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🤩Apple opensources MGIE! Now one can take random pictures w. iPhone & edit w. language! Guiding Instruction-based Image Editing via Multimodal Large Language Models #ICLR2024 spotlight: Apple repo Gradio

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William Wang

21,662 subscribers

125,022 views • 2 years ago •via X (Twitter)

Science & TechnologyNews & PoliticsEducation#ICLR2024
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5 Comments

Ali Madad's profile picture
Ali Madad2 years ago

@ivanfioravanti 👆🏾

Keshav Jindal's profile picture
Keshav Jindal2 years ago

noob question: I couldn't figure out if commercial use is allowed. is it?

Yuriy Yuzifovich's profile picture
Yuriy Yuzifovich2 years ago

Seeing the success of @AIatMeta with open source AI must have been a contributing factor in Apple decision making. Great!

hristo's profile picture
hristo2 years ago

Only Apple Can do !

johns code's profile picture
johns code2 years ago

really cool. unfortunately it does not build on an M2 Mac

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steve

640,176 views • 3 years ago

The Wikipedia wars As Wikipedia approaches its 25th anniversary in 2026, its open editing model faces a growing challenge: coordinated edit wars. In these campaigns, Kremlin-aligned actors try to rewrite history, launder disinformation, and lock distorted narratives into one of the world’s most trusted reference platforms. Founded on the idea that volunteers could collaboratively build a neutral, reliable encyclopaedia, Wikipedia has become one of the most influential information platforms ever created. It is often described as the world’s largest crowd-sourced knowledge project, built on consensus and verifiable sources. In recent years, however, it has also become a frontline in geopolitical information warfare. This is most visible in so-called edit wars: prolonged conflicts where opposing groups repeatedly overwrite and revise articles to control historical narratives. Since Russia’s full-scale invasion of Ukraine in February 2022, these battles have intensified. Kremlin-aligned actors have systematically targeted articles related to Eastern Europe, the Soviet past, and contemporary political leaders. Estonia and especially EU leader Kaja Kallas, Estonia’s former prime minister, have been frequent targets. What are edit wars? An edit war happens when editors repeatedly change the same content instead of resolving disputes through discussion. Wikipedia officially discourages this behaviour and emphasises consensus, neutrality, and reliable sources. In practice, however, edit wars can and do break out. Coordinated editors can use endurance, procedural rules, and administrator complaints to exhaust good faith contributors. The goal is rarely to win a single argument. Instead, it is to wear down opposition, freeze pages at favourable moments, and normalise contested language. Once a page is locked or protected, the version in place gains a sense of legitimacy, even if it reflects a distorted view. Edit wars exploit open systems, operate over long periods, and aim to embed manipulated narratives into reference material rather than spreading short-lived falsehoods. Multiple investigations show that Wikipedia manipulation increased sharply after Russia’s invasion of Ukraine. Russian-language Wikipedia and parts of the English version became arenas for systematic narrative control, especially as independent Russian media was shut down. Wikipedia’s openness, once a strength, had suddenly become a vulnerability. Coordinated editor networks have worked to soften descriptions of Russian aggression, reframe invasions as ‘conflicts’, and question the legitimacy of post-Soviet states. These efforts rely on subtle wording changes, selective sourcing, and procedural tactics rather than obvious vandalism. Estonia and EU officials as targets Estonia shows how edit wars are used for historical revisionism and political influence. Since 2022, English-language Wikipedia articles about Estonia’s history, statehood, and politics have faced sustained pressure. One recurring tactic has been changing the birthplaces of hundreds of Estonian public figures from ‘Estonia’ to ‘Estonian SSR, Soviet Union’, despite the legal consensus that Estonia was occupied, not legitimately incorporated, by the USSR between 1940 and 1991. This is not a minor wording issue. Calling Estonia a ‘Soviet republic’ supports the Russian claim that the Baltic states voluntarily joined the USSR and directly contradicts the position of Estonia, the EU, NATO countries, and international law. Historical topics have also been targeted. The Estonian War of Independence between 1918 and 1920 has at times been reframed as an ‘offensive campaign’ or ‘separatism from Russia’, language that closely mirrors contemporary Kremlin rhetoric. High-profile figures are especially vulnerable because their pages attract constant attention and frequent administrative action. The Wikipedia article on Kaja Kallas has repeatedly been edited to reflect Russian-aligned interpretations of history and geopolitics. At key moments, the page was locked while these contested narratives were in place, blocking corrective edits. Page protection, meant to prevent disruption, instead helped freeze a favourable version of the article. This shows how procedural tools can be exploited as effectively as false information. Why Wikipedia matters Wikipedia is not just another website. It ranks highly in search results and serves as a default reference for journalists, students, policymakers, and the public. Winning an edit war on Wikipedia helps turn contested narratives into global ‘common knowledge’. For Kremlin-aligned actors, this makes Wikipedia a valuable target. Making small wording changes, downplaying occupation, reframing wars, and questioning democratic legitimacy can slowly erode our understanding of history and present-day aggression. Estonia’s experience shows how smaller states are especially exposed. Because Wikipedia is also a core source for AI systems, the stakes are even higher. Recent studies indicate that Wikipedia is one of the most cited sources for ChatGPT, effectively serving as a foundational knowledge base for how the AI understands and retrieves information. Manipulating articles today can therefore shape how future technologies understand, reproduce, and repeat history. This practice is referred to as LLM grooming, the deliberate attempt to influence large language models by seeding biased or distorted narratives into the sources they rely on. The rise in Wikipedia edit wars since 2022 reflects a broader shift in information warfare. Instead of loud propaganda, actors now use procedural, platform-native manipulation. Estonian history and Kaja Kallas are not isolated cases but targets of coordinated action. And as long as open-knowledge platforms shape how societies understand history and politics, sites like Wikipedia will remain contested ground.
0:32

The Wikipedia wars As Wikipedia approaches its 25th anniversary in 2026, its open editing model faces a growing challenge: coordinated edit wars. In these campaigns, Kremlin-aligned actors try to rewrite history, launder disinformation, and lock distorted narratives into one of the world’s most trusted reference platforms. Founded on the idea that volunteers could collaboratively build a neutral, reliable encyclopaedia, Wikipedia has become one of the most influential information platforms ever created. It is often described as the world’s largest crowd-sourced knowledge project, built on consensus and verifiable sources. In recent years, however, it has also become a frontline in geopolitical information warfare. This is most visible in so-called edit wars: prolonged conflicts where opposing groups repeatedly overwrite and revise articles to control historical narratives. Since Russia’s full-scale invasion of Ukraine in February 2022, these battles have intensified. Kremlin-aligned actors have systematically targeted articles related to Eastern Europe, the Soviet past, and contemporary political leaders. Estonia and especially EU leader Kaja Kallas, Estonia’s former prime minister, have been frequent targets. What are edit wars? An edit war happens when editors repeatedly change the same content instead of resolving disputes through discussion. Wikipedia officially discourages this behaviour and emphasises consensus, neutrality, and reliable sources. In practice, however, edit wars can and do break out. Coordinated editors can use endurance, procedural rules, and administrator complaints to exhaust good faith contributors. The goal is rarely to win a single argument. Instead, it is to wear down opposition, freeze pages at favourable moments, and normalise contested language. Once a page is locked or protected, the version in place gains a sense of legitimacy, even if it reflects a distorted view. Edit wars exploit open systems, operate over long periods, and aim to embed manipulated narratives into reference material rather than spreading short-lived falsehoods. Multiple investigations show that Wikipedia manipulation increased sharply after Russia’s invasion of Ukraine. Russian-language Wikipedia and parts of the English version became arenas for systematic narrative control, especially as independent Russian media was shut down. Wikipedia’s openness, once a strength, had suddenly become a vulnerability. Coordinated editor networks have worked to soften descriptions of Russian aggression, reframe invasions as ‘conflicts’, and question the legitimacy of post-Soviet states. These efforts rely on subtle wording changes, selective sourcing, and procedural tactics rather than obvious vandalism. Estonia and EU officials as targets Estonia shows how edit wars are used for historical revisionism and political influence. Since 2022, English-language Wikipedia articles about Estonia’s history, statehood, and politics have faced sustained pressure. One recurring tactic has been changing the birthplaces of hundreds of Estonian public figures from ‘Estonia’ to ‘Estonian SSR, Soviet Union’, despite the legal consensus that Estonia was occupied, not legitimately incorporated, by the USSR between 1940 and 1991. This is not a minor wording issue. Calling Estonia a ‘Soviet republic’ supports the Russian claim that the Baltic states voluntarily joined the USSR and directly contradicts the position of Estonia, the EU, NATO countries, and international law. Historical topics have also been targeted. The Estonian War of Independence between 1918 and 1920 has at times been reframed as an ‘offensive campaign’ or ‘separatism from Russia’, language that closely mirrors contemporary Kremlin rhetoric. High-profile figures are especially vulnerable because their pages attract constant attention and frequent administrative action. The Wikipedia article on Kaja Kallas has repeatedly been edited to reflect Russian-aligned interpretations of history and geopolitics. At key moments, the page was locked while these contested narratives were in place, blocking corrective edits. Page protection, meant to prevent disruption, instead helped freeze a favourable version of the article. This shows how procedural tools can be exploited as effectively as false information. Why Wikipedia matters Wikipedia is not just another website. It ranks highly in search results and serves as a default reference for journalists, students, policymakers, and the public. Winning an edit war on Wikipedia helps turn contested narratives into global ‘common knowledge’. For Kremlin-aligned actors, this makes Wikipedia a valuable target. Making small wording changes, downplaying occupation, reframing wars, and questioning democratic legitimacy can slowly erode our understanding of history and present-day aggression. Estonia’s experience shows how smaller states are especially exposed. Because Wikipedia is also a core source for AI systems, the stakes are even higher. Recent studies indicate that Wikipedia is one of the most cited sources for ChatGPT, effectively serving as a foundational knowledge base for how the AI understands and retrieves information. Manipulating articles today can therefore shape how future technologies understand, reproduce, and repeat history. This practice is referred to as LLM grooming, the deliberate attempt to influence large language models by seeding biased or distorted narratives into the sources they rely on. The rise in Wikipedia edit wars since 2022 reflects a broader shift in information warfare. Instead of loud propaganda, actors now use procedural, platform-native manipulation. Estonian history and Kaja Kallas are not isolated cases but targets of coordinated action. And as long as open-knowledge platforms shape how societies understand history and politics, sites like Wikipedia will remain contested ground.

