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Continuing the FG design trend of every character must share the same tools, here's how Alex can utilize his 5MK low crush properties. Oki shown: OD Chop > Stance Dash > MP Elbow is +6/+2 and in throw / HP cmd grab range. Also charged HP is funny. BGM:...

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Hatson

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29,288 次观看 • 5 个月前 •via X (Twitter)

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🚨New science DEBATE released on HIGH CARB vs KETO: What the new Tim Noakes vs Louise Burke debate actually shows 👇 American Journal of Clinical Nutrition just released it big debate between two of the biggest names in exercise performance asking one loaded question: does a low-carbohydrate diet impair endurance performance? On one side is Louise Burke, one of the most influential sports nutrition researchers in the world. Her argument: yes, low carb can impair performance? She is not saying every athlete must eat high-carb all the time. She is saying performance depends on the event, intensity, environment, training status, sex, and the athlete in front of you. Her biggest physiological point is fuel economy. Near an athlete’s oxidative ceiling (i.e., elite race pace), she argues that carbohydrate produces more usable energy per liter of oxygen than fat. So even if keto adaptation dramatically raises fat oxidation, that does not automatically mean better speed or power. Now I am skeptical of this point, since we have conducted Randomized Controlled Trails in both runners and ironman competitors and showed that athletes at 86% of their VO2max can utilize fat as the predominant fuel while on a very low carb diet and STILL maintain performance. On the other side, Tim Noakes argues: low carb does not necessarily impair performance when athletes are adapted long enough. He points out that many “keto hurts performance” studies are short, often under four weeks, and that several randomized trials lasting four to six weeks report similar performance between high-carb and low-carb groups. He also reframes fatigue. Instead of saying muscle glycogen is always the main limiter, he argues that during prolonged exercise, maintaining blood glucose, the small glucose pool is the most important. In our trial, 10 grams of carbohydrate per hour improved prolonged cycling performance after both diets. Noakes and Burke also did something really special: they developed a CONSENSUS article where they explored key points of AGREEMENT and DISAGREEMENT for future research. This is the coolest part and in my opinion was one of the verty unique aspect of these three studies My take is this: It is not "team high carb" versus "team keto." The weight of the evidence demonstrates that 1) BOTH high and low carb diets can work, and that 2) Carbohydrates during prolonged exercise are valuable...but 3) How this is applied depends on the individual athlete. What's your take?
2:58

🚨New science DEBATE released on HIGH CARB vs KETO: What the new Tim Noakes vs Louise Burke debate actually shows 👇 American Journal of Clinical Nutrition just released it big debate between two of the biggest names in exercise performance asking one loaded question: does a low-carbohydrate diet impair endurance performance? On one side is Louise Burke, one of the most influential sports nutrition researchers in the world. Her argument: yes, low carb can impair performance? She is not saying every athlete must eat high-carb all the time. She is saying performance depends on the event, intensity, environment, training status, sex, and the athlete in front of you. Her biggest physiological point is fuel economy. Near an athlete’s oxidative ceiling (i.e., elite race pace), she argues that carbohydrate produces more usable energy per liter of oxygen than fat. So even if keto adaptation dramatically raises fat oxidation, that does not automatically mean better speed or power. Now I am skeptical of this point, since we have conducted Randomized Controlled Trails in both runners and ironman competitors and showed that athletes at 86% of their VO2max can utilize fat as the predominant fuel while on a very low carb diet and STILL maintain performance. On the other side, Tim Noakes argues: low carb does not necessarily impair performance when athletes are adapted long enough. He points out that many “keto hurts performance” studies are short, often under four weeks, and that several randomized trials lasting four to six weeks report similar performance between high-carb and low-carb groups. He also reframes fatigue. Instead of saying muscle glycogen is always the main limiter, he argues that during prolonged exercise, maintaining blood glucose, the small glucose pool is the most important. In our trial, 10 grams of carbohydrate per hour improved prolonged cycling performance after both diets. Noakes and Burke also did something really special: they developed a CONSENSUS article where they explored key points of AGREEMENT and DISAGREEMENT for future research. This is the coolest part and in my opinion was one of the verty unique aspect of these three studies My take is this: It is not "team high carb" versus "team keto." The weight of the evidence demonstrates that 1) BOTH high and low carb diets can work, and that 2) Carbohydrates during prolonged exercise are valuable...but 3) How this is applied depends on the individual athlete. What's your take?

Andrew Koutnik, Ph.D.

15,181 次观看 • 3 个月前

Lucas Crespo (Lucas Crespo 📧 ) is the mastermind behind Every 📧 's visual vibe—and he does it one prompt at a time. As our creative lead, Lucas uses tools like native image gen in ChatGPT and Midjourney to generate the cover images you see every day. He also designs the interfaces for our products—Cora, Spiral, and Sparkle—and makes everything on our site feel as thoughtful and delightful as possible. We get into: - Why Every’s aesthetic feels familiar and new at the same time. Every’s aesthetic plays with the tension between the old (like Greek statues and Baroque symbols) and the new (like saturated colors and modern motifs) to make the glamor of the past feel fresh. - Art direction matters more than ever today. As AI makes it easier to generate images, Lucas says the real work of design is shifting toward art direction, specifically, curating an aesthetic that feels “organic;” on his X timeline that’s showing up as clouds, earthy landscapes, and textures. - Reimagining what a website can be with AI. Lucas compares most websites to identical buildings—predictable, efficient, and forgettable—and wonders how AI can help us break that mold by designing experiences that prioritize serendipity over speed, and curiosity over control. - Behind the scenes of Cora’s visual aesthetic. How Lucas designed the landing page and launch video for Cora by rooting it in the product’s philosophy: turning the inbox from a source of chaos into something that feels calm, thoughtful—like stepping into spring. - The future of internet interfaces. Lucas believes the future of digital interfaces will be curated with the same care as a film set or ad campaign, where every detail is chosen with intention. Lucas also walks us through how he created the headline image for Every’s consulting page—a human and robotic hand fist-bumping—using Midjourney to iterate from rough prompt to polished visual. This is a must watch for anyone interested in the future of design and making the internet a little more beautiful every day. Watch below! Timestamps: 1. Introduction: 00:01:41 2. How AI changed the course of Lucas’s career: 00:04:02 3. Why Every’s aesthetic feels both familiar and fresh: 00:11:38 4. Why Lucas thinks minimalism is overrated: 00:16:19 5. Art direction matters more than ever in the age of AI: 00:22:01 6. How to reimagine what a website can be with AI: 00:25:06 7. Lucas’s process in Midjourney to generate cover images: 00:33:41 8. Midjourney v. image generation in ChatGPT: 00:43:01 9. Behind the scenes of Cora’s design language: 00:49:43 10. How AI is rewriting the role of a designer: 00:59:57
1:03:52

