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We’re introducing SEAL Showdown, the AI leaderboard that actually captures real preferences, powered by a platform used by real people. Public benchmarks today rely on contrived tasks or narrow user groups. That leaves us guessing which models are actually preferred by people. SEAL Showdown changes that. Model performance can... show more

Jason Droege

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19,832 views • 9 months ago •via X (Twitter)

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Goodbye manus The next generation of ai agents wont be judged by how smart they are they will be judged by ROTI, return on token investment AllyHub just turned that into a real metric, the same task costing less every single time you run it A thread on the agent that gets cheaper the more you use it

Goodbye manus The next generation of ai agents wont be judged by how smart they are they will be judged by ROTI, return on token investment AllyHub just turned that into a real metric, the same task costing less every single time you run it A thread on the agent that gets cheaper the more you use it

Nawi

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Around 1 billion computer-based jobs are at risk of being replaced by AI. This shift is happening whether people are ready for it or not. The real question isn’t if it happens. It’s who owns the value created by it. Right now, that value flows to a handful of billionaires and Big Tech companies. At Action Model, we believe AI should be owned by the people, not controlled by the few. There’s still a small window for people-owned AI to win. Now is your chance to do something about it.

Around 1 billion computer-based jobs are at risk of being replaced by AI. This shift is happening whether people are ready for it or not. The real question isn’t if it happens. It’s who owns the value created by it. Right now, that value flows to a handful of billionaires and Big Tech companies. At Action Model, we believe AI should be owned by the people, not controlled by the few. There’s still a small window for people-owned AI to win. Now is your chance to do something about it.

Action Model

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The goal for the new Grok powered 𝕏 algorithm: "It should be possible for somebody to post content as a new user with no followers and if that content is intrinsically excellent, it can be seen by a lot of people. That's our goal."

The goal for the new Grok powered 𝕏 algorithm: "It should be possible for somebody to post content as a new user with no followers and if that content is intrinsically excellent, it can be seen by a lot of people. That's our goal."

DogeDesigner

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Elon Musk reveals that SpaceX was forced to strap a seal to a board and put headphones on it to test sonic boom sounds "We were forced to kidnap a seal, strap it to a board, put headphones on the seal, and play sonic boom sounds to it to see if it would be distressed. This is an actual thing that happened. This is actually real. I have pictures." Lex Fridman: "I would love to see this." Elon Musk: "Yeah. I mean, sorry, there's a seal with headphones. Yes, it's a seal with headphones strapped to a board. And the amazing part is how calm the seal was." Lex Fridman: "Because if I was a seal, I'd be like, this is the end. They're definitely going to eat me. How will the seal, when the seal goes back to other seal friends, how are they going to explain that?" Elon Musk: "They're never going to believe them. It's sort of like getting kidnapped by aliens and getting an anal probe. You come back and say I swear to god I got kidnapped by aliens and your seal buddies are never going to believe him. And by the way we had to do it twice." Lex Fridman: "They let him go twice? You had to catch the same seal?" Elon Musk: "Well no, different seal."

Elon Musk reveals that SpaceX was forced to strap a seal to a board and put headphones on it to test sonic boom sounds "We were forced to kidnap a seal, strap it to a board, put headphones on the seal, and play sonic boom sounds to it to see if it would be distressed. This is an actual thing that happened. This is actually real. I have pictures." Lex Fridman: "I would love to see this." Elon Musk: "Yeah. I mean, sorry, there's a seal with headphones. Yes, it's a seal with headphones strapped to a board. And the amazing part is how calm the seal was." Lex Fridman: "Because if I was a seal, I'd be like, this is the end. They're definitely going to eat me. How will the seal, when the seal goes back to other seal friends, how are they going to explain that?" Elon Musk: "They're never going to believe them. It's sort of like getting kidnapped by aliens and getting an anal probe. You come back and say I swear to god I got kidnapped by aliens and your seal buddies are never going to believe him. And by the way we had to do it twice." Lex Fridman: "They let him go twice? You had to catch the same seal?" Elon Musk: "Well no, different seal."

