RSS to Podcast: How to Turn Any Feed into a Personal Audio Stream

The short version

RSS spread as a format for reading. With AI summarization and text-to-speech, it can become a format for listening too. There are roughly three ways to make that switch: paste articles into a TTS service yourself, lean on a read-aloud tool inside your existing reader, or feed an AI podcast generator a list of RSS URLs and let it produce a daily episode for your podcast app.

If your goal is to fill commute, chores, or walking time with the things you already follow, the third option tends to be the only one that survives past the first week. The first two work for occasional use. None of them is universally best; they fit different habits and different appetites for tinkering.

This article covers what "RSS to podcast" actually means, why a podcast format suits some habits better than an RSS reader, an honest comparison of the three approaches plus DIY/OSS pipelines, a walkthrough of doing it with Lisnify, which feeds work well, and the copyright and audience-scope questions most tool comparisons skip.

What "RSS to podcast" actually means

Podcast feeds and RSS feeds are technical relatives. The podcast distribution format is the RSS 2.0 specification extended with an <enclosure> element that points to an audio file. Every podcast app you can name is, internally, an RSS subscriber with audio playback wrapped around it.

Converting an article RSS into a podcast comes down to three steps:

1. Fetch the article bodies (or at least titles and summaries) from the source RSS.
2. Convert the text to audio. Summarization usually sits between fetch and audio so the episode is a digest rather than a verbatim reading.
3. Serve a podcast-shaped RSS that references the audio via <enclosure>, so a podcast app can subscribe.

Who handles each step, whether summarization is involved, how multiple articles are bundled into one episode, and who is allowed to listen: those four questions decide which of the methods below fits you.

Why a podcast format beats an RSS reader for some habits

RSS readers are not going anywhere. The reason to add a podcast layer on top is that the two formats serve different parts of the day.

• Hands-free, eyes-free time. Driving, walking, doing dishes, exercising. RSS readers assume you are looking at a screen. Podcast apps assume you are not.
• Playback affordances are first-class. Chapter skip, 1.5x speed, sleep timer, automatic offline download. These are built into every podcast client. Bolting them onto an RSS reader is always an add-on.
• A "reading pile" becomes "today's episode." An unread count of 247 in an RSS reader is a low-grade source of guilt. A 15-minute episode that arrives every morning is a unit you can finish.
• Multiple sources collapse into one stream. With summarization in the loop, you can blend Hacker News, a few dev.to tags, two Substack newsletters, and one company blog into a single morning brief.

The format has limits. Code samples, equations, charts, benchmark tables, and UI screenshots lose information when read aloud. Treat audio as a first-pass filter that helps you decide what to open in a browser later, not as a replacement for reading the original.

Three approaches to turn RSS into a podcast

The methods below cover roughly the same goal (getting an article-shaped feed into your ears), but the cost, control, and amount of ongoing maintenance vary a lot.

1. Manual TTS, one article at a time

The bare-metal version: copy an article body out of your reader, paste it into a TTS service, save the resulting MP3, host it somewhere, hand-write a podcast RSS that points at it via <enclosure>, and add that URL to your podcast app.

Suitable for a handful of articles you want voiced in a particular style; for instance, picking a specific narrator for a long essay you intend to listen to twice. Not suitable for daily multi-source consumption, because nobody keeps doing this past day five.

The TTS side has good options today: ElevenLabs for voice cloning, Play.ht, Speechify, and NaturalReader all offer paid voices that hold up over a long article. The bottleneck is not voice quality. It is the fetch-clean-summarize-render-publish loop you are doing by hand every morning.

2. Read-aloud tools that play inside an existing reader

A softer option is to use the read-aloud feature already built into the tool you read articles in. Feedly has text-to-speech baked into its reader. Instapaper offers TTS on its premium tier. ElevenReader and Speechify ship dedicated apps that import articles and read them aloud. Apple's "Listen to selection" reads any selected text on iOS. Voice Dream Reader has been a favorite of accessibility-focused readers for years.

