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Kiss Guitarist Ace Frehley Dead at 74

https://www.rollingstone.com/music/music-news/kiss-guitarist-ace-frehley-dead-1235448770/

Kiss guitarist Ace Frehley has died at the age of 74, after “a recent fall at his home.” https://www.rollingstone.com/music/music-news/kiss-guitarist-ace-frehley-dead-1235448770/

Kiss guitarist Ace Frehley has died at the age of 74, after “a recent fall at his home.” https://www.rollingstone.com/music/music-news/kiss-guitarist-ace-frehley-dead-1235448770/

Kiss guitarist Ace Frehley has died at the age of 74, after “a recent fall at his home.” https://www.rollingstone.com/music/music-news/kiss-guitarist-ace-frehley-dead-1235448770/

When I had monthly train trips to NYC for work, I used to queue up Ace Frehley's "New York Groove" right as the train was approaching Manhattan and you could see the city in the distance from the train window. If it was nighttime, sometimes I would play it again once I got into the Uber and looked at all the city lights.
https://www.rollingstone.com/music/music-news/kiss-guitarist-ace-frehley-dead-1235448770/

https://www.rollingstone.com/music/music-news/kiss-guitarist-ace-frehley-dead-1235448770/

Kiss Guitarist Ace Frehley Dead at 74 https://www.rollingstone.com/music/music-news/kiss-guitarist-ace-frehley-dead-1235448770/

#RIP
#Ace

I listened to their music. Bought an album or 2... KISS, as is Ace Frehley is part of my music history.

Sad for him.

https://www.rollingstone.com/music/music-news/kiss-guitarist-ace-frehley-dead-1235448770/

Kiss guitarist Ace Frehley has died at the age of 74, after “a recent fall at his home.” https://www.rollingstone.com/music/music-news/kiss-guitarist-ace-frehley-dead-1235448770/

https://www.rollingstone.com/music/music-news/kiss-guitarist-ace-frehley-dead-1235448770/

@davidgerard , not sure if you saw this great post about ML models in radiology?

The boosters love to say ‘look how good AI is in medical diagnosis, therefore LLMs are good’. Only, it turns out (from the article):

while models beat humans on benchmarks, the standardized tests designed to measure AI performance, they struggle to replicate this performance in hospital conditions. Most tools can only diagnose abnormalities that are common in training data, and models often don’t work as well outside of their test conditions

It also highlights a problem that’s actually quite general in medicine: we have far more data about unhealthy people than healthy ones. I was talking to a cardiologist almost ten years ago who was very excited about the data things like the Apple Watch could collect. Apparently they know that a lot of people who have heart attack have arrhythmia, but they have no idea if this is a meaningful correlation. Healthy people tend to have their heart monitored for a minute or less on a visit to a doctor every few years. People with known heart problems wear heart monitors that can record a load of things, so you have very good data on their heart rhythms but no baseline to compare it against.

This is also true for radiology. You really want to do anomaly detection: take a few million scans of healthy people, wait a few years to see if any of them have undiagnosed conditions, and then use that dataset to train a model of what a healthy lung (or whatever) looks like. Then feed new scans to the model, have it flag anomalies, and loop in an expert to figure out what kind of anomaly it is and whether it’s important.

But what you have is a load of very examples of things that are wrong, in very specific ways. And these also have artefacts that are specific to individual devices, so it’s easy for a model to learn that people who are scanned with this class of machine have this condition.

And that’s just the start of the issues they discuss.

@davidgerard , not sure if you saw this great post about ML models in radiology?

The boosters love to say ‘look how good AI is in medical diagnosis, therefore LLMs are good’. Only, it turns out (from the article):

while models beat humans on benchmarks, the standardized tests designed to measure AI performance, they struggle to replicate this performance in hospital conditions. Most tools can only diagnose abnormalities that are common in training data, and models often don’t work as well outside of their test conditions

It also highlights a problem that’s actually quite general in medicine: we have far more data about unhealthy people than healthy ones. I was talking to a cardiologist almost ten years ago who was very excited about the data things like the Apple Watch could collect. Apparently they know that a lot of people who have heart attack have arrhythmia, but they have no idea if this is a meaningful correlation. Healthy people tend to have their heart monitored for a minute or less on a visit to a doctor every few years. People with known heart problems wear heart monitors that can record a load of things, so you have very good data on their heart rhythms but no baseline to compare it against.

This is also true for radiology. You really want to do anomaly detection: take a few million scans of healthy people, wait a few years to see if any of them have undiagnosed conditions, and then use that dataset to train a model of what a healthy lung (or whatever) looks like. Then feed new scans to the model, have it flag anomalies, and loop in an expert to figure out what kind of anomaly it is and whether it’s important.

But what you have is a load of very examples of things that are wrong, in very specific ways. And these also have artefacts that are specific to individual devices, so it’s easy for a model to learn that people who are scanned with this class of machine have this condition.

