It’s relatively rare for a podcast to last 14 years, especially a fiction one. In fact, as far as I can tell, Welcome to Night Vale is the longest continually running fiction podcast out there. (Some will argue it’s actually We’re Alive, but that has taken a few significant breaks between seasons.) The story of Night Vale, the titular desert town, now spans 12 seasons, over 280 episodes, three books, and at least 10 live standalone shows. While dedicating several hundred hours of your life to listening to every episode might seem like a big ask, I believe you’ll be hooked once you dive in.
The show is written by Joseph Fink and Jeffrey Cranor, who draw heavily on the work of H.P. Lovecraft. Every season has its own arc, but broadly, the show tells the story of a town that exists in an alternate version of Earth. In this town Angels are real, but acknowledging their existence is illegal; librarians are dangerous creatures with “thousands of spiny legs” and “pincers”; and there is a Faceless Old Woman who secretly lives in your home.
These are clearly unsettling concepts when taken at face value. But rather than trying to scare the listener, Cranor and Fink lean into the natural absurdity of cosmic horror and refuse to take themselves too seriously. They also routinely subvert the bigotry of their inspiration, using Lovecraftian creations to tell stories rich with LGBTQ+ characters.
Of course, having well-written scripts and telling a compelling story is only part of the equation. What elevates Welcome to Night Vale to true greatness is the cast, especially narrator Cecil Baldwin, who plays the host of the central radio show, Cecil Palmer. Cecil’s voice has the gravitas to tell ominous stories of secretive government agencies and ancient gods. But he has the range to deliver light-hearted banter with a sentient patch of haze (her name is Deb, in case you were wondering).
Cecil Baldwin has the charisma to make even the reading of a repair manual for a toaster compelling. He can be creepy, funny, or soothing, often all within the same episode. (For this reason, I don’t suggest listening to Night Vale at night. I have fallen asleep to the dulcet sounds of Baldwin’s voice several times, only for the more unsettling parts of the show to make their way into my dreams.)
Every episode also features a musical interlude in the guise of “The Weather.” The show mostly features lesser-known artists, but alumni include Jason Isbell, The Mountain Goats, Waxahatchee, Angel Olsen, Open Mike Eagle, and Sylvan Esso.
Welcome to Night Vale is also a great way to introduce younger listeners to horror. I would never suggest my eight-year-old read H.P. Lovecraft. Partly because the man’s unrepentant racism is baked into the very fabric of his stories, but also because the violence is often too much for children. Night Vale, on the other hand, takes those horrors and exposes them for what they are: entertainment. I can put the podcast on, bond with my kid over their burgeoning love of all things creepy and weird, and trust that they’ll walk away with a good message.
Welcome to Night Vale is available on most podcast platforms, including Apple Podcasts, Pocketcasts, YouTube, and Spotify.
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Terrence O’Brien
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#listen #cosmic #horrorcomedy #podcast #Night #Vale
July 31st is National Orgasm Day! (You’re welcome for your positive news dopamine shot of the day.) And, to celebrate, retailers everywhere are slashing prices on best-selling toys, kits, and more. One of my fave retailers, Babeland, is offering up to 50% off its best sellers, including The One Supreme Wand (54% off right now, so it may go even lower by this weekend!) and The One Rabbit (also on sale right now).
There isn’t an official sale tab marked on the site just yet, but I’ve received an email confirmation from the brand that there will be a National Orgasm Day Sale landing page hitting the site soon. It’s a one-day sale, so I thought I’d tell you to mark your calendars now.
If you can’t wait for the sale or you prefer something a little kinkier, Babeland is currently offering up to 50% off BDSM and kink products (handcuffs, restraints, and the like) now through July 31. There are also a ton of vibrators on sale right now, including The One Le’Moan, which is a knockoff of the internet-famous Hello Nancy Lem. And, if you use the code GETHOT at checkout, it’ll get you an additional 10% off select items.