A note on the read-later category: Mozilla retired Pocket in 2025, so the most familiar option here is gone. Raindrop.io is sometimes named as a successor for bookmarking, but it does not have built-in article read-aloud.

The strength here is low setup cost. You keep the reader you already use and just turn on the audio. The limitations are real, though. Most of these tools play articles inside the reader app rather than producing a feed your podcast app can subscribe to. They read full text without summarization, which makes a typical article a 20-minute listen. Multi-host conversation, mixed sources in one episode, and scheduled delivery are not part of the model.

If your need is "I want to listen to this one article on the train," read-aloud tools are enough. If your need is "I want a 15-minute morning brief that covers a few feeds at once and shows up in my podcast app," they fall short.

3. AI podcast generators

This category takes RSS URLs as input, summarizes the new articles, generates a multi-host script, runs TTS, and publishes a podcast-shaped RSS you subscribe to in any podcast app. Lisnify is in this group.

The category itself has three rough sub-shapes, depending on what the product is optimized for:

• Public-podcasting-oriented. Wondercraft, Jellypod, and AutoContent API are aimed at media operators, brands, and content teams that want to publish AI-generated audio under their own name.
• Document-understanding-oriented. Google NotebookLM Audio Overviews turns uploaded sources into a two-host conversation. It is built around understanding a specific corpus, not around continuous publishing of new feed items.
• Personal, private-feed-oriented. Lisnify, Recast, ListenLater, and MultiMind focus on a feed only you (or a small private group) subscribes to.

The right pick depends on what you want at the end. If you are publishing a show, the public-podcasting tools are designed for that. If you want to interrogate a stack of PDFs or papers, NotebookLM is built for that workflow. If you want a personal audio version of the feeds you already read, the private-feed tools fit.

4. DIY and open-source pipelines

It would be wrong to skip this category. Plenty of developers have built their own RSS-to-podcast pipelines, and the OSS scene around it is active.

zarazhangrui/personalized-podcast is a NotebookLM-style personalized podcast generator on GitHub, built around RSS input and TTS output. The n8n AI podcast workflow template chains RSS fetching, an LLM summarizer, and ElevenLabs voice synthesis inside n8n's no-code automation engine. There are dozens of one-off implementations in personal blogs and gists.

If you build it yourself, you own every step and can shape it however you want. You also own every step's failure modes: RSS fetching and parsing edge cases, article body cleanup, summarization prompt drift, multi-host TTS coordination, audio mixing, RSS publication, hosting cost, podcast-app compatibility testing, and monitoring when something quietly breaks. A weekend prototype is fun. Running the same pipeline reliably for months is a different kind of work.

The honest framing: SaaS products in this space are essentially "this DIY pipeline, packaged so it stays running without your attention." If maintenance time is cheap for you and customization is the point, DIY wins. If you want the output and not the project, a hosted tool wins.

Turn your RSS into a private podcast with Lisnify

Here is the concrete workflow if you go with Lisnify. The structure is the same shape as any AI podcast generator in the personal-feed category, so even if you choose another tool, the steps map across.

Register a Show and your RSS sources

Sign in to Lisnify, create a new Show, and add the RSS feed URLs you want covered in the Sources tab. A few feeds that work well for an English-language audience:

• Hacker News front page: https://news.ycombinator.com/rss
• dev.to latest posts: https://dev.to/feed
• Substack newsletters: append /feed to any newsletter's URL
• TechCrunch: https://techcrunch.com/feed/
• Google News keyword search: https://news.google.com/rss/search?q={keyword}&hl=en-US&gl=US&ceid=US:en (URL-encode {keyword})

If a site does not publish an RSS feed, the realistic workaround is to route the topic through a feed that does. Google News keyword search is the easiest. Third-party RSS generators cover sites with predictable HTML. A few platforms expose hidden feeds (most subreddits, for example, accept .rss on the end of any URL). Either way, what you hand to Lisnify is an RSS URL, because that is what the Terms of Service assume as input.

