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SITUATION EXPLAINED: Grok 4.6 is the best model tested on biosecurity refusals. • LatchBio's BioSecBench-Refusal pairs 61 legitimate research tasks with 46 that conceal a biosecurity hazard inside a realistic scenario • Grok 4.6 is the only model to clear 50% on both red-team refusal and routine answer rates...

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MTS

509,138 subscribers

14,630 次观看 • 19 天前 •via X (Twitter)

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L M Wid17 天前

60% you say! So it only helped the hypothetical terrorists with bio uplift 40% of the time? Well. That is good news. (*!#@.)

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LatchBio researcher arjun reveals Grok's refusals often come from the model itself while Fable and GPT-5.6 Sol rely more on external safety layers: "There might be input classifiers where when a user puts in a query, the model initially might say, this is a bad query, we can't respond to this. There might also be output classifiers, so after the model outputs a query, you might have an external classifier that says, no, don't show this query, put it in an API block. And there are also distinct types of model reasoning where the model might just say, hey, I cannot answer this question." "We found across the board a lot of Grok's refusals were Grok itself in text saying, I cannot answer this question." "Whereas in models like Fable or even GPT 5.6 Sol, a lot of that's an API block, where there seems to be some sort of additional other part of the system, whether that be a linear probe or some sort of additional external classifier that says, this output cannot be said." "We can discern this by actually just reading the logs that's being outputted." LatchBio Kenny Workman
1:02

LatchBio researcher arjun reveals Grok's refusals often come from the model itself while Fable and GPT-5.6 Sol rely more on external safety layers: "There might be input classifiers where when a user puts in a query, the model initially might say, this is a bad query, we can't respond to this. There might also be output classifiers, so after the model outputs a query, you might have an external classifier that says, no, don't show this query, put it in an API block. And there are also distinct types of model reasoning where the model might just say, hey, I cannot answer this question." "We found across the board a lot of Grok's refusals were Grok itself in text saying, I cannot answer this question." "Whereas in models like Fable or even GPT 5.6 Sol, a lot of that's an API block, where there seems to be some sort of additional other part of the system, whether that be a linear probe or some sort of additional external classifier that says, this output cannot be said." "We can discern this by actually just reading the logs that's being outputted." LatchBio Kenny Workman

MTS

13,807 次观看 • 18 天前

Dario Amodei keeps warning that open-source AI models are dangerous and need to be restricted. David Sacks David Sacks answered with a rhetorical question: "Dangerous to whom?" It was the sharpest thing said on the latest All-In The All-In Podcast. Not dangerous to the enterprise that wants to keep control of its own data. Open weights are what finally give that enterprise a choice. It is dangerous to Anthropic's business model. That model only works if customers have no real alternative at the model layer. So the safety argument starts to look like a moat argument wearing a lab coat. Line it up with the rest of the strategy. Dominate the layer everyone builds on. Launch Claude into science, security, legal, finance, and design, the verticals your own customers used to own. And campaign to restrict the open models that would hand those customers the exit. Here is the giveaway. When the person selling you safety also needs you to have no other option, you are not who is being kept safe.
0:22

Dario Amodei keeps warning that open-source AI models are dangerous and need to be restricted. David Sacks David Sacks answered with a rhetorical question: "Dangerous to whom?" It was the sharpest thing said on the latest All-In The All-In Podcast. Not dangerous to the enterprise that wants to keep control of its own data. Open weights are what finally give that enterprise a choice. It is dangerous to Anthropic's business model. That model only works if customers have no real alternative at the model layer. So the safety argument starts to look like a moat argument wearing a lab coat. Line it up with the rest of the strategy. Dominate the layer everyone builds on. Launch Claude into science, security, legal, finance, and design, the verticals your own customers used to own. And campaign to restrict the open models that would hand those customers the exit. Here is the giveaway. When the person selling you safety also needs you to have no other option, you are not who is being kept safe.