voided intern

828,155 views • 1 month ago

We are cooked. HeyGen just dropped Digital Twin, powered by Avatar IV. AI that makes a lifelike avatar of you. People won't be able to tell which is real you. Bookmark this, here’s how to set it up:

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Self-Evolving AI : New MIT AI Rewrites its Own Code and it’s Changing Everything | Julian Horsey, Geeky Gadgets TL;DR Key Takeaways : - MIT’s SEAL framework introduces “self-adapting language models” that autonomously enhance their capabilities by generating synthetic training data, self-editing, and updating internal parameters. - SEAL’s self-adaptation process mirrors human learning, allowing continuous improvement and dynamic adaptation to new tasks without relying on external datasets. - Reinforcement learning serves as a feedback mechanism in SEAL, rewarding effective self-edits and making sure sustained progress and goal alignment. SEAL overcomes AI’s reliance on pre-existing datasets by generating its own training material, excelling in long-term task retention and complex problem-solving scenarios. - Potential applications of SEAL include autonomous robotics, personalized education, and advanced problem-solving in fields like healthcare, logistics, and scientific research. --- What if artificial intelligence could not only learn but also rewrite its own code to become smarter over time? This is no longer a futuristic fantasy—MIT’s new “self-adapting language models” (SEAL) framework has made it a reality. Unlike traditional AI systems that rely on external datasets and human intervention to improve, SEAL takes a bold leap forward by autonomously generating its own training data and refining its internal processes. In essence, this AI doesn’t just evolve—it rewires itself, mirroring the way humans adapt through trial, error, and self-reflection. The implications are staggering: a system that can independently enhance its capabilities could redefine the boundaries of what AI can achieve, from solving complex problems to adapting in real time to unforeseen challenges. In this exploration by Wes Roth of MIT’s innovative SEAL framework, you’ll uncover how this self-improving AI works and why it’s a fantastic option for the field of artificial intelligence. From its ability to overcome the “data wall” that limits many current systems to its use of reinforcement learning as a feedback mechanism, SEAL introduces a level of autonomy and adaptability that was previously unimaginable. Imagine AI systems that can retain knowledge over time, dynamically adjust to new tasks, and operate with minimal human oversight. Whether you’re intrigued by its potential for autonomous robotics, personalized education, or advanced problem-solving, SEAL’s ability to rewrite its own rules promises to reshape the future of technology. Could this be the first step toward truly independent, self-evolving AI? What Sets SEAL Apart? The SEAL framework introduces a novel concept of self-adaptation, distinguishing it from traditional AI models. Unlike conventional systems that depend on external datasets for updates, SEAL enables AI to generate synthetic training data independently. This self-generated data is then used to iteratively refine the model, making sure continuous improvement. By persistently updating its internal parameters, SEAL enables AI systems to dynamically adapt to new tasks and inputs. To better illustrate this, consider how humans learn. When faced with a new concept, you might take notes, revisit them, and refine your understanding as you gather more information. SEAL mirrors this process by continuously refining its internal knowledge and performance through iterative self-improvement. This capability allows SEAL to evolve in real time, making it uniquely suited for tasks requiring adaptability and long-term learning. The Role of Reinforcement Learning in SEAL Reinforcement learning plays a critical role in the SEAL framework, acting as a feedback mechanism that evaluates the effectiveness of the model’s self-edits. It rewards changes that enhance performance, creating a cycle of continuous improvement. Over time, this feedback loop optimizes the system’s ability to generate and apply edits, making sure sustained progress. This process is analogous to how humans learn through trial and error. By rewarding effective changes, SEAL aligns its self-generated data and edits with desired outcomes. The integration of reinforcement learning not only enhances the system’s adaptability but also ensures it remains focused on achieving specific goals. This structured feedback mechanism is a cornerstone of SEAL’s ability to refine itself autonomously and efficiently. Real-World Applications and Testing SEAL has demonstrated remarkable performance across various applications, particularly in tasks requiring the integration of factual knowledge and advanced question-answering capabilities. For instance, when tested on benchmarks like the ARC AGI, SEAL outperformed other models by effectively generating and using synthetic data. This ability to create its own training material addresses a significant limitation of current AI systems: their reliance on pre-existing datasets. SEAL’s capacity for long-term task retention and dynamic adaptation further enhances its utility. It excels in scenarios that demand sustained focus and coherence, such as answering complex questions or adapting to evolving objectives. By using its iterative learning process, SEAL is equipped to handle these challenges with exceptional efficiency, making it a valuable tool for a wide range of real-world applications. Overcoming AI’s Data Limitations One of SEAL’s most promising features is its ability to overcome the “data wall” that constrains many AI systems today. By generating synthetic data, SEAL ensures a continuous supply of training material, allowing sustained development without relying on external datasets. This capability is particularly valuable for autonomous AI systems that must operate independently over extended periods. Additionally, SEAL addresses a critical weakness in many current AI models: their struggle with coherence and task retention over long durations. By emulating human learning processes, SEAL enables AI systems to manage complex, long-term tasks with minimal human intervention. This ability to retain and apply knowledge over time positions SEAL as a fantastic tool for advancing AI capabilities. Potential Applications and Future Impact The introduction of SEAL marks a significant milestone in AI research, opening new possibilities for self-improving systems. Its ability to dynamically adapt, retain knowledge, and generate its own training data has far-reaching implications for the future of AI development. Potential applications include: - Autonomous robotics: Systems that can adapt to changing environments and perform tasks with minimal human oversight. - Personalized education: AI-driven platforms that tailor learning experiences to individual needs and preferences. - Advanced problem-solving: Applications in fields such as healthcare, logistics, and scientific research, where adaptability and precision are critical. Read more:

Self-Evolving AI : New MIT AI Rewrites its Own Code and it’s Changing Everything | Julian Horsey, Geeky Gadgets TL;DR Key Takeaways : - MIT’s SEAL framework introduces “self-adapting language models” that autonomously enhance their capabilities by generating synthetic training data, self-editing, and updating internal parameters. - SEAL’s self-adaptation process mirrors human learning, allowing continuous improvement and dynamic adaptation to new tasks without relying on external datasets. - Reinforcement learning serves as a feedback mechanism in SEAL, rewarding effective self-edits and making sure sustained progress and goal alignment. SEAL overcomes AI’s reliance on pre-existing datasets by generating its own training material, excelling in long-term task retention and complex problem-solving scenarios. - Potential applications of SEAL include autonomous robotics, personalized education, and advanced problem-solving in fields like healthcare, logistics, and scientific research. --- What if artificial intelligence could not only learn but also rewrite its own code to become smarter over time? This is no longer a futuristic fantasy—MIT’s new “self-adapting language models” (SEAL) framework has made it a reality. Unlike traditional AI systems that rely on external datasets and human intervention to improve, SEAL takes a bold leap forward by autonomously generating its own training data and refining its internal processes. In essence, this AI doesn’t just evolve—it rewires itself, mirroring the way humans adapt through trial, error, and self-reflection. The implications are staggering: a system that can independently enhance its capabilities could redefine the boundaries of what AI can achieve, from solving complex problems to adapting in real time to unforeseen challenges. In this exploration by Wes Roth of MIT’s innovative SEAL framework, you’ll uncover how this self-improving AI works and why it’s a fantastic option for the field of artificial intelligence. From its ability to overcome the “data wall” that limits many current systems to its use of reinforcement learning as a feedback mechanism, SEAL introduces a level of autonomy and adaptability that was previously unimaginable. Imagine AI systems that can retain knowledge over time, dynamically adjust to new tasks, and operate with minimal human oversight. Whether you’re intrigued by its potential for autonomous robotics, personalized education, or advanced problem-solving, SEAL’s ability to rewrite its own rules promises to reshape the future of technology. Could this be the first step toward truly independent, self-evolving AI? What Sets SEAL Apart? The SEAL framework introduces a novel concept of self-adaptation, distinguishing it from traditional AI models. Unlike conventional systems that depend on external datasets for updates, SEAL enables AI to generate synthetic training data independently. This self-generated data is then used to iteratively refine the model, making sure continuous improvement. By persistently updating its internal parameters, SEAL enables AI systems to dynamically adapt to new tasks and inputs. To better illustrate this, consider how humans learn. When faced with a new concept, you might take notes, revisit them, and refine your understanding as you gather more information. SEAL mirrors this process by continuously refining its internal knowledge and performance through iterative self-improvement. This capability allows SEAL to evolve in real time, making it uniquely suited for tasks requiring adaptability and long-term learning. The Role of Reinforcement Learning in SEAL Reinforcement learning plays a critical role in the SEAL framework, acting as a feedback mechanism that evaluates the effectiveness of the model’s self-edits. It rewards changes that enhance performance, creating a cycle of continuous improvement. Over time, this feedback loop optimizes the system’s ability to generate and apply edits, making sure sustained progress. This process is analogous to how humans learn through trial and error. By rewarding effective changes, SEAL aligns its self-generated data and edits with desired outcomes. The integration of reinforcement learning not only enhances the system’s adaptability but also ensures it remains focused on achieving specific goals. This structured feedback mechanism is a cornerstone of SEAL’s ability to refine itself autonomously and efficiently. Real-World Applications and Testing SEAL has demonstrated remarkable performance across various applications, particularly in tasks requiring the integration of factual knowledge and advanced question-answering capabilities. For instance, when tested on benchmarks like the ARC AGI, SEAL outperformed other models by effectively generating and using synthetic data. This ability to create its own training material addresses a significant limitation of current AI systems: their reliance on pre-existing datasets. SEAL’s capacity for long-term task retention and dynamic adaptation further enhances its utility. It excels in scenarios that demand sustained focus and coherence, such as answering complex questions or adapting to evolving objectives. By using its iterative learning process, SEAL is equipped to handle these challenges with exceptional efficiency, making it a valuable tool for a wide range of real-world applications. Overcoming AI’s Data Limitations One of SEAL’s most promising features is its ability to overcome the “data wall” that constrains many AI systems today. By generating synthetic data, SEAL ensures a continuous supply of training material, allowing sustained development without relying on external datasets. This capability is particularly valuable for autonomous AI systems that must operate independently over extended periods. Additionally, SEAL addresses a critical weakness in many current AI models: their struggle with coherence and task retention over long durations. By emulating human learning processes, SEAL enables AI systems to manage complex, long-term tasks with minimal human intervention. This ability to retain and apply knowledge over time positions SEAL as a fantastic tool for advancing AI capabilities. Potential Applications and Future Impact The introduction of SEAL marks a significant milestone in AI research, opening new possibilities for self-improving systems. Its ability to dynamically adapt, retain knowledge, and generate its own training data has far-reaching implications for the future of AI development. Potential applications include: - Autonomous robotics: Systems that can adapt to changing environments and perform tasks with minimal human oversight. - Personalized education: AI-driven platforms that tailor learning experiences to individual needs and preferences. - Advanced problem-solving: Applications in fields such as healthcare, logistics, and scientific research, where adaptability and precision are critical. Read more:

Owen Gregorian

70,672 views • 1 year ago

BREAKING: Elon Musk shares his goal for the new Grok powered 𝕏 algorithm. "It should be possible for somebody to post content as a new user with no followers and if that content is intrinsically excellent, it can be seen by a lot of people. That's our goal."

BREAKING: Elon Musk shares his goal for the new Grok powered 𝕏 algorithm. "It should be possible for somebody to post content as a new user with no followers and if that content is intrinsically excellent, it can be seen by a lot of people. That's our goal."

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🇨🇳 China: AI should be open, inclusive, people-centered, and a force for good for all. 🇺🇸 America: AI should be led by us, secured by us, and placed in the hands of “trusted defenders.” That is the difference. One sees AI as part of human civilization. The other sees AI as a strategic asset of empire. No wonder Chinese people are becoming more comfortable with AI, while Americans are taught to fear it. You don’t love a weapon. You fear the day it stops obeying.

🇨🇳 China: AI should be open, inclusive, people-centered, and a force for good for all. 🇺🇸 America: AI should be led by us, secured by us, and placed in the hands of “trusted defenders.” That is the difference. One sees AI as part of human civilization. The other sees AI as a strategic asset of empire. No wonder Chinese people are becoming more comfortable with AI, while Americans are taught to fear it. You don’t love a weapon. You fear the day it stops obeying.

