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Comparisons Between Waymo and Tesla Miss How Strange the Robotaxi Race Is

Comparisons Between Waymo and Tesla Miss How Strange the Robotaxi Race Is

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Tesla’s valuation has been spectacular lately, closing at an all-time record of $489.88 earlier this month and still hovering pretty close to that astronomical figure as of this writing. Tesla bulls, notably Dan Ives of Wedbush capital, say this is because Tesla is on the verge of successfully deploying robotaxis, and that Tesla’s stock price could spike to $800 next year.

A New York Times report from Thursday reads like a valiant attempt to talk sanity into anyone who believes the Wedbush Tesla narrative. It’s not going to work, because Tesla is selling a pretty wild fantasy that isn’t mentioned in the Times’ piece.

Central to the Times’ report is the observation that in Austin, Tesla’s proof-of-concept city as a Robocar manufactuer-operator, an estimated 30 self-driving taxis have supposedly rolled onto the roads since June, an absolutely dismal number compared to Waymo’s 200 in the same city since March. The source the Times links to for the Tesla stat is a site called teslarobotaxitracker.com, which is run by an Austin-based robotaxi enthusiast named Ethan McKanna.

And the Times points out that each and every Tesla self-driving taxi with passengers in it still has a human safety monitor—while Waymo’s fleet is unsupervised—at least inside the car. 

The Times is far from the first to claim that Waymo is way ahead of Tesla. Jeff Dean, the chief scientist at Google DeepMind—who shares a parent company, Alphabet, with Waymo, wrote on Twitter earlier this month, “I don’t think Tesla has anywhere near the volume of rider-only autonomous miles that Waymo has (96M for Waymo, as of today). The safety data is quite compelling for Waymo, as well.” 

Elon Musk, the CEO of Tesla, replied to Dean by making one of his famous outlandish predictions: “Waymo never really had a chance against Tesla. This will be obvious in hindsight.”

One issue with any Waymo-Tesla comparison right now, however, is that Waymo’s business is running into some major potholes, and they might be relevant. This past weekend, Waymo had to shut down its service in San Francisco when its vehicles faltered at dark stoplights. It turned out that Waymo’s lack of safety drivers might have contributed to the problem, since the motivation for the shutdown was a logjam caused by the Waymo software’s high volume of requests for human feedback.

But importantly, the bullish case for Tesla’s Robotaxi service doesn’t seem to be based on the existing ride-hailing service that relies on Model Y cars as autonomous taxis. It’s most likely based on the large scale rollout of a two-seater car without a steering wheel or pedals called the Cybercab that Elon Musk unveiled in 2024, and claimed will be available for purchase by the end of 2026.

The supposed silver bullet for Cybercabs is that people will ostensibly buy them, and use them for their own transportation needs, but at other times release them into the wild as robotic servants that make them passive, or passive-ish, income. This would benefit Tesla in theory because it would rely on the Tesla app ecosystem, and Tesla would get a cut, while the car owners have to deal with charging, maintenance, insurance, cleaning, and everything else that’s annoying about owning a car.

And we know Elon Musk has it in his head that he’s going to get something like a million Cybercabs onto the road—or at least some mix of hundreds of thousands of Model Y taxis alongside Cybercabs. We know this because if Tesla doesn’t deploy at least one million self-driving taxis, Elon Musk doesn’t get all of his notorious $1 trillion pay package. 

The Times’s piece isn’t wrong to quote experts saying Tesla is “way behind Waymo.” But it includes passages like this that make near-religious faith in Tesla’s future revenue sound more mysterious than it is:

Some analysts also doubt whether driverless taxis will generate trillions of dollars of revenue, as Mr. Musk has predicted, or be very profitable. For revenue to even reach hundreds of billions of dollars, many people would have to give up their personal vehicles in favor of riding in taxis, which is unlikely anytime soon, said Michael Tyndall, an analyst at HSBC.

It’s not that the Times is comparing apples and oranges. It’s more like they’re comparing otherwise decent apples with worms in them to magical apples from a wizard who claims his apples can grant wishes, but no one can have one yet. It’s more dubious and fantastical than the extremely sane adults in the room are even letting on with their pleas for sanity. But hey, let’s all just wait and see what the wizard has in store for us.

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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. 

As AI content floods the internet, Pangram raises M 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 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 -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
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 M 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 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 -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

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. 

As AI content floods the internet, Pangram raises M 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 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 -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
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. 