And that’s just the start of the issues they discuss.

@davidgerard , not sure if you saw this great post about ML models in radiology?

The boosters love to say ‘look how good AI is in medical diagnosis, therefore LLMs are good’. Only, it turns out (from the article):

while models beat humans on benchmarks, the standardized tests designed to measure AI performance, they struggle to replicate this performance in hospital conditions. Most tools can only diagnose abnormalities that are common in training data, and models often don’t work as well outside of their test conditions

It also highlights a problem that’s actually quite general in medicine: we have far more data about unhealthy people than healthy ones. I was talking to a cardiologist almost ten years ago who was very excited about the data things like the Apple Watch could collect. Apparently they know that a lot of people who have heart attack have arrhythmia, but they have no idea if this is a meaningful correlation. Healthy people tend to have their heart monitored for a minute or less on a visit to a doctor every few years. People with known heart problems wear heart monitors that can record a load of things, so you have very good data on their heart rhythms but no baseline to compare it against.

This is also true for radiology. You really want to do anomaly detection: take a few million scans of healthy people, wait a few years to see if any of them have undiagnosed conditions, and then use that dataset to train a model of what a healthy lung (or whatever) looks like. Then feed new scans to the model, have it flag anomalies, and loop in an expert to figure out what kind of anomaly it is and whether it’s important.

But what you have is a load of very examples of things that are wrong, in very specific ways. And these also have artefacts that are specific to individual devices, so it’s easy for a model to learn that people who are scanned with this class of machine have this condition.

And that’s just the start of the issues they discuss.

@davidgerard , not sure if you saw this great post about ML models in radiology?

The boosters love to say ‘look how good AI is in medical diagnosis, therefore LLMs are good’. Only, it turns out (from the article):

while models beat humans on benchmarks, the standardized tests designed to measure AI performance, they struggle to replicate this performance in hospital conditions. Most tools can only diagnose abnormalities that are common in training data, and models often don’t work as well outside of their test conditions

It also highlights a problem that’s actually quite general in medicine: we have far more data about unhealthy people than healthy ones. I was talking to a cardiologist almost ten years ago who was very excited about the data things like the Apple Watch could collect. Apparently they know that a lot of people who have heart attack have arrhythmia, but they have no idea if this is a meaningful correlation. Healthy people tend to have their heart monitored for a minute or less on a visit to a doctor every few years. People with known heart problems wear heart monitors that can record a load of things, so you have very good data on their heart rhythms but no baseline to compare it against.

This is also true for radiology. You really want to do anomaly detection: take a few million scans of healthy people, wait a few years to see if any of them have undiagnosed conditions, and then use that dataset to train a model of what a healthy lung (or whatever) looks like. Then feed new scans to the model, have it flag anomalies, and loop in an expert to figure out what kind of anomaly it is and whether it’s important.

But what you have is a load of very examples of things that are wrong, in very specific ways. And these also have artefacts that are specific to individual devices, so it’s easy for a model to learn that people who are scanned with this class of machine have this condition.

And that’s just the start of the issues they discuss.

@davidgerard , not sure if you saw this great post about ML models in radiology?

The boosters love to say ‘look how good AI is in medical diagnosis, therefore LLMs are good’. Only, it turns out (from the article):

while models beat humans on benchmarks, the standardized tests designed to measure AI performance, they struggle to replicate this performance in hospital conditions. Most tools can only diagnose abnormalities that are common in training data, and models often don’t work as well outside of their test conditions

It also highlights a problem that’s actually quite general in medicine: we have far more data about unhealthy people than healthy ones. I was talking to a cardiologist almost ten years ago who was very excited about the data things like the Apple Watch could collect. Apparently they know that a lot of people who have heart attack have arrhythmia, but they have no idea if this is a meaningful correlation. Healthy people tend to have their heart monitored for a minute or less on a visit to a doctor every few years. People with known heart problems wear heart monitors that can record a load of things, so you have very good data on their heart rhythms but no baseline to compare it against.

This is also true for radiology. You really want to do anomaly detection: take a few million scans of healthy people, wait a few years to see if any of them have undiagnosed conditions, and then use that dataset to train a model of what a healthy lung (or whatever) looks like. Then feed new scans to the model, have it flag anomalies, and loop in an expert to figure out what kind of anomaly it is and whether it’s important.

But what you have is a load of very examples of things that are wrong, in very specific ways. And these also have artefacts that are specific to individual devices, so it’s easy for a model to learn that people who are scanned with this class of machine have this condition.

And that’s just the start of the issues they discuss.

@davidgerard , not sure if you saw this great post about ML models in radiology?