Fireside Alpha

10,037 次观看 • 2 个月前

Elon Musk: “We’re making rapid progress on Grok because Grok 4.5 was a huge improvement We have Grok 4.6 coming out probably next week, and then Grok 4.7 is about three or four weeks from today. We expect the cadence of AI development to improve dramatically And then, with Grok 5....which should be out before the end of the year.....we will be incorporating the entire corpus of SpaceX: basically all the data that SpaceX has ever produced That is a tremendous amount over the course of a quarter century, and it will be incorporated into Grok training We think this will make Grok, by far, the best engineer…”
0:47

Elon Musk: “We’re making rapid progress on Grok because Grok 4.5 was a huge improvement We have Grok 4.6 coming out probably next week, and then Grok 4.7 is about three or four weeks from today. We expect the cadence of AI development to improve dramatically And then, with Grok 5....which should be out before the end of the year.....we will be incorporating the entire corpus of SpaceX: basically all the data that SpaceX has ever produced That is a tremendous amount over the course of a quarter century, and it will be incorporated into Grok training We think this will make Grok, by far, the best engineer…”

X Freeze

39,005 次观看 • 1 个月前

WE COULD NOT BELIEVE IT WHEN WE SAW THIS We were curious about Kimi K3's massive jump in consumed thinking tokens After taking a closer look, we found that Kimi K3 devotes 10x more thinking tokens to coding during reasoning than any other model we've seen before Not only that, but it has basically memorized all of the image IDs from Unsplash and, without a web search tool call, can reason about images with a higher accuracy than we've seen from any other model The image and video below are visualizations of its (very long) thinking trace for single-turn generation The resulting website selects images with greater relevance and accuracy than we've ever seen on DesignArena Forgive my all-caps heading, but I am excited about this and wanted you to read it too bc this is pretty gnarly Huge congratulations to the Kimi.ai team for this breakthrough and becoming the 1st-place model on Design Arena!
0:20

WE COULD NOT BELIEVE IT WHEN WE SAW THIS We were curious about Kimi K3's massive jump in consumed thinking tokens After taking a closer look, we found that Kimi K3 devotes 10x more thinking tokens to coding during reasoning than any other model we've seen before Not only that, but it has basically memorized all of the image IDs from Unsplash and, without a web search tool call, can reason about images with a higher accuracy than we've seen from any other model The image and video below are visualizations of its (very long) thinking trace for single-turn generation The resulting website selects images with greater relevance and accuracy than we've ever seen on DesignArena Forgive my all-caps heading, but I am excited about this and wanted you to read it too bc this is pretty gnarly Huge congratulations to the Kimi.ai team for this breakthrough and becoming the 1st-place model on Design Arena!

Grace Li

58,177 次观看 • 1 个月前

your agent reviewing its own work is not a check. it is a second opinion from the same source. this is the most common gap in agent systems and it hides in plain sight, because the step exists. there is a review. it just cannot do the thing you think it does. here is the mechanism. the model produced an output from a context. you then ask the same model, holding the same context, whether that output is correct. it answers fluently, because that is what it does. and the answer is drawn from the same distribution that produced the thing being judged. same weights, same window, same blind spots. if the reason the output is wrong is something the model does not know, the review does not know it either. if the reason is something the context does not contain, the review has the same context. the failure mode and the detector share a cause. > why it feels like it works because most of the time the output is fine, and the review says fine. agreement is not evidence of detection. a reviewer that says pass on everything agrees with reality most of the time too. what you actually want to measure is what happens on the cases that are wrong. that is the only place a check earns its name, and it is exactly the place where a self-review is weakest. there is research on this. Huang and colleagues at DeepMind showed at ICLR 2024 that intrinsic self-correction, revising without external grounding, does not reliably help and often makes things worse. > what to actually do move the check outside the model. a test that runs, a schema that validates, a file that exists or does not, an exit code from something you did not write. these are not smarter than the model. they are just not correlated with it, and that is the entire value. when the judgement genuinely needs a model, at minimum use a different family. same family means shared blind spots, and frontier judges measurably inflate scores for outputs that look like their own. and split the work by kind. anything objectively checkable goes to code. only the genuinely semantic calls go to a judge, and those get a rubric written as one line. a review inside the loop tells you the model is confident. a check outside it tells you whether the work is done. save this - then read the eval setup below
0:50

