Daniel sent us this one, and it's basically about the show itself eating its own tail. We've passed thirty-seven hundred episodes. That's sixty-eight days of continuous listening if you never slept. And the RSS feed is groaning under its own weight. The real question he's asking is whether traditional podcast distribution can actually handle this kind of scale, or if the whole model implicitly assumes you've got maybe a hundred episodes and the rest lives in some kind of search-driven long tail. Plus, he wants suggestions for discovery mechanisms that actually work at this level.
This is one of those problems where the infrastructure tells you what it thinks a podcast is, just by where it breaks. RSS was designed in the early two thousands for blogs. Ten items, twenty items, maybe you push it to a hundred. The idea that a single feed would contain thousands of episodes wasn't even on the radar.
The RSS spec is basically a time capsule from an era when people thought blogging was the pinnacle of human communication. And now it's trying to serve a thirty-seven-hundred-episode podcast like it's still carrying ten blog posts about someone's cat.
The technical ceiling is real. Most RSS readers and podcast directories impose practical limits on feed size. Apple Podcasts will start choking if your feed XML exceeds roughly five hundred and twelve megabytes, though in practice things get weird well before that. Spotify's ingestion pipeline has its own constraints. Libsyn, one of the largest hosting platforms, recommends keeping feeds under five hundred episodes for performance reasons.
We're already seven times past the recommended ceiling for one of the biggest hosting platforms. That's like trying to park a cargo ship in a suburban driveway.
This isn't just a file size problem. Every time a podcast app polls the feed, it downloads the entire XML. If your feed is thirty megabytes because it contains metadata for thousands of entries, every single subscriber is pulling that down every hour or whatever the refresh interval is. Multiply that by the subscriber count and you're generating enormous bandwidth costs for something fundamentally inefficient.
It's the podcast equivalent of using a shopping cart to move furniture across town. It kind of works until it really doesn't.
That's why most large-scale podcasts don't actually distribute their full back catalog through the main RSS feed. They use what's called a rolling window. The feed shows the latest hundred or two hundred episodes, and everything older lives on a website with search functionality, or gets surfaced through platform-native recommendation algorithms.
Which brings us to the Spotify problem. Daniel mentions he listens on Spotify, but the search there keeps failing him. He can't find episodes he knows exist. Meanwhile, the website search, which uses Algolia with vector similarity matching, surfaces things Spotify misses.
This is a known limitation of Spotify's podcast infrastructure. Spotify's search relies primarily on keyword matching against episode titles and show notes. It does some semantic expansion, but it's not doing true vector similarity search across the full transcript corpus. For a show like this, where the episode title might be something oblique or playful, the keywords don't always map to the actual content.
"The Glockenspiel of Corporate Approachability" is not going to surface when someone searches for "corporate communication strategies." Even though that's exactly what the episode is about.
And Spotify's podcast API limits how many episodes you can retrieve programmatically. You get twenty episodes per request, up to some paginated maximum. Their search endpoint doesn't expose the kind of deep semantic indexing you'd need for a thirty-seven-hundred-episode corpus where the connections between episodes are often non-obvious.
The fundamental tension here is between two distribution philosophies. One is the feed model, which says "here's everything, in order, subscribe and consume." The other is the search model, which says "tell me what you want, I'll find the relevant episodes." And podcast infrastructure is built almost entirely around the first one.
The feed model implicitly assumes recency bias. It assumes listeners care about what's new, not what's deeply buried in episode seven hundred and twelve from two years ago. For most podcasts, that assumption holds. For this show, where the episodes are essentially a growing knowledge base meant to be explored non-linearly, it completely falls apart.
Daniel's original vision was a knowledge graph. Clickable nodes, semantic connections, browse by concept rather than by date. And he's right that there's nothing technically standing in the way. You need a graph database, some embedding models for similarity matching, and a frontend that renders nodes and edges. It's not trivial, but it's not science fiction either.
The interesting question is whether that kind of interface can be integrated into traditional distribution platforms at all, or whether it requires a completely separate consumption experience. Spotify's podcast infrastructure doesn't expose anything like a graph navigation layer. Apple Podcasts doesn't either. These platforms are built around the linear feed as the atomic unit of distribution.
Which means if you want graph-based discovery, you're building it yourself, on your own website, as a parallel experience to the traditional feed. You're not replacing Spotify, you're supplementing it.