As AI content floods the internet, Pangram raises M 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 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 -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
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
reports, Vivo has started preparations for launching its S series phones back in India with the release of the Vivo S2 smartphone. While Vivo hasn’t officially confirmed a release date, some reports indicate the phone could launch on August 6th, with sales expected to begin on August 11. This would mark the revival of the S series of phones in India following the debut of the Vivo S1 phone back in 2019. Reports have also given an early look at its design while revealing details about its expected specifications, color options, and launch plans.

Expected Specifications and Camera Features

Leaks suggest the Vivo S2 will feature an OLED display with a refresh rate of up to 120Hz. The phone may use the MediaTek Dimensity 7360 chipset, built on a 4nm process. Reports also suggest the phone will come in 8GB + 128GB and 8GB + 256GB variants. On the camera front, the Vivo S2 is tipped to include a 50MP Sony main sensor. Some information also suggests that the phone would be capable of recording videos in 4K quality through both front and rear cameras.

According to some recent leaks, the Vivo S2 would come with a sleek design. It would have a 3D curved display with narrow borders and a punch-hole selfie camera at the center of the device. On the back side, there would be a camera module in the shape of a pill with two big rings and an LED flash. Unlike many smartphones, the camera module matches the rear panel color for a more seamless finish. Vivo could offer the phone in Sapphire Blue, Regal Bronze, and Silk White. Its design closely resembles the Vivo X300 FE. Vivo has not shared any battery details yet. However, one report suggests the Vivo S2 may pack a 7,000mAh battery. This information is still based on leaks and awaits official confirmation.

Expected Price of Vivo S2

The Vivo S2 could target India’s premium mid-range smartphone market. It might cost anywhere between ₹40,000 and ₹50,000, though Vivo has not yet officially announced its pricing. Some leaks also reveal internal listing prices, but they may differ from the final retail price. On launch, the Vivo S2 will challenge the OnePlus Nord 6 and the Motorola Edge 70 Pro smartphones. It will also take on the Realme 16 Pro series and Vivo V70 FE.

#Vivo #India #Launch #Tipped #August #Design #Key #Specs #LeakedVivo">Vivo S2 India Launch Tipped for August 6: Design and Key Specs Leaked According to reports, Vivo has started preparations for launching its S series phones back in India with the release of the Vivo S2 smartphone. While Vivo hasn’t officially confirmed a release date, some reports indicate the phone could launch on August 6th, with sales expected to begin on August 11. This would mark the revival of the S series of phones in India following the debut of the Vivo S1 phone back in 2019. Reports have also given an early look at its design while revealing details about its expected specifications, color options, and launch plans. Expected Specifications and Camera Features Leaks suggest the Vivo S2 will feature an OLED display with a refresh rate of up to 120Hz. The phone may use the MediaTek Dimensity 7360 chipset, built on a 4nm process. Reports also suggest the phone will come in 8GB + 128GB and 8GB + 256GB variants. On the camera front, the Vivo S2 is tipped to include a 50MP Sony main sensor. Some information also suggests that the phone would be capable of recording videos in 4K quality through both front and rear cameras. According to some recent leaks, the Vivo S2 would come with a sleek design. It would have a 3D curved display with narrow borders and a punch-hole selfie camera at the center of the device. On the back side, there would be a camera module in the shape of a pill with two big rings and an LED flash. Unlike many smartphones, the camera module matches the rear panel color for a more seamless finish. Vivo could offer the phone in Sapphire Blue, Regal Bronze, and Silk White. Its design closely resembles the Vivo X300 FE. Vivo has not shared any battery details yet. However, one report suggests the Vivo S2 may pack a 7,000mAh battery. This information is still based on leaks and awaits official confirmation. Expected Price of Vivo S2 The Vivo S2 could target India’s premium mid-range smartphone market. It might cost anywhere between ₹40,000 and ₹50,000, though Vivo has not yet officially announced its pricing. Some leaks also reveal internal listing prices, but they may differ from the final retail price. On launch, the Vivo S2 will challenge the OnePlus Nord 6 and the Motorola Edge 70 Pro smartphones. It will also take on the Realme 16 Pro series and Vivo V70 FE. #Vivo #India #Launch #Tipped #August #Design #Key #Specs #LeakedVivo

, Vivo has started preparations for launching its S series phones back in India with the release of the Vivo S2 smartphone. While Vivo hasn’t officially confirmed a release date, some reports indicate the phone could launch on August 6th, with sales expected to begin on August 11. This would mark the revival of the S series of phones in India following the debut of the Vivo S1 phone back in 2019. Reports have also given an early look at its design while revealing details about its expected specifications, color options, and launch plans.