July 31st is National Orgasm Day! (You’re welcome for your positive news dopamine shot of the day.) And, to celebrate, retailers everywhere are slashing prices on best-selling toys, kits, and more. One of my fave retailers, Babeland, is offering up to 50% off its best sellers, including The One Supreme Wand (54% off right now, so it may go even lower by this weekend!) and The One Rabbit (also on sale right now).
There isn’t an official sale tab marked on the site just yet, but I’ve received an email confirmation from the brand that there will be a National Orgasm Day Sale landing page hitting the site soon. It’s a one-day sale, so I thought I’d tell you to mark your calendars now.
If you can’t wait for the sale or you prefer something a little kinkier, Babeland is currently offering up to 50% off BDSM and kink products (handcuffs, restraints, and the like) now through July 31. There are also a ton of vibrators on sale right now, including The One Le’Moan, which is a knockoff of the internet-famous Hello Nancy Lem. And, if you use the code GETHOT at checkout, it’ll get you an additional 10% off select items.
#Babeland #National #Orgasm #Day #Sale #sex #toys">Babeland National Orgasm Day Sale 2026: 50% off sex toys
50% OFF: Celebrate National Orgasm Day on July 31 with Babeland’s one-day flash sale. Save 50% on best-selling vibrators, rabbits, wands, and more.
Get up to 50% off best-selling toys.
July 31st is National Orgasm Day! (You’re welcome for your positive news dopamine shot of the day.) And, to celebrate, retailers everywhere are slashing prices on best-selling toys, kits, and more. One of my fave retailers, Babeland, is offering up to 50% off its best sellers, including The One Supreme Wand (54% off right now, so it may go even lower by this weekend!) and The One Rabbit (also on sale right now).
There isn’t an official sale tab marked on the site just yet, but I’ve received an email confirmation from the brand that there will be a National Orgasm Day Sale landing page hitting the site soon. It’s a one-day sale, so I thought I’d tell you to mark your calendars now.
If you can’t wait for the sale or you prefer something a little kinkier, Babeland is currently offering up to 50% off BDSM and kink products (handcuffs, restraints, and the like) now through July 31. There are also a ton of vibrators on sale right now, including The One Le’Moan, which is a knockoff of the internet-famous Hello Nancy Lem. And, if you use the code GETHOT at checkout, it’ll get you an additional 10% off select items.
DoorDash said it has received a Part 135 air carrier certification from the FAA that enable it to delivery small packages via drone. Other companies to receive this designation include Alphabet’s Wing, Zipline, and Amazon.
Previously, DoorDash worked with Alphabet’s drone delivery division Wing on a number of pilots in places like Dallas-Fort Worth, Virginia, and Australia. Now the company says it will deploy its own drones, designed specifically for food delivery. The drones will be designed and assembled within the company’s DoorDash Labs division, which houses its robotics and AI teams responsible for building its Dot sidewalk delivery robots.
“We’re designing an aircraft purpose-built for the gaps we see in local commerce, and it complements the short- and long-range designs our partners are scaling,” the company said in a press release. “Our drone is also proudly American-designed and built, with the majority of components made right here in the U.S.” (To be sure, the vast majority of drones are manufactured in China.)
In addition, DoorDash says it will also build all the necessary ground-level infrastructure to enable restaurants to hand off their deliveries to drones. As seen with other drone delivery pilots, there is a complex system of structures and additional equipment that needs to be built on the ground to enable drones to pickup their payloads before embarking on their deliveries. And there are flight plans, airspace management, and drop-off logistics that needs to be established to track the drones in flight. DoorDash says its committed to building all that infrastructure itself.
“The aircraft is what everyone sees,” the company says. “The harder problem is the infrastructure and integration on the ground, and that’s our advantage. Solving hard physical-world problems for local businesses is our focus, from real-time inventory reconciliation to universal handoff systems for drive-throughs, rooftops, or merchant back doors. We are building that complete end-to-end system.”