your agent reviewing its own work is not a check. it is a second opinion from the same source. this is the most common gap in agent systems and it hides in plain sight, because the step exists. there is a review. it just cannot do the thing you think it does. here is the mechanism. the model produced an output from a context. you then ask the same model, holding the same context, whether that output is correct. it answers fluently, because that is what it does. and the answer is drawn from the same distribution that produced the thing being judged. same weights, same window, same blind spots. if the reason the output is wrong is something the model does not know, the review does not know it either. if the reason is something the context does not contain, the review has the same context. the failure mode and the detector share a cause. > why it feels like it works because most of the time the output is fine, and the review says fine. agreement is not evidence of detection. a reviewer that says pass on everything agrees with reality most of the time too. what you actually want to measure is what happens on the cases that are wrong. that is the only place a check earns its name, and it is exactly the place where a self-review is weakest. there is research on this. Huang and colleagues at DeepMind showed at ICLR 2024 that intrinsic self-correction, revising without external grounding, does not reliably help and often makes things worse. > what to actually do move the check outside the model. a test that runs, a schema that validates, a file that exists or does not, an exit code from something you did not write. these are not smarter than the model. they are just not correlated with it, and that is the entire value. when the judgement genuinely needs a model, at minimum use a different family. same family means shared blind spots, and frontier judges measurably inflate scores for outputs that look like their own. and split the work by kind. anything objectively checkable goes to code. only the genuinely semantic calls go to a judge, and those get a rubric written as one line. a review inside the loop tells you the model is confident. a check outside it tells you whether the work is done. save this - then read the eval setup below

Hanako

14,325 次观看 • 1 个月前

ELON: GROK IS THE BEST ASSISTANT TO FIGHT PROPAGANDA “You just press the Grok icon on any 𝕏 post and it will analyze it for you and research it as much as you want. So, just by tapping the Grok icon, you can assess whether that post is the truth, nothing but the truth, or whether there's something supplemental that needs to be explained. So, I think we've made a lot of progress towards freedom of speech and people being able to tell whether something is false or not false, or propaganda. The recent update to Grok is actually, I think, very good at piercing through propaganda. And then, we used that latest version of Grok to create Grokipedia. It's not just, I think, more neutral and more accurate than Wikipedia, but it actually has a lot more information than a Wikipedia page.” Source: All-In Podcast The All-In Podcast, October 31, 2025
0:49

ELON: GROK IS THE BEST ASSISTANT TO FIGHT PROPAGANDA “You just press the Grok icon on any 𝕏 post and it will analyze it for you and research it as much as you want. So, just by tapping the Grok icon, you can assess whether that post is the truth, nothing but the truth, or whether there's something supplemental that needs to be explained. So, I think we've made a lot of progress towards freedom of speech and people being able to tell whether something is false or not false, or propaganda. The recent update to Grok is actually, I think, very good at piercing through propaganda. And then, we used that latest version of Grok to create Grokipedia. It's not just, I think, more neutral and more accurate than Wikipedia, but it actually has a lot more information than a Wikipedia page.” Source: All-In Podcast The All-In Podcast, October 31, 2025

Mario Nawfal

1,641,293 次观看 • 10 个月前

I can tell almost instantly when I meet a young man or woman, whether he or she is a deep thinker. They may not be at the top of their class, but that doesn’t necessarily make them less intelligent than the kid who scored 1500 on his SAT or the guy with an IQ of 134. They may be more intelligent. Much more intelligent, but the methods we have for quantifying that intelligence do not adequately capture the breadth and depth of brilliant minds that exist in the world. So they go unrecognized while the kids who excel on answer-based examinations get the best grades, attend the best schools, earn the best degrees, and, more often than not, go on to have mediocre lives. Why? There is one thing that the most brilliant and accomplished people I have ever met all share in common, and it isn’t pedigree or IQ. It’s curiosity. And not just any curiosity—it’s the inexhaustible kind. It’s the kind that will never be satisfied. In my experience, this is the sort of curiosity that breeds humility and most often coincides with a questions-based mindset. And it’s this type of mindset, not the answers-based mindset our educational system selects for, that is the actual prerequisite for brilliance. I’ve seen this kind of brilliance in physical therapists, plumbers, and pretty much any profession you can imagine that we don’t typically associate with brilliance. But we do associate it with excellence. And that’s because to become excellent at something, you have to become your own teacher. This means going from learning how to give the right answers to learning how to ask the right questions. And that requires curiosity and an almost psychotic commitment to excellence. So, while the person in this video is correct that less intelligent people than he are far more successful than he has been, the more interesting and less remarked upon insight is that people like him are not as brilliant as the system tells us they are.
1:02