Lucas Crespo (Lucas Crespo 📧 ) is the mastermind behind Every 📧 's visual vibe—and he does it one prompt at a time. As our creative lead, Lucas uses tools like native image gen in ChatGPT and Midjourney to generate the cover images you see every day. He also designs the interfaces for our products—Cora, Spiral, and Sparkle—and makes everything on our site feel as thoughtful and delightful as possible. We get into: - Why Every’s aesthetic feels familiar and new at the same time. Every’s aesthetic plays with the tension between the old (like Greek statues and Baroque symbols) and the new (like saturated colors and modern motifs) to make the glamor of the past feel fresh. - Art direction matters more than ever today. As AI makes it easier to generate images, Lucas says the real work of design is shifting toward art direction, specifically, curating an aesthetic that feels “organic;” on his X timeline that’s showing up as clouds, earthy landscapes, and textures. - Reimagining what a website can be with AI. Lucas compares most websites to identical buildings—predictable, efficient, and forgettable—and wonders how AI can help us break that mold by designing experiences that prioritize serendipity over speed, and curiosity over control. - Behind the scenes of Cora’s visual aesthetic. How Lucas designed the landing page and launch video for Cora by rooting it in the product’s philosophy: turning the inbox from a source of chaos into something that feels calm, thoughtful—like stepping into spring. - The future of internet interfaces. Lucas believes the future of digital interfaces will be curated with the same care as a film set or ad campaign, where every detail is chosen with intention. Lucas also walks us through how he created the headline image for Every’s consulting page—a human and robotic hand fist-bumping—using Midjourney to iterate from rough prompt to polished visual. This is a must watch for anyone interested in the future of design and making the internet a little more beautiful every day. Watch below! Timestamps: 1. Introduction: 00:01:41 2. How AI changed the course of Lucas’s career: 00:04:02 3. Why Every’s aesthetic feels both familiar and fresh: 00:11:38 4. Why Lucas thinks minimalism is overrated: 00:16:19 5. Art direction matters more than ever in the age of AI: 00:22:01 6. How to reimagine what a website can be with AI: 00:25:06 7. Lucas’s process in Midjourney to generate cover images: 00:33:41 8. Midjourney v. image generation in ChatGPT: 00:43:01 9. Behind the scenes of Cora’s design language: 00:49:43 10. How AI is rewriting the role of a designer: 00:59:57

Dan Shipper 📧

16,038 次观看 • 1 年前

My favorite weapons in Where Winds Meet! What’s yours so far? Full video on how to get all of them ⬇️ - The Vernal Umbrella: Immobilizing enemies and then hovering in the air to shoot a flurry of projectiles is a killer combo. Not to mention the fact that you can throw the umbrella and let it act like a drone, constantly dealing damage while you switch to your second weapon. And come on: it’s an umbrella! - The Mortal Rope Dart: This weapon lets you pull enemies toward you and drop them down with a kick. You can also drop rats that attack the same target you’re hitting with your light attacks. A really fun weapon once you get the hang of it. - The Panacea Fan: Amazing as an off-hand healing device. Switch to it to create an area-of-effect heal, then grab your DPS weapons again to stay alive while dealing damage. Another super unique weapon. - The Thundercry Blade: Way stronger than I expected. It lets you create a shield based on your HP and grants you Fighting Spirit, which you can use for a powerful charged heavy attack combo that slams enemies to the ground. You can also pull enemies for some crowd control, and after that, your heavy attacks charge 50% faster. #WhereWindsMeet #Ad
0:42

My favorite weapons in Where Winds Meet! What’s yours so far? Full video on how to get all of them ⬇️ - The Vernal Umbrella: Immobilizing enemies and then hovering in the air to shoot a flurry of projectiles is a killer combo. Not to mention the fact that you can throw the umbrella and let it act like a drone, constantly dealing damage while you switch to your second weapon. And come on: it’s an umbrella! - The Mortal Rope Dart: This weapon lets you pull enemies toward you and drop them down with a kick. You can also drop rats that attack the same target you’re hitting with your light attacks. A really fun weapon once you get the hang of it. - The Panacea Fan: Amazing as an off-hand healing device. Switch to it to create an area-of-effect heal, then grab your DPS weapons again to stay alive while dealing damage. Another super unique weapon. - The Thundercry Blade: Way stronger than I expected. It lets you create a shield based on your HP and grants you Fighting Spirit, which you can use for a powerful charged heavy attack combo that slams enemies to the ground. You can also pull enemies for some crowd control, and after that, your heavy attacks charge 50% faster. #WhereWindsMeet #Ad

JorRaptor

29,984 次观看 • 9 个月前

If you take a movement to unpack this visualization... You'll see how it simply breaks down how reality works. At frame 0 you have a static image. Everything is one, this is the monad. As soon as you hit frame 1 there is movement, there is change. Now you have two states, moving, or static. When Nikola Tesla says you can explain everything in frequency and vibration. The difference between frame 0 and 1, is vibration. The difference between movement and no movement. This is like binary logic we use in code which is made up of 0's and 1's. After frame 1, is when frequency emerges. Because the difference between frame 1 and all frames after is about how fast is the vibration/movement happening. If we skip forward to frame 50... You have a shape that begins to emerge, this is the 8 dots, then the 6 dots. Notice how unstable it is, it's 8 dots, then 6, then a moment with 4 in a rectangle These shapes are emergent properties. The first two emergent properties after the monad was vibration and frequency. Next comes shape (i'm skipping over rotation and direction). These shapes of dots can only exist when you have frequency and rotation. This frequency and rotation creates vortex energy. It's the same energy that things like your chakras use. Or the same energy we harness in devices like engines, airplanes, fans, blenders, hard drives, etc. It's also the same vortex energy you'll see in a tornado or hurricane. They are powered because they harness rotation and frequency(change/movement). Going back to the video, notice that it is inside the entire shape, the internal structure is manifesting before the external structure does. Then around frame 60 the hexagon of circles begins to rotate. First it was the two dots that moved and now it's a complex shape that is coming to life. This is a higher dimension (or lower depending on how you look at it) manifesting into existence. The internal state is "awakening" and experiencing it's own change like what happened to the whole shape in the first frames. But it is unstable. That's why it doesn't persist for long. If you think of the 8 dots being the octahedron, they map to the element of air. Air is in the material world, but it is not something you can see. The brief moments the 8 dots are visible is similar to that effect. They are only experienceable between a small frequency band of frames. Now here's where stability begins to appear in the internal structure. This is when the 4 dots appear. You'll see that the four dots, the square, is stable and persists the most visibly for the most amount of frames. The square represents earth in the platonic solids to elements mapping. Earth, is material, it's stable. We build our buildings in squares and with earth because it is a solid shape to build on. This visualization shows you why. Across different vibrations (frame rates) it can self sustain. Between this point and frame 180, you'll see a new emergent property. Which is depth. A new dimension is introduced at around frame 90 but really becomes visible at around frame 110. You can see a foreground and background. There is the shape of the dots, but also the triskellion wave happening in the background. Let's jump to frame 180. Notice how it is the same as frame 0 except... It's flashing. If you were paying attention, you'll notice you could see flashing at frame 90 and frame 120, but they didn't persist for long. At around 150 it started to reach stability and 180 it was solidified. Between frames 150 and 180 there is flashing, but the image is still moving. Only for a brief moment at frame 180 is the movement frozen and the flashing persists. Think of that like your computer screen. It's what your screen is doing right now as you read this. Even tho the text isn't moving, the screen is flashing at 60 or 120hz. The images appear on your device because this flashing brings things to life. The entire material realm and your physical body right now, is doing the same thing. While you look solid... You're flashing in and out of existence at very high frequencies. You can look at frame 180 and frame 0 as the same essence but it is the mid point between an octave change. In the video, the ying and yang was vertical, now it is horizontal. This is a phase shift. If you notice at exactly frame 180, the rotation freezes and then the direction of rotation changes. The process then repeats all the way to frame 360 but in the opposite sequence. Once it reaches frame 360, that is an octave change and the process repeats. Each time you repeat the process is a layering of the same patterns into higher octaves. This is the same as your chakras or how other things work. They are like russian nesting dolls where every octave is layering onto the next. The complexity of your body is a layering of basic principles that emerged in earlier stages. Your organs are built of systems that are built with cells that are built with proteins that are built with atoms and so on. The atoms, work just like your body at a basic level. Your body works just like the galaxies. At each level you'll have the same pattern. This is where the idea "As Above, So Below" from. The monad, splits in two, and so on and so on. One cell, splits into two through mitosis in the same logic. We could spend all day going through examples of how biology, physics, spirituality, etc. aren't really different. They are just categories that we use to dissect these frequencies and octaves of energy but they only start paying attention within the confines of materialism. The problem is, none of the sciences start at the root patterns. Because that is reserved for religion or spirituality. It's too woo-woo to take seriously so it's dismissed. And because of that... We're left ignorant on the simple explanations for how things work. Now you need some expert with tools you don't have access to in order to explain things. When you could be understanding them without the tools. The Yin and Yang symbol in this video is 3,000 years old. It's simple. Yet I just showed you how it explains deeper layers of reality.
1:59