DoorDash says drone delivery could solve the problem of midrange deliveries that still end up taking longer because couriers are hard to find. The company says more than 20 percent of its orders involve distances of three to five miles, but that those orders can take on average nearly 25 percent longer than shorter deliveries, largely because finding a courier takes longer for these mid-range trips.
“Routing mid-range deliveries to drones lets Dashers focus on the orders they often favor: shorter deliveries that can be done quickly, staying near a high concentration of merchants for optimal routing, and maximizing their earning potential by getting them to their next order faster,” the company says.
DoorDash didn’t reveal which markets it was targeting for its initial launch, noting that it would announce more details in the months to come.
DoorDash said it has received a Part 135 air carrier certification from the FAA that enable it to delivery small packages via drone. Other companies to receive this designation include Alphabet’s Wing, Zipline, and Amazon.
Previously, DoorDash worked with Alphabet’s drone delivery division Wing on a number of pilots in places like Dallas-Fort Worth, Virginia, and Australia. Now the company says it will deploy its own drones, designed specifically for food delivery. The drones will be designed and assembled within the company’s DoorDash Labs division, which houses its robotics and AI teams responsible for building its Dot sidewalk delivery robots.
“We’re designing an aircraft purpose-built for the gaps we see in local commerce, and it complements the short- and long-range designs our partners are scaling,” the company said in a press release. “Our drone is also proudly American-designed and built, with the majority of components made right here in the U.S.” (To be sure, the vast majority of drones are manufactured in China.)
In addition, DoorDash says it will also build all the necessary ground-level infrastructure to enable restaurants to hand off their deliveries to drones. As seen with other drone delivery pilots, there is a complex system of structures and additional equipment that needs to be built on the ground to enable drones to pickup their payloads before embarking on their deliveries. And there are flight plans, airspace management, and drop-off logistics that needs to be established to track the drones in flight. DoorDash says its committed to building all that infrastructure itself.
“The aircraft is what everyone sees,” the company says. “The harder problem is the infrastructure and integration on the ground, and that’s our advantage. Solving hard physical-world problems for local businesses is our focus, from real-time inventory reconciliation to universal handoff systems for drive-throughs, rooftops, or merchant back doors. We are building that complete end-to-end system.”
DoorDash says drone delivery could solve the problem of midrange deliveries that still end up taking longer because couriers are hard to find. The company says more than 20 percent of its orders involve distances of three to five miles, but that those orders can take on average nearly 25 percent longer than shorter deliveries, largely because finding a courier takes longer for these mid-range trips.
“Routing mid-range deliveries to drones lets Dashers focus on the orders they often favor: shorter deliveries that can be done quickly, staying near a high concentration of merchants for optimal routing, and maximizing their earning potential by getting them to their next order faster,” the company says.
DoorDash didn’t reveal which markets it was targeting for its initial launch, noting that it would announce more details in the months to come.
#DoorDash #airborne #drone #delivery #divisionDrones,Food,News,Science,Tech,Transportation">DoorDash is going airborne with new drone delivery division
DoorDash is launching a new drone delivery program called DoorDash Air. The largest food delivery app in the US said that it has approval from the Federal Aviation Administration that clears the way for drone delivery in the near future.
DoorDash said it has received a Part 135 air carrier certification from the FAA that enable it to delivery small packages via drone. Other companies to receive this designation include Alphabet’s Wing, Zipline, and Amazon.
Previously, DoorDash worked with Alphabet’s drone delivery division Wing on a number of pilots in places like Dallas-Fort Worth, Virginia, and Australia. Now the company says it will deploy its own drones, designed specifically for food delivery. The drones will be designed and assembled within the company’s DoorDash Labs division, which houses its robotics and AI teams responsible for building its Dot sidewalk delivery robots.
“We’re designing an aircraft purpose-built for the gaps we see in local commerce, and it complements the short- and long-range designs our partners are scaling,” the company said in a press release. “Our drone is also proudly American-designed and built, with the majority of components made right here in the U.S.” (To be sure, the vast majority of drones are manufactured in China.)