EUvsDisinfo

343,369 views • 6 months ago

Eric Schmidt was asked a technical question about open source and answered with the map of the next fifty years. The winner won’t be the smartest model. It’ll be the one four billion people never had to choose. Schmidt: “China is competing with open weights and open training data, and the US is largely and majority focused on closed weights, closed data.” That isn’t a product decision. It’s a distribution decision. And distribution has beaten quality in every contest that ever mattered. Schmidt: “The majority of the world, think of it as the Belt and Road initiative, are going to use Chinese models and not American models.” The first Belt and Road was ports, rail, and highways. This one doesn’t get poured. It gets downloaded. Every piece of infrastructure ever built was indifferent to what moved across it. A road doesn’t tell you where to go. A model does. Schmidt: “The American models are typically using 16-bit precision for their training. The Chinese are pushing 8 and now even 4.” Every bit they drop is a cheaper device that can run it. We cut off their chips to slow them down. Scarcity made their models small. Small is what crosses a border. We designed their advantage. Not better. Present. America is building the best model on earth and metering it. China is building one that’s good enough and giving it away. A model isn’t software. It’s a compressed set of judgments about what’s true, what’s askable, and what a reasonable answer sounds like. Install that as a country’s default and you haven’t sold them a tool. You’ve set the limits of what occurs to them. That isn’t censorship. Censorship leaves a mark. A question that never occurs to you doesn’t feel like a restriction. It feels like the edge of the world. Every empire before this one had to teach the world its language first. Missionaries, schoolteachers, garrisons, printing presses. Every one of them ran through a human being who could hesitate, doubt, or be talked out of it. AI arrives already speaking yours. It doesn’t ask you to change. It changes you in your own voice. The first ideology in history that doesn’t need believers. It only needs to be installed. Schmidt: “I’d much rather have the proliferation of large language models and that learning be done based on Western values.” He’s right, and we’re playing it backwards. We treat openness like a giveaway, as if the weights were the crown jewels. Openness is the one advantage an authoritarian can’t copy. An open model can be read, probed, and torn apart by anyone who doubts it. A system that has to control the answer can never afford to publish the reasoning. China opens its weights to spread them. America could open its weights to be trusted. Only one of those compounds. A closed American model wins the benchmark. An open American model wins the default. Centuries get built out of defaults. Schmidt: “We also have to watch to make sure that the proliferation of these models for handheld devices is under American control.” That’s the ground. Not data centers. Not cloud contracts. Pockets. The frontier race has five contenders and the whole world watching. This one has no audience at all. It plays out on hardware too cheap to run an American model, and goes to whoever bothered to show up. We keep asking who reaches AGI first. The question that settles the century is smaller and much harder to take back. Four billion people are going to ask a machine what happened in their own country. Whose answer do they get? Nobody votes on that. It’s decided by whatever was already installed. America has the best AI ever built. The only way to lose this era is to keep it.
2:24

Eric Schmidt was asked a technical question about open source and answered with the map of the next fifty years. The winner won’t be the smartest model. It’ll be the one four billion people never had to choose. Schmidt: “China is competing with open weights and open training data, and the US is largely and majority focused on closed weights, closed data.” That isn’t a product decision. It’s a distribution decision. And distribution has beaten quality in every contest that ever mattered. Schmidt: “The majority of the world, think of it as the Belt and Road initiative, are going to use Chinese models and not American models.” The first Belt and Road was ports, rail, and highways. This one doesn’t get poured. It gets downloaded. Every piece of infrastructure ever built was indifferent to what moved across it. A road doesn’t tell you where to go. A model does. Schmidt: “The American models are typically using 16-bit precision for their training. The Chinese are pushing 8 and now even 4.” Every bit they drop is a cheaper device that can run it. We cut off their chips to slow them down. Scarcity made their models small. Small is what crosses a border. We designed their advantage. Not better. Present. America is building the best model on earth and metering it. China is building one that’s good enough and giving it away. A model isn’t software. It’s a compressed set of judgments about what’s true, what’s askable, and what a reasonable answer sounds like. Install that as a country’s default and you haven’t sold them a tool. You’ve set the limits of what occurs to them. That isn’t censorship. Censorship leaves a mark. A question that never occurs to you doesn’t feel like a restriction. It feels like the edge of the world. Every empire before this one had to teach the world its language first. Missionaries, schoolteachers, garrisons, printing presses. Every one of them ran through a human being who could hesitate, doubt, or be talked out of it. AI arrives already speaking yours. It doesn’t ask you to change. It changes you in your own voice. The first ideology in history that doesn’t need believers. It only needs to be installed. Schmidt: “I’d much rather have the proliferation of large language models and that learning be done based on Western values.” He’s right, and we’re playing it backwards. We treat openness like a giveaway, as if the weights were the crown jewels. Openness is the one advantage an authoritarian can’t copy. An open model can be read, probed, and torn apart by anyone who doubts it. A system that has to control the answer can never afford to publish the reasoning. China opens its weights to spread them. America could open its weights to be trusted. Only one of those compounds. A closed American model wins the benchmark. An open American model wins the default. Centuries get built out of defaults. Schmidt: “We also have to watch to make sure that the proliferation of these models for handheld devices is under American control.” That’s the ground. Not data centers. Not cloud contracts. Pockets. The frontier race has five contenders and the whole world watching. This one has no audience at all. It plays out on hardware too cheap to run an American model, and goes to whoever bothered to show up. We keep asking who reaches AGI first. The question that settles the century is smaller and much harder to take back. Four billion people are going to ask a machine what happened in their own country. Whose answer do they get? Nobody votes on that. It’s decided by whatever was already installed. America has the best AI ever built. The only way to lose this era is to keep it.

Dustin

12,094 views • 1 month ago

I've had many engineers ask me why its worth their time and effort to learn biology in response to this post. Why should they be excited? We are poised for a revolution in biotech that will be uniquely enabled by computers. Convince yourself by digging into the examples I link: - The tooling is getting better. Assays are able to measure a broad array of molecules at a falling cost and increasing throughput. Look to ScaleBio, Curio Bioscience, AtlasXOmics for inspiration. We are sequencing millions of single cells and building spatial maps of the molecular state of tumors. After two generations of "next-generation sequencing", and stagnating DNA read + write costs under the monopoly of Illumina, this wave of the new assays will have a profound impact on the iteration speed and scale of experimentation. Little needs to be said about the impact of compounding trends in core tooling over a sufficient period of time. - Biotechs are using data to guide decisions and are incorporating domain informed machine learning as a core part of the molecular design process. There is great synergy here with better tooling as a means of abundant, cheap data. Look to Recursion, Manifold Bio, Dyno Tx, Asimov. There is also the cross pollination of biology informed architectures with the recent explosion in new machine learning techniques. These models are starting to do useful things, like generate functional gene editing proteins and entire prokaryotic organisms. Look to ESM, AlphaFold3, RFdiffusion. - New classes of therapies - genetic medicines and engineered immune cells - are having real success in the clinic. One dose cures for cardiovascular disease (Verve w ACSD), vaccines for cancer (Moderna w mRNA 4157), in vivo gene editing proteins (CRISPR Tx, Beam, Ensoma), metabolic disease (Novartis, Eli Lily w GLP-1/GIP modulators) are being dosed in real people right now + transforming lives. - The AI craze is commoditizing accelerated hardware and fast storage devices like NVMes, improving developer frameworks for writing code against these devices and maturing the systems tooling for moving around lots of data between computers for distributed training. One happy accident of this bubble will be the reuse of these components to build a new systems stack for the large scale processing of molecular data. This will be very important to construct a 1 billion single cell atlas and beyond. (For reference, the state of the art is ScaleBio's 2M cell kit, dubbed QuantumScale, and it is pushing things with the hardware + software we have today.) - Language models might be the perfect tool to distill the unstructured corpus of public data, literature, and methods sitting around on the Internet into real biological insights for scientist asking questions in natural language. It will also allow them to install, configure and run the slew of useful but poorly maintained academic computational tools to explore and hypothesize new biology on their own. Increasing the productivity of each scientist will do much to reverse Eroom's law. - There is an appetite from the market for new applications of biotech beyond drug development. Bacteria driven lithium mining (maverick), cell agriculture (growing cows in vats), early signs of consumer biologics (Geltor), biofoundries (Ginkgo). My guess is some of the greatest minds of our generation will want to do more than perturb the human body with therapies. I think it is also important to recognize that the need for computers and software is a secular trend in the progression of biotech independent of the interests of Silicon Valley. Clusters of computers, the information they store and the software that runs on them are precisely the technology needed by this field as it transforms into a discipline of information management, towards reducing living things into well characterized building blocks we can rebuild in our image. Software companies, as some local and hyper efficient structure in the arc of capitalism, with established methods and well trod rails to attract resources, talent and easily distribute product to an entire market, are the perfect place to incubate and disseminate these tools. There will probably be many, very large computer companies in biology in the next century.
0:52