The boosters love to say ‘look how good AI is in medical diagnosis, therefore LLMs are good’. Only, it turns out (from the article):

while models beat humans on benchmarks, the standardized tests designed to measure AI performance, they struggle to replicate this performance in hospital conditions. Most tools can only diagnose abnormalities that are common in training data, and models often don’t work as well outside of their test conditions

It also highlights a problem that’s actually quite general in medicine: we have far more data about unhealthy people than healthy ones. I was talking to a cardiologist almost ten years ago who was very excited about the data things like the Apple Watch could collect. Apparently they know that a lot of people who have heart attack have arrhythmia, but they have no idea if this is a meaningful correlation. Healthy people tend to have their heart monitored for a minute or less on a visit to a doctor every few years. People with known heart problems wear heart monitors that can record a load of things, so you have very good data on their heart rhythms but no baseline to compare it against.

This is also true for radiology. You really want to do anomaly detection: take a few million scans of healthy people, wait a few years to see if any of them have undiagnosed conditions, and then use that dataset to train a model of what a healthy lung (or whatever) looks like. Then feed new scans to the model, have it flag anomalies, and loop in an expert to figure out what kind of anomaly it is and whether it’s important.

But what you have is a load of very examples of things that are wrong, in very specific ways. And these also have artefacts that are specific to individual devices, so it’s easy for a model to learn that people who are scanned with this class of machine have this condition.

And that’s just the start of the issues they discuss.

@davidgerard , not sure if you saw this great post about ML models in radiology?

The boosters love to say ‘look how good AI is in medical diagnosis, therefore LLMs are good’. Only, it turns out (from the article):

while models beat humans on benchmarks, the standardized tests designed to measure AI performance, they struggle to replicate this performance in hospital conditions. Most tools can only diagnose abnormalities that are common in training data, and models often don’t work as well outside of their test conditions

It also highlights a problem that’s actually quite general in medicine: we have far more data about unhealthy people than healthy ones. I was talking to a cardiologist almost ten years ago who was very excited about the data things like the Apple Watch could collect. Apparently they know that a lot of people who have heart attack have arrhythmia, but they have no idea if this is a meaningful correlation. Healthy people tend to have their heart monitored for a minute or less on a visit to a doctor every few years. People with known heart problems wear heart monitors that can record a load of things, so you have very good data on their heart rhythms but no baseline to compare it against.

This is also true for radiology. You really want to do anomaly detection: take a few million scans of healthy people, wait a few years to see if any of them have undiagnosed conditions, and then use that dataset to train a model of what a healthy lung (or whatever) looks like. Then feed new scans to the model, have it flag anomalies, and loop in an expert to figure out what kind of anomaly it is and whether it’s important.

But what you have is a load of very examples of things that are wrong, in very specific ways. And these also have artefacts that are specific to individual devices, so it’s easy for a model to learn that people who are scanned with this class of machine have this condition.

And that’s just the start of the issues they discuss.

@davidgerard , not sure if you saw this great post about ML models in radiology?

The boosters love to say ‘look how good AI is in medical diagnosis, therefore LLMs are good’. Only, it turns out (from the article):

while models beat humans on benchmarks, the standardized tests designed to measure AI performance, they struggle to replicate this performance in hospital conditions. Most tools can only diagnose abnormalities that are common in training data, and models often don’t work as well outside of their test conditions

It also highlights a problem that’s actually quite general in medicine: we have far more data about unhealthy people than healthy ones. I was talking to a cardiologist almost ten years ago who was very excited about the data things like the Apple Watch could collect. Apparently they know that a lot of people who have heart attack have arrhythmia, but they have no idea if this is a meaningful correlation. Healthy people tend to have their heart monitored for a minute or less on a visit to a doctor every few years. People with known heart problems wear heart monitors that can record a load of things, so you have very good data on their heart rhythms but no baseline to compare it against.

This is also true for radiology. You really want to do anomaly detection: take a few million scans of healthy people, wait a few years to see if any of them have undiagnosed conditions, and then use that dataset to train a model of what a healthy lung (or whatever) looks like. Then feed new scans to the model, have it flag anomalies, and loop in an expert to figure out what kind of anomaly it is and whether it’s important.

But what you have is a load of very examples of things that are wrong, in very specific ways. And these also have artefacts that are specific to individual devices, so it’s easy for a model to learn that people who are scanned with this class of machine have this condition.

And that’s just the start of the issues they discuss.

@davidgerard , not sure if you saw this great post about ML models in radiology?

The boosters love to say ‘look how good AI is in medical diagnosis, therefore LLMs are good’. Only, it turns out (from the article):

while models beat humans on benchmarks, the standardized tests designed to measure AI performance, they struggle to replicate this performance in hospital conditions. Most tools can only diagnose abnormalities that are common in training data, and models often don’t work as well outside of their test conditions

It also highlights a problem that’s actually quite general in medicine: we have far more data about unhealthy people than healthy ones. I was talking to a cardiologist almost ten years ago who was very excited about the data things like the Apple Watch could collect. Apparently they know that a lot of people who have heart attack have arrhythmia, but they have no idea if this is a meaningful correlation. Healthy people tend to have their heart monitored for a minute or less on a visit to a doctor every few years. People with known heart problems wear heart monitors that can record a load of things, so you have very good data on their heart rhythms but no baseline to compare it against.