In addition, DoorDash says it will also build all the necessary ground-level infrastructure to enable restaurants to hand off their deliveries to drones. As seen with other drone delivery pilots, there is a complex system of structures and additional equipment that needs to be built on the ground to enable drones to pickup their payloads before embarking on their deliveries. And there are flight plans, airspace management, and drop-off logistics that needs to be established to track the drones in flight. DoorDash says its committed to building all that infrastructure itself.
“The aircraft is what everyone sees,” the company says. “The harder problem is the infrastructure and integration on the ground, and that’s our advantage. Solving hard physical-world problems for local businesses is our focus, from real-time inventory reconciliation to universal handoff systems for drive-throughs, rooftops, or merchant back doors. We are building that complete end-to-end system.”
DoorDash says drone delivery could solve the problem of midrange deliveries that still end up taking longer because couriers are hard to find. The company says more than 20 percent of its orders involve distances of three to five miles, but that those orders can take on average nearly 25 percent longer than shorter deliveries, largely because finding a courier takes longer for these mid-range trips.
“Routing mid-range deliveries to drones lets Dashers focus on the orders they often favor: shorter deliveries that can be done quickly, staying near a high concentration of merchants for optimal routing, and maximizing their earning potential by getting them to their next order faster,” the company says.
DoorDash didn’t reveal which markets it was targeting for its initial launch, noting that it would announce more details in the months to come.
New York-based AI detection startup Pangram is on a mission to combat the AI slop infestation spreading across the internet, and it just raised $9 million on a bet that demand for tools that distinguish human-generated content from AI-generated text will only grow.
Pangram’s fundraise — led by Menlo Ventures, with participation from Haystack, ScOp, Script Capital, and Cadenza — comes as the startup also launches its next-generation AI text detection model, Pangram 4, and an AI image detection model, Pangram Image.
Pangram says the new text detection model is over 99% accurate at finding AI-assisted writing and mixed human-AI content, plus it can more easily detect AI humanizer programs. The AI image detector is only available via research preview for now; Pangram plans to release it more widely in the coming weeks.
Stanford AI and machine learning grads Max Spero and Bradley Emi launched Pangram about two years ago, after the launch of ChatGPT opened the floodgates for an internet full of bots, AI-generated SEO slop content, and what Spero calls “LLM-powered Russian disinformation campaigns and UAE-influenced campaigns on Twitter.”
“I think it’s just incredibly valuable to know whether what you’re looking at is something that’s AI-generated or not,” Spero told TechCrunch. “Especially text that you’re reading, because it changes how people approach the text. Is this something that I’m going to have to look out for hallucinations and jump in skeptically, or is this something that I trust was well-researched from an actual journalist?”
Pangram’s AI detection system is essentially a large machine learning model that was trained on tens of millions of known human documents. The startup then created a “synthetic mirror” for each document, replicating the topic, length, and tone of voice, but written by a frontier LLM.
“Our model is learning the stylistic differences and the choices that AI makes consistently and is able to use that to learn what makes something AI-generated with high confidence,” Spero said, adding that the AI detector isn’t relying on copy-paste metadata or hidden watermarks.
For Pangram, AI detection isn’t just about whether or not a piece of text was written entirely by AI. It’s also about distinguishing between levels of AI assistance — like in the case of someone who writes something themselves, but then asks AI to edit or clean it up. Spero believes AI assistance can be acceptable, just so long as the writer discloses their use of AI.
Image Credits:Pangram
Pangram’s emergence comes at a time when AI usage is becoming more commonplace. In some cases, like the Canadian politician who read an AI prompt aloud in a speech to lawmakers, the mistakes result in ridicule. In other cases, as with certain lawyers making their case using fake citations created by ChatGPT, the consequences could be sanctions and fines.