Expected Specifications and Camera Features

Leaks suggest the Vivo S2 will feature an OLED display with a refresh rate of up to 120Hz. The phone may use the MediaTek Dimensity 7360 chipset, built on a 4nm process. Reports also suggest the phone will come in 8GB + 128GB and 8GB + 256GB variants. On the camera front, the Vivo S2 is tipped to include a 50MP Sony main sensor. Some information also suggests that the phone would be capable of recording videos in 4K quality through both front and rear cameras.

According to some recent leaks, the Vivo S2 would come with a sleek design. It would have a 3D curved display with narrow borders and a punch-hole selfie camera at the center of the device. On the back side, there would be a camera module in the shape of a pill with two big rings and an LED flash. Unlike many smartphones, the camera module matches the rear panel color for a more seamless finish. Vivo could offer the phone in Sapphire Blue, Regal Bronze, and Silk White. Its design closely resembles the Vivo X300 FE. Vivo has not shared any battery details yet. However, one report suggests the Vivo S2 may pack a 7,000mAh battery. This information is still based on leaks and awaits official confirmation.

Expected Price of Vivo S2

The Vivo S2 could target India’s premium mid-range smartphone market. It might cost anywhere between ₹40,000 and ₹50,000, though Vivo has not yet officially announced its pricing. Some leaks also reveal internal listing prices, but they may differ from the final retail price. On launch, the Vivo S2 will challenge the OnePlus Nord 6 and the Motorola Edge 70 Pro smartphones. It will also take on the Realme 16 Pro series and Vivo V70 FE.

#Vivo #India #Launch #Tipped #August #Design #Key #Specs #LeakedVivo">Vivo S2 India Launch Tipped for August 6: Design and Key Specs Leaked

According to reports, Vivo has started preparations for launching its S series phones back in India with the release of the Vivo S2 smartphone. While Vivo hasn’t officially confirmed a release date, some reports indicate the phone could launch on August 6th, with sales expected to begin on August 11. This would mark the revival of the S series of phones in India following the debut of the Vivo S1 phone back in 2019. Reports have also given an early look at its design while revealing details about its expected specifications, color options, and launch plans.

Expected Specifications and Camera Features

Leaks suggest the Vivo S2 will feature an OLED display with a refresh rate of up to 120Hz. The phone may use the MediaTek Dimensity 7360 chipset, built on a 4nm process. Reports also suggest the phone will come in 8GB + 128GB and 8GB + 256GB variants. On the camera front, the Vivo S2 is tipped to include a 50MP Sony main sensor. Some information also suggests that the phone would be capable of recording videos in 4K quality through both front and rear cameras.

According to some recent leaks, the Vivo S2 would come with a sleek design. It would have a 3D curved display with narrow borders and a punch-hole selfie camera at the center of the device. On the back side, there would be a camera module in the shape of a pill with two big rings and an LED flash. Unlike many smartphones, the camera module matches the rear panel color for a more seamless finish. Vivo could offer the phone in Sapphire Blue, Regal Bronze, and Silk White. Its design closely resembles the Vivo X300 FE. Vivo has not shared any battery details yet. However, one report suggests the Vivo S2 may pack a 7,000mAh battery. This information is still based on leaks and awaits official confirmation.

Expected Price of Vivo S2

The Vivo S2 could target India’s premium mid-range smartphone market. It might cost anywhere between ₹40,000 and ₹50,000, though Vivo has not yet officially announced its pricing. Some leaks also reveal internal listing prices, but they may differ from the final retail price. On launch, the Vivo S2 will challenge the OnePlus Nord 6 and the Motorola Edge 70 Pro smartphones. It will also take on the Realme 16 Pro series and Vivo V70 FE.

#Vivo #India #Launch #Tipped #August #Design #Key #Specs #LeakedVivo

Before Denis Villeneuve came aboard as the next James Bond movie director, another intriguing project was sitting in his potential “to-do” pile: an adaptation of Nuclear War: A Scenario, Annie Jacobsen‘s 2024 book that uses extensive interviews and research to examine, minute by minute, what would happen if nuclear war broke out. Her latest book sounds very much in that same vein; it’s titled Biological War: A Scenario, and though it’s just been released today, it’s already been scooped up for adaptation.