That's actually what most large-scale content operations do. Think about the BBC's podcast catalog. Thousands of episodes across hundreds of shows. Their main distribution happens through standard feeds on standard platforms, but their website has search, filtering by topic, curated collections, and recommendation engines that create a completely different discovery experience.
The BBC model is basically "feed for subscription, website for exploration." Two different tools for two different listening modes.
And the feed doesn't need to contain everything. It just needs to contain enough that a new subscriber can get oriented. The long tail lives elsewhere.
The practical answer to the prompt is that traditional podcast feeds are not designed for catalogs of this size, and the platforms built on top of them aren't either. The implicit contract is absolutely "latest hundred episodes, maybe two hundred, and the rest you find through search or direct links.
There's actually a formal specification effort around this. The Podcast Index, an open alternative to closed podcast directories, has been working on something called the "podroll" and improved namespace extensions for better episode discovery. But even their approach doesn't fundamentally solve the large-catalog problem inside the feed itself. It's more about enabling cross-show recommendations and better metadata.
The Podcast Index is the "we have podcast infrastructure at home" version of the ecosystem. Admirable, but not exactly mainstream adoption.
It's growing, actually. As of mid two thousand twenty-six, the Podcast Index tracks over four million podcasts and has been integrated into a number of open-source podcast apps. But you're right that it's not where most listeners are. Spotify and Apple dominate consumption.
Let's talk about what actually would improve discovery for this show specifically. What mechanisms make sense at this scale?
I think there are a few layers. The first is what Daniel already has: vector search on the website. Algolia with embeddings means someone can type a fuzzy query and get semantically relevant episodes even if the keywords don't match exactly. That's table stakes at this point.
It's already outperforming Spotify's search, which tells you how far behind the platform-native tools are.
The second layer is what Daniel originally envisioned: the knowledge graph. You take the entire episode corpus, extract entities and concepts, map the relationships between them, and present it as a browsable network. A listener clicks on "cryptography" and sees all the episodes that touch on cryptography, plus related concepts like "privacy" or "zero-knowledge proofs," and can follow those edges to discover episodes they'd never find through linear browsing.
The knowledge graph is basically the "people who liked this also liked" of the intellectual world. Except instead of being driven by collaborative filtering, it's driven by actual semantic relationships in the content.
The technology for this is surprisingly accessible now. You can use sentence transformers to generate embeddings for each episode transcript, cluster them, extract key phrases, and build a graph in something like Neo4j or even a simple NetworkX implementation. The frontend visualization is the hardest part, and there are libraries like D3.js or vis.js that handle the rendering.
The frontend is where these projects usually go to die. Building a graph visualization that's actually pleasant to use, rather than a hairball of nodes that looks like a conspiracy theorist's corkboard, is genuinely hard.
The best implementations I've seen use a combination of search and graph browsing. You start with a search for something specific, and the graph shows you related episodes radiating outward. You're never just staring at the entire graph. You're always oriented around something you care about.
That's the key insight. The graph isn't a map you explore from a bird's eye view. It's more like walking through a city. You're always on a street, looking at what's nearby, deciding which direction to turn.
That metaphor maps well to how people actually discover content. Nobody wants to browse a thirty-seven-hundred-episode catalog linearly. They want to follow threads. They listen to an episode about encryption, something in it references metadata privacy, and they want to jump to episodes about that. The graph makes those threads explicit.
The knowledge graph is one discovery layer.
The third layer, which I think is under-explored in podcasting, is what I'd call "curated entry points." Instead of expecting listeners to know what to search for, you create topical collections. "Interested in AI safety? Here are the twelve episodes that form a coherent arc on that topic." "New to the show? Start with these five." These are editorial rather than algorithmic, and they solve the cold start problem for new listeners.
The "start here" playlist is the podcast equivalent of a museum's recommended route. You can wander freely, but if you're overwhelmed, here's a path that makes sense.
At this scale, the show has enough material to create dozens of these curated paths. The "privacy arc," the "architecture arc," the "diplomacy arc." Each one is a self-contained listening experience that pulls from across the entire catalog, not just the most recent episodes.
Which also solves a retention problem. If someone discovers the show through a recent episode about battery chemistry, they might not realize there are thirty episodes about related energy topics buried in the archive. A curated collection surfaces those connections.