This is also true for radiology. You really want to do anomaly detection: take a few million scans of healthy people, wait a few years to see if any of them have undiagnosed conditions, and then use that dataset to train a model of what a healthy lung (or whatever) looks like. Then feed new scans to the model, have it flag anomalies, and loop in an expert to figure out what kind of anomaly it is and whether it’s important.

But what you have is a load of very examples of things that are wrong, in very specific ways. And these also have artefacts that are specific to individual devices, so it’s easy for a model to learn that people who are scanned with this class of machine have this condition.

And that’s just the start of the issues they discuss.

@davidgerard , not sure if you saw this great post about ML models in radiology?

The boosters love to say ‘look how good AI is in medical diagnosis, therefore LLMs are good’. Only, it turns out (from the article):

while models beat humans on benchmarks, the standardized tests designed to measure AI performance, they struggle to replicate this performance in hospital conditions. Most tools can only diagnose abnormalities that are common in training data, and models often don’t work as well outside of their test conditions

It also highlights a problem that’s actually quite general in medicine: we have far more data about unhealthy people than healthy ones. I was talking to a cardiologist almost ten years ago who was very excited about the data things like the Apple Watch could collect. Apparently they know that a lot of people who have heart attack have arrhythmia, but they have no idea if this is a meaningful correlation. Healthy people tend to have their heart monitored for a minute or less on a visit to a doctor every few years. People with known heart problems wear heart monitors that can record a load of things, so you have very good data on their heart rhythms but no baseline to compare it against.

This is also true for radiology. You really want to do anomaly detection: take a few million scans of healthy people, wait a few years to see if any of them have undiagnosed conditions, and then use that dataset to train a model of what a healthy lung (or whatever) looks like. Then feed new scans to the model, have it flag anomalies, and loop in an expert to figure out what kind of anomaly it is and whether it’s important.

But what you have is a load of very examples of things that are wrong, in very specific ways. And these also have artefacts that are specific to individual devices, so it’s easy for a model to learn that people who are scanned with this class of machine have this condition.

And that’s just the start of the issues they discuss.

Can You Hear Us?: Five Voices from Gaza – Hot Press Magazine, Ireland

https://www.hotpress.com/opinion/can-you-hear-us-five-voices-from-gaza-23113207

The five voices are those of my partner at Gaza Verified, @joynewacc – who also helped curate and translate the pieces from Arabic – and our Gaza Verified members, @aseelfromgz, @soma23, @Mohammedjhad, and @mohshbair.

A huge thank you to all of you for taking time in the middle of surviving famine and genocide to share your experiences with us and to @gerrymcgovern for commissioning the piece.

💕

#Gaza #Palestine #israel #genocide #famine #HotPressMagazine #Ireland #article #StopIsrael #FreePalestine

Can You Hear Us?: Five Voices from Gaza – Hot Press Magazine, Ireland

https://www.hotpress.com/opinion/can-you-hear-us-five-voices-from-gaza-23113207

The five voices are those of my partner at Gaza Verified, @joynewacc – who also helped curate and translate the pieces from Arabic – and our Gaza Verified members, @aseelfromgz, @soma23, @Mohammedjhad, and @mohshbair.

A huge thank you to all of you for taking time in the middle of surviving famine and genocide to share your experiences with us and to @gerrymcgovern for commissioning the piece.

💕

#Gaza #Palestine #israel #genocide #famine #HotPressMagazine #Ireland #article #StopIsrael #FreePalestine

@liaizon @mohshbair You can also find a short piece Mohammed wrote here: https://www.hotpress.com/opinion/can-you-hear-us-five-voices-from-gaza-23113207

Can You Hear Us?: Five Voices from Gaza – Hot Press Magazine, Ireland

https://www.hotpress.com/opinion/can-you-hear-us-five-voices-from-gaza-23113207

The five voices are those of my partner at Gaza Verified, @joynewacc – who also helped curate and translate the pieces from Arabic – and our Gaza Verified members, @aseelfromgz, @soma23, @Mohammedjhad, and @mohshbair.

A huge thank you to all of you for taking time in the middle of surviving famine and genocide to share your experiences with us and to @gerrymcgovern for commissioning the piece.

💕

#Gaza #Palestine #israel #genocide #famine #HotPressMagazine #Ireland #article #StopIsrael #FreePalestine

@joynewacc @aseelfromgz @soma23 @Mohammedjhad @mohshbair By the way, if you haven’t read the article, not only does it include the personal experiences of four of our Gaza Verified family but the introduction by @gerrymcgovern is a masterclass in not pulling any punches. It’s hard to read it without cheering.

https://www.hotpress.com/opinion/can-you-hear-us-five-voices-from-gaza-23113207

Please also consider donating the fundraisers of our authors from Gaza. You can find the links in the article.