I've had many engineers ask me why its worth their time and effort to learn biology in response to this post. Why should they be excited? We are poised for a revolution in biotech that will be uniquely enabled by computers. Convince yourself by digging into the examples I link: - The tooling is getting better. Assays are able to measure a broad array of molecules at a falling cost and increasing throughput. Look to ScaleBio, Curio Bioscience, AtlasXOmics for inspiration. We are sequencing millions of single cells and building spatial maps of the molecular state of tumors. After two generations of "next-generation sequencing", and stagnating DNA read + write costs under the monopoly of Illumina, this wave of the new assays will have a profound impact on the iteration speed and scale of experimentation. Little needs to be said about the impact of compounding trends in core tooling over a sufficient period of time. - Biotechs are using data to guide decisions and are incorporating domain informed machine learning as a core part of the molecular design process. There is great synergy here with better tooling as a means of abundant, cheap data. Look to Recursion, Manifold Bio, Dyno Tx, Asimov. There is also the cross pollination of biology informed architectures with the recent explosion in new machine learning techniques. These models are starting to do useful things, like generate functional gene editing proteins and entire prokaryotic organisms. Look to ESM, AlphaFold3, RFdiffusion. - New classes of therapies - genetic medicines and engineered immune cells - are having real success in the clinic. One dose cures for cardiovascular disease (Verve w ACSD), vaccines for cancer (Moderna w mRNA 4157), in vivo gene editing proteins (CRISPR Tx, Beam, Ensoma), metabolic disease (Novartis, Eli Lily w GLP-1/GIP modulators) are being dosed in real people right now + transforming lives. - The AI craze is commoditizing accelerated hardware and fast storage devices like NVMes, improving developer frameworks for writing code against these devices and maturing the systems tooling for moving around lots of data between computers for distributed training. One happy accident of this bubble will be the reuse of these components to build a new systems stack for the large scale processing of molecular data. This will be very important to construct a 1 billion single cell atlas and beyond. (For reference, the state of the art is ScaleBio's 2M cell kit, dubbed QuantumScale, and it is pushing things with the hardware + software we have today.) - Language models might be the perfect tool to distill the unstructured corpus of public data, literature, and methods sitting around on the Internet into real biological insights for scientist asking questions in natural language. It will also allow them to install, configure and run the slew of useful but poorly maintained academic computational tools to explore and hypothesize new biology on their own. Increasing the productivity of each scientist will do much to reverse Eroom's law. - There is an appetite from the market for new applications of biotech beyond drug development. Bacteria driven lithium mining (maverick), cell agriculture (growing cows in vats), early signs of consumer biologics (Geltor), biofoundries (Ginkgo). My guess is some of the greatest minds of our generation will want to do more than perturb the human body with therapies. I think it is also important to recognize that the need for computers and software is a secular trend in the progression of biotech independent of the interests of Silicon Valley. Clusters of computers, the information they store and the software that runs on them are precisely the technology needed by this field as it transforms into a discipline of information management, towards reducing living things into well characterized building blocks we can rebuild in our image. Software companies, as some local and hyper efficient structure in the arc of capitalism, with established methods and well trod rails to attract resources, talent and easily distribute product to an entire market, are the perfect place to incubate and disseminate these tools. There will probably be many, very large computer companies in biology in the next century.

Kenny Workman

113,534 views • 1 year ago

In this post I will explain why people become borderline religious when they discover Qubic. Now with video. Please repost. I want people to learn about QUBIC. The ecosystem consists of 3 separate universes: AI, Mining, and Tickchain. AI is the primary product and purpose of QUBIC and it is supported by Mining to train the AI and by Tickchin for validation and decentralization. Here is how this whole thing works: AI: Let’s start with the AI. The main purpose of QUBIC is creating AGI (Artificial General Intelligence). It’s a type of AI that can self-develop, set tasks, grow, and learn on its own—much like the human brain does. This product is called AIGarth and it uses many cool ideas where AIs can create their own agents and have them compete against one another to evolve. It is basically robots creating robots with the survival-of-the-fittest evolution approach. Very impressive and thought out. To develop such an AI there are several requirements that even the industry giants like OpenAI, Microsoft, and Tesla are missing. One of them is the data processing for AI training. I mean they have their Datacenters, but those are only good enough to train limited Large Language Models such as ChatGPT and Grok. Mining / Training: Now QUBIC solves this problem with its mining architecture. Keep in mind, mining in QUBIC does not secure the chain, primarily it provides the processing power for training the AI. In a sense, QUBIC mining creates the largest distributed datacenter in the world, where individual miners provide their computers for training the AI and get paid with newly issued QUBIC coins. This way QUBIC gets constantly increasing processing power without having to really pay for the infrastructure. And here is another impressive bit of info. QUBIC’s distributed mining network currently ranks above the #1 supercomputer in the world - El Capitan. QUBIC Tickchain The QUBIC chain ties its AI and Mining together to create decentralization, the reward system for miners, it acts as a decision voting system for future development, and it allows AIGarth to function independently through Smart Contracts. In this summary I will not go over the specifics of QUBIC Tickchain. It’s pretty complex so it will be a separate post. Now, it’s an absolute genius piece of tech, which I consider the most advanced product within crypto industry. It is important to know that QUBIC chain runs directly out of Random Access Memory of its validators. It has instant finality and acts as its own operating system. That allows for speeds only bound by current hardware capabilities and it only increases as technology progresses. As I am writing this, QUBIC Tickchain is fully functional and it already hosts several smart-contract based web3 applications. QUBIC has designed its chain to be this fast for a single purpose, to give its future AI the speed it needs to evolve and to react quickly to the outside world. Ilya Shutskever the scientist, who developed ChatGPT clearly states that next generation superintelligence will make decisions in split second with less data. I believe QUBIC is that next generation. Why QUBIC? So out of the sea of AI projects in crypto why is QUBIC my #1 pick? Well, the first reason is that QUBIC is a unicorn AI startup that happens to use blockchain tech to reach it’s goals. In the real world of Venture Capital it would be fully funded instantly and you would not be invited. Second reason is that the industry admits that Large Language Models have plateaued. Even with enough processing power there is only so much information they can add to their data. Even Google CEO admits that. New approach is needed because the future progress is not possible with LLMs. The third reason is because Large Language Models will not create true AGI. It is evident by Ilya Shutskver latest presentation. Sam Altman of OpenAI is trying to change the definition of what is considered AGI just to lower the plank for his own product. Microsoft’s AI chief is now claiming that it would take 10 years to reach AGI, while QUBIC aims to do this in 2027. All these big players are using wrong technology for what they are trying to achieve and there isn’t enough investor funding for them to pivot. The fourth reason is that QUBIC is headed by Sergey Ivancheglo and 2 renowned AI scientists. Many claim Segey is the creator of Bitcoin. He was the 3rd person to mine bitcoin, he invented Proof of Stake consensus, which Ethereum uses now, he ran the first ICO, and he created 2 of the top gainers in crypto NXT and IOTA. QUBIC is his grand finale after 12 years of development and trials. I am including links below the post as the proof of my claims. Thank you for your time. Please live a like or a comment. It helps me continue making these extensive posts and videos.
5:57

In this post I will explain why people become borderline religious when they discover Qubic. Now with video. Please repost. I want people to learn about QUBIC. The ecosystem consists of 3 separate universes: AI, Mining, and Tickchain. AI is the primary product and purpose of QUBIC and it is supported by Mining to train the AI and by Tickchin for validation and decentralization. Here is how this whole thing works: AI: Let’s start with the AI. The main purpose of QUBIC is creating AGI (Artificial General Intelligence). It’s a type of AI that can self-develop, set tasks, grow, and learn on its own—much like the human brain does. This product is called AIGarth and it uses many cool ideas where AIs can create their own agents and have them compete against one another to evolve. It is basically robots creating robots with the survival-of-the-fittest evolution approach. Very impressive and thought out. To develop such an AI there are several requirements that even the industry giants like OpenAI, Microsoft, and Tesla are missing. One of them is the data processing for AI training. I mean they have their Datacenters, but those are only good enough to train limited Large Language Models such as ChatGPT and Grok. Mining / Training: Now QUBIC solves this problem with its mining architecture. Keep in mind, mining in QUBIC does not secure the chain, primarily it provides the processing power for training the AI. In a sense, QUBIC mining creates the largest distributed datacenter in the world, where individual miners provide their computers for training the AI and get paid with newly issued QUBIC coins. This way QUBIC gets constantly increasing processing power without having to really pay for the infrastructure. And here is another impressive bit of info. QUBIC’s distributed mining network currently ranks above the #1 supercomputer in the world - El Capitan. QUBIC Tickchain The QUBIC chain ties its AI and Mining together to create decentralization, the reward system for miners, it acts as a decision voting system for future development, and it allows AIGarth to function independently through Smart Contracts. In this summary I will not go over the specifics of QUBIC Tickchain. It’s pretty complex so it will be a separate post. Now, it’s an absolute genius piece of tech, which I consider the most advanced product within crypto industry. It is important to know that QUBIC chain runs directly out of Random Access Memory of its validators. It has instant finality and acts as its own operating system. That allows for speeds only bound by current hardware capabilities and it only increases as technology progresses. As I am writing this, QUBIC Tickchain is fully functional and it already hosts several smart-contract based web3 applications. QUBIC has designed its chain to be this fast for a single purpose, to give its future AI the speed it needs to evolve and to react quickly to the outside world. Ilya Shutskever the scientist, who developed ChatGPT clearly states that next generation superintelligence will make decisions in split second with less data. I believe QUBIC is that next generation. Why QUBIC? So out of the sea of AI projects in crypto why is QUBIC my #1 pick? Well, the first reason is that QUBIC is a unicorn AI startup that happens to use blockchain tech to reach it’s goals. In the real world of Venture Capital it would be fully funded instantly and you would not be invited. Second reason is that the industry admits that Large Language Models have plateaued. Even with enough processing power there is only so much information they can add to their data. Even Google CEO admits that. New approach is needed because the future progress is not possible with LLMs. The third reason is because Large Language Models will not create true AGI. It is evident by Ilya Shutskver latest presentation. Sam Altman of OpenAI is trying to change the definition of what is considered AGI just to lower the plank for his own product. Microsoft’s AI chief is now claiming that it would take 10 years to reach AGI, while QUBIC aims to do this in 2027. All these big players are using wrong technology for what they are trying to achieve and there isn’t enough investor funding for them to pivot. The fourth reason is that QUBIC is headed by Sergey Ivancheglo and 2 renowned AI scientists. Many claim Segey is the creator of Bitcoin. He was the 3rd person to mine bitcoin, he invented Proof of Stake consensus, which Ethereum uses now, he ran the first ICO, and he created 2 of the top gainers in crypto NXT and IOTA. QUBIC is his grand finale after 12 years of development and trials. I am including links below the post as the proof of my claims. Thank you for your time. Please live a like or a comment. It helps me continue making these extensive posts and videos.