If you take a movement to unpack this visualization... You'll see how it simply breaks down how reality works. At frame 0 you have a static image. Everything is one, this is the monad. As soon as you hit frame 1 there is movement, there is change. Now you have two states, moving, or static. When Nikola Tesla says you can explain everything in frequency and vibration. The difference between frame 0 and 1, is vibration. The difference between movement and no movement. This is like binary logic we use in code which is made up of 0's and 1's. After frame 1, is when frequency emerges. Because the difference between frame 1 and all frames after is about how fast is the vibration/movement happening. If we skip forward to frame 50... You have a shape that begins to emerge, this is the 8 dots, then the 6 dots. Notice how unstable it is, it's 8 dots, then 6, then a moment with 4 in a rectangle These shapes are emergent properties. The first two emergent properties after the monad was vibration and frequency. Next comes shape (i'm skipping over rotation and direction). These shapes of dots can only exist when you have frequency and rotation. This frequency and rotation creates vortex energy. It's the same energy that things like your chakras use. Or the same energy we harness in devices like engines, airplanes, fans, blenders, hard drives, etc. It's also the same vortex energy you'll see in a tornado or hurricane. They are powered because they harness rotation and frequency(change/movement). Going back to the video, notice that it is inside the entire shape, the internal structure is manifesting before the external structure does. Then around frame 60 the hexagon of circles begins to rotate. First it was the two dots that moved and now it's a complex shape that is coming to life. This is a higher dimension (or lower depending on how you look at it) manifesting into existence. The internal state is "awakening" and experiencing it's own change like what happened to the whole shape in the first frames. But it is unstable. That's why it doesn't persist for long. If you think of the 8 dots being the octahedron, they map to the element of air. Air is in the material world, but it is not something you can see. The brief moments the 8 dots are visible is similar to that effect. They are only experienceable between a small frequency band of frames. Now here's where stability begins to appear in the internal structure. This is when the 4 dots appear. You'll see that the four dots, the square, is stable and persists the most visibly for the most amount of frames. The square represents earth in the platonic solids to elements mapping. Earth, is material, it's stable. We build our buildings in squares and with earth because it is a solid shape to build on. This visualization shows you why. Across different vibrations (frame rates) it can self sustain. Between this point and frame 180, you'll see a new emergent property. Which is depth. A new dimension is introduced at around frame 90 but really becomes visible at around frame 110. You can see a foreground and background. There is the shape of the dots, but also the triskellion wave happening in the background. Let's jump to frame 180. Notice how it is the same as frame 0 except... It's flashing. If you were paying attention, you'll notice you could see flashing at frame 90 and frame 120, but they didn't persist for long. At around 150 it started to reach stability and 180 it was solidified. Between frames 150 and 180 there is flashing, but the image is still moving. Only for a brief moment at frame 180 is the movement frozen and the flashing persists. Think of that like your computer screen. It's what your screen is doing right now as you read this. Even tho the text isn't moving, the screen is flashing at 60 or 120hz. The images appear on your device because this flashing brings things to life. The entire material realm and your physical body right now, is doing the same thing. While you look solid... You're flashing in and out of existence at very high frequencies. You can look at frame 180 and frame 0 as the same essence but it is the mid point between an octave change. In the video, the ying and yang was vertical, now it is horizontal. This is a phase shift. If you notice at exactly frame 180, the rotation freezes and then the direction of rotation changes. The process then repeats all the way to frame 360 but in the opposite sequence. Once it reaches frame 360, that is an octave change and the process repeats. Each time you repeat the process is a layering of the same patterns into higher octaves. This is the same as your chakras or how other things work. They are like russian nesting dolls where every octave is layering onto the next. The complexity of your body is a layering of basic principles that emerged in earlier stages. Your organs are built of systems that are built with cells that are built with proteins that are built with atoms and so on. The atoms, work just like your body at a basic level. Your body works just like the galaxies. At each level you'll have the same pattern. This is where the idea "As Above, So Below" from. The monad, splits in two, and so on and so on. One cell, splits into two through mitosis in the same logic. We could spend all day going through examples of how biology, physics, spirituality, etc. aren't really different. They are just categories that we use to dissect these frequencies and octaves of energy but they only start paying attention within the confines of materialism. The problem is, none of the sciences start at the root patterns. Because that is reserved for religion or spirituality. It's too woo-woo to take seriously so it's dismissed. And because of that... We're left ignorant on the simple explanations for how things work. Now you need some expert with tools you don't have access to in order to explain things. When you could be understanding them without the tools. The Yin and Yang symbol in this video is 3,000 years old. It's simple. Yet I just showed you how it explains deeper layers of reality.