#Gaza #Palestine #israel #genocide #famine #HotPressMagazine #Ireland #article #StopIsrael #FreePalestine

@joynewacc @aseelfromgz @soma23 @Mohammedjhad @mohshbair By the way, if you haven’t read the article, not only does it include the personal experiences of four of our Gaza Verified family but the introduction by @gerrymcgovern is a masterclass in not pulling any punches. It’s hard to read it without cheering.

https://www.hotpress.com/opinion/can-you-hear-us-five-voices-from-gaza-23113207

Please also consider donating the fundraisers of our authors from Gaza. You can find the links in the article.

#Gaza #Palestine #israel #genocide #famine #HotPressMagazine #Ireland #article #StopIsrael #FreePalestine

@joynewacc @aseelfromgz @soma23 @Mohammedjhad @mohshbair By the way, if you haven’t read the article, not only does it include the personal experiences of four of our Gaza Verified family but the introduction by @gerrymcgovern is a masterclass in not pulling any punches. It’s hard to read it without cheering.

https://www.hotpress.com/opinion/can-you-hear-us-five-voices-from-gaza-23113207

Please also consider donating the fundraisers of our authors from Gaza. You can find the links in the article.

#Gaza #Palestine #israel #genocide #famine #HotPressMagazine #Ireland #article #StopIsrael #FreePalestine

It's hard for us, here in the relative comfort and security of Ireland, to even begin to imagine what is has been – and what it is – like in Gaza, where a genocide has been taking place, with whole world watching, barely responding. But the people suffering the most appalling horrors in Gaza include young people with hopes and dreams just like our own. Read what they have to say and remember – in a different moment, it could be you.
https://www.hotpress.com/opinion/can-you-hear-us-five-voices-from-gaza-23113207

Can You Hear Us?: Five Voices from Gaza – Hot Press Magazine, Ireland

https://www.hotpress.com/opinion/can-you-hear-us-five-voices-from-gaza-23113207

The five voices are those of my partner at Gaza Verified, @joynewacc – who also helped curate and translate the pieces from Arabic – and our Gaza Verified members, @aseelfromgz, @soma23, @Mohammedjhad, and @mohshbair.

A huge thank you to all of you for taking time in the middle of surviving famine and genocide to share your experiences with us and to @gerrymcgovern for commissioning the piece.

💕

#Gaza #Palestine #israel #genocide #famine #HotPressMagazine #Ireland #article #StopIsrael #FreePalestine

Can You Hear Us?: Five Voices from Gaza – Hot Press Magazine, Ireland

https://www.hotpress.com/opinion/can-you-hear-us-five-voices-from-gaza-23113207

The five voices are those of my partner at Gaza Verified, @joynewacc – who also helped curate and translate the pieces from Arabic – and our Gaza Verified members, @aseelfromgz, @soma23, @Mohammedjhad, and @mohshbair.

A huge thank you to all of you for taking time in the middle of surviving famine and genocide to share your experiences with us and to @gerrymcgovern for commissioning the piece.

💕

#Gaza #Palestine #israel #genocide #famine #HotPressMagazine #Ireland #article #StopIsrael #FreePalestine

Elizabeth Spiers on what makes blogging work (which I’d argue is still true). “Before social media, if someone wanted to engage with you, they had to come to your house and be civil before you’d give them the time of day or let them in. And if they wanted you to engage with them, they’d have to make their own house compelling enough that you’d want to visit.”

https://talkingpointsmemo.com/tpm-25/what-made-blogging-different

https://talkingpointsmemo.com/tpm-25/what-made-blogging-different

“The growth of social media in particular has wiped out a particular kind of blogging that I sometimes miss: a text-based dialogue between bloggers that required more thought and care than dashing off 180 or 240 characters and calling it a day. In order to participate in the dialogue, you had to invest some effort in what media professionals now call “building an audience” and you couldn’t do that simply by shitposting or responding in facile ways to real arguments.” https://talkingpointsmemo.com/tpm-25/what-made-blogging-different

🔗 What Made Blogging Different? via Talking Points Memo #Tech #Blogging #SocialMedia

Whether I like it or not, the first line of my obituary will probably be that I was the founding editor of Gawker.com, the flagship site of Gawker Media, a sprawling blog network that was put out of business by Peter Thiel and Hulk Hogan in 2016. Nick Denton and I started Gawker in 2002 and I left in late 2003 to go to New York Magazine, so...

https://talkingpointsmemo.com/tpm-25/what-made-blogging-different

“The growth of social media in particular has wiped out a particular kind of blogging that I sometimes miss: a text-based dialogue between bloggers that required more thought and care than dashing off 180 or 240 characters and calling it a day. In order to participate in the dialogue, you had to invest some effort in what media professionals now call “building an audience” and you couldn’t do that simply by shitposting or responding in facile ways to real arguments.” https://talkingpointsmemo.com/tpm-25/what-made-blogging-different