That backlash isn’t just costing individuals embarrassment or sanctions — it’s starting to show up in institutional rules, too.
The open-access archive arXiv introduced a new enforcement policy this year, stating that submissions containing evidence that authors failed to review LLM output (like hallucinated references or meta comments such as, “Would you like me to make any changes?”) can trigger a one-year submission ban.
Pangram isn’t the only one betting that AI detection will become more sought after. Competitors like Winston AI, Originality.ai, Copyleaks, and GPTZero are are chasing the same demand, each building its own detector.
Pangram’s technology, while not perfect, could help fuel the resistance to accepting the AI-generated content flooding the internet, the courtroom, and academic papers.
Users can access Pangram via a $20-per-month subscription on the web or download the Chrome extension, which automatically labels posts in real time on X, LinkedIn, Substack, Reddit, and Medium. It also provides a feed health score with a percentage breakdown of human versus AI content on your screen.
Pangram also offers its technology via API. Notably, Substack recently integrated Pangram’s technology into its platform to show readers which of their favorite authors write their newsletters using AI. Other API customers include Quora, schools and universities, publishers and agents, and recruiters, among others, per Spero.
Does Pangram work?
Pangram detected AI-generated content even when lightly edited by a human. Image Credits:Pangram/TechCrunch
Spero said roughly one in 10,000 human documents are incorrectly labeled as AI with Pangram’s model, so I decided to put it to the test. The text detection model was very impressive but not perfect. It easily flagged entirely AI-generated news articles written by both ChatGPT and Claude, and was rarely fooled by my attempts to edit the AI-generated text into sounding more human. At the same time, Pangram did flag sentences that I completely rewrote as being AI-written. Pangram also wasn’t at all fooled by my attempts to prompt ChatGPT and Claude into evading AI detectors when generating content.
I also gave ChatGPT and Claude one of my own articles and asked them to polish it up. Pangram gave it a 13% AI assisted score, which was probably close to accurate, but the model was able to detect subtle word-choice changes in some sentences and ignored them in others. It also flagged some sentences as AI-assisted when they were human written. That was notable because when I gave Pangram that same article in its entirety, as I had written it, it got a 100% human score.
Maybe the problem was that news articles can be a bit dry and could easily sound like AI. So I tried a different tactic. I tested Pangram on my own more voicey, personal Substack newsletter content, pasting the first half of the text into Pangram and then asking ChatGPT and Claude to copy my style and write the second half. For the most part, Pangram easily detected human-written text versus AI-written text.
My limited testing of Pangram’s new image detection model turned out to be equally impressive.
Image Credits:Pangram/TechCrunch
Pangram’s AI image detection system promises to spot AI-generated images across AI models, unlike OpenAI’s or Google DeepMind’s watermark-based checks, which mostly detect their own output. It works on pixel-level distributions, learning subtle statistical differences between real photos and AI-generated images. Spero says the model can even detect an AI image that appears inside a real-world photo.
In my testing, the model easily detected AI-generated imagery, whether it was photorealistic or cartoonish. I can also confirm the model could detect an AI image appearing in a real-world photo — the heat map Pangram provides clearly lighting up over the image — though in one instance it incorrectly labeled a photo of an AI-generated image as human content.
Spero says he doesn’t want his technology to fuel a witch hunt against people using AI for writing, but that there needs to be some sort of mechanism to push back against the slop.
“The future that I see is that AI content just continues to proliferate,” Spero said. “We’re getting new GPUs faster than new people are being born. If we do not actively discriminate in favor of human content, then we’re just gonna see more and more AI, and it’s just gonna drown out any human signal that we have.”
When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.
New York-based AI detection startup Pangram is on a mission to combat the AI slop infestation spreading across the internet, and it just raised $9 million on a bet that demand for tools that distinguish human-generated content from AI-generated text will only grow.
Pangram’s fundraise — led by Menlo Ventures, with participation from Haystack, ScOp, Script Capital, and Cadenza — comes as the startup also launches its next-generation AI text detection model, Pangram 4, and an AI image detection model, Pangram Image.