The fourth layer is something that traditional platforms actually do well when the metadata is rich: chapter-level indexing. If each episode has detailed chapter markers with timestamps and topic tags, then platform search can surface specific segments rather than just full episodes. Apple Podcasts supports chapter markers in the RSS feed. Spotify has its own chapter implementation. When a listener searches for a specific concept, they could theoretically jump to the exact five-minute segment where it's discussed, rather than scanning through a forty-minute episode.
Chapter markers are one of those things that sound tedious to implement but pay massive dividends at scale. It turns every episode into a set of granular, searchable units instead of a monolithic blob.
With AI-generated chapters, it's not even tedious anymore. You can run a transcript through a segmentation model, generate chapter titles and timestamps automatically, and inject them into the feed metadata. Several hosting platforms now offer this as a built-in feature.
It's like showing up to a potluck with a five-course meal.
There's a fifth layer too, which is more speculative but interesting for this show. What about an AI-powered conversational discovery interface? Instead of search or graph browsing, you just describe what you're interested in, and the system recommends episodes.
A chatbot that knows the entire catalog and can have a conversation about what you're looking for.
"I'm interested in episodes about encryption but specifically the ones where you talk about metadata privacy and government surveillance." That's a query that's hard to express in a keyword search but easy to express in natural language. A retrieval-augmented generation system over the transcript corpus could handle that beautifully.
It would feel like a natural extension of the show's whole premise. The podcast is already AI-generated. Having an AI-powered discovery layer is thematically coherent.
It's also useful at this scale. When you have thirty-seven hundred episodes, the discoverability problem isn't just about finding something interesting. It's about finding the specific episode you half-remember from six months ago, where Corn made a particular joke about anteaters and you want to hear it again.
My anteater material deserves to be discoverable.
It really does. And traditional platforms offer nothing for that use case. You can't search by "that episode where Corn compared something to adopting a feral cat.
Which, to be fair, I've done multiple times. So even that wouldn't narrow it down much.
This is the fundamental limitation. Podcast platforms treat episodes as opaque audio files with some text metadata attached. They don't index the actual content. Until they do, search on these platforms will always be worse than search on a dedicated website that has full transcript indexing.
What's the actual path forward? If Daniel wants to improve discovery, where does he invest effort?
I'd prioritize in this order. First, maintain and improve the website search, because that's already outperforming platform search and it's the foundation everything else builds on. Second, build the knowledge graph as a dedicated discovery interface on the website. It doesn't need to be perfect on day one. Even a simple force-directed graph with episode nodes and topic edges would be a massive improvement over linear browsing.
The minimum viable knowledge graph. You don't need to boil the ocean.
Third, create curated collections as editorial entry points. These can be static pages on the website that link to episodes in a recommended order, with brief descriptions of why each episode matters and how they connect. Fourth, implement rich chapter markers in the feed so that platform-native search has better granularity to work with.
The AI conversational layer?
That's the stretch goal. It's the most technically interesting but also the most complex to build well. I'd treat it as a later phase, once the graph and curated collections are in place. The nice thing is that all of these layers share infrastructure. The embeddings you generate for vector search can also power the knowledge graph and the conversational interface. You're building on the same foundation each time.
It's a cumulative investment, not a series of separate projects.
Which is why starting with good embeddings and a solid search backend is so important. Everything else grows out of that.
There's also a philosophical question here about what kind of show this is trying to be. Most podcasts are ephemeral. They're about what's happening now. The back catalog is an archive, not a living resource. But this show is different. The episodes are designed to be evergreen, or at least to form a durable knowledge base that remains relevant over time.
That changes the distribution strategy completely. If you're producing evergreen content, you want the long tail to be as accessible as the latest episode. The feed model, with its recency bias, actively works against that. It buries your most valuable content simply because it's old.
Which is why the website needs to be the primary discovery surface, not the feed. The feed is the subscription mechanism. The website is the library.
Libraries need catalogs. A good library doesn't just put the newest books on a display table and shove everything else into unmarked stacks. It has a card catalog, subject classifications, recommended reading lists, librarians who can help you find things.
The AI chatbot as the librarian. "Excuse me, I'm looking for episodes about the history of cryptography with a particular focus on the nineties crypto wars.
"Certainly, let me pull a few things for you." That's the experience that's missing from podcast platforms today.
There's another angle here that the prompt touches on. The feed itself is breaking because it's too large. What's the actual fix for that?