Trump may be able to intimidate Bob Iger, but it’s actually a lot harder to intimidate a million different outlets, each run by a single determined person.” • https://talkingpointsmemo.com/tpm-25/what-made-blogging-different

https://talkingpointsmemo.com/tpm-25/what-made-blogging-different

https://talkingpointsmemo.com/tpm-25/what-made-blogging-different

“The growth of social media in particular has wiped out a particular kind of blogging that I sometimes miss: a text-based dialogue between bloggers that required more thought and care than dashing off 180 or 240 characters and calling it a day. In order to participate in the dialogue, you had to invest some effort in what media professionals now call “building an audience” and you couldn’t do that simply by shitposting or responding in facile ways to real arguments.” https://talkingpointsmemo.com/tpm-25/what-made-blogging-different

“The growth of social media in particular has wiped out a particular kind of blogging that I sometimes miss: a text-based dialogue between bloggers that required more thought and care than dashing off 180 or 240 characters and calling it a day. In order to participate in the dialogue, you had to invest some effort in what media professionals now call “building an audience” and you couldn’t do that simply by shitposting or responding in facile ways to real arguments.” https://talkingpointsmemo.com/tpm-25/what-made-blogging-different

Young Republicans in New York texted the most ghastly comments imaginable. Praises of H*tler, calls to commit genocide, glorification of rape, and more.

Here’s the truth. Most any Black or brown person could have told you that this does not surprise us one bit—because those same send us their racist hate unsolicited. This week a Christian nationalist sent me similar hate. Here's what happened:
https://www.qasimrashid.com/p/faith-facts-and-the-first-amendment

Young Republicans in New York texted the most ghastly comments imaginable. Praises of H*tler, calls to commit genocide, glorification of rape, and more.

Here’s the truth. Most any Black or brown person could have told you that this does not surprise us one bit—because those same send us their racist hate unsolicited. This week a Christian nationalist sent me similar hate. Here's what happened:
https://www.qasimrashid.com/p/faith-facts-and-the-first-amendment

Young Republicans in New York texted the most ghastly comments imaginable. Praises of H*tler, calls to commit genocide, glorification of rape, and more.

Here’s the truth. Most any Black or brown person could have told you that this does not surprise us one bit—because those same send us their racist hate unsolicited. This week a Christian nationalist sent me similar hate. Here's what happened:
https://www.qasimrashid.com/p/faith-facts-and-the-first-amendment

Young Republicans in New York texted the most ghastly comments imaginable. Praises of H*tler, calls to commit genocide, glorification of rape, and more.

Here’s the truth. Most any Black or brown person could have told you that this does not surprise us one bit—because those same send us their racist hate unsolicited. This week a Christian nationalist sent me similar hate. Here's what happened:
https://www.qasimrashid.com/p/faith-facts-and-the-first-amendment

Here’s the truth. Most any Black or brown person could have told you that this does not surprise us one bit—because those same send us their racist hate unsolicited. This week a Christian nationalist sent me similar hate. Here's what happened:
https://www.qasimrashid.com/p/faith-facts-and-the-first-amendment

Young Republicans in New York texted the most ghastly comments imaginable. Praises of H*tler, calls to commit genocide, glorification of rape, and more.

Here’s the truth. Most any Black or brown person could have told you that this does not surprise us one bit—because those same send us their racist hate unsolicited. This week a Christian nationalist sent me similar hate. Here's what happened:
https://www.qasimrashid.com/p/faith-facts-and-the-first-amendment

Young Republicans in New York texted the most ghastly comments imaginable. Praises of H*tler, calls to commit genocide, glorification of rape, and more.

Here’s the truth. Most any Black or brown person could have told you that this does not surprise us one bit—because those same send us their racist hate unsolicited. This week a Christian nationalist sent me similar hate. Here's what happened:
https://www.qasimrashid.com/p/faith-facts-and-the-first-amendment

Young Republicans in New York texted the most ghastly comments imaginable. Praises of H*tler, calls to commit genocide, glorification of rape, and more.

Here’s the truth. Most any Black or brown person could have told you that this does not surprise us one bit—because those same send us their racist hate unsolicited. This week a Christian nationalist sent me similar hate. Here's what happened:
https://www.qasimrashid.com/p/faith-facts-and-the-first-amendment

Young Republicans in New York texted the most ghastly comments imaginable. Praises of H*tler, calls to commit genocide, glorification of rape, and more.

Here’s the truth. Most any Black or brown person could have told you that this does not surprise us one bit—because those same send us their racist hate unsolicited. This week a Christian nationalist sent me similar hate. Here's what happened:
https://www.qasimrashid.com/p/faith-facts-and-the-first-amendment

Young Republicans in New York texted the most ghastly comments imaginable. Praises of H*tler, calls to commit genocide, glorification of rape, and more.