Pangram says the new text detection model is over 99% accurate at finding AI-assisted writing and mixed human-AI content, plus it can more easily detect AI humanizer programs. The AI image detector is only available via research preview for now; Pangram plans to release it more widely in the coming weeks.
Stanford AI and machine learning grads Max Spero and Bradley Emi launched Pangram about two years ago, after the launch of ChatGPT opened the floodgates for an internet full of bots, AI-generated SEO slop content, and what Spero calls “LLM-powered Russian disinformation campaigns and UAE-influenced campaigns on Twitter.”
“I think it’s just incredibly valuable to know whether what you’re looking at is something that’s AI-generated or not,” Spero told TechCrunch. “Especially text that you’re reading, because it changes how people approach the text. Is this something that I’m going to have to look out for hallucinations and jump in skeptically, or is this something that I trust was well-researched from an actual journalist?”
Pangram’s AI detection system is essentially a large machine learning model that was trained on tens of millions of known human documents. The startup then created a “synthetic mirror” for each document, replicating the topic, length, and tone of voice, but written by a frontier LLM.
“Our model is learning the stylistic differences and the choices that AI makes consistently and is able to use that to learn what makes something AI-generated with high confidence,” Spero said, adding that the AI detector isn’t relying on copy-paste metadata or hidden watermarks.
For Pangram, AI detection isn’t just about whether or not a piece of text was written entirely by AI. It’s also about distinguishing between levels of AI assistance — like in the case of someone who writes something themselves, but then asks AI to edit or clean it up. Spero believes AI assistance can be acceptable, just so long as the writer discloses their use of AI.
Image Credits:Pangram
Pangram’s emergence comes at a time when AI usage is becoming more commonplace. In some cases, like the Canadian politician who read an AI prompt aloud in a speech to lawmakers, the mistakes result in ridicule. In other cases, as with certain lawyers making their case using fake citations created by ChatGPT, the consequences could be sanctions and fines.
That backlash isn’t just costing individuals embarrassment or sanctions — it’s starting to show up in institutional rules, too.
The open-access archive arXiv introduced a new enforcement policy this year, stating that submissions containing evidence that authors failed to review LLM output (like hallucinated references or meta comments such as, “Would you like me to make any changes?”) can trigger a one-year submission ban.
Pangram isn’t the only one betting that AI detection will become more sought after. Competitors like Winston AI, Originality.ai, Copyleaks, and GPTZero are are chasing the same demand, each building its own detector.
Pangram’s technology, while not perfect, could help fuel the resistance to accepting the AI-generated content flooding the internet, the courtroom, and academic papers.
Users can access Pangram via a $20-per-month subscription on the web or download the Chrome extension, which automatically labels posts in real time on X, LinkedIn, Substack, Reddit, and Medium. It also provides a feed health score with a percentage breakdown of human versus AI content on your screen.
Pangram also offers its technology via API. Notably, Substack recently integrated Pangram’s technology into its platform to show readers which of their favorite authors write their newsletters using AI. Other API customers include Quora, schools and universities, publishers and agents, and recruiters, among others, per Spero.
Does Pangram work?
Pangram detected AI-generated content even when lightly edited by a human. Image Credits:Pangram/TechCrunch
Spero said roughly one in 10,000 human documents are incorrectly labeled as AI with Pangram’s model, so I decided to put it to the test. The text detection model was very impressive but not perfect. It easily flagged entirely AI-generated news articles written by both ChatGPT and Claude, and was rarely fooled by my attempts to edit the AI-generated text into sounding more human. At the same time, Pangram did flag sentences that I completely rewrote as being AI-written. Pangram also wasn’t at all fooled by my attempts to prompt ChatGPT and Claude into evading AI detectors when generating content.
