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I Didn’t Care for Dildos Until I Tried This One From Lelo

I Didn’t Care for Dildos Until I Tried This One From Lelo

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My first sex toy was a bright blue dildo. I was about 19, and as a college student in New Hampshire, I did what anyone in my position would do: hotfooted it to the closest city to find a sex shop. With my best friend in tow, I jetted in and out of the Condom World on Newbury Street so fast that I only had time to grab the first sex toy I saw and pay for it, keeping my head down the whole time. To say the early 2000s were different when it came to sex toy acceptance would be a gross understatement.

In my small-town mind, they were something that shouldn’t be discussed and the very definition of the word taboo. But they weren’t taboo enough to keep me from buying that battery-operated blue monstrosity with its exaggerated veins and head. I was embarrassed by it from day one, even more so after I used it, with zero understanding of what I was supposed to get out of it.

With Age Came Wisdom

As I got to know my body better, exploring on my own or during partnered sex, with other toys I purchased, I came to a realization. As much as I love penetration with my partner(s), I didn’t particularly enjoy dildos. I found most impersonal, poorly designed, and because it wouldn’t be until my late 30s that I’d finally have an orgasm from G-spot simulation, on a pleasure level, they weren’t for me. This didn’t stop me from trying to get something out of them, especially as I started writing about sexual wellness and suddenly had a world of sex toys and the newest innovations at the tip of my fingers.

My feelings on dildos changed drastically with the Lelo Gigi 3. Finally, an internal dildo with a head that was flattened, as opposed to cone-shaped, meaning it covered more of the G-spot area. Breaking news, that’s exactly what I needed after all this time. While so many other dildos have rounded heads, which many people love, the flat head of the Gigi 3 is what really sets it apart.

It’s not just the shape of the head, but the way it disperses stimulation. Because the eight vibration modes are delivered through the flattened head, the sensations are more intense and rumbly, meaning I can feel them branching out and reaching more nerve endings. People with vulvas are far too often taught that when it comes to sexual pleasure, the focus should be on the clitoris or the G-spot—or both at once—but the reality is that the entire region is chock-full of nerve endings. Hence, the reason people with vulvas can experience orgasms outside these two zones, like via the A-spot (anterior fornix) and the U-spot (urethral sponge).

When Size Matters

I’m not a size queen. I’m the first to admit that smaller is better when it comes to external vibrators. However, when it comes to internal vibrators, the size and shape of the shaft matter. The Gigi 3 is the ideal size for G-spot stimulation. Even those who have yet to officially find their G-spot can’t possibly miss it when using the Gigi 3 because the length and slight arc of the shaft puts that flattened head exactly where every person with a vulva wants it. For me, I love to put a few drops of water-based lube on the head and shaft of the Gigi 3, get myself comfy, then slide it inside. From there, I can melt into the moment without fumbling with buttons (the Gigi 3 is app-controlled).

When I’m not in the mood for internal play, the flat head of the Gigi 3 is great for direct clitoral stimulation or teasing other parts of my vulva. As I’ve learned, just because the labia doesn’t have as many nerve endings as the clitoris doesn’t mean it likes to be ignored.

Is the Lelo Gigi 3 my favorite sex toy? No. Depending on the day and my mood, my favorite sex toys and vibrators change. But, if you’re someone with a vulva who has never liked dildos, the Gigi 3 could be your ticket to sexual pleasure. It could also hit the spot if you prefer clitoral stimulation, but want the vibrations to encompass more than just the external tip of the clit. Although the Gigi 3 can be used anywhere, internally and externally, if you have a vulva, this is a must-have.

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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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