The Android phone maker says go ahead, fix your own phone.

The right-to-repair movement continues to gain steam as another big tech company shows its support for letting people fix their own broken devices.

Google endorsed an Oregon right-to-repair legislation Thursday calling it a “common sense repair bill” and saying it would be a “win for consumers.” This marks the first time the Android phone maker has officially backed any right-to-repair law.

The ability to repair a phone, for example, empowers people by saving money on devices while creating less waste,” said Steven Nickel, devices and services director of operations for Google, in a blog post Thursday. “It also critically supports sustainability in manufacturing. Repair must be easy enough for anyone to do, whether they are technicians or do-it-yourselfers.”

In the Oregon repair bill, manufacturers will be required to provide replacement parts, software, physical tools, documentation and schematics needed for repair to authorized repair providers or individuals. The legislation covers any digital electronics with a computer chip although cars, farm equipment, medical devices, solar power systems, and any heavy or industrial equipment that is not sold to consumers are exempt from the bill.

Google has made strides in making its Pixel phones easier to fix. The company enabled a Repair Mode for the phones last month allowing the protection of data on the device while it’s being serviced. There’s also a diagnostic feature that helps determine if your Pixel phone is working properly or not. That said, Google’s Pixel Watch is another story as the company said in October it will not provide parts to repair its smartwatch.

Apple jumped on the right-to-repair bandwagon back in October. The iPhone maker showed its support for a federal law to make it easier to repair its phones after years of being a staunch opponent.

The Android phone maker says go ahead, fix your own phone.

The right-to-repair movement continues to gain steam as another big tech company shows its support for letting people fix their own broken devices.

Google endorsed an Oregon right-to-repair legislation Thursday calling it a “common sense repair bill” and saying it would be a “win for consumers.” This marks the first time the Android phone maker has officially backed any right-to-repair law.

The ability to repair a phone, for example, empowers people by saving money on devices while creating less waste,” said Steven Nickel, devices and services director of operations for Google, in a blog post Thursday. “It also critically supports sustainability in manufacturing. Repair must be easy enough for anyone to do, whether they are technicians or do-it-yourselfers.”

In the Oregon repair bill, manufacturers will be required to provide replacement parts, software, physical tools, documentation and schematics needed for repair to authorized repair providers or individuals. The legislation covers any digital electronics with a computer chip although cars, farm equipment, medical devices, solar power systems, and any heavy or industrial equipment that is not sold to consumers are exempt from the bill.

Google has made strides in making its Pixel phones easier to fix. The company enabled a Repair Mode for the phones last month allowing the protection of data on the device while it’s being serviced. There’s also a diagnostic feature that helps determine if your Pixel phone is working properly or not. That said, Google’s Pixel Watch is another story as the company said in October it will not provide parts to repair its smartwatch.

Apple jumped on the right-to-repair bandwagon back in October. The iPhone maker showed its support for a federal law to make it easier to repair its phones after years of being a staunch opponent.

The Android phone maker says go ahead, fix your own phone.

The right-to-repair movement continues to gain steam as another big tech company shows its support for letting people fix their own broken devices.

Google endorsed an Oregon right-to-repair legislation Thursday calling it a “common sense repair bill” and saying it would be a “win for consumers.” This marks the first time the Android phone maker has officially backed any right-to-repair law.

The ability to repair a phone, for example, empowers people by saving money on devices while creating less waste,” said Steven Nickel, devices and services director of operations for Google, in a blog post Thursday. “It also critically supports sustainability in manufacturing. Repair must be easy enough for anyone to do, whether they are technicians or do-it-yourselfers.”

In the Oregon repair bill, manufacturers will be required to provide replacement parts, software, physical tools, documentation and schematics needed for repair to authorized repair providers or individuals. The legislation covers any digital electronics with a computer chip although cars, farm equipment, medical devices, solar power systems, and any heavy or industrial equipment that is not sold to consumers are exempt from the bill.

Google has made strides in making its Pixel phones easier to fix. The company enabled a Repair Mode for the phones last month allowing the protection of data on the device while it’s being serviced. There’s also a diagnostic feature that helps determine if your Pixel phone is working properly or not. That said, Google’s Pixel Watch is another story as the company said in October it will not provide parts to repair its smartwatch.

Apple jumped on the right-to-repair bandwagon back in October. The iPhone maker showed its support for a federal law to make it easier to repair its phones after years of being a staunch opponent.

The Android phone maker says go ahead, fix your own phone.

The right-to-repair movement continues to gain steam as another big tech company shows its support for letting people fix their own broken devices.

Google endorsed an Oregon right-to-repair legislation Thursday calling it a “common sense repair bill” and saying it would be a “win for consumers.” This marks the first time the Android phone maker has officially backed any right-to-repair law.

The ability to repair a phone, for example, empowers people by saving money on devices while creating less waste,” said Steven Nickel, devices and services director of operations for Google, in a blog post Thursday. “It also critically supports sustainability in manufacturing. Repair must be easy enough for anyone to do, whether they are technicians or do-it-yourselfers.”