Variety reports that Patrick Macmanus (the creator of Peacock’s Devil in Disguise: John Wayne Gacy) secured the rights to a television adaptation for his production company; he and Jacobsen will co-create and serve as executive producers on Biological War.

The book takes a close look at the seconds, minutes, hours, and days after the release of a microscopic agent that could bring down civilization if it’s allowed to spread across the globe. And it’s a weapon, not unlike The Stand‘s Captain Trips, so presumably the show will include a heavy government-military aspect to things as well.

“What begins with a routine day leads to the end of the world six days later,” Macmamus told Variety. “But it isn’t ultimately a nihilistic show. Yes. There’s the dark excitement and exploration of the apocalypse. But there’s a parallel story of the instinct to protect and rebuild. A celebration of community and family. The wish-fulfillment of starting the human experiment over again in the hopes of getting it right this time around.”

The Variety piece mentions the potential Villeneuve adaptation of Nuclear War, so maybe we’ll get to see that on screens eventually too. So far, there’s no streamer or network locked in for Biological War, but the trade writes “the television adaptation will be taken out to buyers in the upcoming weeks.”

Want more io9 news? Check out when to expect the latest Marvel, Star Wars, and Star Trek releases, what’s next for the DC Universe on film and TV, and everything you need to know about the future of Doctor Who.

#Annie #Jacobsens #Biological #War #ComingAnnie Jacobsen,Biological War">Annie Jacobsen’s ‘Biological War’ Is Coming to TV Before Denis Villeneuve came aboard as the next James Bond movie director, another intriguing project was sitting in his potential “to-do” pile: an adaptation of Nuclear War: A Scenario, Annie Jacobsen‘s 2024 book that uses extensive interviews and research to examine, minute by minute, what would happen if nuclear war broke out. Her latest book sounds very much in that same vein; it’s titled Biological War: A Scenario, and though it’s just been released today, it’s already been scooped up for adaptation. Variety reports that Patrick Macmanus (the creator of Peacock’s Devil in Disguise: John Wayne Gacy) secured the rights to a television adaptation for his production company; he and Jacobsen will co-create and serve as executive producers on Biological War. The book takes a close look at the seconds, minutes, hours, and days after the release of a microscopic agent that could bring down civilization if it’s allowed to spread across the globe. And it’s a weapon, not unlike The Stand‘s Captain Trips, so presumably the show will include a heavy government-military aspect to things as well. “What begins with a routine day leads to the end of the world six days later,” Macmamus told Variety. “But it isn’t ultimately a nihilistic show. Yes. There’s the dark excitement and exploration of the apocalypse. But there’s a parallel story of the instinct to protect and rebuild. A celebration of community and family. The wish-fulfillment of starting the human experiment over again in the hopes of getting it right this time around.” The Variety piece mentions the potential Villeneuve adaptation of Nuclear War, so maybe we’ll get to see that on screens eventually too. So far, there’s no streamer or network locked in for Biological War, but the trade writes “the television adaptation will be taken out to buyers in the upcoming weeks.” Want more io9 news? Check out when to expect the latest Marvel, Star Wars, and Star Trek releases, what’s next for the DC Universe on film and TV, and everything you need to know about the future of Doctor Who. #Annie #Jacobsens #Biological #War #ComingAnnie Jacobsen,Biological War

came aboard as the next James Bond movie director, another intriguing project was sitting in his potential “to-do” pile: an adaptation of Nuclear War: A Scenario, Annie Jacobsen‘s 2024 book that uses extensive interviews and research to examine, minute by minute, what would happen if nuclear war broke out. Her latest book sounds very much in that same vein; it’s titled Biological War: A Scenario, and though it’s just been released today, it’s already been scooped up for adaptation.

Variety reports that Patrick Macmanus (the creator of Peacock’s Devil in Disguise: John Wayne Gacy) secured the rights to a television adaptation for his production company; he and Jacobsen will co-create and serve as executive producers on Biological War.

The book takes a close look at the seconds, minutes, hours, and days after the release of a microscopic agent that could bring down civilization if it’s allowed to spread across the globe. And it’s a weapon, not unlike The Stand‘s Captain Trips, so presumably the show will include a heavy government-military aspect to things as well.

“What begins with a routine day leads to the end of the world six days later,” Macmamus told Variety. “But it isn’t ultimately a nihilistic show. Yes. There’s the dark excitement and exploration of the apocalypse. But there’s a parallel story of the instinct to protect and rebuild. A celebration of community and family. The wish-fulfillment of starting the human experiment over again in the hopes of getting it right this time around.”

