Hermes makes a weekly podcast highlights mix
A listener reports a Hermes workflow that transcribes weekly podcast episodes, ranks segments with Mistral and delivers a one-to-two-hour highlights mix on Telegram.
Suggested prompt
Suggested prompt written by Agents In Use, not the source author. Replace bracketed inputs and review tool permissions first.
For [my selected podcasts], collect new episodes each week, transcribe them and rank segments against [interests]. Prepare a highlights mix with episode references for review and deliver it on [day]. Preserve context and flag uncertain transcription.
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See placements →What happened
A listener reports a Hermes workflow that transcribes weekly podcast episodes, ranks segments with Mistral and delivers a one-to-two-hour highlights mix on Telegram.
Reported by u/SelectionCalm70 on RedditHow the agent did it
- Pull new episodes from the selected podcasts weekly.
- Transcribe the source audio with Voxtral.
- Use Mistral Large 3 to rank segments against retained preferences.
Where a person stepped in
The user selects podcasts and preferences and listens to the results. The account does not document per-segment approval, complete costs or independent accuracy checks.
A Reddit user describes a Hermes Agent workflow for a podcast backlog that had exceeded the time available to listen. The agent pulls new episodes each week and sends their audio through Voxtral transcription. Mistral Large 3 scores and ranks segments against the user's preferences, while Mistral Small supplies the stitching logic.
The result is a reported one-to-two-hour mix delivered every Sunday through Telegram. The title compares that with more than ten hours of normal listening, but no before-and-after log or independent assessment accompanies the claim. Hermes memory retains what the user finds interesting, so selection can use accumulated preferences rather than treating each episode as an unrelated summarisation request.
The author says Voxtral handled different accents and overlapping speakers better than the previous transcription setup. This remains a personal observation, not a benchmark of transcription accuracy. The post does not identify the final audio-cutting tool, a full cost breakdown or an approval step for every segment. The concrete copyable design separates acquisition, transcription, relevance scoring and assembly, with a weekly delivery deadline. Reviewing excerpts still matters: a highly ranked fragment can lose context, and the source does not establish that every selected passage accurately represents the speaker's wider argument.
Prompt
Suggested prompt (written by Agents In Use, not the author's)
Source: Reddit · u/SelectionCalm70 on Reddit
Frequently asked questions
Which agent runs the workflow?
The author explicitly names Hermes Agent as the host. Voxtral and Mistral models perform transcription, ranking and stitching tasks inside that workflow.
What is the delivered result?
The user reports a one-to-two-hour selection of podcast segments delivered through Telegram every Sunday.
Is transcription quality measured?
No. Better handling of accents and crosstalk is the author’s personal comparison; no accuracy benchmark or full cost breakdown is supplied.
More details
Remaining steps
- Use Mistral Small for stitching logic.
- Deliver the reported highlights mix on Telegram each Sunday.
About this account
Hermes coordinates weekly acquisition, transcription, preference-based segment selection and assembly, using memory to retain the listener’s interests.
Firsthand account by u/SelectionCalm70 on Reddit; original publication date verified against Reddit created_utc 1776143201 = 2026-04-14T05:06:41Z. Outcomes are the author’s reports, not independently reproduced. Suggested prompt written by Agents In Use; not the author’s original prompt.
Credit: u/SelectionCalm70 on Reddit
Source published 14 Apr 2026
Source checked 14 Oct 2026
Tools and topics
Hermes Agent · Voxtral · Mistral Large 3 · Mistral Small · Telegram
Podcast highlights · Preference memory · Segment ranking
Editorial completeness: 100/100. Measures documented fields, not agent performance or verified outcomes.