Jamal ☯︎ 🔆🧘🏽🧠

13,149 次观看 • 4 个月前

If your MCP server has dozens of tools, it’s probably built wrong. You need tools that are specific and clear for each use case—but you also can’t have too many. This creates an almost impossible tradeoff that most companies don’t know how to solve. That’s why I interviewed my friend Alex Rattray (Alex Rattray), the founder and CEO of Stainless. Stainless builds APIs, SDKs, and MCP servers for companies like OpenAI and Anthropic. Alex has spent years mastering how to make software talk to software, and he came on the show to share what he knows. I had him on Every 📧’s AI & I to talk about MCP and the future of the AI-native internet. We get into: • Design MCP servers to be lean and precise. Alex’s best practices for building reliable MCP servers start with keeping the toolset small, giving each tool a precise name and description, and minimizing the inputs and outputs the model has to handle. At Stainless, they also often add a JSON filter on top to strip out unnecessary data. • Make complex APIs manageable with dynamic mode. To solve the problem of how an AI figures out which tool to use in larger APIs, Stainless switches to “dynamic mode,” where the model gets only three tools: List the endpoints, pick one and learn about it, and then execute it. • MCP servers as business copilots. At Stainless, Alex uses MCP servers to connect tools like Notion and HubSpot, so he can ask questions like, “Which customers signed up last week?” The system queries multiple databases and returns a summary that would’ve otherwise taken multiple logins and searches. • Create a “brain” for your company with Claude Code. Alex built a shared company brain at Stainless by keeping Claude Code running on his system and asking it to save useful inputs—like customer feedback and SQL queries—into GitHub. Over time, this creates a curated archive his team can query easily. • The future of MCP is code execution. Instead of giving models hundreds of tools, Alex believes the most powerful setup will be a simple code execution tool and a doc search tool. The AI writes code against an API’s SDK, runs it on a server, and checks the docs when it gets stuck. This is a must-watch for anyone who wants to understand MCP—and learn how to use them as a competitive edge. Watch below! Timestamps: Introduction: 00:01:14 Why Alex likes running barefoot: 00:02:54 APIs and MCP, the connectors of the new internet: 00:05:09 Why MCP servers are hard to get right: 00:10:53 Design principles for reliable MCP servers: 00:20:07 Scaling MCP servers for large APIs: 00:23:50 Using MCP for business ops at Stainless: 00:25:14 Building a company brain with Claude Code: 00:28:12 Where MCP goes from here: 00:33:59 Alex’s take on the security model for MCP: 00:41:10
51:39

If your MCP server has dozens of tools, it’s probably built wrong. You need tools that are specific and clear for each use case—but you also can’t have too many. This creates an almost impossible tradeoff that most companies don’t know how to solve. That’s why I interviewed my friend Alex Rattray (Alex Rattray), the founder and CEO of Stainless. Stainless builds APIs, SDKs, and MCP servers for companies like OpenAI and Anthropic. Alex has spent years mastering how to make software talk to software, and he came on the show to share what he knows. I had him on Every 📧’s AI & I to talk about MCP and the future of the AI-native internet. We get into: • Design MCP servers to be lean and precise. Alex’s best practices for building reliable MCP servers start with keeping the toolset small, giving each tool a precise name and description, and minimizing the inputs and outputs the model has to handle. At Stainless, they also often add a JSON filter on top to strip out unnecessary data. • Make complex APIs manageable with dynamic mode. To solve the problem of how an AI figures out which tool to use in larger APIs, Stainless switches to “dynamic mode,” where the model gets only three tools: List the endpoints, pick one and learn about it, and then execute it. • MCP servers as business copilots. At Stainless, Alex uses MCP servers to connect tools like Notion and HubSpot, so he can ask questions like, “Which customers signed up last week?” The system queries multiple databases and returns a summary that would’ve otherwise taken multiple logins and searches. • Create a “brain” for your company with Claude Code. Alex built a shared company brain at Stainless by keeping Claude Code running on his system and asking it to save useful inputs—like customer feedback and SQL queries—into GitHub. Over time, this creates a curated archive his team can query easily. • The future of MCP is code execution. Instead of giving models hundreds of tools, Alex believes the most powerful setup will be a simple code execution tool and a doc search tool. The AI writes code against an API’s SDK, runs it on a server, and checks the docs when it gets stuck. This is a must-watch for anyone who wants to understand MCP—and learn how to use them as a competitive edge. Watch below! Timestamps: Introduction: 00:01:14 Why Alex likes running barefoot: 00:02:54 APIs and MCP, the connectors of the new internet: 00:05:09 Why MCP servers are hard to get right: 00:10:53 Design principles for reliable MCP servers: 00:20:07 Scaling MCP servers for large APIs: 00:23:50 Using MCP for business ops at Stainless: 00:25:14 Building a company brain with Claude Code: 00:28:12 Where MCP goes from here: 00:33:59 Alex’s take on the security model for MCP: 00:41:10

Dan Shipper 📧

15,645 次观看 • 10 个月前

I was very happy to see how enthusiastic people were about the first hand review, so here's another one. I'm using some screenshots from vision to show where I made the right play or made a mistake. This isn't a 1 to 1 solution to this exact spot, because vision has a limited number of positions so the results a merely indicative not final. A like on this post is much appreciated. We wake up with KK84ds in the big blind vs a button open. We push quite a bit of equity here vs his opening range so we elect a 3-bet. The flop comes K42r and here we have our first serious decision. Our entire 3-bet range does okay on this board, so we want to both bet our strong hands and add in quite a few bluffs. For that reason KKxx should bet here at a healthy frequency, but also blocking the 4 makes this one of the few candidates that we want to consider playing slow (screenshot 1). We check and our opponent bets, we want him to continue bluffing with air and protect our calling range, so most of the time we are calling here (screenshot 2). The turn brings a queen with our flush draw and we check, which is our first mistake. The queen is benefitial for our range (we 3-bet a lot of high cards) and the air portion of my range is gone, while he still has some air in his stabbing range. Since we have a sizeable equity advantage now, we should start betting with a lot of value and start adding in some bluffs (screenshot 3). After checking our opponent bets 50% pot and here we have yet another interesting spot. In a vacuum betting here makes a lot of money, but we want to consider all the hands that we are playing in this spot. This hand is so strong that it doesn't need that much protection and we still want our opponent to keep his air in the hand. We also want to protect our calling range here (screenshot 4). The river brings in the flush and yet another tough decision for us. We block a decent part of the board, so I still give my opponent credit for not having anything. Most if the flushes that will call my bet here, will also bet themselves when checked to. That's why I decided to check here. I was very happy with how I played this hand, but still made a crucial mistake on the turn. Even at higher stakes, people are constantly screwing up if you compare their strategy to a solver output. Knowing what the solver would do in every spot is impossible. What is possible though, is understanding the broader concepts. Understanding why the solver bets here on the turn is information that you can extrapolate in any situation that is somewhat similar. That's why is it so valuable. Hopefully you enjoyed this breakdown as much as you did with the last one. A like on this post is much appreciated. Stay tuned!
1:15