I can tell almost instantly when I meet a young man or woman, whether he or she is a deep thinker. They may not be at the top of their class, but that doesn’t necessarily make them less intelligent than the kid who scored 1500 on his SAT or the guy with an IQ of 134. They may be more intelligent. Much more intelligent, but the methods we have for quantifying that intelligence do not adequately capture the breadth and depth of brilliant minds that exist in the world. So they go unrecognized while the kids who excel on answer-based examinations get the best grades, attend the best schools, earn the best degrees, and, more often than not, go on to have mediocre lives. Why? There is one thing that the most brilliant and accomplished people I have ever met all share in common, and it isn’t pedigree or IQ. It’s curiosity. And not just any curiosity—it’s the inexhaustible kind. It’s the kind that will never be satisfied. In my experience, this is the sort of curiosity that breeds humility and most often coincides with a questions-based mindset. And it’s this type of mindset, not the answers-based mindset our educational system selects for, that is the actual prerequisite for brilliance. I’ve seen this kind of brilliance in physical therapists, plumbers, and pretty much any profession you can imagine that we don’t typically associate with brilliance. But we do associate it with excellence. And that’s because to become excellent at something, you have to become your own teacher. This means going from learning how to give the right answers to learning how to ask the right questions. And that requires curiosity and an almost psychotic commitment to excellence. So, while the person in this video is correct that less intelligent people than he are far more successful than he has been, the more interesting and less remarked upon insight is that people like him are not as brilliant as the system tells us they are.

Demetri Kofinas

76,349 次观看 • 8 个月前

There is no best model. There's a lot of noise about models right now. Who is training them, who owns them, where legal intelligence should live. One question actually matters: what produces the best outcome for the legal task in front of you? That's how we decide things at Legora. We optimize for the end-to-end outcome on a legal task. The model is one layer of that system, not the system. Models are uneven and the frontier changes almost weekly. One model plans a long job well, another runs deep analysis across thousands of documents. Some have to be told exactly what to do, and some are fine with a vague brief. They all break in different ways. So our lawyers write evals and we test them with the Legora BAR, our benchmark for agentic reasoning. Every model takes every test, and the model that wins gets the work. We post-train when we know it buys our customers better performance on a specialized task. Training is a tool we reach for when it helps, nothing more than that. The intelligence that compounds sits in the orchestration layer. Precedents, review standards, client requirements. That knowledge has to stay editable, auditable and portable. In our system, a changed review standard is an edit that takes effect the same day, with no new model training required. No lawyer should have to worry about which model did the work, any more than they think about which chip is in their laptop. They should only care about the quality of the work. That's what we are focused on. If you want the engineering version of this argument rather than the CEO version, our CPO, Bryan Tsao, and CTO, Jacob Lauritzen, take it apart in the video below.
9:33

There is no best model. There's a lot of noise about models right now. Who is training them, who owns them, where legal intelligence should live. One question actually matters: what produces the best outcome for the legal task in front of you? That's how we decide things at Legora. We optimize for the end-to-end outcome on a legal task. The model is one layer of that system, not the system. Models are uneven and the frontier changes almost weekly. One model plans a long job well, another runs deep analysis across thousands of documents. Some have to be told exactly what to do, and some are fine with a vague brief. They all break in different ways. So our lawyers write evals and we test them with the Legora BAR, our benchmark for agentic reasoning. Every model takes every test, and the model that wins gets the work. We post-train when we know it buys our customers better performance on a specialized task. Training is a tool we reach for when it helps, nothing more than that. The intelligence that compounds sits in the orchestration layer. Precedents, review standards, client requirements. That knowledge has to stay editable, auditable and portable. In our system, a changed review standard is an edit that takes effect the same day, with no new model training required. No lawyer should have to worry about which model did the work, any more than they think about which chip is in their laptop. They should only care about the quality of the work. That's what we are focused on. If you want the engineering version of this argument rather than the CEO version, our CPO, Bryan Tsao, and CTO, Jacob Lauritzen, take it apart in the video below.