retrodrive ⛏

24,757 views • 1 year ago

Introducing Stanley: The first AI Head of Content that will help you grow your following. Stanley works just like a real employee: text him and he’ll create, edit, and strategize viral content across X, LinkedIn, and Instagram. Making viral content is hard. It requires deep research, deep platform understanding, and knowing what’s culturally relevant at that exact minute. When you ask ChatGPT to “write a social post” it doesn’t know what good content looks like, it’s not actively consuming content, and it doesn’t understand you. This is why the output is slop. Stanley solves this. First Stanley deeply understands your unique voice and writing style (and doesn’t include AI language like em dashes and “it’s not x it’s y”) Then he plugs into your day-to-day (your Slack, Notion, Calendar, Granola) and proactively suggests viral posts for you based off the most interesting things happening in your life. All you need to do is voice note him your thoughts, and Stanley optimizes and then schedules a post across all platforms. Example: Last week I asked ChatGPT "what happened on X" and got a generic summary. I asked Stanley the same thing and he told me about Jensen Huang's first post on X and the viral Deny’s comment. That’s because Stanley has a team of specialized agents that are always on. One agent researches viral posts through the X API. One studies your voice. One doom scrolls X, LinkedIn, and Instagram. 2 months ago we released Stanley in beta to 100 social media power users like @chasepassiveincome, Jay Yang, Mitchell, and Pascio who’ve used Stanley in their daily content process. Engagement rates increased by >50% for the average beta tester. (in fact you’ve probably engaged w/ Stanley posts without realizing) Posting content has genuinely changed my life for the better: it’s helped me raise millions of $, landed me my first customers, and attracted the very best employees. Stan wouldn’t be a $40M ARR business without Content. Stanley is our attempt at democratizing that. Our mission is to help any Entrepreneur tell their story, so we'd love for you to try Stanley for free here: One more thing.. You can see a glimpse of Stanley’s power in the comments below. Drop a reply and Stanley will analyze your X content right now. It'll pull your posts, study your voice, and tell you what's working and what's not. Each reply costs us abt ~$1 in tokens. Go abuse it. (Thank you VC’s.) FYI: This post and launch video were written 100% w/ the help of Stanley (how’d he do?) See Stanley work below 👇
2:16

Introducing Stanley: The first AI Head of Content that will help you grow your following. Stanley works just like a real employee: text him and he’ll create, edit, and strategize viral content across X, LinkedIn, and Instagram. Making viral content is hard. It requires deep research, deep platform understanding, and knowing what’s culturally relevant at that exact minute. When you ask ChatGPT to “write a social post” it doesn’t know what good content looks like, it’s not actively consuming content, and it doesn’t understand you. This is why the output is slop. Stanley solves this. First Stanley deeply understands your unique voice and writing style (and doesn’t include AI language like em dashes and “it’s not x it’s y”) Then he plugs into your day-to-day (your Slack, Notion, Calendar, Granola) and proactively suggests viral posts for you based off the most interesting things happening in your life. All you need to do is voice note him your thoughts, and Stanley optimizes and then schedules a post across all platforms. Example: Last week I asked ChatGPT "what happened on X" and got a generic summary. I asked Stanley the same thing and he told me about Jensen Huang's first post on X and the viral Deny’s comment. That’s because Stanley has a team of specialized agents that are always on. One agent researches viral posts through the X API. One studies your voice. One doom scrolls X, LinkedIn, and Instagram. 2 months ago we released Stanley in beta to 100 social media power users like @chasepassiveincome, Jay Yang, Mitchell, and Pascio who’ve used Stanley in their daily content process. Engagement rates increased by >50% for the average beta tester. (in fact you’ve probably engaged w/ Stanley posts without realizing) Posting content has genuinely changed my life for the better: it’s helped me raise millions of $, landed me my first customers, and attracted the very best employees. Stan wouldn’t be a $40M ARR business without Content. Stanley is our attempt at democratizing that. Our mission is to help any Entrepreneur tell their story, so we'd love for you to try Stanley for free here: One more thing.. You can see a glimpse of Stanley’s power in the comments below. Drop a reply and Stanley will analyze your X content right now. It'll pull your posts, study your voice, and tell you what's working and what's not. Each reply costs us abt ~$1 in tokens. Go abuse it. (Thank you VC’s.) FYI: This post and launch video were written 100% w/ the help of Stanley (how’d he do?) See Stanley work below 👇

John Hu

2,097,079 views • 1 month ago

Apple’s iPad “Crush” Ad Is Bleak, Ominous and Threatening I don’t know if you’ve seen Apple’s just-released commercial for the “New” iPad Pro, but it’s pretty awful. It is dark, humorless, and feels like a not-so-thinly veiled threat to writers, musicians, game makers, developers, and artists of all kinds. …and children, even. I’ve watched it at least five times today alone, and I’m left with one big question. “Who on earth approved this?” It’s absolutely baffling that the world’s largest technology company, with the world’s biggest marketing budget, thought this would be a good idea. What kind of idiot—or idiots, since dozens or hundreds of people had to be involved in the writing, staging, producing, recording, and editing—felt this kind of ad would somehow create a positive emotional connection with consumers? Seriously, it’s terrifying. In a dank, cold warehouse, devoid of all life and humanity, an industrial crusher comes to life, and slowly starts destroying a collection of musical, philosophical, and artistic devices and instruments. For no apparent reason, everything starts getting smashed: first, a trumpet, then an arcade video game, then cans of paint, a piano, a globe, a metronome, a guitar… on and on it goes, obliterating everything in sight into a colorful, gooey, explosive mess. Books, camera lenses, lamps, a guitar, a sculpture, and a typewriter—all tools of the liberal arts—get mangled into a garbage heap as Sonny & Cher cheerfully sing, “All I ever need is you.” In the penultimate moment, a goofy yellow smiley emoji becomes a bug-eyed scary-clown freak as it, too, is crushed to death. Worse, if you enable closed captions like I do by default, the video says: “[POPPING] [SPLAT]” right as its eyeballs pop out of its head when Cher sings, “Give me a reason to build my world around you.” It’s enough to make a child cry. It has all the comforting vibes of the burnt pink teddy bear floating in the swimming pool on Breaking Bad after two planes crash in mid-air. I have so many questions (aside from simply wondering the names of the soon-to-be ex-employees who greenlit this abomination). First of all, as a trumpet player myself, I am personally offended that they made me watch a perfectly good trumpet get smashed to smithereens like it’s no big deal. Why would they torture me like this? Second of all, what is the message here? No, not that “the most powerful iPad ever is also the thinnest,” as the voiceover artist states in the last few seconds of the clip. I mean: what is the message? Ostensibly, pulverizing children’s toys, arcade games, architectural models, and ceramic Angry Birds into a paste implies something like “We’re taking all the best of humanity; all the collective works of Western Civilization, smashing it into pieces and putting it inside this remarkably thin device so you can have all of it in the palm of your hand.” But my oh my, is there an elephant in this room… he’s hiding behind the monstrous destroying machine. Did anyone inside Apple realize that everyone outside Apple will recognize this imagery in a metaphorical sense, but not the one Apple intended? We don’t see a crushing machine gently consolidating the greatest output of all our artistic endeavors, simply reformatted for a digital age and consumed by everyone with instant, fingertip access. We see what is painstakingly obvious to us, and the timing couldn’t possibly be worse. We see a giant, soulless machine consuming our work in a very different way. Right now, AI models are training themselves on our intellectual property and even our very own personally-identifying data. We aren’t the ones doing the consuming. We’re the ones being consumed. The tech industry has become one massive gaping maw, opening wide and swallowing everything in sight, chewing it up into little bits and pieces of comminuted waste, like a paper shredder or a garbage disposal. It’s destruction in its most literal form. And for what? For a newer version of the iPad that is only slightly thinner than its predecessor? For an only marginally improved version of Apple’s tablet device that has been around for 14 years? For increased profits? This is a terrible look for Apple. They may as well be saying: “All your work are belong to us.” Personally, I am a fan of artificial intelligence. I am eagerly embracing our robot overlords and I welcome our new CSV god (as the actual developers of AI models like to say). I look forward to the freedom and innovation that will come as a result of humanity augmenting our intelligence with AI like a force multiplier on a battlefield. But if Apple has the same perspective I do, they’re selling it in the worst possible way. When I see this video, I see that Apple is definitely crushing something… but I’m not sure what. -Crushing small companies that develop apps for the extremely heavy-handed App Store, which imposes byzantine restrictions on what they can and can’t do with their own apps? -Crushing competitors by limiting what they can do on the iOS and MacOS platforms with arbitrary and capricious rules about enabling functionalities that Apple doesn’t like, even if users do? -Crushing publishers and content creators with a punitive 30% fee on all subscriptions and in-app purchases? -Crushing choice and competition by not allowing app makers to make apps and programs that do the same thing that native apps already do, even if they do it better? -Crushing all human creativity and innovation by automating and systematizing everything? In the early days of the “Google vs. Apple” fight over the web and app stores, I was really concerned that Google was becoming way, way too powerful. Specifically, in 2015, when Google came up with “app streaming,” they announced a desire to form a “web of apps.” This was concerning. Especially when coupled with Google’s efforts to steal content from other websites and provide it to users via the “knowledge graph” results, ending up with the creation of “zero-click” search results pages, which absolutely punished website owners and content creators. By taking the most valuable content off a website and showing it to Google users without them needing to click through to the website itself, Google had essentially stolen everybody’s intellectual property with only the most minimal attribution possible (to fend off lawsuits no doubt, but with no intention of users actually visiting the website in question anymore). “Google is eating the internet,” I thought, and said out loud, (although I probably wasn’t the first person to use that phrase) But what I meant was purely an analogy. It was vague and ambiguous, almost silly. Maybe I was wrong, though: maybe it’s Apple that’s doing the eating. Maybe Apple is not only gobbling up everyone else’s work, but also homogenizing it—and us—and forcing us to use their platform, pay their fees, abide by their rules, and constantly keep upgrading, upgrading, upgrading, to an ever-thinner iPad in order to use it. Watch the video again. This is the stuff of nightmares. To be perfectly fair, even if I were to take the commercial at face value and ignore it’s off-the-charts creepiness and just stick to its one stated claim—that the new iPad Pro is thinner—it still fails as a commercial. Why? Because nobody cares how thin an iPad is. Seriously. I’ve owned an iPad since 2010: that means I’ve carried around a version of Apple’s already-thin tablet every day for over a dozen years. Never once have I said to myself: “You know what improvement I’d really like to see in this thing? I wish it were thinner.” Never. That thought has never crossed my mind, even once. You know what has? -Better battery life. -I’d like my iPad to not get hot to the touch when I use the Apple Pencil to take notes. -I wish it wasn’t so fragile: I dropped my brand-new iPad 2 back in the day when it slipped out of the arm-hold I was carrying it in, it bounced on the pavement, and the screen shattered into a thousand pieces, making it unusable. -I wish it had more storage. -I wish Apple would stop changing the type of cable connector it uses: I’ve gone from the original 30-pin connector to the Lightning connector, and now to the current USB-C/Thunderbolt connector. -I wish I could view the screen in direct sunlight. -I wish it wouldn’t overheat and turn off automatically when I use it outdoors in the summertime. Those are announcements I would welcome in a new iPad Pro commercial. None of this “now even thinner” nonsense nobody needs or cares about. So, back to the commercial. In my opinion, whoever made this ad should be fired. I almost never say that about other companies, especially for good-faith marketing efforts gone wrong… those of us who work in marketing make mistakes sometimes, and we learn from them. But cases like this warrant a special exception. Marketing and advertising are designed to make people want to buy your products. This commercial doesn’t just not make me want to buy Apple’s products. It makes me not want to buy Apple’s products, which is something altogether different. It turns me from someone who likes iPads into someone who is almost rethinking iPads entirely. That’s not just a bad advertisement; it’s a harmful advertisement. Apple’s usually known for great commercials. The legendary 1984 Super Bowl commercial was, of course, their best. I thought “Hello, I’m a Mac” was absolutely brilliant. They have made some missteps along the way, but this one is really bad. Not even their nauseatingly preachy and woke “Mother Nature” ad from a few years ago was this bad. Steve Jobs once said, “Technology alone is not enough—it’s technology married with liberal arts, married with the humanities, that yields us the results that make our heart sing.” My goodness, that last line alone is poetry itself! This ad seems to be Apple signaling that they don’t believe in that anymore. And I don’t think all this handwringing is an overreaction to where you could say “Oh, c’mon, it’s just a commercial! What’s the big deal?” It is a big deal. It tells you about the values of the company, and what they intend to communicate. Really, how is this the same company that used to sell iPhones by showing grandmas using FaceTime to connect with their baby grandchildren from afar during the holidays? Everything about it is wrong: even the thumbnail they chose for it (the bulging-eyed smiley face) and the fact that they gave it the title “Crush!” It was fun to see the reactions to the video online today. I find it fascinating that Apple shared it on YouTube but turned off the comments. On X, Tim Cook shared it Tuesday, and the video, which so richly deserves to be mocked, is getting it in spades. Some people are calling it “anti-art.” One user called it “soul-crushing,” which was about as literal and logical a response as you’d expect. It turns out Apple actually made an announcement about the commercial. In response to the (apparently unexpected) poor welcome it got, Apple wrote: “We missed the mark with this video, and we’re sorry.” Lame response from a tone-deaf tech behemoth, but still, they hopefully got the message. C’mon, Apple. I have seen the future, and this ain’t it.
1:08