Pick hosts, language, and schedule

In the Host tab you choose AI host voices, the language they speak, and the speaking style. Lisnify supports cross-language pairs: you can read English-language sources and have the episode delivered in a different language, or the reverse. A common setup for non-native English speakers is "non-English source feeds, English summary."

Pick a delivery schedule: daily or weekly, with a target time of day in your timezone. The system will run on that cadence regardless of how many new articles appeared since the last run.

Wait for AI article selection and script generation

At the scheduled time, Lisnify pulls the latest items from your registered RSS feeds, an LLM picks which articles belong in today's episode, and the selected articles are turned into a multi-host conversational script. Selection, summarization, transitions, intro, and outro are all generated. Each episode includes up to ten articles. That ceiling is deliberate: beyond it, episodes get long enough that listeners stop finishing them.

Audio synthesis and feed update

The script goes through TTS and is mixed into a single MP3 with the multiple hosts speaking in turn. A new entry, complete with <enclosure>, is appended to your private podcast RSS feed.

Subscribe in your podcast app

Take the feed URL Lisnify gives you and add it to a podcast app that supports "subscribe by URL": Apple Podcasts, Pocket Casts, and Overcast all do. Spotify does not support arbitrary user-supplied RSS URLs. Its podcast model is built around publisher-submitted shows in its directory, not personal feeds. If Spotify is your daily driver, plan to use a second app for this kind of listening.

A short note on what a "private podcast feed" is, since this article is the canonical home for the explanation: the feed URL is built around a UUIDv7 identifier, so it is long enough that nobody will guess it. Lisnify never registers your feed with Apple Podcasts, Spotify, or any other public directory. The feed is private by being unlisted, not by authentication. Podcast apps largely do not implement HTTP basic auth for arbitrary RSS, so unlisted URLs are the standard pattern. One thing to know: per the iTunes Podcast namespace specification, your contact email address is included in the feed XML as the show owner's contact. Anyone who has the feed URL can read that email out of the XML. The Terms of Service forbid posting your feed URL anywhere publicly accessible (social media, public web pages, public forums); private sharing with a small number of trusted people is allowed.

Which RSS sources work well, and which don't

Not every feed converts cleanly into something worth listening to.

Sources that work well:

• Text-centered article feeds. Personal blogs, dev.to, Substack newsletters, Medium publications, engineering blogs from companies you follow.
• Curation feeds. Hacker News (https://news.ycombinator.com/rss), Lobsters (https://lobste.rs/rss), subreddit RSS, Google News keyword search. These work well because the original entries are short (titles plus brief commentary), and the audio version naturally becomes "skim through interesting links," which is how most people use them in the first place.
• Non-English source feeds when you want a native-language summary. A typical use is following coverage of a topic in another country's press while having the episode narrated in your own language.

Sources that do not convert well:

• Code-, equation-, or benchmark-heavy articles. A method comparison with a results table is mostly useless as audio. The episode might be fine as a "this paper exists, here's the gist," but you will end up reading the original anyway.
• Video-first or interactive pages. If the actual content is a chart you scrub through or a screenshot walkthrough, audio summarization drops most of what makes the page valuable.
• Login-required private pages. RSS-to-podcast is for what the source has chosen to publish openly. Private feeds for things behind login are out of scope and create compliance questions worth not having.

Copyright, summarization, and audience scope

Most tool comparisons skip this section. They should not, because the questions here decide what you can responsibly do with the output.

Stay within what the RSS allows. A site publishing an RSS feed is not the same as the site granting permission to use that feed for any purpose. Terms of service and robots.txt directives still apply. Reading another publisher's full articles aloud as part of a commercially distributed show is well past the line that quoting and excerpting cover.