I was very happy to see how enthusiastic people were about the first hand review, so here's another one. I'm using some screenshots from vision to show where I made the right play or made a mistake. This isn't a 1 to 1 solution to this exact spot, because vision has a limited number of positions so the results a merely indicative not final. A like on this post is much appreciated. We wake up with KK84ds in the big blind vs a button open. We push quite a bit of equity here vs his opening range so we elect a 3-bet. The flop comes K42r and here we have our first serious decision. Our entire 3-bet range does okay on this board, so we want to both bet our strong hands and add in quite a few bluffs. For that reason KKxx should bet here at a healthy frequency, but also blocking the 4 makes this one of the few candidates that we want to consider playing slow (screenshot 1). We check and our opponent bets, we want him to continue bluffing with air and protect our calling range, so most of the time we are calling here (screenshot 2). The turn brings a queen with our flush draw and we check, which is our first mistake. The queen is benefitial for our range (we 3-bet a lot of high cards) and the air portion of my range is gone, while he still has some air in his stabbing range. Since we have a sizeable equity advantage now, we should start betting with a lot of value and start adding in some bluffs (screenshot 3). After checking our opponent bets 50% pot and here we have yet another interesting spot. In a vacuum betting here makes a lot of money, but we want to consider all the hands that we are playing in this spot. This hand is so strong that it doesn't need that much protection and we still want our opponent to keep his air in the hand. We also want to protect our calling range here (screenshot 4). The river brings in the flush and yet another tough decision for us. We block a decent part of the board, so I still give my opponent credit for not having anything. Most if the flushes that will call my bet here, will also bet themselves when checked to. That's why I decided to check here. I was very happy with how I played this hand, but still made a crucial mistake on the turn. Even at higher stakes, people are constantly screwing up if you compare their strategy to a solver output. Knowing what the solver would do in every spot is impossible. What is possible though, is understanding the broader concepts. Understanding why the solver bets here on the turn is information that you can extrapolate in any situation that is somewhat similar. That's why is it so valuable. Hopefully you enjoyed this breakdown as much as you did with the last one. A like on this post is much appreciated. Stay tuned!

Venividi1993

93,143 次观看 • 2 年前

How Missionaries groom Hindus? 1. Tell Hindus they are sinners. 2. Tell them Prabhu Yeshu Masih is Parmeshwar ka putra who came to earth to end their problems. 3. Tell Hindus that Yeshu sacrificed his life for their sins. 4. Prabhu Yeshu never asks for conversion and is accepting. All are welcomed. 5. Only visiting a prayer meeting can take away their problems. He is Virender Ahirwar, who goes by the same name but has attended prayer meetings like this for the past eight years. In many places, the Ahirwar caste falls under the Scheduled Caste category, but in Madhya Pradesh, it is also classified as OBC. So, Virender Ahirwar, being a Hindu by birth, has become a Masihi Bhai for the past eight years now. I am sure he must have been baptized as well. His wife wears no bindi, sindoor, or anything to look like a married woman. I have met people who have been Christians for years and then reverted to the Sanatan fold. They tell me how every symbol of Hindu identity is slowly stripped away by the missionaries, from temple visits to bindi, nothing remains. Case from Sehore, MP: House Church of Jabbar Khan and family. All booked.
0:59

How Missionaries groom Hindus? 1. Tell Hindus they are sinners. 2. Tell them Prabhu Yeshu Masih is Parmeshwar ka putra who came to earth to end their problems. 3. Tell Hindus that Yeshu sacrificed his life for their sins. 4. Prabhu Yeshu never asks for conversion and is accepting. All are welcomed. 5. Only visiting a prayer meeting can take away their problems. He is Virender Ahirwar, who goes by the same name but has attended prayer meetings like this for the past eight years. In many places, the Ahirwar caste falls under the Scheduled Caste category, but in Madhya Pradesh, it is also classified as OBC. So, Virender Ahirwar, being a Hindu by birth, has become a Masihi Bhai for the past eight years now. I am sure he must have been baptized as well. His wife wears no bindi, sindoor, or anything to look like a married woman. I have met people who have been Christians for years and then reverted to the Sanatan fold. They tell me how every symbol of Hindu identity is slowly stripped away by the missionaries, from temple visits to bindi, nothing remains. Case from Sehore, MP: House Church of Jabbar Khan and family. All booked.

Subhi Vishwakarma

23,618 次观看 • 1 年前

A central bank. And Bitcoin. My speech at The Bitcoin Conference 2026 in Las Vegas on 28 April 2026. Video and text; the slide link is below: Today, I want to talk about a strange combination: A central bank. And Bitcoin. Most people do not put these two things together. I do. In monetary policy, a central bank must be conservative. But it must think ahead. When I became Governor of the Czech National Bank in mid-2022, inflation in my country was close to 20 percent. Twenty percent. It was a serious moment. When I took office, I said we would bring inflation back to 2 percent within two years. And we did. Not with magic. With discipline. I said this clearly: Even before covid, money was too cheap for too long. For too long, the system promoted borrowing. For too long, the currency was weakened. We changed that. We kept policy tighter for longer. We supported saving. And the koruna became strong. That, for me, is conservative monetary policy. Our rule is simple: stay hawkish forever. We also manage very large foreign exchange reserves. Very large. We manage about 180 billion dollars in reserves. That is about 44 percent of GDP. Relative to the size of our economy, our reserves are among the largest in the world. So we have to build the right portfolio for the future. Here, you can see the long-term risk and return. It is based on Czech koruna data, the currency in which our books are kept. Bonds are at the low end. Low risk. Low return. Stocks and gold can offer higher returns. But they also bring higher risk. The next point is the Czech National Bank’s portfolio. Over the past four years, we increased the share of equities from 15 to 26 percent. We also increased the share of gold from almost zero to 6 percent. We built a diversified portfolio. A higher expected return than before. Lower risk than an all-stock portfolio. And even lower risk than an all-bond portfolio. But then came the next question. Can we do more? Can we build an even stronger portfolio for the future This is where Bitcoin comes in. The first time I used Bitcoin, I bought a coffee in Prague about ten years ago. Today, that coffee comes to about 350 dollars. It was the most expensive coffee of my life. Bitcoin has had very high returns. But honestly, it looks risky. It is much more volatile than other assets. One day, its price may be much higher. Or it could go to zero. Yes, zero. And that is true for other assets too. A stock can go to zero. Even a bond can fail. That is why it is not wise to bet on just one asset. We have to think about the whole portfolio. The next point on the chart is what we found in our new analysis. This is our model portfolio with 1 percent in Bitcoin. And here comes the interesting part. With 1 percent in Bitcoin, the expected return goes up. And the overall risk stays about the same. That is what our new study shows. Why? Because Bitcoin has low long-term correlation with many traditional assets. It does not move in the same way. And that matters. When you add an asset like this, the whole portfolio can work better. The return can go up. And the risk can stay about the same. That is diversification. Over the long term, Bitcoin can provide returns that are not closely linked to other assets. In some ways, it is similar to venture capital. But it is much more liquid. So we started a separate test portfolio with Bitcoin. A test portfolio. Not a revolution. Not a political statement. A test. We will run it for two years. Then we will publish the results. Then we will decide what comes next. Be conservative in monetary policy. Be innovative in how we work. This is the future. Česká národní banka
8:09