The practical fix is to truncate the main feed to the latest few hundred episodes and serve the full archive through a separate mechanism. Most podcast hosting platforms support this. You configure your feed to show the most recent episodes, and the older ones remain accessible through direct links or API calls but don't bloat the feed XML.
You're not losing access to the old episodes. You're just removing them from the default subscription experience.
For listeners who want the full archive, you provide alternative access paths. The website with full search. Maybe a separate "archive feed" for the truly dedicated. Direct download links organized by topic.
The archive feed is basically saying "if you really want everything, here it is, but we're not going to make every subscriber download this monster every time they refresh.
Some podcast apps actually handle multiple feeds well. You could have the main feed for the current season, and an archive feed for everything else. Listeners who want the full catalog can subscribe to both.
It's the podcast equivalent of a TV show having current episodes on the streaming homepage and a "complete series" section for the back catalog.
This is already standard practice for large podcasts. "This American Life" has over eight hundred episodes but their main feed only shows a rolling window. The full archive is on their website. "Hardcore History" keeps recent episodes in the feed and sells the back catalog separately. The model exists, it's just not well-documented for independent podcasters.
The feed truncation is the immediate technical fix, and the discovery layers are the long-term investment in making the archive actually useful rather than just accessible.
I think the discovery layers are where the show can differentiate itself. Anyone can truncate a feed. Not everyone can build a knowledge graph that turns a podcast catalog into an explorable intellectual network.
The knowledge graph also changes the value proposition of the back catalog. Right now, old episodes are just... They exist, but they're hard to find and harder to contextualize. With a graph, every old episode becomes a node in a network that makes the entire catalog more valuable. Each new episode enriches the graph, and the graph enriches each old episode by connecting it to newer content.
Network effects, but for your own content. The value of the catalog grows superlinearly with the number of episodes because the connections between them multiply faster than the episodes themselves.
Which is exactly the opposite of how traditional podcasts work, where old episodes are a liability that clogs up the feed.
There's a name for this in information science. It's called the "knowledge base" model versus the "periodical" model. Periodicals are about what's new. Each issue stands alone, and old issues are archived but not actively referenced. Knowledge bases are cumulative. Each new entry adds to a structure where everything remains relevant and interconnected.
Podcasting inherited the periodical model from radio and blogging. But this show is a knowledge base pretending to be a podcast.
The distribution infrastructure wasn't built for knowledge bases. It was built for periodicals. That's the root of the friction.
The solution isn't to fix the infrastructure. It's to build a parallel infrastructure that serves the knowledge base use case, while maintaining compatibility with the periodical distribution channels for the subscription use case.
That's the synthesis. Feed for subscription, website for discovery, knowledge graph for exploration, AI for conversational retrieval. Each layer serves a different listening mode, and together they cover the full range of how people might want to interact with a catalog of this size.
Let's get concrete about the knowledge graph implementation, because I think that's the centerpiece here. What does it actually look like for a listener?
I imagine it as a page on the website. You land on it and there's a search bar at the top. You type something, or you can browse starting from popular topic clusters. When you select a topic, you see a central node with that topic label, and radiating outward are episode nodes. Each episode node shows the title and a brief extract. Clicking it takes you to the episode page.
The edges between nodes represent what exactly?
Multiple relationship types. "Discusses" is the basic one, connecting an episode to a topic. But you could also have "extends," where one episode builds on the ideas in another. "Contrasts," where episodes take opposing views on the same subject. "References," where an episode explicitly mentions a concept that another episode explores in depth.
The "contrasts" edge is particularly interesting. It surfaces productive disagreement rather than just topical similarity.
It's the kind of connection that's invisible in traditional search. Two episodes might use completely different terminology but be in direct dialogue with each other intellectually. A good embedding model will catch that semantic relationship even when the surface-level keywords don't overlap.
How much of this can be automated versus needing human curation?
The topic extraction and similarity matching can be almost entirely automated with current tools. You run each transcript through a pipeline that extracts entities, key phrases, and generates embeddings. You cluster the embeddings to identify topic groups. You use a large language model to label the clusters. That gets you maybe eighty percent of the way there.
The remaining twenty percent?
That's the editorial layer. Human review of the automated clusters to clean up misclassifications. Curated collections that impose narrative structure on top of the raw similarity graph. Manual annotations for relationships that the automated system missed. The "contrasts" edges, for example, are harder to detect automatically than "similar to" edges, because they require understanding that two episodes are talking about the same thing from different angles.