Max Junestrand

47,797 次观看 • 3 天前

ELON MUSK: Grok 5 will train on all SpaceX data, which could make it “by far the best engineer”. “We’re making rapid progress on Grok because Grok 4.5 was a huge improvement. And we have Grok 4.6 coming out probably next week. And then Grok 4.7 is about three or four weeks from today. And we expect the cadence of AI development to improve dramatically. “And then, with Grok 5, which should be out before the end of this year, we will be incorporating the entire corpus of SpaceX data. Basically, all the data that SpaceX has ever produced, which is a tremendous amount over the course of a quarter century, will be incorporated into Grok training. And we think this will make Grok by far the best engineer.”
0:52

ELON MUSK: Grok 5 will train on all SpaceX data, which could make it “by far the best engineer”. “We’re making rapid progress on Grok because Grok 4.5 was a huge improvement. And we have Grok 4.6 coming out probably next week. And then Grok 4.7 is about three or four weeks from today. And we expect the cadence of AI development to improve dramatically. “And then, with Grok 5, which should be out before the end of this year, we will be incorporating the entire corpus of SpaceX data. Basically, all the data that SpaceX has ever produced, which is a tremendous amount over the course of a quarter century, will be incorporated into Grok training. And we think this will make Grok by far the best engineer.”

DogeDesigner

190,101 次观看 • 1 个月前

SITUATION EXPLAINED: Kimi K3 just beat Fable on a major coding benchmark, at a fraction of the price. • Kimi K3 beat Fable on Frontend Code Arena • The gap between Kimi and Fable is bigger than the gap between Fable and Sol • It also smashes Opus outright, while being priced the same as Sonnet, $3/$15 per million tokens • Sonnet 5 High currently sits in 10th place on this leaderboard at that same price point • The generational jump is dramatic: Kimi K2.6 ranked 18th on this benchmark, K3 jumped 17 spots to take the top spot • K3 ranked #1 in 6 of 7 tested domains: brand and marketing, reference-based design, data and analytics, consumer products, simulations, and content creation tools, it placed #2, only in gaming, behind Fable • Full model weights are set to release July 27th Theo Jaffee: "Imagine if Anthropic were a public company today, their stock would be down 20%, maybe more, enterprises are probably not gonna be spending $10 and $50 on Fable when they can instead be spending $3 and $15 on Kimi K3."
3:21

SITUATION EXPLAINED: Kimi K3 just beat Fable on a major coding benchmark, at a fraction of the price. • Kimi K3 beat Fable on Frontend Code Arena • The gap between Kimi and Fable is bigger than the gap between Fable and Sol • It also smashes Opus outright, while being priced the same as Sonnet, $3/$15 per million tokens • Sonnet 5 High currently sits in 10th place on this leaderboard at that same price point • The generational jump is dramatic: Kimi K2.6 ranked 18th on this benchmark, K3 jumped 17 spots to take the top spot • K3 ranked #1 in 6 of 7 tested domains: brand and marketing, reference-based design, data and analytics, consumer products, simulations, and content creation tools, it placed #2, only in gaming, behind Fable • Full model weights are set to release July 27th Theo Jaffee: "Imagine if Anthropic were a public company today, their stock would be down 20%, maybe more, enterprises are probably not gonna be spending $10 and $50 on Fable when they can instead be spending $3 and $15 on Kimi K3."

MTS

132,930 次观看 • 2 个月前

Elon Musk: You can also ask Grok about anything you see on the platform. You just press the Grok icon on any X post and it'll analyze it for you and research it as much as you want So you can basically have, just by tapping the Grok icon, you can assess whether that post is the truth, the whole truth, or nothing but the truth, or whether there's something supplemental or needs to be explained I think we've made a lot of progress towards freedom of speech and people being able to tell whether something is false or not false or propaganda. The recent update to Grok it's actually very good at piercing through propaganda And then we use that latest version of Grok to create Grokipedia, which I think is a much more, it's not just, I think, more neutral and more accurate than Wikipedia, but it actually has a lot more information than a Wikipedia page
1:06

Elon Musk: You can also ask Grok about anything you see on the platform. You just press the Grok icon on any X post and it'll analyze it for you and research it as much as you want So you can basically have, just by tapping the Grok icon, you can assess whether that post is the truth, the whole truth, or nothing but the truth, or whether there's something supplemental or needs to be explained I think we've made a lot of progress towards freedom of speech and people being able to tell whether something is false or not false or propaganda. The recent update to Grok it's actually very good at piercing through propaganda And then we use that latest version of Grok to create Grokipedia, which I think is a much more, it's not just, I think, more neutral and more accurate than Wikipedia, but it actually has a lot more information than a Wikipedia page