In the Oregon repair bill, manufacturers will be required to provide replacement parts, software, physical tools, documentation and schematics needed for repair to authorized repair providers or individuals. The legislation covers any digital electronics with a computer chip although cars, farm equipment, medical devices, solar power systems, and any heavy or industrial equipment that is not sold to consumers are exempt from the bill.

Google has made strides in making its Pixel phones easier to fix. The company enabled a Repair Mode for the phones last month allowing the protection of data on the device while it’s being serviced. There’s also a diagnostic feature that helps determine if your Pixel phone is working properly or not. That said, Google’s Pixel Watch is another story as the company said in October it will not provide parts to repair its smartwatch.

Apple jumped on the right-to-repair bandwagon back in October. The iPhone maker showed its support for a federal law to make it easier to repair its phones after years of being a staunch opponent.

The New York Times is suing OpenAI and Microsoft for copyright infringement, claiming the two companies built their AI models by “copying and using millions” of the publication’s articles and now “directly compete” with its content as a result.

As outlined in the lawsuit, the Times alleges OpenAI and Microsoft’s large language models (LLMs), which power ChatGPT and Copilot, “can generate output that recites Times content verbatim, closely summarizes it, and mimics its expressive style.” This “undermine[s] and damage[s]” the Times’ relationship with readers, the outlet alleges, while also depriving it of “subscription, licensing, advertising, and affiliate revenue.”

The complaint also argues that these AI models “threaten high-quality journalism” by hurting the ability of news outlets to protect and monetize content. “Through Microsoft’s Bing Chat (recently rebranded as “Copilot”) and OpenAI’s ChatGPT, Defendants seek to free-ride on The Times’s massive investment in its journalism by using it to build substitutive products without permission or payment,” the lawsuit states.

The full text of the lawsuit can be found here

The New York Times is suing OpenAI and Microsoft for copyright infringement, claiming the two companies built their AI models by “copying and using millions” of the publication’s articles and now “directly compete” with its content as a result.

As outlined in the lawsuit, the Times alleges OpenAI and Microsoft’s large language models (LLMs), which power ChatGPT and Copilot, “can generate output that recites Times content verbatim, closely summarizes it, and mimics its expressive style.” This “undermine[s] and damage[s]” the Times’ relationship with readers, the outlet alleges, while also depriving it of “subscription, licensing, advertising, and affiliate revenue.”

The complaint also argues that these AI models “threaten high-quality journalism” by hurting the ability of news outlets to protect and monetize content. “Through Microsoft’s Bing Chat (recently rebranded as “Copilot”) and OpenAI’s ChatGPT, Defendants seek to free-ride on The Times’s massive investment in its journalism by using it to build substitutive products without permission or payment,” the lawsuit states.

The full text of the lawsuit can be found here

The New York Times is suing OpenAI and Microsoft for copyright infringement, claiming the two companies built their AI models by “copying and using millions” of the publication’s articles and now “directly compete” with its content as a result.

As outlined in the lawsuit, the Times alleges OpenAI and Microsoft’s large language models (LLMs), which power ChatGPT and Copilot, “can generate output that recites Times content verbatim, closely summarizes it, and mimics its expressive style.” This “undermine[s] and damage[s]” the Times’ relationship with readers, the outlet alleges, while also depriving it of “subscription, licensing, advertising, and affiliate revenue.”

The complaint also argues that these AI models “threaten high-quality journalism” by hurting the ability of news outlets to protect and monetize content. “Through Microsoft’s Bing Chat (recently rebranded as “Copilot”) and OpenAI’s ChatGPT, Defendants seek to free-ride on The Times’s massive investment in its journalism by using it to build substitutive products without permission or payment,” the lawsuit states.

The full text of the lawsuit can be found here

A controversial developer circumvented one of Mastodon's primary tools for blocking bad actors, all so that his servers could connect to Threads.

We’ve criticized the security and privacy mechanisms of Mastodon in the past, but this new development should be eye-opening. Alex Gleason, the former Truth Social developer behind Soapbox and Rebased, has come up with a sneaky workaround to how Authorized Fetch functions: if your domain is blocked for a fetch, just sign it with a different domain name instead.

Gleason was originally investigating Threads federation to determine whether or not a failure to fetch posts indicated a software compatibility issue, or if Threads had blocked his server. After checking some logs and experimenting, he came to a conclusion.

“Fellas,” Gleason writes, “I think threads.net might be blocking some servers already.”

What Alex found was that Threads attempts to verify domain names before allowing access to a resource, a very similar approach to what Authorized Fetch does in Mastodon.

You can see Threads fetching your own server by looking at the facebookexternalua user agent. Try this command on your server:

grep facebookexternalua /var/log/nginx/access.log

If you see logs there, that means Threads is attempting to verify your signatures and allow you to access their data.

I got interested in this question a few years ago, when I started writing about the “denominator problem”. A great deal of social media research focuses on finding unwanted behavior – mis/disinformation, hate speech – on platforms. This isn’t that hard to do: search for “white genocide” or “ivermectin” and count the results. Indeed, a lot of eye-catching research does just this – consider Avaaz’s August 2020 report about COVID misinformation. It reports 3.8 billion views of COVID misinfo in a year, which is a very big number. But it’s a numerator without a denominator – Facebook generates dozens or hundreds of views a day for each of its 3 billion users – 3.8 billion views is actually a very small number, contextualized with a denominator.