I also gave ChatGPT and Claude one of my own articles and asked them to polish it up. Pangram gave it a 13% AI assisted score, which was probably close to accurate, but the model was able to detect subtle word-choice changes in some sentences and ignored them in others. It also flagged some sentences as AI-assisted when they were human written. That was notable because when I gave Pangram that same article in its entirety, as I had written it, it got a 100% human score.
Maybe the problem was that news articles can be a bit dry and could easily sound like AI. So I tried a different tactic. I tested Pangram on my own more voicey, personal Substack newsletter content, pasting the first half of the text into Pangram and then asking ChatGPT and Claude to copy my style and write the second half. For the most part, Pangram easily detected human-written text versus AI-written text.
My limited testing of Pangram’s new image detection model turned out to be equally impressive.
Image Credits:Pangram/TechCrunch
Pangram’s AI image detection system promises to spot AI-generated images across AI models, unlike OpenAI’s or Google DeepMind’s watermark-based checks, which mostly detect their own output. It works on pixel-level distributions, learning subtle statistical differences between real photos and AI-generated images. Spero says the model can even detect an AI image that appears inside a real-world photo.
In my testing, the model easily detected AI-generated imagery, whether it was photorealistic or cartoonish. I can also confirm the model could detect an AI image appearing in a real-world photo — the heat map Pangram provides clearly lighting up over the image — though in one instance it incorrectly labeled a photo of an AI-generated image as human content.
Spero says he doesn’t want his technology to fuel a witch hunt against people using AI for writing, but that there needs to be some sort of mechanism to push back against the slop.
“The future that I see is that AI content just continues to proliferate,” Spero said. “We’re getting new GPUs faster than new people are being born. If we do not actively discriminate in favor of human content, then we’re just gonna see more and more AI, and it’s just gonna drown out any human signal that we have.”
When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.
#content #floods #internet #Pangram #raises #detect #TechCrunchai slop">As AI content floods the internet, Pangram raises $9M to detect it | TechCrunch
New York-based AI detection startup Pangram is on a mission to combat the AI slop infestation spreading across the internet, and it just raised $9 million on a bet that demand for tools that distinguish human-generated content from AI-generated text will only grow.
Pangram’s fundraise — led by Menlo Ventures, with participation from Haystack, ScOp, Script Capital, and Cadenza — comes as the startup also launches its next-generation AI text detection model, Pangram 4, and an AI image detection model, Pangram Image.
Pangram says the new text detection model is over 99% accurate at finding AI-assisted writing and mixed human-AI content, plus it can more easily detect AI humanizer programs. The AI image detector is only available via research preview for now; Pangram plans to release it more widely in the coming weeks.
Stanford AI and machine learning grads Max Spero and Bradley Emi launched Pangram about two years ago, after the launch of ChatGPT opened the floodgates for an internet full of bots, AI-generated SEO slop content, and what Spero calls “LLM-powered Russian disinformation campaigns and UAE-influenced campaigns on Twitter.”
“I think it’s just incredibly valuable to know whether what you’re looking at is something that’s AI-generated or not,” Spero told TechCrunch. “Especially text that you’re reading, because it changes how people approach the text. Is this something that I’m going to have to look out for hallucinations and jump in skeptically, or is this something that I trust was well-researched from an actual journalist?”
Pangram’s AI detection system is essentially a large machine learning model that was trained on tens of millions of known human documents. The startup then created a “synthetic mirror” for each document, replicating the topic, length, and tone of voice, but written by a frontier LLM.
“Our model is learning the stylistic differences and the choices that AI makes consistently and is able to use that to learn what makes something AI-generated with high confidence,” Spero said, adding that the AI detector isn’t relying on copy-paste metadata or hidden watermarks.
For Pangram, AI detection isn’t just about whether or not a piece of text was written entirely by AI. It’s also about distinguishing between levels of AI assistance — like in the case of someone who writes something themselves, but then asks AI to edit or clean it up. Spero believes AI assistance can be acceptable, just so long as the writer discloses their use of AI.