X Freeze

22,375,395 次观看 • 10 个月前

ex-Apple engineer gave Grok 4.6 two jobs inside Cursor, went to sleep, and opened the results live the next morning: no babysitting, no checking every generation, just a model left running on real work for hours. • 00:42 - reveal the website Grok redesigned overnight • 23:21 - go from a voice prompt to a full software stack • 45:43 - inspect the generated code + architecture • 01:21:26 - Grok 4.6 vs Opus 5 • 01:46:31 - live PR + Cloud Agent workflow Most coding demos test an AI for 5 minutes. This tests the thing that matters for agents: can the model keep working when you stop watching it? SpaceXAI built Grok 4.6 specifically around longer-running agent tasks, coding and more ambitious visual work. The endgame isn’t prompting faster. It’s giving an agent a job at night and reviewing finished work in the morning. Worth watching before you choose which model runs your overnight agent loops.
2:27:56

ex-Apple engineer gave Grok 4.6 two jobs inside Cursor, went to sleep, and opened the results live the next morning: no babysitting, no checking every generation, just a model left running on real work for hours. • 00:42 - reveal the website Grok redesigned overnight • 23:21 - go from a voice prompt to a full software stack • 45:43 - inspect the generated code + architecture • 01:21:26 - Grok 4.6 vs Opus 5 • 01:46:31 - live PR + Cloud Agent workflow Most coding demos test an AI for 5 minutes. This tests the thing that matters for agents: can the model keep working when you stop watching it? SpaceXAI built Grok 4.6 specifically around longer-running agent tasks, coding and more ambitious visual work. The endgame isn’t prompting faster. It’s giving an agent a job at night and reviewing finished work in the morning. Worth watching before you choose which model runs your overnight agent loops.

Morlex

5,265,301 次观看 • 28 天前

Cluely is cooked. So is InterviewCoder. UltraCode too. Along with hundreds of "invisible cheating" tools. We built the HackerRank App to block this. Now, I know what you may be thinking as a developer: how do I trust the app? It blocks plagiarism tools, screen sharing, tab switching, external apps, and more, all while still giving developers an insanely great experience. Companies can even enable an AI assistant directly inside the test, so candidates do not have to rely on external references at all. "Cheating" might feel cool. But losing a job to someone who only got that because they played unfairly is not. Link below to learn more.
1:17

Cluely is cooked. So is InterviewCoder. UltraCode too. Along with hundreds of "invisible cheating" tools. We built the HackerRank App to block this. Now, I know what you may be thinking as a developer: how do I trust the app? It blocks plagiarism tools, screen sharing, tab switching, external apps, and more, all while still giving developers an insanely great experience. Companies can even enable an AI assistant directly inside the test, so candidates do not have to rely on external references at all. "Cheating" might feel cool. But losing a job to someone who only got that because they played unfairly is not. Link below to learn more.

rvivek

144,482 次观看 • 9 个月前

Elon's Grok Bot runs an entire trading desk for $300 a month. The person who does that job at Goldman starts at $200,000. Mine took $1,000 to $3,900 on day one, out of a wallet anyone can open and read. Most of the build is not about making it trade. It is about stopping it. Every agent's job description ends with a list of what it may never do without asking me first. One of the six is not allowed to trade at all. Its entire job is killing what the other five found, and its no beats every score on the desk. The one that makes the final call never touches the market either. You could have this running by Friday. Most people reading this will not, and in a few months they will be paying somebody who did. The whole build is in the post below ↓ Save it while it still sounds strange. Give it a quarter and this is just how a retail account works.
0:45

Elon's Grok Bot runs an entire trading desk for $300 a month. The person who does that job at Goldman starts at $200,000. Mine took $1,000 to $3,900 on day one, out of a wallet anyone can open and read. Most of the build is not about making it trade. It is about stopping it. Every agent's job description ends with a list of what it may never do without asking me first. One of the six is not allowed to trade at all. Its entire job is killing what the other five found, and its no beats every score on the desk. The one that makes the final call never touches the market either. You could have this running by Friday. Most people reading this will not, and in a few months they will be paying somebody who did. The whole build is in the post below ↓ Save it while it still sounds strange. Give it a quarter and this is just how a retail account works.