The paper this post describes can be found here
Abstract:

YouTube is one of the largest, most important communication platforms in the world, but while there is a great deal of research about the site, many of its fundamental characteristics remain unknown. To better understand YouTube as a whole, we created a random sample of videos using a new method. Through a description of the sample’s metadata, we provide answers to many essential questions about, for example, the distribution of views, comments, likes, subscribers, and categories. Our method also allows us to estimate the total number of publicly visible videos on YouTube and its growth over time. To learn more about video content, we hand-coded a subsample to answer questions like how many are primarily music, video games, or still images. Finally, we processed the videos’ audio using language detection software to determine the distribution of spoken languages. In providing basic information about YouTube as a whole, we not only learn more about an influential platform, but also provide baseline context against which samples in more focused studies can be compared.

I got interested in this question a few years ago, when I started writing about the “denominator problem”. A great deal of social media research focuses on finding unwanted behavior – mis/disinformation, hate speech – on platforms. This isn’t that hard to do: search for “white genocide” or “ivermectin” and count the results. Indeed, a lot of eye-catching research does just this – consider Avaaz’s August 2020 report about COVID misinformation. It reports 3.8 billion views of COVID misinfo in a year, which is a very big number. But it’s a numerator without a denominator – Facebook generates dozens or hundreds of views a day for each of its 3 billion users – 3.8 billion views is actually a very small number, contextualized with a denominator.

The paper this post describes can be found here
Abstract:

YouTube is one of the largest, most important communication platforms in the world, but while there is a great deal of research about the site, many of its fundamental characteristics remain unknown. To better understand YouTube as a whole, we created a random sample of videos using a new method. Through a description of the sample’s metadata, we provide answers to many essential questions about, for example, the distribution of views, comments, likes, subscribers, and categories. Our method also allows us to estimate the total number of publicly visible videos on YouTube and its growth over time. To learn more about video content, we hand-coded a subsample to answer questions like how many are primarily music, video games, or still images. Finally, we processed the videos’ audio using language detection software to determine the distribution of spoken languages. In providing basic information about YouTube as a whole, we not only learn more about an influential platform, but also provide baseline context against which samples in more focused studies can be compared.

I got interested in this question a few years ago, when I started writing about the “denominator problem”. A great deal of social media research focuses on finding unwanted behavior – mis/disinformation, hate speech – on platforms. This isn’t that hard to do: search for “white genocide” or “ivermectin” and count the results. Indeed, a lot of eye-catching research does just this – consider Avaaz’s August 2020 report about COVID misinformation. It reports 3.8 billion views of COVID misinfo in a year, which is a very big number. But it’s a numerator without a denominator – Facebook generates dozens or hundreds of views a day for each of its 3 billion users – 3.8 billion views is actually a very small number, contextualized with a denominator.

The paper this post describes can be found here
Abstract:

YouTube is one of the largest, most important communication platforms in the world, but while there is a great deal of research about the site, many of its fundamental characteristics remain unknown. To better understand YouTube as a whole, we created a random sample of videos using a new method. Through a description of the sample’s metadata, we provide answers to many essential questions about, for example, the distribution of views, comments, likes, subscribers, and categories. Our method also allows us to estimate the total number of publicly visible videos on YouTube and its growth over time. To learn more about video content, we hand-coded a subsample to answer questions like how many are primarily music, video games, or still images. Finally, we processed the videos’ audio using language detection software to determine the distribution of spoken languages. In providing basic information about YouTube as a whole, we not only learn more about an influential platform, but also provide baseline context against which samples in more focused studies can be compared.

I got interested in this question a few years ago, when I started writing about the “denominator problem”. A great deal of social media research focuses on finding unwanted behavior – mis/disinformation, hate speech – on platforms. This isn’t that hard to do: search for “white genocide” or “ivermectin” and count the results. Indeed, a lot of eye-catching research does just this – consider Avaaz’s August 2020 report about COVID misinformation. It reports 3.8 billion views of COVID misinfo in a year, which is a very big number. But it’s a numerator without a denominator – Facebook generates dozens or hundreds of views a day for each of its 3 billion users – 3.8 billion views is actually a very small number, contextualized with a denominator.

The paper this post describes can be found here
Abstract:

YouTube is one of the largest, most important communication platforms in the world, but while there is a great deal of research about the site, many of its fundamental characteristics remain unknown. To better understand YouTube as a whole, we created a random sample of videos using a new method. Through a description of the sample’s metadata, we provide answers to many essential questions about, for example, the distribution of views, comments, likes, subscribers, and categories. Our method also allows us to estimate the total number of publicly visible videos on YouTube and its growth over time. To learn more about video content, we hand-coded a subsample to answer questions like how many are primarily music, video games, or still images. Finally, we processed the videos’ audio using language detection software to determine the distribution of spoken languages. In providing basic information about YouTube as a whole, we not only learn more about an influential platform, but also provide baseline context against which samples in more focused studies can be compared.