Generate a LinkedIn post from a podcast episode

Input

The episode file (MP3, WAV, M4A, FLAC, MP4, MOV, WebM) or a YouTube, Vimeo, TikTok, Facebook, X, Twitch, or Dailymotion link

Output

One plain-text LinkedIn post — hook line, short paragraphs, optional “→” list, closing takeaway or question, at most 3 hashtags

Length

About 120–220 words at standard; short roughly halves it, long roughly doubles the material

Tone

Professional, casual, or punchy

Cost

1 AI credit per post draft; reopening a generated draft is free

Source of truth

Written only from the transcript — no invented numbers, names, or handles

How to get a LinkedIn post out of a podcast episode

1. Transcribe the episode

   Upload the audio or paste the episode link in Transcribe Studio. You get a timestamped transcript in the episode’s language (99 languages, auto-detected).

2. Repurpose → LinkedIn post

   Pick the LinkedIn format, a tone, and a length. Professional tends to suit LinkedIn; try punchy for a shorter, more declarative version.

3. Check the hook against the transcript

   The first line is the claim or number LinkedIn shows before “see more”. Click the transcript to confirm it is exactly what was said, then adjust the wording to your voice.

4. Paste, then make variants

   Paste the plain text into LinkedIn — no Markdown to strip. Generate a second draft in another tone for later in the week, or switch to X thread or newsletter for the other channels.

What makes a podcast-derived LinkedIn post work

LinkedIn shows roughly the first two lines of a post before the “see more” fold, so the post lives or dies on its opening line. Posts built from an episode usually fail in one of two ways: they open with an announcement (“New episode out now!”) or they try to compress the whole conversation into one post. The first gives the reader no reason to expand; the second gives them too many.

DomeScribe’s LinkedIn format is written against both failures. The brief instructs the model to make line one a specific, surprising claim or number from the recording that stands on its own, then to tell one idea with one concrete story or figure from the transcript across six to twelve short lines, optionally a three-to-five-line list with “→” bullets, and to end with a takeaway or one genuine question. It is told to avoid emoji walls, “I’m excited to share”, and “Here’s the thing”, and to use at most three hashtags, only if they are relevant.

Plain text, because LinkedIn is plain text

The post is generated as plain text with line breaks, not Markdown. Asterisks and pound signs that would need cleaning up in other tools are simply not there; you paste the draft into the LinkedIn composer as-is. The blog-post and show-notes formats use Markdown because their destinations render it; LinkedIn, X threads, YouTube descriptions, and newsletter sections are plain text for the same reason.

Three tones, three lengths, nine posts from one episode

Each tone is a contract with observable markers. Professional: sentences averaging 15–22 words, no contractions, no exclamation marks, claims stated with their evidence. Casual: sentences averaging 10–16 words, contractions, second person, light warmth. Punchy: sentences averaging 6–12 words, strong verbs, the claim first, no hedges.

Length scales the word target around a standard of about 120–220 words. Short keeps only the single strongest idea and its best supporting detail; long uses more of the transcript — a second figure, the fuller story — without padding. Because every combination is a separate cached draft, a single episode can supply a professional standard post for launch day, a punchy short post mid-week, and a casual long post for the weekend, all at one credit each.

• Every fact, number, name, and quote must be present in the transcript.
• The model declines to invent links or handles; add your episode link yourself.
• Drafts are written in the transcript’s language.

Multiple speakers, one idea

DomeScribe’s transcripts do not label speakers, so the LinkedIn draft does not attribute lines to named hosts or guests unless the transcript itself makes it clear who is speaking (for example, the host introducing a guest by name). The brief asks the model to quote sparingly and verbatim only when a line is strong; otherwise it tells the idea in a neutral voice. If you want a guest-attributed quote, check the transcript at the timestamp and add the attribution yourself.

What it costs and what happens to the audio

A LinkedIn post draft costs one AI credit. Accounts start with 30 minutes of transcription and 5 credits, no credit card; Pro ($19 per month) includes 1,000 transcription minutes and 300 credits monthly. Transcription is server-side — the audio is processed by a speech-recognition model and the uploaded original is deleted afterwards; the transcript is encrypted at rest with a per-user key and auto-deletes after 30 days. A pasted link is used only to produce the transcript.

Questions, answered

Related

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• Turn a podcast episode into a blog post
• Convert a YouTube video to a blog post

Try it on your own recording

Free to start — 30 minutes of transcription and 5 AI credits, no card required.

Write a LinkedIn post from an episode · See pricing