & Chapters Generator

Turn any YouTube video into a ready-to-publish Twitter/X thread, LinkedIn article, podcast show notes, or timestamped chapter list — generated from the actual transcript, not inferred from metadata. No hallucinations. No fabricated quotes.

This Actor transcribes your YouTube video using the Wisprs API, then passes the real transcript to the Wisprs repurpose engine to generate structured content. Every tweet, every paragraph, every quote is drawn from what was actually said in the video. The output is grounded in the source material — not invented by an LLM reading a title and description.

What does this Actor do?

Generic AI writing tools that claim to "turn your YouTube video into a thread" have a structural problem: they do not actually watch the video. They read metadata and generate plausible-sounding content around a topic. The output feels authentic until you notice that the specific statistics your guest cited, the exact analogy they used, the memorable line that made the episode worth sharing — none of it is there. It was made up.

This Actor solves that by transcribing the source material first, then generating content from the real transcript.

1. Accepts YouTube video URLs (also works with podcast episodes, Loom recordings, direct mp3/mp4)
2. Transcribes the audio asynchronously via Wisprs (no timeouts — handles videos of any length)
3. Generates your chosen output format from the transcript:
   • Thread — 8–15 tweet Twitter/X thread with hook, body, and CTA
   • Blog — LinkedIn article or blog post in Markdown, 600–1,200 words
   • Show notes — structured podcast-style notes with summary, chapters, and verbatim quotes
   • Summary — 2–4 sentences for newsletter intros or email subjects
   • Chapters — timestamped chapter markers for YouTube descriptions
   • Quotes — top verbatim quotes with speaker attribution and timestamps
4. Saves the generated content plus transcript to your Apify Dataset

How do I turn a YouTube video into a Twitter/X thread?

Set repurposeMode to "thread":

{
"startUrls":[{"url":"https://www.youtube.com/watch?v=YOUR_VIDEO_ID"}],
"repurposeMode":"thread"
}

The repurposed_thread field in your dataset row will contain an array of tweet objects with the text for each tweet in the thread. Every tweet is drawn verbatim from what was said in the video.

How do I convert a YouTube video to a LinkedIn article or blog post?

Set repurposeMode to "blog":

{
"startUrls":[{"url":"https://www.youtube.com/watch?v=YOUR_VIDEO_ID"}],
"repurposeMode":"blog"
}

The output is a 600–1,200 word Markdown article in repurposed_blog — ready to paste into LinkedIn, your CMS, or a newsletter.

How do I auto-generate YouTube chapter markers?

Set repurposeMode to "chapters" to get timestamped chapter markers suitable for pasting directly into a YouTube video description:

{
"startUrls":[{"url":"https://www.youtube.com/watch?v=YOUR_VIDEO_ID"}],
"repurposeMode":"chapters"
}

The repurposed_chapters field contains an array of {title, startSeconds, description} objects — ready to inject via the YouTube Data API or format for manual copy-paste.

How do I repurpose multiple YouTube videos in batch?

Add all URLs to startUrls. The Actor processes each one sequentially and saves a separate dataset row per video:

{
"startUrls":[
{"url":"https://www.youtube.com/watch?v=VIDEO_1"},
{"url":"https://www.youtube.com/watch?v=VIDEO_2"},
{"url":"https://www.youtube.com/watch?v=VIDEO_3"}
],
"repurposeMode":"thread",
"exportFormats":["txt"]
}

How do I receive results via webhook (n8n, Make, Zapier)?

Pass a webhookUrl to get a POST callback as each video completes instead of polling:

{
"startUrls":[{"url":"https://www.youtube.com/watch?v=YOUR_VIDEO_ID"}],
"repurposeMode":"blog",
"webhookUrl":"https://your-n8n-instance.com/webhook/YOUR_HOOK_ID"
}

Using with AI agents (MCP)

This Actor is published on the Apify Store and automatically available as an MCP tool. AI agents using Claude Desktop, LangChain, CrewAI, or any MCP-compatible framework can discover and call this Actor directly — no custom integration required.