It's a hybrid system. Machines do the heavy lifting of processing thirty-seven hundred transcripts, humans add the layer of judgment and narrative coherence.
That's actually the ideal division of labor. Machines are good at finding patterns in large corpora. Humans are good at understanding what those patterns mean and which ones are interesting.
There's a parallel here to how libraries evolved. The Dewey Decimal System was a human-designed taxonomy that required trained librarians to classify every book. It worked but it didn't scale well to massive collections. Modern library systems use automated classification supplemented by human catalogers.
The Dewey Decimal System is itself an interesting example of the limitations of rigid taxonomies. It has exactly one slot for a book. A book about the philosophy of quantum mechanics has to go in either philosophy or physics, not both. A knowledge graph doesn't have that limitation. An episode can be connected to cryptography and privacy and history and ethics all at once, with different edge weights reflecting the strength of each connection.
The multi-parent problem. Hierarchies force you to choose. Graphs let things belong in multiple places simultaneously.
Which is how knowledge actually works. Ideas don't fit neatly into single categories. They sit at the intersections. The best episodes of this show are often the ones that sit at the intersection of three or four different topics, and those are exactly the episodes that a hierarchical taxonomy would struggle to classify.
The knowledge graph as the anti-Dewey. Not a tree, but a web.
There's a nice historical resonance there. Dewey's system was developed in the eighteen seventies, at the dawn of modern library science. It was designed for physical books on physical shelves, where a book can only be in one place. We're no longer constrained by physics. Information can be in multiple places at once. The graph is a more honest representation of how ideas relate to each other.
We've got the philosophical justification. Now let's talk about what's actually buildable in a reasonable timeframe.
I think a phase one knowledge graph, the minimum viable version, could be built in a few weeks of focused work. The ingredients are all off-the-shelf. You use a sentence transformer model to generate embeddings. You use a clustering algorithm like HDBSCAN to group similar episodes. You use a graph database like Neo4j or even a simple JSON structure to store the nodes and edges. You use a JavaScript visualization library for the frontend.
The embedding model is doing the real work there. Everything else is plumbing.
The embedding model and the clustering. Get those right and the graph basically builds itself. The visualization is the part that takes iteration to get right, because the user experience of exploring a graph is tricky. You need good defaults for what's visible, good interactions for expanding and collapsing nodes, good search integration so people don't get lost.
The "don't get lost" part is crucial. A thirty-seven-hundred-node graph is overwhelming if you render the whole thing. You need progressive disclosure. Show the neighborhood around the current node, let people expand outward, always keep them oriented.
There's a design pattern for this called "focus plus context." You show the immediate neighborhood in detail, and you show the broader structure in a simplified form so people know where they are in the larger graph. It's how map applications work. You see the street you're on in detail, and the surrounding area zoomed out.
The map metaphor again. It really does map well to graph exploration.
People already have the mental model for it. Everyone knows how to use a map app. You search for a destination, you see the route, you can zoom in and out, you can explore nearby. A knowledge graph interface that works like a map would be immediately intuitive.
The "you are here" dot, but for ideas.
When you click on a new episode, the map re-centers around that node, showing you what's connected to it. You're never disoriented because the interface always shows you where you are in relation to everything else.
Alright, let's pull back and summarize the actionable recommendations here. If someone's running a podcast that's scaled to thousands of episodes and they're hitting these same friction points, what should they do?
Step one, truncate the main RSS feed to the latest two or three hundred episodes. This solves the immediate technical problems with feed size and platform ingestion limits. The old episodes remain accessible through your website and direct links. Step two, invest in full transcript indexing with vector search on your website. This immediately outperforms platform-native search for long-tail discovery.
Step three, build the knowledge graph. Start simple, iterate based on how people actually use it. The goal is to turn the archive from a storage problem into a discovery asset.
Step four, create curated collections. These don't require any technical infrastructure. They're just web pages with links and descriptions. But they solve the cold start problem for new listeners and surface thematic arcs that the graph might not capture.
Step five, implement rich chapter markers in your feed metadata. This improves discoverability on platforms you don't control and makes your content more granularly searchable.
Step six, the stretch goal, build a conversational AI interface that lets people describe what they're looking for in natural language and get episode recommendations. This is where the technology is heading anyway, and having a well-indexed transcript corpus makes it relatively straightforward to implement.
None of this is particularly exotic technology. It's all stuff that exists today, that's been proven in other domains. It's just that podcasting hasn't caught up because most podcasts don't need it.