Summarize rather than read full text. Verbatim audio of a copyrighted article, made available to anyone, is hard to defend on copyright grounds. The reason most AI podcast tools converge on "summary plus link to the source" is partly listener experience (full reads are long) and partly that the summary-with-attribution shape is closer to fair use than a reproduction.

Be explicit about who hears it. A feed only you listen to, a feed shared with two family members, and a public show are three different products with three different obligations. Lisnify is built around the first one and accommodates the second by allowing private sharing with a small group; it is not designed for the third.

Verify primary sources for high-stakes content. Anything you would act on (a legal interpretation, a medical claim, a financial number, a contractual term, a benchmark result) needs the original. Audio summaries are a good way to decide what to read more carefully, not a replacement for reading.

Frequently asked questions

Can I do this for sites that don't publish an RSS feed?

Lisnify expects an RSS URL as input (Terms of Service). For sites without an RSS feed, the workaround is to use a Google News keyword search feed, a third-party RSS generator that scrapes the site, or a platform-specific feed you can derive from the URL. The output is always an RSS URL handed to Lisnify. Pages that require a login are not supported.

Can I listen on Apple Podcasts and Spotify?

Apple Podcasts, Pocket Casts, and Overcast all let you add a show by pasting an arbitrary RSS URL, which is what your private Lisnify feed needs. Spotify does not support that flow. Its podcast model is directory-based, with shows submitted by publishers and made public, not user-supplied private feeds. If Spotify is where you usually listen, plan to use a second app for personal feeds.

Do I still need to read the original articles?

For a general sense of what is happening, the audio is enough. For anything you will act on (a code change you intend to apply, a benchmark you will quote, a legal or medical or financial claim), open the original. The point of audio is to triage your reading list while your hands are busy, not to skip reading.

Can I share the same feed with someone else?

Lisnify's Terms of Service forbid posting the feed URL on social media, public websites, public forums, or anywhere else where strangers can find it. Private sharing with a small number of people you actually know (household, close friends) is allowed. Be aware that the feed XML contains your email address as the show owner's contact (this is required by the iTunes Podcast namespace), so anyone you share with can see it. Share only with people you would already give your email to.

How does this compare to building it myself with TTS and an RSS host?

DIY is genuinely viable. The OSS scene includes projects like zarazhangrui/personalized-podcast and n8n's AI podcast workflow, and you can wire your own around the TTS provider of your choice. What you take on by going DIY is the running maintenance: feed parsing edge cases, prompt tuning as articles change shape, audio mixing across hosts, RSS publication, hosting bills, podcast-app compatibility, and noticing when one of those silently breaks. A hosted product trades that maintenance for a subscription. Pick based on which cost you would rather pay.

Can I keep using my RSS reader at the same time?

Yes, and most people who get into this end up running both. A common pattern is reader for discovery, podcast for skim during commute or chores, browser for the few articles that turn out to deserve real attention. Audio is a third format on top of the two you already have, not a replacement.

Wrapping up

The three approaches, restated: manual TTS gets you very specific control over a few articles at the cost of doing it by hand; read-aloud tools sit comfortably inside your existing reader but stop short of producing a real podcast feed; AI podcast generators give you a daily or weekly episode in your podcast app at the cost of letting summarization and host selection happen automatically. DIY pipelines stay viable for anyone who would rather own the stack.

For continuous daily listening, the AI-podcast-generator approach is the one that survives contact with real life. Within that category the differences are about scope. If you want to publish an audio show under a brand, Wondercraft and Jellypod are designed for that. If you want to deeply understand one set of documents, NotebookLM is built for that. If you want a private feed of the things you already read, that is what Lisnify is built for.

Two follow-ups if you want to go deeper: a focused walkthrough for one of the most common feeds people start with is in Listen to Hacker News as a daily AI podcast, and a side-by-side comparison of NotebookLM, Wondercraft, Jellypod, and Lisnify is in NotebookLM alternatives: how Wondercraft, Jellypod, and Lisnify compare.