𝘊𝘰𝘳𝘳𝘪𝘯𝘦

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Microsoft's Chief of AI just announced that AI will replace almost all white-collar jobs within 18 months. Big Tech used our data to train their AI. Now that same AI is coming for our jobs. And we get nothing in return? If AI replaces us…we should own it. That’s why we built Action Model Not another chatbot. A community-owned AI model that acts. An AI trained by the people. Owned by the people. No billionaires controlling it. No extracting value from users. No “you are the product” model. Over 150,000+ people are already earning ownership in the AI future with Action Model. You should be too.

Microsoft's Chief of AI just announced that AI will replace almost all white-collar jobs within 18 months. Big Tech used our data to train their AI. Now that same AI is coming for our jobs. And we get nothing in return? If AI replaces us…we should own it. That’s why we built Action Model Not another chatbot. A community-owned AI model that acts. An AI trained by the people. Owned by the people. No billionaires controlling it. No extracting value from users. No “you are the product” model. Over 150,000+ people are already earning ownership in the AI future with Action Model. You should be too.

Georgia | Action Model

13,198 views • 4 months ago

One of the biggest bottlenecks in deploying visual AI and computer vision is annotation, which can be both costly and time-consuming. Today, we’re introducing Verified Auto Labeling, a new approach to AI-assisted annotation that achieves up to 95% of human-level performance while cutting labeling costs by up to 100,000x and time by 5,000x. Read the full paper:

One of the biggest bottlenecks in deploying visual AI and computer vision is annotation, which can be both costly and time-consuming. Today, we’re introducing Verified Auto Labeling, a new approach to AI-assisted annotation that achieves up to 95% of human-level performance while cutting labeling costs by up to 100,000x and time by 5,000x. Read the full paper:

Voxel51

12,722 views • 1 year ago

Malvina used to be daunted by new technology – now, she embraces it. Discover how this programme is forging a new pathway for getting people online and connecting them with tech that can boost their lives. #TechnologysGoldenAge Consumer Tech Association #Ad paid and presented by CTA Foundation

Malvina used to be daunted by new technology – now, she embraces it. Discover how this programme is forging a new pathway for getting people online and connecting them with tech that can boost their lives. #TechnologysGoldenAge Consumer Tech Association #Ad paid and presented by CTA Foundation

BBC StoryWorks

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TikTok Media censorship is no longer a warning, it is here. Platforms that are not owned by the people cannot be trusted to protect free speech or creators. Fanbase was built for the people, by the people. Creator owned. Community powered. This is how we fight back. We build what we own. DOWNLOAD Fanbase and you can migrate your TikTok content today.

TikTok Media censorship is no longer a warning, it is here. Platforms that are not owned by the people cannot be trusted to protect free speech or creators. Fanbase was built for the people, by the people. Creator owned. Community powered. This is how we fight back. We build what we own. DOWNLOAD Fanbase and you can migrate your TikTok content today.

Isaac Hayes III

31,270 views • 5 months ago

We’re excited to be integrating SERV Reasoning by OpenServ to bring our no-code robotics solution to market, and had the pleasure of joining their closed beta. Advanced robotics used to require a full team of specialists. Now you can build real behavior in our no-code studio — this Unitree G1 hand grasp function was entirely written by the serv-standard model inside ROBA's no-code studio. This is just a small glimpse of what can be done in our platform. In our benchmarks across real builder tasks, SERV's models matched the quality bar we needed while cutting AI costs by over 80%. SERV Reasoning will now be the default inference engine for the upcoming release of our ROBA no-code studio on June 1st.

We’re excited to be integrating SERV Reasoning by OpenServ to bring our no-code robotics solution to market, and had the pleasure of joining their closed beta. Advanced robotics used to require a full team of specialists. Now you can build real behavior in our no-code studio — this Unitree G1 hand grasp function was entirely written by the serv-standard model inside ROBA's no-code studio. This is just a small glimpse of what can be done in our platform. In our benchmarks across real builder tasks, SERV's models matched the quality bar we needed while cutting AI costs by over 80%. SERV Reasoning will now be the default inference engine for the upcoming release of our ROBA no-code studio on June 1st.