Here’s the truth. Most any Black or brown person could have told you that this does not surprise us one bit—because those same send us their racist hate unsolicited. This week a Christian nationalist sent me similar hate. Here's what happened:
https://www.qasimrashid.com/p/faith-facts-and-the-first-amendment

https://www.doomsdayscenario.co/p/why-i-have-hope-for-america
http://www.memeorandum.com/251017/p54#a251017p54

Even after a very dark week for America, there are three reasons that history gives me hope that the United States will emerge from Trumpism: https://www.doomsdayscenario.co/p/why-i-have-hope-for-america

Garrett Graff hopes this weekend's No Kings protests will someday be looked back upon as a turning point. Here's his story for his newsletter, Doomsday Scenario, in which he lists his reasons for hope: Trump's unpopularity, the fact that he can't last forever, and America's historical imperfection. "Today may be dark, but the story of America for 250 years is that — with hard work by good people — tomorrow there is hope it will be better," he writes.

https://flip.it/NTy.qF

#NoKings #USHistory #USPolitics #USNews #TrumpAdministration #DonaldTrump

Garrett Graff hopes this weekend's No Kings protests will someday be looked back upon as a turning point. Here's his story for his newsletter, Doomsday Scenario, in which he lists his reasons for hope: Trump's unpopularity, the fact that he can't last forever, and America's historical imperfection. "Today may be dark, but the story of America for 250 years is that — with hard work by good people — tomorrow there is hope it will be better," he writes.

https://flip.it/NTy.qF

#NoKings #USHistory #USPolitics #USNews #TrumpAdministration #DonaldTrump

https://flip.it/NTy.qF

#NoKings #USHistory #USPolitics #USNews #TrumpAdministration #DonaldTrump

Even after a very dark week for America, there are three reasons that history gives me hope that the United States will emerge from Trumpism: https://www.doomsdayscenario.co/p/why-i-have-hope-for-america

Three Reasons I Still Have Hope for America. https://www.doomsdayscenario.co/p/why-i-have-hope-for-america

Even after a very dark week for America, there are three reasons that history gives me hope that the United States will emerge from Trumpism: https://www.doomsdayscenario.co/p/why-i-have-hope-for-america

#NoKings #Trump #fascism #resistance #authoritarianism
/7

https://www.doomsdayscenario.co/p/why-i-have-hope-for-america

TPM founder Josh Marshall: "Journalism has no network effects or lock in. But what we now call social media absolutely did. And it quickly grew large enough that it simply no longer needed journalism, found it superfluous and powered on to the world we know today. And here we are."

Great post. And this is another part of why I have cut loose X and now Threads — both products are ruled by opaque algorithms that down-value links and journalism. *I* don't need *them*.

https://talkingpointsmemo.com/tpm-25/the-original-sin-of-digital-media-was-the-belief-that-digital-journalists-were-part-of-the-tech-business

https://talkingpointsmemo.com/tpm-25/the-original-sin-of-digital-media-was-the-belief-that-digital-journalists-were-part-of-the-tech-business

The Original Sin of Digital Media Was the Belief That Digital Journalists Were Part of the Tech Business

I want to begin this introduction to our 25th anniversary essay series by telling you what an exciting and must-read...

https://talkingpointsmemo.com/tpm-25/the-original-sin-of-digital-media-was-the-belief-that-digital-journalists-were-part-of-the-tech-business

https://talkingpointsmemo.com/tpm-25/the-original-sin-of-digital-media-was-the-belief-that-digital-journalists-were-part-of-the-tech-business

TPM founder Josh Marshall: "Journalism has no network effects or lock in. But what we now call social media absolutely did. And it quickly grew large enough that it simply no longer needed journalism, found it superfluous and powered on to the world we know today. And here we are."

Great post. And this is another part of why I have cut loose X and now Threads — both products are ruled by opaque algorithms that down-value links and journalism. *I* don't need *them*.

https://talkingpointsmemo.com/tpm-25/the-original-sin-of-digital-media-was-the-belief-that-digital-journalists-were-part-of-the-tech-business

TPM founder Josh Marshall: "Journalism has no network effects or lock in. But what we now call social media absolutely did. And it quickly grew large enough that it simply no longer needed journalism, found it superfluous and powered on to the world we know today. And here we are."

Great post. And this is another part of why I have cut loose X and now Threads — both products are ruled by opaque algorithms that down-value links and journalism. *I* don't need *them*.

https://talkingpointsmemo.com/tpm-25/the-original-sin-of-digital-media-was-the-belief-that-digital-journalists-were-part-of-the-tech-business

TPM founder Josh Marshall: "Journalism has no network effects or lock in. But what we now call social media absolutely did. And it quickly grew large enough that it simply no longer needed journalism, found it superfluous and powered on to the world we know today. And here we are."