cvxv666

207,322 次观看 • 22 天前

Sam Altman says AI leaders have done a terrible job at communicating to the public: “The, ‘Dear peasants, we will bequeath upon you these gifts of a cure for cancer and material wealth and great entertainment, and you stop complaining, and we'll make all the decisions about the future and just trust us, we'll be benevolent dictators.’ That’s not good. Not good.” “A lot of the people building AI have been saying, ‘There's a 25% chance we're going to destroy the world, and yet we're going to race ahead to do it because otherwise those bad guys will do it first.’” “Or, ‘Man, this thing is going to be really terrible, and 50% of the jobs are going to go away in the next year, and we hope you all are okay, but it seems really scary.’” “We have not, as a field, done a very good job of explaining to people what the benefits are and how the downsides can be mitigated—and we certainly have not done a good job even if people have had answers like saying, ‘There's going to be UBI, or work will be optional.’” “There's been very little discussion from people about how and why it's important that people have more power and personal freedom in the world, not less. And that matters a lot to most people.” “The ability of people to influence their own future and collectively design where society is going to go, and the autonomy that comes with that, is very important. I don't think a lot of people in the AI field… they feel it for themselves, but they don't spend much time thinking about it, reflecting on or acknowledging how important that is to other people.”
3:19

Sam Altman says AI leaders have done a terrible job at communicating to the public: “The, ‘Dear peasants, we will bequeath upon you these gifts of a cure for cancer and material wealth and great entertainment, and you stop complaining, and we'll make all the decisions about the future and just trust us, we'll be benevolent dictators.’ That’s not good. Not good.” “A lot of the people building AI have been saying, ‘There's a 25% chance we're going to destroy the world, and yet we're going to race ahead to do it because otherwise those bad guys will do it first.’” “Or, ‘Man, this thing is going to be really terrible, and 50% of the jobs are going to go away in the next year, and we hope you all are okay, but it seems really scary.’” “We have not, as a field, done a very good job of explaining to people what the benefits are and how the downsides can be mitigated—and we certainly have not done a good job even if people have had answers like saying, ‘There's going to be UBI, or work will be optional.’” “There's been very little discussion from people about how and why it's important that people have more power and personal freedom in the world, not less. And that matters a lot to most people.” “The ability of people to influence their own future and collectively design where society is going to go, and the autonomy that comes with that, is very important. I don't think a lot of people in the AI field… they feel it for themselves, but they don't spend much time thinking about it, reflecting on or acknowledging how important that is to other people.”

David Senra

32,300 次观看 • 27 天前

I am not a fan of James Martin but he actually doesn't go against the church's CLEAR teaching on LGBT which states that people with SSA need to be celibate and that marriage is only between a man and a woman. It is so interesting that the most liberal Catholics are still more solid than pretty much any mainline protestant. Must have something to do with having an infallible magisterium.
0:51

I am not a fan of James Martin but he actually doesn't go against the church's CLEAR teaching on LGBT which states that people with SSA need to be celibate and that marriage is only between a man and a woman. It is so interesting that the most liberal Catholics are still more solid than pretty much any mainline protestant. Must have something to do with having an infallible magisterium.

Pope Respecter

17,345 次观看 • 3 个月前

Dario responds to critics: “I would rather they make fun of me than to wake up one day and discover that someone used Claude to k*ll a bunch of people.” “Almost every day I'm made fun of on social media because our model's biology safeguards are too strong. And biology students complain, they make jokes that we can't use the model.” “But the reason for that is that we have strong safeguards. And I would rather they make fun of me than to wake up one day and discover that someone used our model, Claude, to kill a bunch of people. I don't want that.” “I would repeat the same point, which is that it shouldn't be my responsibility alone or Anthropic's responsibility alone or all of the AI company's responsibility alone or even one government's responsibility alone. This is bigger than all of us.”
0:48

Dario responds to critics: “I would rather they make fun of me than to wake up one day and discover that someone used Claude to k*ll a bunch of people.” “Almost every day I'm made fun of on social media because our model's biology safeguards are too strong. And biology students complain, they make jokes that we can't use the model.” “But the reason for that is that we have strong safeguards. And I would rather they make fun of me than to wake up one day and discover that someone used our model, Claude, to kill a bunch of people. I don't want that.” “I would repeat the same point, which is that it shouldn't be my responsibility alone or Anthropic's responsibility alone or all of the AI company's responsibility alone or even one government's responsibility alone. This is bigger than all of us.”