Twitter/X thread output example

{
"thread":[
{
"tweetNumber":1,
"text":"The biggest mistake founders make in year one? Optimizing for signups instead of retention. Here's what we learned the hard way. [Thread]"
},
{
"tweetNumber":2,
"text":"We hit 500 signups in week 2. Felt amazing. Then checked week-3 usage. 12 people. The signup metric was lying to us."
},
{
"tweetNumber":3,
"text":"The question that changed everything: 'If this product disappeared tomorrow, would you be disappointed?' We asked 50 users. 48 said no."
},
{
"tweetNumber":10,
"text":"TL;DR: Track the metric that breaks your heart when it's low. That's your north star.\n\nFollow for more."
}
]
}

Every tweet is drawn from what was actually said in the video. Speaker-aware quotes pull the exact sentence with the speaker's name. Claims can be traced to a timestamp in the source.

What data does the Actor return?

Wisprs vs AI writing tools that "summarize" YouTube videos

How much does it cost?

Pricing is pay-per-event:

• $0.005 per video submitted
• $0.015 per audio minute (20-min video = $0.30)
• $0.075 per repurpose result generated

Example: 20-minute YouTube video → Twitter thread

• Submit: $0.005
• Audio: 20 × $0.015 = $0.30
• Thread: $0.075
• Total: ~$0.38 per video

Example: 10 videos → LinkedIn articles

• Submit: 10 × $0.005 = $0.05
• Audio: 10 × 20 × $0.015 = $3.00
• Blog posts: 10 × $0.075 = $0.75
• Total: ~$3.80 for 10 LinkedIn articles

What can I build with this?

Daily thread content operation — connect to a YouTube channel RSS feed Actor, submit new video URLs as they publish, and deliver a draft thread to the creator via Slack or email within 5 minutes of upload. Charge creators $19/month for this one automation.

Ghostwriting tool — ghostwriters charge $500–2,000 per thread for high-profile clients. Submit the client's YouTube appearance or interview, get a draft thread in minutes, edit to voice, publish. Handle 5× the client volume.

Podcast-to-LinkedIn pipeline — take any interview podcast, transcribe the episodes, generate LinkedIn posts attributed to the guest's exact words. Guest gets content distribution. You get an automated content operation.

YouTube chapter automation — submit videos in batch and get back chapters arrays for every video. Inject into YouTube video descriptions via the YouTube Data API. An entire channel's chapters in one run.

Newsletter from YouTube content — submit relevant videos published that week, generate summary outputs, curate the best into a newsletter section automatically. Spend your time on selection and framing, not transcription.

Supported URL formats

• YouTube videos, Shorts, and long-form
• Podcast RSS episode links
• Loom recordings
• Direct mp3, mp4, wav, m4a
• TikTok, Vimeo, and most public video/audio hosts

Language support

100+ languages with automatic detection. Repurpose output is generated in the same language as the transcript. An English video produces an English thread; a Spanish video produces Spanish output.

Related Actors

• Wisprs — Podcast Show Notes Generator — podcast-optimized with chapters, quotes, and diarization
• Wisprs — Audio & Video Transcription — transcript-only with all export formats
• Wisprs — Social Media Transcriber — TikTok, Reels, Shorts

FAQ

Is the generated content based on the real transcript? Yes. Wisprs transcribes the audio first, then generates content from the actual words spoken. It cannot invent quotes or statistics that weren't in the video.

Can I repurpose videos in languages other than English? Yes. Wisprs supports 100+ languages. The repurposed output is in the same language as the video.

What if the video is long (90+ minutes)? The async job queue handles videos of any length. A 90-minute video typically completes transcription in 5–10 minutes. maxPollSeconds defaults to 1800 (30 min) — increase to 3600 for very long content.

Can I process a full YouTube channel at once? Yes — pair this Actor with Apify's YouTube Scraper to extract all video URLs from a channel, then pass them into this Actor.

Support

• Documentation: wisprs.co/docs
• Email: tosh@belvadigital.com

Real transcripts. Real content. No hallucinations.