Roba Labs

34,042 views • 1 month ago

Serious Question. If people are told they can’t sing church Hymns outside church grounds. How is it acceptable for public streets to be blocked so that Muslims can pray and loudspeakers used in public spaces. Surely the same rules should apply to everyone SHAMEFUL BY POLICE!

Serious Question. If people are told they can’t sing church Hymns outside church grounds. How is it acceptable for public streets to be blocked so that Muslims can pray and loudspeakers used in public spaces. Surely the same rules should apply to everyone SHAMEFUL BY POLICE!

Benonwine

188,397 views • 4 months ago

🦖: There was once someone who used to be a fan who loved us, and one day that person said they felt disgusted by us. I can understand that to some extent. It comes from the kind of news they were exposed to and the anger they felt. I myself probably wouldn’t do the same thing that person did to us. 🦖: What I want is for people to look more at the reality of what is happening, what actually occurred between Thailand and Cambodia. And to try to understand it, because in truth, ordinary people like us, and people on the Cambodian side as well, are directly affected by this situation. But this is not something we should be blaming or attacking each other over.

🦖: There was once someone who used to be a fan who loved us, and one day that person said they felt disgusted by us. I can understand that to some extent. It comes from the kind of news they were exposed to and the anger they felt. I myself probably wouldn’t do the same thing that person did to us. 🦖: What I want is for people to look more at the reality of what is happening, what actually occurred between Thailand and Cambodia. And to try to understand it, because in truth, ordinary people like us, and people on the Cambodian side as well, are directly affected by this situation. But this is not something we should be blaming or attacking each other over.

𝓢𝓽𝓱𝓮𝓯 ࿔⋆🎀

71,792 views • 7 months ago

Just walked through Union Station in DC for the first time in years, and I can’t believe it... It’s clean, safe, and beautiful. Exactly how it should be. A place Americans can be proud of. It used to be overrun by homeless people and drug addicts. Now it’s a place I can bring my family. Well done, Trump Administration. Well done.

Just walked through Union Station in DC for the first time in years, and I can’t believe it... It’s clean, safe, and beautiful. Exactly how it should be. A place Americans can be proud of. It used to be overrun by homeless people and drug addicts. Now it’s a place I can bring my family. Well done, Trump Administration. Well done.

Benny Johnson

928,689 views • 9 months ago

Stop being so easy to trick. Is the humanoid robot - Joy actually a breakthrough in AI, or just a tall, skinny girl in a wig cap and 10/10 makeup? We’re calling it innovation, but it looks more like a high-budget publicity stunt to see how easily we'll bow down to machines. Is this performance art, or are they conditioning us to accept our new overlords? Joy is clearly a woman in a costume, but the real story is how many people actually believe she’s metal and wires. If we’re this easily fooled by a performance artist, how are we going to survive the actual AI takeover?

Stop being so easy to trick. Is the humanoid robot - Joy actually a breakthrough in AI, or just a tall, skinny girl in a wig cap and 10/10 makeup? We’re calling it innovation, but it looks more like a high-budget publicity stunt to see how easily we'll bow down to machines. Is this performance art, or are they conditioning us to accept our new overlords? Joy is clearly a woman in a costume, but the real story is how many people actually believe she’s metal and wires. If we’re this easily fooled by a performance artist, how are we going to survive the actual AI takeover?

Pretty Genius

19,296 views • 4 months ago

On 1 January 1962, the first SEAL Teams were officially established: SEAL Team ONE in Coronado, California and SEAL Team TWO in Little Creek, Virginia. On 6 June of that year, President John F. Kennedy made the following remarks at West Point to the U.S. Military Academy graduating class: “This is another type of warfare, new in its intensity, ancient in its origin – war by guerrillas, subversives, insurgents, assassins, war by ambush instead of by combat; by infiltration, instead of aggression, seeking victory by eroding and exhausting the enemy instead of engaging him. It is a form of warfare uniquely adapted to what has been strangely called ‘wars of liberation,’ to undermine the efforts of new and poor countries to maintain the freedom that they have finally achieved. It prays on economic unrest and ethnic conflicts. It requires in those situations where we must counter it, and these are the kinds of challenges that will be before us in the next decade if freedom is to be saved, a whole new kind of strategy, a wholly different kind of force, and therefore a new and wholly different kind of military training.” For more on the history of the SEAL Teams, visit the Navy SEAL Museum website and make plans to visit this year.