Apple’s iPad “Crush” Ad Is Bleak, Ominous and Threatening I don’t know if you’ve seen Apple’s just-released commercial for the “New” iPad Pro, but it’s pretty awful. It is dark, humorless, and feels like a not-so-thinly veiled threat to writers, musicians, game makers, developers, and artists of all kinds. …and children, even. I’ve watched it at least five times today alone, and I’m left with one big question. “Who on earth approved this?” It’s absolutely baffling that the world’s largest technology company, with the world’s biggest marketing budget, thought this would be a good idea. What kind of idiot—or idiots, since dozens or hundreds of people had to be involved in the writing, staging, producing, recording, and editing—felt this kind of ad would somehow create a positive emotional connection with consumers? Seriously, it’s terrifying. In a dank, cold warehouse, devoid of all life and humanity, an industrial crusher comes to life, and slowly starts destroying a collection of musical, philosophical, and artistic devices and instruments. For no apparent reason, everything starts getting smashed: first, a trumpet, then an arcade video game, then cans of paint, a piano, a globe, a metronome, a guitar… on and on it goes, obliterating everything in sight into a colorful, gooey, explosive mess. Books, camera lenses, lamps, a guitar, a sculpture, and a typewriter—all tools of the liberal arts—get mangled into a garbage heap as Sonny & Cher cheerfully sing, “All I ever need is you.” In the penultimate moment, a goofy yellow smiley emoji becomes a bug-eyed scary-clown freak as it, too, is crushed to death. Worse, if you enable closed captions like I do by default, the video says: “[POPPING] [SPLAT]” right as its eyeballs pop out of its head when Cher sings, “Give me a reason to build my world around you.” It’s enough to make a child cry. It has all the comforting vibes of the burnt pink teddy bear floating in the swimming pool on Breaking Bad after two planes crash in mid-air. I have so many questions (aside from simply wondering the names of the soon-to-be ex-employees who greenlit this abomination). First of all, as a trumpet player myself, I am personally offended that they made me watch a perfectly good trumpet get smashed to smithereens like it’s no big deal. Why would they torture me like this? Second of all, what is the message here? No, not that “the most powerful iPad ever is also the thinnest,” as the voiceover artist states in the last few seconds of the clip. I mean: what is the message? Ostensibly, pulverizing children’s toys, arcade games, architectural models, and ceramic Angry Birds into a paste implies something like “We’re taking all the best of humanity; all the collective works of Western Civilization, smashing it into pieces and putting it inside this remarkably thin device so you can have all of it in the palm of your hand.” But my oh my, is there an elephant in this room… he’s hiding behind the monstrous destroying machine. Did anyone inside Apple realize that everyone outside Apple will recognize this imagery in a metaphorical sense, but not the one Apple intended? We don’t see a crushing machine gently consolidating the greatest output of all our artistic endeavors, simply reformatted for a digital age and consumed by everyone with instant, fingertip access. We see what is painstakingly obvious to us, and the timing couldn’t possibly be worse. We see a giant, soulless machine consuming our work in a very different way. Right now, AI models are training themselves on our intellectual property and even our very own personally-identifying data. We aren’t the ones doing the consuming. We’re the ones being consumed. The tech industry has become one massive gaping maw, opening wide and swallowing everything in sight, chewing it up into little bits and pieces of comminuted waste, like a paper shredder or a garbage disposal. It’s destruction in its most literal form. And for what? For a newer version of the iPad that is only slightly thinner than its predecessor? For an only marginally improved version of Apple’s tablet device that has been around for 14 years? For increased profits? This is a terrible look for Apple. They may as well be saying: “All your work are belong to us.” Personally, I am a fan of artificial intelligence. I am eagerly embracing our robot overlords and I welcome our new CSV god (as the actual developers of AI models like to say). I look forward to the freedom and innovation that will come as a result of humanity augmenting our intelligence with AI like a force multiplier on a battlefield. But if Apple has the same perspective I do, they’re selling it in the worst possible way. When I see this video, I see that Apple is definitely crushing something… but I’m not sure what. -Crushing small companies that develop apps for the extremely heavy-handed App Store, which imposes byzantine restrictions on what they can and can’t do with their own apps? -Crushing competitors by limiting what they can do on the iOS and MacOS platforms with arbitrary and capricious rules about enabling functionalities that Apple doesn’t like, even if users do? -Crushing publishers and content creators with a punitive 30% fee on all subscriptions and in-app purchases? -Crushing choice and competition by not allowing app makers to make apps and programs that do the same thing that native apps already do, even if they do it better? -Crushing all human creativity and innovation by automating and systematizing everything? In the early days of the “Google vs. Apple” fight over the web and app stores, I was really concerned that Google was becoming way, way too powerful. Specifically, in 2015, when Google came up with “app streaming,” they announced a desire to form a “web of apps.” This was concerning. Especially when coupled with Google’s efforts to steal content from other websites and provide it to users via the “knowledge graph” results, ending up with the creation of “zero-click” search results pages, which absolutely punished website owners and content creators. By taking the most valuable content off a website and showing it to Google users without them needing to click through to the website itself, Google had essentially stolen everybody’s intellectual property with only the most minimal attribution possible (to fend off lawsuits no doubt, but with no intention of users actually visiting the website in question anymore). “Google is eating the internet,” I thought, and said out loud, (although I probably wasn’t the first person to use that phrase) But what I meant was purely an analogy. It was vague and ambiguous, almost silly. Maybe I was wrong, though: maybe it’s Apple that’s doing the eating. Maybe Apple is not only gobbling up everyone else’s work, but also homogenizing it—and us—and forcing us to use their platform, pay their fees, abide by their rules, and constantly keep upgrading, upgrading, upgrading, to an ever-thinner iPad in order to use it. Watch the video again. This is the stuff of nightmares. To be perfectly fair, even if I were to take the commercial at face value and ignore it’s off-the-charts creepiness and just stick to its one stated claim—that the new iPad Pro is thinner—it still fails as a commercial. Why? Because nobody cares how thin an iPad is. Seriously. I’ve owned an iPad since 2010: that means I’ve carried around a version of Apple’s already-thin tablet every day for over a dozen years. Never once have I said to myself: “You know what improvement I’d really like to see in this thing? I wish it were thinner.” Never. That thought has never crossed my mind, even once. You know what has? -Better battery life. -I’d like my iPad to not get hot to the touch when I use the Apple Pencil to take notes. -I wish it wasn’t so fragile: I dropped my brand-new iPad 2 back in the day when it slipped out of the arm-hold I was carrying it in, it bounced on the pavement, and the screen shattered into a thousand pieces, making it unusable. -I wish it had more storage. -I wish Apple would stop changing the type of cable connector it uses: I’ve gone from the original 30-pin connector to the Lightning connector, and now to the current USB-C/Thunderbolt connector. -I wish I could view the screen in direct sunlight. -I wish it wouldn’t overheat and turn off automatically when I use it outdoors in the summertime. Those are announcements I would welcome in a new iPad Pro commercial. None of this “now even thinner” nonsense nobody needs or cares about. So, back to the commercial. In my opinion, whoever made this ad should be fired. I almost never say that about other companies, especially for good-faith marketing efforts gone wrong… those of us who work in marketing make mistakes sometimes, and we learn from them. But cases like this warrant a special exception. Marketing and advertising are designed to make people want to buy your products. This commercial doesn’t just not make me want to buy Apple’s products. It makes me not want to buy Apple’s products, which is something altogether different. It turns me from someone who likes iPads into someone who is almost rethinking iPads entirely. That’s not just a bad advertisement; it’s a harmful advertisement. Apple’s usually known for great commercials. The legendary 1984 Super Bowl commercial was, of course, their best. I thought “Hello, I’m a Mac” was absolutely brilliant. They have made some missteps along the way, but this one is really bad. Not even their nauseatingly preachy and woke “Mother Nature” ad from a few years ago was this bad. Steve Jobs once said, “Technology alone is not enough—it’s technology married with liberal arts, married with the humanities, that yields us the results that make our heart sing.” My goodness, that last line alone is poetry itself! This ad seems to be Apple signaling that they don’t believe in that anymore. And I don’t think all this handwringing is an overreaction to where you could say “Oh, c’mon, it’s just a commercial! What’s the big deal?” It is a big deal. It tells you about the values of the company, and what they intend to communicate. Really, how is this the same company that used to sell iPhones by showing grandmas using FaceTime to connect with their baby grandchildren from afar during the holidays? Everything about it is wrong: even the thumbnail they chose for it (the bulging-eyed smiley face) and the fact that they gave it the title “Crush!” It was fun to see the reactions to the video online today. I find it fascinating that Apple shared it on YouTube but turned off the comments. On X, Tim Cook shared it Tuesday, and the video, which so richly deserves to be mocked, is getting it in spades. Some people are calling it “anti-art.” One user called it “soul-crushing,” which was about as literal and logical a response as you’d expect. It turns out Apple actually made an announcement about the commercial. In response to the (apparently unexpected) poor welcome it got, Apple wrote: “We missed the mark with this video, and we’re sorry.” Lame response from a tone-deaf tech behemoth, but still, they hopefully got the message. C’mon, Apple. I have seen the future, and this ain’t it.