A central bank. And Bitcoin. My speech at The Bitcoin Conference 2026 in Las Vegas on 28 April 2026. Video and text; the slide link is below: Today, I want to talk about a strange combination: A central bank. And Bitcoin. Most people do not put these two things together. I do. In monetary policy, a central bank must be conservative. But it must think ahead. When I became Governor of the Czech National Bank in mid-2022, inflation in my country was close to 20 percent. Twenty percent. It was a serious moment. When I took office, I said we would bring inflation back to 2 percent within two years. And we did. Not with magic. With discipline. I said this clearly: Even before covid, money was too cheap for too long. For too long, the system promoted borrowing. For too long, the currency was weakened. We changed that. We kept policy tighter for longer. We supported saving. And the koruna became strong. That, for me, is conservative monetary policy. Our rule is simple: stay hawkish forever. We also manage very large foreign exchange reserves. Very large. We manage about 180 billion dollars in reserves. That is about 44 percent of GDP. Relative to the size of our economy, our reserves are among the largest in the world. So we have to build the right portfolio for the future. Here, you can see the long-term risk and return. It is based on Czech koruna data, the currency in which our books are kept. Bonds are at the low end. Low risk. Low return. Stocks and gold can offer higher returns. But they also bring higher risk. The next point is the Czech National Bank’s portfolio. Over the past four years, we increased the share of equities from 15 to 26 percent. We also increased the share of gold from almost zero to 6 percent. We built a diversified portfolio. A higher expected return than before. Lower risk than an all-stock portfolio. And even lower risk than an all-bond portfolio. But then came the next question. Can we do more? Can we build an even stronger portfolio for the future This is where Bitcoin comes in. The first time I used Bitcoin, I bought a coffee in Prague about ten years ago. Today, that coffee comes to about 350 dollars. It was the most expensive coffee of my life. Bitcoin has had very high returns. But honestly, it looks risky. It is much more volatile than other assets. One day, its price may be much higher. Or it could go to zero. Yes, zero. And that is true for other assets too. A stock can go to zero. Even a bond can fail. That is why it is not wise to bet on just one asset. We have to think about the whole portfolio. The next point on the chart is what we found in our new analysis. This is our model portfolio with 1 percent in Bitcoin. And here comes the interesting part. With 1 percent in Bitcoin, the expected return goes up. And the overall risk stays about the same. That is what our new study shows. Why? Because Bitcoin has low long-term correlation with many traditional assets. It does not move in the same way. And that matters. When you add an asset like this, the whole portfolio can work better. The return can go up. And the risk can stay about the same. That is diversification. Over the long term, Bitcoin can provide returns that are not closely linked to other assets. In some ways, it is similar to venture capital. But it is much more liquid. So we started a separate test portfolio with Bitcoin. A test portfolio. Not a revolution. Not a political statement. A test. We will run it for two years. Then we will publish the results. Then we will decide what comes next. Be conservative in monetary policy. Be innovative in how we work. This is the future. Česká národní banka

Aleš Michl

68,654 次观看 • 3 个月前

Video content creation sounds simple, but what if you don’t have time to: • Write the script, • Prepare the visuals, • Generate the voiceover, • Create the subtitles, • And finally render the video? This is why we built Noustiny on top of Nous Research Hermes Agent by adding 12 generic Hermes tools + 13 generic Hermes skills, bringing the whole process into one single flow. How does it work? Let’s take a closer look 👇 ———— 1- Story state: context, tree, motifs: Hermes had no built-in narrative-state primitive for tracking canon, branching story structure, and recurring motifs. So we added three generic Hermes tools for this: → story_tree_graph: Manages the story tree structure. It handles operations like canon path, descendants, and splice insertion points. → narrative_context_builder: Walks the canon chain and returns the live context every narrative skill should reason against. This includes recent chain, mood, and character state. → motif_tracker: Remembers recurring motifs across the story arc. For example, a sword introduced in beat 2 can reappear meaningfully in later scenes. ———— 2- Character / cast pipeline: Hermes had no built-in primitive for cast extraction or character continuity. So we added a four-tool character pipeline: → story_copyright_detector: Handles IP scrubbing. For example, “Iron Man” is converted into an IP-free character description before the image API ever sees it. → character_sheet_builder: Produces 1 to 4 characters. For each character, it creates an IP-free visual description and a hero-portrait prompt. These portraits become the reference frames used across later storyboard scenes. → character_registry_lookup: Finds a character by name inside the cast sheet and attaches the correct portrait reference to each beat. → character_alias_resolver: Resolves aliases like “Mr. Stark” into the main character name. This way, the same character keeps one portrait reference even if they appear under different names. ———— 3- Voice pipeline: Hermes had no built-in primitive for audio acquisition or voice cloning. So we added the full voice chain, and the agent dispatches it autonomously in order: → narration_voice_director: The director-agent reads the seed + story and returns persona_label, search_query, and fallback_query. → voice_sample_builder: Uses yt-dlp + ffmpeg. It accepts a URL, an 11-character ID, or a free-text query. It runs ytsearch5 with dead-video tolerance and normalizes the audio to 24 kHz mono PCM. → voice_clone_synthesize: Wraps ElevenLabs IVC + timestamps. The voice ID is cached by reference SHA. Per-character alignment comes through the same audio call at no extra cost. → voice_clone_cleanup: Frees the cached voice ID after render so orphan voices do not accumulate. ———— 4- Render: Hermes had no built-in video-render entry. So we added the final render tool: → noustiny_storybook: The agent dispatches it as the final step of the chain. One tool call drives the FastAPI render service end to end and emits the mp4. ———— 5- Skills: 13 generic Hermes skills added into skills/creative/: The branching engine in Noustiny works like a council of narrative skills. Each skill is loaded by the gateway as a system prompt and orchestrated in this order: → narrative-brainstorm: Proposes 2 to 3 next-checkpoint options from the canon chain. → narrative-writer-assist: Writes a spliced insert beat that fits the parent and child. → narrative-continuity-critic: Audits downstream beats against the new insert. → narrative-rewriter: Updates the stale beats flagged by the continuity critic. → narrative-judge: Approves or rejects the rewrite against the original flow. → narrative-scene-qa: Checks each beat for consistency, length, and register. → narrative-writer: Finalizes the chosen branch as polished prose. After one splice, this cascade walks downstream by itself until the canon becomes coherent again. ———— 6- Visual + IP pipeline: On the visual side, the goal is not just generating scenes. It is also preserving character continuity and IP safety. This pipeline runs through these skills: → visual-prompt-builder: Turns a beat into an IP-free image prompt and reads the character-sheet references. → scene-composition: Defines shot framing, scene composition, and layout rules. → story-copyright-detector: Skill counterpart of the same-named tool. It can be used for direct slash-command invocation. → character-sheet-builder: Skill counterpart of the same-named tool. Defines cast extraction rules and the IP-free portrait-prompt format used to seed character consistency across the storyboard. → storybook-intro: Generates the cinematic intro page for the render. ———— 7- Voice skill: → narration-voice-director: Defines persona reasoning rules and supports the decision logic behind the same-named voice tool. ———— 8- Pattern: Hermes baseline already had the gateway, agent loop, skill registry, and tool registry. We extended that foundation with 12 generic Hermes tools + 13 generic Hermes skills and organized the system into four main pipelines: • story-state • character continuity • voice • render The important part is this: Noustiny is not a hardcoded system locked inside a single app. A Telegram bot, Discord bot, CLI session, or third-party Next.js app can call the same gateway and use the same tool + skill chains. - No app glue. - No hardcoded prompts. - A drop-in, registry-compatible, agent-native video creation flow. ✅Github:
6:43