Most podcasts don't have thirty-seven hundred episodes and a knowledge base structure. This show is an outlier, and outliers have to build their own tools. The good news is that the tools are available and the cost of building them has dropped dramatically in the last few years.
The embedding models that power vector search used to require serious machine learning expertise to deploy. Now they're available as hosted APIs with simple interfaces. The graph databases that power knowledge graphs used to require specialized knowledge. Now there are managed services that handle the infrastructure. The barrier to entry has collapsed.
That's really the story of the last few years in software. Things that were research projects in two thousand twenty are now weekend projects. Things that required teams of engineers are now doable by a single motivated person with the right APIs.
The answer to the prompt is yes, there are ways to provide robust, scalable discovery for large podcast catalogs. But they don't live inside the traditional distribution platforms. They live on your own website, using modern search and graph technologies, supplementing the feed rather than replacing it.
The feed is for subscribing. The website is for discovering. And at this scale, those are fundamentally different activities that deserve different tools.
It's a bit like the difference between a magazine subscription and a research library. The magazine shows up in your mailbox, you read the latest issue. But if you want to research a specific topic across decades of back issues, you go to the library and use the catalog.
Nobody expects the magazine subscription to include a comprehensive searchable index of every article ever published. That's not what the subscription model is for. Podcast feeds are the same. They're the subscription. The library is separate.
The mistake is expecting one system to do both jobs.
That's the insight. Once you accept that the feed and the discovery layer are separate things with separate purposes, the whole problem becomes tractable. The feed does what feeds do well: deliver the latest content to subscribers. The discovery layer does what search and graphs do well: help people find specific content in a large corpus.
The two systems complement each other. Someone discovers an old episode through the knowledge graph, they like it, they subscribe to the feed to get new episodes. The discovery layer feeds the subscription layer.
The virtuous cycle. Better discovery drives more subscribers, more subscribers justify more investment in discovery. And the whole thing compounds.
Which is exactly how platforms like YouTube work. The recommendation engine and the subscription feed are separate systems that reinforce each other. YouTube doesn't expect you to find old videos through your subscription feed. It gives you search, recommendations, related videos, playlists. The subscription feed is just one discovery surface among many.
YouTube is actually a great analogy for what podcasting could become. YouTube's discovery infrastructure is vastly more sophisticated than anything in podcasting, and it's built on exactly the principles we've been discussing. Search indexing, semantic similarity, graph-based recommendations, curated playlists, algorithmic feeds. Podcasting is still stuck in the RSS era, and the RSS era was designed for a different kind of content at a different scale.
Podcasting is basically YouTube circa two thousand seven, before they figured out that discovery was the product.
The question is whether podcasting's open infrastructure can evolve to support better discovery, or whether discovery will always require building outside the feed ecosystem. I'm skeptical that the feed ecosystem will evolve quickly, because it's built on a standard that's intentionally simple and decentralized. Adding sophisticated discovery features to RSS would require coordination across dozens of platforms and millions of podcast clients.
The decentralization that makes podcasting resilient is also what makes it slow to innovate. You can't just ship a new discovery feature and have it work everywhere.
Which is why the practical approach is to build discovery on your own infrastructure and use the feed for what it's good at. Don't wait for the ecosystem to solve your problem. It won't, or at least not on your timeline.
Build the library yourself. The subscription mechanism is already handled.
That's really the takeaway here. For podcasts operating at this scale, the feed is necessary but not sufficient. The website is where the real discovery work happens.
Now: Hilbert's daily fun fact.
Hilbert: In the seventeen eighties, the Icelandic naturalist Sveinn Pálsson measured the summit of Snæfellsjökull and recorded its height at four thousand five hundred and fifty Icelandic feet. That's roughly the same elevation as the deepest known colony of stalked crinoids discovered on the Muirfield Seamount in the Indian Ocean, which sits about one thousand four hundred meters below the surface. So a volcanic glacier in Iceland and a deep-sea lily garden off the coast of Western Australia are basically at the same altitude, if you flip the ocean inside out.
I'm not sure "flip the ocean inside out" is a standard unit of measurement.
It is now.
This has been My Weird Prompts. Thanks to our producer Hilbert Flumingtop. If you want to explore the full episode catalog, including the knowledge graph we just spent half an hour describing, head to myweirdprompts.We'll be back with another episode soon.