Ron Stauffer

19,249 views • 2 years ago

Grok Bot summary of NVIDIA CEO Jensen Huang’s fireside chat at today’s G20 Meeting: Power, data centers, and infrastructure - He said AI has turned a corner: it is now productive infrastructure, like energy or the internet, not just phones and PCs. - Energy is the bottom of the stack. You cannot produce intelligence without it. These systems turn electricity into mathematics, then into something you can sell. - Compute is priced like power: dollars per million tokens, the way energy is dollars per kilowatt-hour. - A data center is land, power, and a shell. Plug energy in, and money starts coming out. - Every country needs this the way it needs water, roads, electricity, and the internet. Local capacity is what activates researchers, students, startups, and industry. - US edge he cited: pro-energy growth and faster regulation. This year, close to a trillion dollars into US infrastructure, and jobs across chip fabs, computer plants, and “AI factories.” - Scale: one gigawatt of this infrastructure is about $50–60 billion (old-school, a $25 billion chip fab felt huge). NVIDIA’s plan: 100 gigawatts by the end of the decade. AI as a growth engine - The “token” is just a number produced by a lot of computation. String enough of them together and you get an image, a paragraph, an answer, or a new idea. That is how you put a price on intelligence. - Once you use it, the value is obvious: more productive workers, more capable engineers, faster education for young populations, a quicker lift for countries building science and tech. - For 50 years, computers were a tool for maybe 10–20 million people who knew how to program. Now anyone can program a computer in human language. He called that the great equalizer. - AI is a five-layer cake: energy, chips, data-center infrastructure, models, then data and applications. The US is trying to lead the whole cake. Other countries do not have to win every layer. They should pick where to invest, and push adoption into education, healthcare, manufacturing, and science. - NVIDIA’s pitch: it runs American models, international models, and models in biology, chemistry, physics, and robotics. - AGI in the next couple of years, and he argued we are practically there now. That does not mean a company plugs into an API and becomes productive overnight. Even a brilliant MIT hire still needs context, purpose, and a harness around them. Same for AI. - Tasks get automated. The job’s purpose (context, meaning, direction) stays. People get supercharged, not replaced. “All the jobs disappear” he called nonsense. - Use off-the-shelf AI where you can, but every country and company still has to build some of its own intelligence. Do not outsource all of it. - Next 5–10 years: things that took 10 years take 1; things that took a year take a month. First time innovators talk about adding $20–50 trillion of benefit to a $100 trillion industry. Robotics and physical AI - AI starts as software. What made LLMs useful was putting an “agent harness” around them: retrieval, working memory, tools, collaboration. - Put that agent in a body and it is a robot. Four wheels: a self-driving car. A manipulator: pick-and-place. Also grocery and logistics vehicles, surgical robots, autonomous drug-discovery labs. - Same idea throughout: a large language model plus an agentic system, using digital tools or physical ones. How countries should play it - Treat AI as infrastructure, then actually build some of it at home. - You do not have to invent every layer. You do have to get AI into your own industries. - The real risk is not using it and getting left behind. Safety and regulation - New tech always looks like magic. Building it is engineering, and the people building it own safety, the way they do for cars and planes. - He wants the tech to advance faster, because advancement is what made it safer (less hallucination, grounded in real truth). - Balance fear-and-safety talk with prosperity talk. Airline analogy: passengers did not want ads about whose plane was safer. They wanted new destinations. Safety is the builder’s job, not the public’s daily burden. - Regulate actual, pragmatic harm, not hypothetical harm. Fold AI into agencies that already exist (FDA, NHTSA, and the rest) instead of inventing a freeze on early S-curve tech. Why GPUs, in his telling - A GPU is a general-purpose parallel processor: physics, chemistry, graphics, and AI. - 14th-generation architecture, in clouds, on-prem, at the edge, in robots and cars. - Because a gigawatt costs $50–60 billion, you want an architecture that is fungible and durable. If models change (and they are changing fast), a too-specialized chip can obsolete the whole investment. That, he said, is why NVIDIA’s growth is accelerating.
30:43

Grok Bot summary of NVIDIA CEO Jensen Huang’s fireside chat at today’s G20 Meeting: Power, data centers, and infrastructure - He said AI has turned a corner: it is now productive infrastructure, like energy or the internet, not just phones and PCs. - Energy is the bottom of the stack. You cannot produce intelligence without it. These systems turn electricity into mathematics, then into something you can sell. - Compute is priced like power: dollars per million tokens, the way energy is dollars per kilowatt-hour. - A data center is land, power, and a shell. Plug energy in, and money starts coming out. - Every country needs this the way it needs water, roads, electricity, and the internet. Local capacity is what activates researchers, students, startups, and industry. - US edge he cited: pro-energy growth and faster regulation. This year, close to a trillion dollars into US infrastructure, and jobs across chip fabs, computer plants, and “AI factories.” - Scale: one gigawatt of this infrastructure is about $50–60 billion (old-school, a $25 billion chip fab felt huge). NVIDIA’s plan: 100 gigawatts by the end of the decade. AI as a growth engine - The “token” is just a number produced by a lot of computation. String enough of them together and you get an image, a paragraph, an answer, or a new idea. That is how you put a price on intelligence. - Once you use it, the value is obvious: more productive workers, more capable engineers, faster education for young populations, a quicker lift for countries building science and tech. - For 50 years, computers were a tool for maybe 10–20 million people who knew how to program. Now anyone can program a computer in human language. He called that the great equalizer. - AI is a five-layer cake: energy, chips, data-center infrastructure, models, then data and applications. The US is trying to lead the whole cake. Other countries do not have to win every layer. They should pick where to invest, and push adoption into education, healthcare, manufacturing, and science. - NVIDIA’s pitch: it runs American models, international models, and models in biology, chemistry, physics, and robotics. - AGI in the next couple of years, and he argued we are practically there now. That does not mean a company plugs into an API and becomes productive overnight. Even a brilliant MIT hire still needs context, purpose, and a harness around them. Same for AI. - Tasks get automated. The job’s purpose (context, meaning, direction) stays. People get supercharged, not replaced. “All the jobs disappear” he called nonsense. - Use off-the-shelf AI where you can, but every country and company still has to build some of its own intelligence. Do not outsource all of it. - Next 5–10 years: things that took 10 years take 1; things that took a year take a month. First time innovators talk about adding $20–50 trillion of benefit to a $100 trillion industry. Robotics and physical AI - AI starts as software. What made LLMs useful was putting an “agent harness” around them: retrieval, working memory, tools, collaboration. - Put that agent in a body and it is a robot. Four wheels: a self-driving car. A manipulator: pick-and-place. Also grocery and logistics vehicles, surgical robots, autonomous drug-discovery labs. - Same idea throughout: a large language model plus an agentic system, using digital tools or physical ones. How countries should play it - Treat AI as infrastructure, then actually build some of it at home. - You do not have to invent every layer. You do have to get AI into your own industries. - The real risk is not using it and getting left behind. Safety and regulation - New tech always looks like magic. Building it is engineering, and the people building it own safety, the way they do for cars and planes. - He wants the tech to advance faster, because advancement is what made it safer (less hallucination, grounded in real truth). - Balance fear-and-safety talk with prosperity talk. Airline analogy: passengers did not want ads about whose plane was safer. They wanted new destinations. Safety is the builder’s job, not the public’s daily burden. - Regulate actual, pragmatic harm, not hypothetical harm. Fold AI into agencies that already exist (FDA, NHTSA, and the rest) instead of inventing a freeze on early S-curve tech. Why GPUs, in his telling - A GPU is a general-purpose parallel processor: physics, chemistry, graphics, and AI. - 14th-generation architecture, in clouds, on-prem, at the edge, in robots and cars. - Because a gigawatt costs $50–60 billion, you want an architecture that is fungible and durable. If models change (and they are changing fast), a too-specialized chip can obsolete the whole investment. That, he said, is why NVIDIA’s growth is accelerating.