Video content creation sounds simple, but what if you don’t have time to: • Write the script, • Prepare the visuals, • Generate the voiceover, • Create the subtitles, • And finally render the video? This is why we built Noustiny on top of Nous Research Hermes Agent by adding 12 generic Hermes tools + 13 generic Hermes skills, bringing the whole process into one single flow. How does it work? Let’s take a closer look 👇 ———— 1- Story state: context, tree, motifs: Hermes had no built-in narrative-state primitive for tracking canon, branching story structure, and recurring motifs. So we added three generic Hermes tools for this: → story_tree_graph: Manages the story tree structure. It handles operations like canon path, descendants, and splice insertion points. → narrative_context_builder: Walks the canon chain and returns the live context every narrative skill should reason against. This includes recent chain, mood, and character state. → motif_tracker: Remembers recurring motifs across the story arc. For example, a sword introduced in beat 2 can reappear meaningfully in later scenes. ———— 2- Character / cast pipeline: Hermes had no built-in primitive for cast extraction or character continuity. So we added a four-tool character pipeline: → story_copyright_detector: Handles IP scrubbing. For example, “Iron Man” is converted into an IP-free character description before the image API ever sees it. → character_sheet_builder: Produces 1 to 4 characters. For each character, it creates an IP-free visual description and a hero-portrait prompt. These portraits become the reference frames used across later storyboard scenes. → character_registry_lookup: Finds a character by name inside the cast sheet and attaches the correct portrait reference to each beat. → character_alias_resolver: Resolves aliases like “Mr. Stark” into the main character name. This way, the same character keeps one portrait reference even if they appear under different names. ———— 3- Voice pipeline: Hermes had no built-in primitive for audio acquisition or voice cloning. So we added the full voice chain, and the agent dispatches it autonomously in order: → narration_voice_director: The director-agent reads the seed + story and returns persona_label, search_query, and fallback_query. → voice_sample_builder: Uses yt-dlp + ffmpeg. It accepts a URL, an 11-character ID, or a free-text query. It runs ytsearch5 with dead-video tolerance and normalizes the audio to 24 kHz mono PCM. → voice_clone_synthesize: Wraps ElevenLabs IVC + timestamps. The voice ID is cached by reference SHA. Per-character alignment comes through the same audio call at no extra cost. → voice_clone_cleanup: Frees the cached voice ID after render so orphan voices do not accumulate. ———— 4- Render: Hermes had no built-in video-render entry. So we added the final render tool: → noustiny_storybook: The agent dispatches it as the final step of the chain. One tool call drives the FastAPI render service end to end and emits the mp4. ———— 5- Skills: 13 generic Hermes skills added into skills/creative/: The branching engine in Noustiny works like a council of narrative skills. Each skill is loaded by the gateway as a system prompt and orchestrated in this order: → narrative-brainstorm: Proposes 2 to 3 next-checkpoint options from the canon chain. → narrative-writer-assist: Writes a spliced insert beat that fits the parent and child. → narrative-continuity-critic: Audits downstream beats against the new insert. → narrative-rewriter: Updates the stale beats flagged by the continuity critic. → narrative-judge: Approves or rejects the rewrite against the original flow. → narrative-scene-qa: Checks each beat for consistency, length, and register. → narrative-writer: Finalizes the chosen branch as polished prose. After one splice, this cascade walks downstream by itself until the canon becomes coherent again. ———— 6- Visual + IP pipeline: On the visual side, the goal is not just generating scenes. It is also preserving character continuity and IP safety. This pipeline runs through these skills: → visual-prompt-builder: Turns a beat into an IP-free image prompt and reads the character-sheet references. → scene-composition: Defines shot framing, scene composition, and layout rules. → story-copyright-detector: Skill counterpart of the same-named tool. It can be used for direct slash-command invocation. → character-sheet-builder: Skill counterpart of the same-named tool. Defines cast extraction rules and the IP-free portrait-prompt format used to seed character consistency across the storyboard. → storybook-intro: Generates the cinematic intro page for the render. ———— 7- Voice skill: → narration-voice-director: Defines persona reasoning rules and supports the decision logic behind the same-named voice tool. ———— 8- Pattern: Hermes baseline already had the gateway, agent loop, skill registry, and tool registry. We extended that foundation with 12 generic Hermes tools + 13 generic Hermes skills and organized the system into four main pipelines: • story-state • character continuity • voice • render The important part is this: Noustiny is not a hardcoded system locked inside a single app. A Telegram bot, Discord bot, CLI session, or third-party Next.js app can call the same gateway and use the same tool + skill chains. - No app glue. - No hardcoded prompts. - A drop-in, registry-compatible, agent-native video creation flow. ✅Github:

Ufuk

28,972 次观看 • 3 个月前

Goldman Sachs just published the list of jobs AI will eliminate first. 300 million jobs globally. 25% of all US work hours. And that's not in 10 years, it's starting NOW. Highest risk of displacement according to Goldman: 1. Computer programmers 2. Accountants 3. Auditors 4. Legal assistants 5. Administrative assistants 6. Customer service reps 7. Telemarketers 8. Proofreaders 9. Copy editors 10. Credit analysts. 46% of all office and administrative tasks can be automated. 44% of legal work. 37% of architecture and engineering. 36% of science. 35% of business and finance. These aren't warehouse jobs. These aren't factory floor positions. These are the careers parents told their kids to pursue. "Go to college. Get a degree. Get a desk job. You'll be safe." Goldman Sachs just told you that desk is getting emptied. And the data is already showing up in real time: Tech employment as a share of the US economy has dropped below its long-term trend for the first time since records began. Marketing consulting, graphic design, office administration, and call centers are all seeing employment growth fall below trend. Younger workers are getting hit first and hardest. Goldman's lead economist said it directly: "The big story in 2026 in labor will be AI." But here's what the report doesn't mention: Goldman Sachs is one of the biggest investors in the companies BUILDING the AI that eliminates these jobs. They underwrote OpenAI's funding rounds. They're advising on the $700 billion in AI infrastructure spending this year. They profit from every merger, every capex deal, every stock offering tied to AI. The same bank telling you 300 million jobs are at risk is making billions helping the companies that will take them. And the corporate playbook is already locked in: Meta is firing 16,000 people. 20% of its entire workforce. While doubling AI spending to $135 billion. Stock went up 3% on the announcement. Block fired 40% of its staff. Stock surged 24%. Atlassian cut 10%. Same pattern. Over 61,000 AI-linked layoffs since November. 764 people per day losing their jobs in tech alone. Every single time a company announces mass layoffs and says "AI," the stock price goes up. Wall Street has created a system where firing humans is the most profitable announcement a CEO can make. Goldman's report says the jobs most PROTECTED from AI are air traffic controllers, chief executives, radiologists, pharmacists, and members of the clergy. Notice who's safe? The people at the top and the people praying. Everyone in the middle is exposed. The entry-level white-collar worker who spent four years and $200,000 on a degree is now competing against software that works 24/7, never takes vacation, never asks for a raise, and improves every single week. Goldman even admits younger workers in their 20s and 30s entering knowledge and content creation sectors will be "most affected." The generation that was told AI would make their lives better is the one getting displaced by it first. And it gets even WORSE: Goldman says if this displacement happens faster than their 10 year base case, the economic impact "could be much larger." Basically: if companies move fast, which they already are, the fallout will be worse than their projections. They're already moving fast. $700 billion in AI infrastructure this year. Mass layoffs at every major tech company. Stock prices rewarding every single one. The report is 50 pages of data telling you exactly what's coming. Most people won't read past the headline. But you just did.
1:14