On 1 January 1962, the first SEAL Teams were officially established: SEAL Team ONE in Coronado, California and SEAL Team TWO in Little Creek, Virginia. On 6 June of that year, President John F. Kennedy made the following remarks at West Point to the U.S. Military Academy graduating class: “This is another type of warfare, new in its intensity, ancient in its origin – war by guerrillas, subversives, insurgents, assassins, war by ambush instead of by combat; by infiltration, instead of aggression, seeking victory by eroding and exhausting the enemy instead of engaging him. It is a form of warfare uniquely adapted to what has been strangely called ‘wars of liberation,’ to undermine the efforts of new and poor countries to maintain the freedom that they have finally achieved. It prays on economic unrest and ethnic conflicts. It requires in those situations where we must counter it, and these are the kinds of challenges that will be before us in the next decade if freedom is to be saved, a whole new kind of strategy, a wholly different kind of force, and therefore a new and wholly different kind of military training.” For more on the history of the SEAL Teams, visit the Navy SEAL Museum website and make plans to visit this year.

Jack Carr

39,723 views • 6 months ago

🚨ZUCK: "Probably in 2025, Meta, as well as other companies, will have an AI that can effectively be a mid-level engineer that you have at your company that can write code. In the beginning that will be very expensive to run, then at some point a lot of the code we have in our apps will be built by AI engineers instead of people engineers. In my opinion that will augment the people working on it. My view is that in the future people will be so much more creative and freed up to do crazy things."

🚨ZUCK: "Probably in 2025, Meta, as well as other companies, will have an AI that can effectively be a mid-level engineer that you have at your company that can write code. In the beginning that will be very expensive to run, then at some point a lot of the code we have in our apps will be built by AI engineers instead of people engineers. In my opinion that will augment the people working on it. My view is that in the future people will be so much more creative and freed up to do crazy things."

Autism Capital 🧩

1,666,456 views • 1 year ago

I made tons of money building websites for people and small companies. Most of that is now gone. You can now generate, customize, and deploy a website using natural language. I recorded myself building a site from scratch using 10Web.io. 4 minutes later, I had a full website generated just for me. Crazy! That's mostly how I paid rent when I graduated college: I'd either build you a new site or make changes to an existing one. It's nuts anyone can do that today by just asking. But this doesn't stop with generating a website. 10Web also released an AI Copilot that you can use to edit a website with a chat-based AI assistant. Here is what you can do with the AI Copilot: 1. You can edit individual elements and widgets on your site by selecting them and describing the changes you want. 2. You can generate new images, create sections, and generate or edit text. 3. You can also add any new elements to the site. The best way to understand how much progress we've made is by looking at the AI Copilot in action: And the funny part is that we are just figuring out how to use AI models. 2025 will be crazy.

I made tons of money building websites for people and small companies. Most of that is now gone. You can now generate, customize, and deploy a website using natural language. I recorded myself building a site from scratch using 10Web.io. 4 minutes later, I had a full website generated just for me. Crazy! That's mostly how I paid rent when I graduated college: I'd either build you a new site or make changes to an existing one. It's nuts anyone can do that today by just asking. But this doesn't stop with generating a website. 10Web also released an AI Copilot that you can use to edit a website with a chat-based AI assistant. Here is what you can do with the AI Copilot: 1. You can edit individual elements and widgets on your site by selecting them and describing the changes you want. 2. You can generate new images, create sections, and generate or edit text. 3. You can also add any new elements to the site. The best way to understand how much progress we've made is by looking at the AI Copilot in action: And the funny part is that we are just figuring out how to use AI models. 2025 will be crazy.

Santiago

57,917 views • 1 year ago