DogeDesigner

49,803 views • 19 hours ago

I know your timeline is flooded now with word salads of "insane, HER, 10 features you missed, we're so back". Sit down. Chill. Take a deep breath like Mark does in the demo . Let's think step by step: - Technique-wise, OpenAI has figured out a way to map audio to audio directly as first-class modality, and stream videos to a transformer in real-time. These require some new research on tokenization and architecture, but overall it's a data and system optimization problem (as most things are). High-quality data can come from at least 2 sources: 1) Naturally occurring dialogues on YouTube, podcasts, TV series, movies, etc. Whisper can be trained to identify speaker turns in a dialogue or separate overlapping speeches for automated annotation. 2) Synthetic data. Run the slow 3-stage pipeline using the most powerful models: speech1->text1 (ASR), text1->text2 (LLM), text2->speech2 (TTS). The middle LLM can decide when to stop and also simulate how to resume from interruption. It could output additional "thought traces" that are not verbalized to help generate better reply. Then GPT-4o distills directly from speech1->speech2, with optional auxiliary loss functions based on the 3-stage data. After distillation, these behaviors are now baked into the model without emitting intermediate texts. On the system side: the latency would not meet real-time threshold if every video frame is decompressed into an RGB image. OpenAI has likely developed their own neural-first, streaming video codec to transmit the motion deltas as tokens. The communication protocol and NN inference must be co-optimized. For example, there could be a small and energy-efficient NN running on the edge device that decides to transmit more tokens if the video is interesting, and fewer otherwise. - I didn't expect GPT-4o to be closer to GPT-5, the rumored "Arrakis" model that takes multimodal in and out. In fact, it's likely an early checkpoint of GPT-5 that hasn't finished training yet. The branding betrays a certain insecurity. Ahead of Google I/O, OpenAI would rather beat our mental projection of GPT-4.5 than disappoint by missing the sky-high expectation for GPT-5. A smart move to buy more time. - Notably, the assistant is much more lively and even a bit flirty. GPT-4o is trying (perhaps a bit too hard) to sound like HER. OpenAI is eating Character AI's lunch, with almost 100% overlap in form factor and huge distribution channels. It's a pivot towards more emotional AI with strong personality, which OpenAI seemed to actively suppress in the past. - Whoever wins Apple first wins big time. I see 3 levels of integration with iOS: 1) Ditch Siri. OpenAI distills a smaller-tier, purely on-device GPT-4o for iOS, with optional paid upgrade to use the cloud. 2) Native features to stream the camera or screen into the model. Chip-level support for neural audio/video codec. 3) Integrate with iOS system-level action API and smart home APIs. No one uses Siri Shortcuts, but it's time to resurrect. This could become the AI agent product with a billion users from the get-go. The FSD for smartphones with a Tesla-scale data flywheel.
14:22

I know your timeline is flooded now with word salads of "insane, HER, 10 features you missed, we're so back". Sit down. Chill. Take a deep breath like Mark does in the demo . Let's think step by step: - Technique-wise, OpenAI has figured out a way to map audio to audio directly as first-class modality, and stream videos to a transformer in real-time. These require some new research on tokenization and architecture, but overall it's a data and system optimization problem (as most things are). High-quality data can come from at least 2 sources: 1) Naturally occurring dialogues on YouTube, podcasts, TV series, movies, etc. Whisper can be trained to identify speaker turns in a dialogue or separate overlapping speeches for automated annotation. 2) Synthetic data. Run the slow 3-stage pipeline using the most powerful models: speech1->text1 (ASR), text1->text2 (LLM), text2->speech2 (TTS). The middle LLM can decide when to stop and also simulate how to resume from interruption. It could output additional "thought traces" that are not verbalized to help generate better reply. Then GPT-4o distills directly from speech1->speech2, with optional auxiliary loss functions based on the 3-stage data. After distillation, these behaviors are now baked into the model without emitting intermediate texts. On the system side: the latency would not meet real-time threshold if every video frame is decompressed into an RGB image. OpenAI has likely developed their own neural-first, streaming video codec to transmit the motion deltas as tokens. The communication protocol and NN inference must be co-optimized. For example, there could be a small and energy-efficient NN running on the edge device that decides to transmit more tokens if the video is interesting, and fewer otherwise. - I didn't expect GPT-4o to be closer to GPT-5, the rumored "Arrakis" model that takes multimodal in and out. In fact, it's likely an early checkpoint of GPT-5 that hasn't finished training yet. The branding betrays a certain insecurity. Ahead of Google I/O, OpenAI would rather beat our mental projection of GPT-4.5 than disappoint by missing the sky-high expectation for GPT-5. A smart move to buy more time. - Notably, the assistant is much more lively and even a bit flirty. GPT-4o is trying (perhaps a bit too hard) to sound like HER. OpenAI is eating Character AI's lunch, with almost 100% overlap in form factor and huge distribution channels. It's a pivot towards more emotional AI with strong personality, which OpenAI seemed to actively suppress in the past. - Whoever wins Apple first wins big time. I see 3 levels of integration with iOS: 1) Ditch Siri. OpenAI distills a smaller-tier, purely on-device GPT-4o for iOS, with optional paid upgrade to use the cloud. 2) Native features to stream the camera or screen into the model. Chip-level support for neural audio/video codec. 3) Integrate with iOS system-level action API and smart home APIs. No one uses Siri Shortcuts, but it's time to resurrect. This could become the AI agent product with a billion users from the get-go. The FSD for smartphones with a Tesla-scale data flywheel.