Image Credits:Pangram
Pangram’s emergence comes at a time when AI usage is becoming more commonplace. In some cases, like the Canadian politician who read an AI prompt aloud in a speech to lawmakers, the mistakes result in ridicule. In other cases, as with certain lawyers making their case using fake citations created by ChatGPT, the consequences could be sanctions and fines.
That backlash isn’t just costing individuals embarrassment or sanctions — it’s starting to show up in institutional rules, too.
The open-access archive arXiv introduced a new enforcement policy this year, stating that submissions containing evidence that authors failed to review LLM output (like hallucinated references or meta comments such as, “Would you like me to make any changes?”) can trigger a one-year submission ban.
Pangram isn’t the only one betting that AI detection will become more sought after. Competitors like Winston AI, Originality.ai, Copyleaks, and GPTZero are are chasing the same demand, each building its own detector.
Pangram’s technology, while not perfect, could help fuel the resistance to accepting the AI-generated content flooding the internet, the courtroom, and academic papers.
Users can access Pangram via a $20-per-month subscription on the web or download the Chrome extension, which automatically labels posts in real time on X, LinkedIn, Substack, Reddit, and Medium. It also provides a feed health score with a percentage breakdown of human versus AI content on your screen.
Pangram also offers its technology via API. Notably, Substack recently integrated Pangram’s technology into its platform to show readers which of their favorite authors write their newsletters using AI. Other API customers include Quora, schools and universities, publishers and agents, and recruiters, among others, per Spero.
Does Pangram work?
Pangram detected AI-generated content even when lightly edited by a human. Image Credits:Pangram/TechCrunch
Spero said roughly one in 10,000 human documents are incorrectly labeled as AI with Pangram’s model, so I decided to put it to the test. The text detection model was very impressive but not perfect. It easily flagged entirely AI-generated news articles written by both ChatGPT and Claude, and was rarely fooled by my attempts to edit the AI-generated text into sounding more human. At the same time, Pangram did flag sentences that I completely rewrote as being AI-written. Pangram also wasn’t at all fooled by my attempts to prompt ChatGPT and Claude into evading AI detectors when generating content.
I also gave ChatGPT and Claude one of my own articles and asked them to polish it up. Pangram gave it a 13% AI assisted score, which was probably close to accurate, but the model was able to detect subtle word-choice changes in some sentences and ignored them in others. It also flagged some sentences as AI-assisted when they were human written. That was notable because when I gave Pangram that same article in its entirety, as I had written it, it got a 100% human score.
Maybe the problem was that news articles can be a bit dry and could easily sound like AI. So I tried a different tactic. I tested Pangram on my own more voicey, personal Substack newsletter content, pasting the first half of the text into Pangram and then asking ChatGPT and Claude to copy my style and write the second half. For the most part, Pangram easily detected human-written text versus AI-written text.
My limited testing of Pangram’s new image detection model turned out to be equally impressive.
Image Credits:Pangram/TechCrunch
Pangram’s AI image detection system promises to spot AI-generated images across AI models, unlike OpenAI’s or Google DeepMind’s watermark-based checks, which mostly detect their own output. It works on pixel-level distributions, learning subtle statistical differences between real photos and AI-generated images. Spero says the model can even detect an AI image that appears inside a real-world photo.
In my testing, the model easily detected AI-generated imagery, whether it was photorealistic or cartoonish. I can also confirm the model could detect an AI image appearing in a real-world photo — the heat map Pangram provides clearly lighting up over the image — though in one instance it incorrectly labeled a photo of an AI-generated image as human content.
Spero says he doesn’t want his technology to fuel a witch hunt against people using AI for writing, but that there needs to be some sort of mechanism to push back against the slop.
“The future that I see is that AI content just continues to proliferate,” Spero said. “We’re getting new GPUs faster than new people are being born. If we do not actively discriminate in favor of human content, then we’re just gonna see more and more AI, and it’s just gonna drown out any human signal that we have.”
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