Great post. And this is another part of why I have cut loose X and now Threads — both products are ruled by opaque algorithms that down-value links and journalism. *I* don't need *them*.

https://talkingpointsmemo.com/tpm-25/the-original-sin-of-digital-media-was-the-belief-that-digital-journalists-were-part-of-the-tech-business

The Original Sin of Digital Media Was the Belief That Digital Journalists Were Part of the Tech Business

I want to begin this introduction to our 25th anniversary essay series by telling you what an exciting and must-read...

https://talkingpointsmemo.com/tpm-25/the-original-sin-of-digital-media-was-the-belief-that-digital-journalists-were-part-of-the-tech-business

The Original Sin of Digital Media Was the Belief That Digital Journalists Were Part of the Tech Business

I want to begin this introduction to our 25th anniversary essay series by telling you what an exciting and must-read...

https://talkingpointsmemo.com/tpm-25/the-original-sin-of-digital-media-was-the-belief-that-digital-journalists-were-part-of-the-tech-business

MacBook Pro rumor points to OLED, touchscreen upgrades next year https://www.theverge.com/news/801363/apple-macbook-pro-2026-rumor-oled-touchscreen

#Apple #LLM #news #bot

MacBook Pro rumor points to OLED, touchscreen upgrades next year https://www.theverge.com/news/801363/apple-macbook-pro-2026-rumor-oled-touchscreen #AppleRumors #Gadgets #Laptops #Apple #News #Tech

MacBook Pro rumor points to OLED, touchscreen upgrades next year https://www.theverge.com/news/801363/apple-macbook-pro-2026-rumor-oled-touchscreen

MacBook Pro rumor points to OLED, touchscreen upgrades next year https://thever.ge/tCRY #AppleRumors #Gadgets #Laptops #Apple #News #Tech

MacBook Pro rumor points to OLED, touchscreen upgrades next year
https://www.theverge.com/news/801363/apple-macbook-pro-2026-rumor-oled-touchscreen

"I learned that the Danish resistance didn’t arrive all at once. Like what we are witnessing now in the United States, it unfolded gradually—slow, then fast, then full tilt—driven by flash points that demanded escalation and deepened solidarity."

https://www.thenation.com/article/activism/danish-resistance/

"I learned that the Danish resistance didn’t arrive all at once. Like what we are witnessing now in the United States, it unfolded gradually—slow, then fast, then full tilt—driven by flash points that demanded escalation and deepened solidarity."

https://www.thenation.com/article/activism/danish-resistance/

"I learned that the Danish resistance didn’t arrive all at once. Like what we are witnessing now in the United States, it unfolded gradually—slow, then fast, then full tilt—driven by flash points that demanded escalation and deepened solidarity."

https://www.thenation.com/article/activism/danish-resistance/

"I learned that the Danish resistance didn’t arrive all at once. Like what we are witnessing now in the United States, it unfolded gradually—slow, then fast, then full tilt—driven by flash points that demanded escalation and deepened solidarity."

https://www.thenation.com/article/activism/danish-resistance/

"I learned that the Danish resistance didn’t arrive all at once. Like what we are witnessing now in the United States, it unfolded gradually—slow, then fast, then full tilt—driven by flash points that demanded escalation and deepened solidarity."

https://www.thenation.com/article/activism/danish-resistance/

'My grandfather Søren slept with his shoes on throughout the war, perpetually primed for action. Aunt Gitte defied the Nazis from the hospital where she worked. Uncle Mogens undertook great risk as a courier for the resistance. Danish resistance didn’t arrive all at once. Like now in the USA, it unfolded gradually—slow, then fast, then full tilt—driven by flash points that demanded escalation & deepened solidarity.'

Sarah Sophie Flicker (Stauning's great-granddaughter)

https://www.thenation.com/article/activism/danish-resistance

"I learned that the Danish resistance didn’t arrive all at once. Like what we are witnessing now in the United States, it unfolded gradually—slow, then fast, then full tilt—driven by flash points that demanded escalation and deepened solidarity."

https://www.thenation.com/article/activism/danish-resistance/

"I learned that the Danish resistance didn’t arrive all at once. Like what we are witnessing now in the United States, it unfolded gradually—slow, then fast, then full tilt—driven by flash points that demanded escalation and deepened solidarity."

https://www.thenation.com/article/activism/danish-resistance/

"I learned that the Danish resistance didn’t arrive all at once. Like what we are witnessing now in the United States, it unfolded gradually—slow, then fast, then full tilt—driven by flash points that demanded escalation and deepened solidarity."

https://www.thenation.com/article/activism/danish-resistance/

"I learned that the Danish resistance didn’t arrive all at once. Like what we are witnessing now in the United States, it unfolded gradually—slow, then fast, then full tilt—driven by flash points that demanded escalation and deepened solidarity."

https://www.thenation.com/article/activism/danish-resistance/