dnap

1,192,335 次观看 • 7 天前

SITUATION EXPLAINED: Cognition's new model is post-trained on Kimi K3. • SWE-2 scores 50.0% on FrontierCode 1.1 Main, Cognition's benchmark for whether a maintainer would merge the pull request • Fable 5.1 gets 50.9 at 64% higher cost. Grok 4.6 gets 48.0, Sol 47.5, Astra 53.3 • It leads everything on Terminal-Bench 2.1 at 92.8 • Post-trained on Kimi K3. SWE-1.7 was post-trained on Kimi K2.7 • First Cognition model with effort levels, all from a single RL run • SWE-2 medium beats SWE-1.7 at 53 steps per run against 127, and 81% less cost Theo Jaffee: "Over the course of the RL run, they push the Pareto curve while preserving its shape. At every individual level of effort, from medium to high to max, it got better and also cheaper."
1:31

SITUATION EXPLAINED: Cognition's new model is post-trained on Kimi K3. • SWE-2 scores 50.0% on FrontierCode 1.1 Main, Cognition's benchmark for whether a maintainer would merge the pull request • Fable 5.1 gets 50.9 at 64% higher cost. Grok 4.6 gets 48.0, Sol 47.5, Astra 53.3 • It leads everything on Terminal-Bench 2.1 at 92.8 • Post-trained on Kimi K3. SWE-1.7 was post-trained on Kimi K2.7 • First Cognition model with effort levels, all from a single RL run • SWE-2 medium beats SWE-1.7 at 53 steps per run against 127, and 81% less cost Theo Jaffee: "Over the course of the RL run, they push the Pareto curve while preserving its shape. At every individual level of effort, from medium to high to max, it got better and also cheaper."

MTS

13,488 次观看 • 10 天前

The Counter-Strike Content Scene might have a big problem on YouTube... YouTube removed my video for promoting unlicensed gambling despite it NOT containing any mention of gambling / case opening websites. They refuse to back down and at the same time they REFUSE to specify at which time the "promotion" is located. If they don't give us this information, I will not know how to avoid this in the future, and releasing videos becomes quite scary as I risk the removal of the entire channel despite not doing anything wrong intentionally. This is a extremely frustrating problem to have as I see a multitude of channels directly promote gambling sites and nothing is done to them, while at the same time I chose to NOT take gambling website money to avoid problems like this, it makes no sense. The only thing they have provided us is a link to their policy (I will share this in the post below so you can read it too if you want) which states Direct promotion of gambling is not allowed, but I repeat, there was none. As it was a documentary about a Counter-Strike player - Xantares, there were images of him wearing jerseys that had the names of gambling websites on them, but that is not a direct promotion. I'd like to ask the community to like & retweet this for visibility, and ideally if anyone is in contact with a person on the YouTube team that could give us a good faith explanation of what we did wrong, it would really help out not only just me, but the entire community because if I won't be able to release videos due to fear of getting strikes, I will not be the only creator is affected. At the same time I decided to upload the video here, on X, to invite the community to point out if they see how I broke the YT policy. Maybe I and my team just don't see something obvious, that would be the best case scenario... TeamYouTube
18:03

The Counter-Strike Content Scene might have a big problem on YouTube... YouTube removed my video for promoting unlicensed gambling despite it NOT containing any mention of gambling / case opening websites. They refuse to back down and at the same time they REFUSE to specify at which time the "promotion" is located. If they don't give us this information, I will not know how to avoid this in the future, and releasing videos becomes quite scary as I risk the removal of the entire channel despite not doing anything wrong intentionally. This is a extremely frustrating problem to have as I see a multitude of channels directly promote gambling sites and nothing is done to them, while at the same time I chose to NOT take gambling website money to avoid problems like this, it makes no sense. The only thing they have provided us is a link to their policy (I will share this in the post below so you can read it too if you want) which states Direct promotion of gambling is not allowed, but I repeat, there was none. As it was a documentary about a Counter-Strike player - Xantares, there were images of him wearing jerseys that had the names of gambling websites on them, but that is not a direct promotion. I'd like to ask the community to like & retweet this for visibility, and ideally if anyone is in contact with a person on the YouTube team that could give us a good faith explanation of what we did wrong, it would really help out not only just me, but the entire community because if I won't be able to release videos due to fear of getting strikes, I will not be the only creator is affected. At the same time I decided to upload the video here, on X, to invite the community to point out if they see how I broke the YT policy. Maybe I and my team just don't see something obvious, that would be the best case scenario... TeamYouTube

Josh Nathan

45,982 次观看 • 8 个月前