The Variety piece mentions the potential Villeneuve adaptation of Nuclear War, so maybe we’ll get to see that on screens eventually too. So far, there’s no streamer or network locked in for Biological War, but the trade writes “the television adaptation will be taken out to buyers in the upcoming weeks.”

Want more io9 news? Check out when to expect the latest Marvel, Star Wars, and Star Trek releases, what’s next for the DC Universe on film and TV, and everything you need to know about the future of Doctor Who.

#Annie #Jacobsens #Biological #War #ComingAnnie Jacobsen,Biological War">Annie Jacobsen’s ‘Biological War’ Is Coming to TVAnnie Jacobsen’s ‘Biological War’ Is Coming to TV Before Denis Villeneuve came aboard as the next James Bond movie director, another intriguing project was sitting in his potential “to-do” pile: an adaptation of Nuclear War: A Scenario, Annie Jacobsen‘s 2024 book that uses extensive interviews and research to examine, minute by minute, what would happen if nuclear war broke out. Her latest book sounds very much in that same vein; it’s titled Biological War: A Scenario, and though it’s just been released today, it’s already been scooped up for adaptation. Variety reports that Patrick Macmanus (the creator of Peacock’s Devil in Disguise: John Wayne Gacy) secured the rights to a television adaptation for his production company; he and Jacobsen will co-create and serve as executive producers on Biological War. The book takes a close look at the seconds, minutes, hours, and days after the release of a microscopic agent that could bring down civilization if it’s allowed to spread across the globe. And it’s a weapon, not unlike The Stand‘s Captain Trips, so presumably the show will include a heavy government-military aspect to things as well. “What begins with a routine day leads to the end of the world six days later,” Macmamus told Variety. “But it isn’t ultimately a nihilistic show. Yes. There’s the dark excitement and exploration of the apocalypse. But there’s a parallel story of the instinct to protect and rebuild. A celebration of community and family. The wish-fulfillment of starting the human experiment over again in the hopes of getting it right this time around.” The Variety piece mentions the potential Villeneuve adaptation of Nuclear War, so maybe we’ll get to see that on screens eventually too. So far, there’s no streamer or network locked in for Biological War, but the trade writes “the television adaptation will be taken out to buyers in the upcoming weeks.” Want more io9 news? Check out when to expect the latest Marvel, Star Wars, and Star Trek releases, what’s next for the DC Universe on film and TV, and everything you need to know about the future of Doctor Who. #Annie #Jacobsens #Biological #War #ComingAnnie Jacobsen,Biological War

Before Denis Villeneuve came aboard as the next James Bond movie director, another intriguing project was sitting in his potential “to-do” pile: an adaptation of Nuclear War: A Scenario, Annie Jacobsen‘s 2024 book that uses extensive interviews and research to examine, minute by minute, what would happen if nuclear war broke out. Her latest book sounds very much in that same vein; it’s titled Biological War: A Scenario, and though it’s just been released today, it’s already been scooped up for adaptation.

Variety reports that Patrick Macmanus (the creator of Peacock’s Devil in Disguise: John Wayne Gacy) secured the rights to a television adaptation for his production company; he and Jacobsen will co-create and serve as executive producers on Biological War.

The book takes a close look at the seconds, minutes, hours, and days after the release of a microscopic agent that could bring down civilization if it’s allowed to spread across the globe. And it’s a weapon, not unlike The Stand‘s Captain Trips, so presumably the show will include a heavy government-military aspect to things as well.

“What begins with a routine day leads to the end of the world six days later,” Macmamus told Variety. “But it isn’t ultimately a nihilistic show. Yes. There’s the dark excitement and exploration of the apocalypse. But there’s a parallel story of the instinct to protect and rebuild. A celebration of community and family. The wish-fulfillment of starting the human experiment over again in the hopes of getting it right this time around.”

The Variety piece mentions the potential Villeneuve adaptation of Nuclear War, so maybe we’ll get to see that on screens eventually too. So far, there’s no streamer or network locked in for Biological War, but the trade writes “the television adaptation will be taken out to buyers in the upcoming weeks.”

Want more io9 news? Check out when to expect the latest Marvel, Star Wars, and Star Trek releases, what’s next for the DC Universe on film and TV, and everything you need to know about the future of Doctor Who.

#Annie #Jacobsens #Biological #War #ComingAnnie Jacobsen,Biological War

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