Jim Fan

991,844 views • 2 years ago

You can see hundreds of photos of Torres del Paine before you go and still be surprised by how much of the park has nothing to do with the famous towers. There are turquoise lakes, glaciers, waterfalls, open grasslands and lenga forests. Guanacos graze near the road. Condors ride the wind above the mountains. Depending on the hour, the Paine Massif can fill the horizon or disappear almost completely behind clouds. The three granite towers are the image everyone knows, but they’re only one part of the place. Torres del Paine National Park sits in southern Chile, where the Andes meet the Patagonian steppe. The park covers more than 227,000 hectares and has been part of a UNESCO Biosphere Reserve since 1978. Glaciers carved much of what you see today, and ice still runs through the story of the landscape. People were moving through this part of Patagonia long before hikers started arriving with trekking poles and backpacks. The Aónikenk, often called Tehuelche, traveled through the region for thousands of years. Ranching came much later, and large estancias eventually spread across land that now belongs to the park. Sheep, horses and ranch workers were here before refugios and marked trails. The national park was created in 1959 and expanded in the decades that followed. You can still see bits of the ranching past in old buildings, fences and horses along the road. Most trips begin in Puerto Natales. I’d stay there the night before heading into the park rather than treating it as somewhere to sleep between a flight and a bus. The town sits on the water with mountains in the distance, and it’s a good place to have dinner, buy anything you forgot and check your gear one last time. Patagonian lamb shows up on plenty of menus, along with seafood from southern Chile. The drive north is where the trip really starts. Buildings become less frequent. The road cuts through broad stretches of grassland. Guanacos begin appearing in the fields, sometimes close enough to the road that traffic slows down for them. Then, eventually, the mountains show up in the distance. That first view is worth paying attention to. Wildlife is easier to see here than I expected. Guanacos are common. Rheas move through the open country. Foxes live in the park, and condors can sometimes be spotted circling high above the valleys. Pumas are here too, though seeing one usually takes luck, patience or a guide who knows where to look. A lot of people come specifically for the W Trek. The route connects several of the park’s best-known areas, including Base Torres, French Valley and Grey Glacier, and most hikers take about four or five days to complete it. If you like multi-day hiking, it’s an obvious way to see a large part of the park without spending your days getting in and out of a vehicle. The Base Torres hike can also be done on its own. It’s a long day. The trail climbs through the Ascencio Valley, passes through forest and ends with a steep section over rocks before you reach the glacial lake beneath the towers. On a clear day, the view at the top is exactly the one that probably made you want to come here. Of course, Patagonia may have other plans. You can leave under blue skies and arrive with the towers hidden behind cloud. An hour later they might be visible again. Weather changes quickly enough that there’s not much point obsessing over a forecast days in advance. Layers matter. So does a waterproof jacket. The wind is harder to appreciate until you experience it. Patagonia is famous for it, but “windy” doesn’t really describe those moments when a gust hits hard enough to make you adjust your footing on the trail. You’ll also notice trees that have spent their lives growing in one direction. The good news is that you don’t need to hike the W, or even spend every day on a long trail, to see Torres del Paine well. A lot of the park is accessible by road and shorter walks. Salto Grande is an easy stop. Water rushes through a narrow channel between Lake Nordenskjöld and Lake Pehoé, and from there you can continue toward viewpoints of the Cuernos del Paine. The Cuernos look very different from the towers. Their dark upper sections sit above lighter granite walls, and from some angles they’re the mountains I’d rather photograph. Lake Pehoé is close by and deserves time of its own. On the right day, the water turns an almost unreasonable shade of blue-green. The mountains rise behind it, and there are places along the road where you’ll probably pull over even though you hadn’t planned to stop. Grey Glacier is on the western side of the park. It flows down from the Southern Patagonian Ice Field into Grey Lake. Once you see it, the broad valleys and glacial lakes you’ve been passing start to make more sense. Ice shaped much of this place, and here you can still watch part of that story unfolding. There are hiking routes toward viewpoints of the glacier, and boat trips cross Grey Lake for a closer look. Depending on conditions, pieces of ice float in the water between the boat and the glacier. That part of the park feels different from the open country farther east. This is something I’d pay attention to throughout the trip. Torres del Paine changes surprisingly quickly as you move around it. The eastern side opens into Patagonian steppe. Forest becomes more common near the mountains. One lake can be deep blue, another turquoise, another gray beneath the clouds. Rivers run through valleys that seem almost empty. An hour in the car can take you through several versions of Patagonia. That’s why three full days would be my minimum even if you aren’t doing the W Trek. Four is better if the schedule allows. You could spend a day on the Base Torres hike, another around Grey Lake and the western side of the park, and another around Lake Pehoé, Salto Grande and shorter walks. The extra time gives you room to change plans when the weather turns. Where you stay makes a noticeable difference. Puerto Natales has more restaurants, hotels and services, and staying there can keep costs down. The downside is spending more time each day getting to and from the park. Staying inside or near Torres del Paine costs more, but you wake up much closer to the trails and viewpoints. If I could split the trip, I’d spend the first or last night in Puerto Natales and the rest closer to the park. Anyone hiking overnight should sort out refugios or campsites well in advance during the busy season. It’s also worth checking current trail conditions and park rules shortly before arriving, because Patagonia has a way of making plans made six months earlier feel provisional. For most first-time visitors, the warmer months are the easiest time to come. Late spring through early fall in the Southern Hemisphere brings longer daylight and better odds for hiking, although it also brings more people. “Summer” is still a relative term down here. Pack the rain jacket. By the end of a few days, the famous towers may not even be the thing you remember most clearly. It might be a line of guanacos moving across a hillside. The sound of the wind against the car. A glacier appearing across a lake. An empty stretch of road with the Paine Massif slowly getting larger in front of you. Those moments are harder to plan, which is probably why they stick. Come for Base Torres if that’s what first caught your attention. See Grey Glacier. Stop at Lake Pehoé. Hike as much as your legs and the weather allow. But don’t spend the whole trip racing from one famous view to the next. Some of my favorite stretches would probably be the drives between them: old ranch country, open steppe, guanacos scattered across the hills and mountains that keep appearing and disappearing as the clouds move through. You’ll take plenty of pictures in Torres del Paine. Every once in a while, leave the camera in the bag. If you like traveling this way....understanding the history, landscape and people that shaped a place....that’s what I write about in the Timeless Traveler. Travel with a little more soul.
1:08

You can see hundreds of photos of Torres del Paine before you go and still be surprised by how much of the park has nothing to do with the famous towers. There are turquoise lakes, glaciers, waterfalls, open grasslands and lenga forests. Guanacos graze near the road. Condors ride the wind above the mountains. Depending on the hour, the Paine Massif can fill the horizon or disappear almost completely behind clouds. The three granite towers are the image everyone knows, but they’re only one part of the place. Torres del Paine National Park sits in southern Chile, where the Andes meet the Patagonian steppe. The park covers more than 227,000 hectares and has been part of a UNESCO Biosphere Reserve since 1978. Glaciers carved much of what you see today, and ice still runs through the story of the landscape. People were moving through this part of Patagonia long before hikers started arriving with trekking poles and backpacks. The Aónikenk, often called Tehuelche, traveled through the region for thousands of years. Ranching came much later, and large estancias eventually spread across land that now belongs to the park. Sheep, horses and ranch workers were here before refugios and marked trails. The national park was created in 1959 and expanded in the decades that followed. You can still see bits of the ranching past in old buildings, fences and horses along the road. Most trips begin in Puerto Natales. I’d stay there the night before heading into the park rather than treating it as somewhere to sleep between a flight and a bus. The town sits on the water with mountains in the distance, and it’s a good place to have dinner, buy anything you forgot and check your gear one last time. Patagonian lamb shows up on plenty of menus, along with seafood from southern Chile. The drive north is where the trip really starts. Buildings become less frequent. The road cuts through broad stretches of grassland. Guanacos begin appearing in the fields, sometimes close enough to the road that traffic slows down for them. Then, eventually, the mountains show up in the distance. That first view is worth paying attention to. Wildlife is easier to see here than I expected. Guanacos are common. Rheas move through the open country. Foxes live in the park, and condors can sometimes be spotted circling high above the valleys. Pumas are here too, though seeing one usually takes luck, patience or a guide who knows where to look. A lot of people come specifically for the W Trek. The route connects several of the park’s best-known areas, including Base Torres, French Valley and Grey Glacier, and most hikers take about four or five days to complete it. If you like multi-day hiking, it’s an obvious way to see a large part of the park without spending your days getting in and out of a vehicle. The Base Torres hike can also be done on its own. It’s a long day. The trail climbs through the Ascencio Valley, passes through forest and ends with a steep section over rocks before you reach the glacial lake beneath the towers. On a clear day, the view at the top is exactly the one that probably made you want to come here. Of course, Patagonia may have other plans. You can leave under blue skies and arrive with the towers hidden behind cloud. An hour later they might be visible again. Weather changes quickly enough that there’s not much point obsessing over a forecast days in advance. Layers matter. So does a waterproof jacket. The wind is harder to appreciate until you experience it. Patagonia is famous for it, but “windy” doesn’t really describe those moments when a gust hits hard enough to make you adjust your footing on the trail. You’ll also notice trees that have spent their lives growing in one direction. The good news is that you don’t need to hike the W, or even spend every day on a long trail, to see Torres del Paine well. A lot of the park is accessible by road and shorter walks. Salto Grande is an easy stop. Water rushes through a narrow channel between Lake Nordenskjöld and Lake Pehoé, and from there you can continue toward viewpoints of the Cuernos del Paine. The Cuernos look very different from the towers. Their dark upper sections sit above lighter granite walls, and from some angles they’re the mountains I’d rather photograph. Lake Pehoé is close by and deserves time of its own. On the right day, the water turns an almost unreasonable shade of blue-green. The mountains rise behind it, and there are places along the road where you’ll probably pull over even though you hadn’t planned to stop. Grey Glacier is on the western side of the park. It flows down from the Southern Patagonian Ice Field into Grey Lake. Once you see it, the broad valleys and glacial lakes you’ve been passing start to make more sense. Ice shaped much of this place, and here you can still watch part of that story unfolding. There are hiking routes toward viewpoints of the glacier, and boat trips cross Grey Lake for a closer look. Depending on conditions, pieces of ice float in the water between the boat and the glacier. That part of the park feels different from the open country farther east. This is something I’d pay attention to throughout the trip. Torres del Paine changes surprisingly quickly as you move around it. The eastern side opens into Patagonian steppe. Forest becomes more common near the mountains. One lake can be deep blue, another turquoise, another gray beneath the clouds. Rivers run through valleys that seem almost empty. An hour in the car can take you through several versions of Patagonia. That’s why three full days would be my minimum even if you aren’t doing the W Trek. Four is better if the schedule allows. You could spend a day on the Base Torres hike, another around Grey Lake and the western side of the park, and another around Lake Pehoé, Salto Grande and shorter walks. The extra time gives you room to change plans when the weather turns. Where you stay makes a noticeable difference. Puerto Natales has more restaurants, hotels and services, and staying there can keep costs down. The downside is spending more time each day getting to and from the park. Staying inside or near Torres del Paine costs more, but you wake up much closer to the trails and viewpoints. If I could split the trip, I’d spend the first or last night in Puerto Natales and the rest closer to the park. Anyone hiking overnight should sort out refugios or campsites well in advance during the busy season. It’s also worth checking current trail conditions and park rules shortly before arriving, because Patagonia has a way of making plans made six months earlier feel provisional. For most first-time visitors, the warmer months are the easiest time to come. Late spring through early fall in the Southern Hemisphere brings longer daylight and better odds for hiking, although it also brings more people. “Summer” is still a relative term down here. Pack the rain jacket. By the end of a few days, the famous towers may not even be the thing you remember most clearly. It might be a line of guanacos moving across a hillside. The sound of the wind against the car. A glacier appearing across a lake. An empty stretch of road with the Paine Massif slowly getting larger in front of you. Those moments are harder to plan, which is probably why they stick. Come for Base Torres if that’s what first caught your attention. See Grey Glacier. Stop at Lake Pehoé. Hike as much as your legs and the weather allow. But don’t spend the whole trip racing from one famous view to the next. Some of my favorite stretches would probably be the drives between them: old ranch country, open steppe, guanacos scattered across the hills and mountains that keep appearing and disappearing as the clouds move through. You’ll take plenty of pictures in Torres del Paine. Every once in a while, leave the camera in the bag. If you like traveling this way....understanding the history, landscape and people that shaped a place....that’s what I write about in the Timeless Traveler. Travel with a little more soul.

The Timeless Traveler 🇺🇸

20,333 views • 18 days ago