Goldman Sachs just published the list of jobs AI will eliminate first. 300 million jobs globally. 25% of all US work hours. And that's not in 10 years, it's starting NOW. Highest risk of displacement according to Goldman: 1. Computer programmers 2. Accountants 3. Auditors 4. Legal assistants 5. Administrative assistants 6. Customer service reps 7. Telemarketers 8. Proofreaders 9. Copy editors 10. Credit analysts. 46% of all office and administrative tasks can be automated. 44% of legal work. 37% of architecture and engineering. 36% of science. 35% of business and finance. These aren't warehouse jobs. These aren't factory floor positions. These are the careers parents told their kids to pursue. "Go to college. Get a degree. Get a desk job. You'll be safe." Goldman Sachs just told you that desk is getting emptied. And the data is already showing up in real time: Tech employment as a share of the US economy has dropped below its long-term trend for the first time since records began. Marketing consulting, graphic design, office administration, and call centers are all seeing employment growth fall below trend. Younger workers are getting hit first and hardest. Goldman's lead economist said it directly: "The big story in 2026 in labor will be AI." But here's what the report doesn't mention: Goldman Sachs is one of the biggest investors in the companies BUILDING the AI that eliminates these jobs. They underwrote OpenAI's funding rounds. They're advising on the $700 billion in AI infrastructure spending this year. They profit from every merger, every capex deal, every stock offering tied to AI. The same bank telling you 300 million jobs are at risk is making billions helping the companies that will take them. And the corporate playbook is already locked in: Meta is firing 16,000 people. 20% of its entire workforce. While doubling AI spending to $135 billion. Stock went up 3% on the announcement. Block fired 40% of its staff. Stock surged 24%. Atlassian cut 10%. Same pattern. Over 61,000 AI-linked layoffs since November. 764 people per day losing their jobs in tech alone. Every single time a company announces mass layoffs and says "AI," the stock price goes up. Wall Street has created a system where firing humans is the most profitable announcement a CEO can make. Goldman's report says the jobs most PROTECTED from AI are air traffic controllers, chief executives, radiologists, pharmacists, and members of the clergy. Notice who's safe? The people at the top and the people praying. Everyone in the middle is exposed. The entry-level white-collar worker who spent four years and $200,000 on a degree is now competing against software that works 24/7, never takes vacation, never asks for a raise, and improves every single week. Goldman even admits younger workers in their 20s and 30s entering knowledge and content creation sectors will be "most affected." The generation that was told AI would make their lives better is the one getting displaced by it first. And it gets even WORSE: Goldman says if this displacement happens faster than their 10 year base case, the economic impact "could be much larger." Basically: if companies move fast, which they already are, the fallout will be worse than their projections. They're already moving fast. $700 billion in AI infrastructure this year. Mass layoffs at every major tech company. Stock prices rewarding every single one. The report is 50 pages of data telling you exactly what's coming. Most people won't read past the headline. But you just did.

Ricardo

235,712 次观看 • 5 个月前

This guy runs his entire marketing department out of folders and markdown files. No code or crazy automations. He calls it CMOHQ, it works with any AI (Claude, ChatGPT, Hermes, Codex), and he sells the whole thing as a zip file. He went 3 weeks without opening his laptop and the business kept running. Justin Brooke ❤️‍🔥 is back on the pod to walk us through the entire system. Here's what I learned: 1. The whole OS is folders and markdown files. He tried n8n, LangChain, and CrewAI. Too technical for a copywriter. 2. One HQ folder for who you are. One folder per brand with intelligence, departments, and metrics inside. Say "work in the FaithFunnels brand" and Claude switches context. 3. He built the finance folder from a YouTube transcript. It ingests ThriveCart, Kit, Google Ads, and Zeni exports and auto-generates max CPA, spend ceilings, and dashboards. 4. Build it in iCloud, not on a Mac Mini. It's just text files. Phone, laptop, office, same brain. 5. Nothing skips the pipeline. Pope mentions "digital sobriety," Claude writes the article, he approves, it publishes to Ghost via MCP with an ElevenLabs audio version. 6. Reporting is wired in through MCPs. Kit, Google Analytics, Microsoft Clarity. Clicks to leads to sales, plus a monthly P&L drop. 7. He uses Claude Cowork, not Claude Code. 90% of the features, and his work is mostly writing anyway. 8. Start with Jobs To Be Done. Every job becomes a folder. Every folder gets instructions. 9. Delivery is the product. The zip holds an ops manual and an installer.md that interviews the buyer and builds their system. Zero onboarding calls. 10. If they want to build it themselves, they're not your customer. Millionaires look at the system and say "can I just pay you?" His 2 key takeaways: 1. Every agent needs three things: memory, instructions, and tools. Get those right and you can replicate any process in any business. 2. Stop doing sales calls on the fly. Run a slide deck, tweak one slide per call, and by call five it's dialed in and trainable. That's how you get out of sales. Justin has been marketing for 20 years and this is the simplest AI operating system I've seen. Go follow Justin Brooke ❤️‍🔥 Full video below. (Also available on the Build With AI podcast wherever you get your pods)
33:48

This guy runs his entire marketing department out of folders and markdown files. No code or crazy automations. He calls it CMOHQ, it works with any AI (Claude, ChatGPT, Hermes, Codex), and he sells the whole thing as a zip file. He went 3 weeks without opening his laptop and the business kept running. Justin Brooke ❤️‍🔥 is back on the pod to walk us through the entire system. Here's what I learned: 1. The whole OS is folders and markdown files. He tried n8n, LangChain, and CrewAI. Too technical for a copywriter. 2. One HQ folder for who you are. One folder per brand with intelligence, departments, and metrics inside. Say "work in the FaithFunnels brand" and Claude switches context. 3. He built the finance folder from a YouTube transcript. It ingests ThriveCart, Kit, Google Ads, and Zeni exports and auto-generates max CPA, spend ceilings, and dashboards. 4. Build it in iCloud, not on a Mac Mini. It's just text files. Phone, laptop, office, same brain. 5. Nothing skips the pipeline. Pope mentions "digital sobriety," Claude writes the article, he approves, it publishes to Ghost via MCP with an ElevenLabs audio version. 6. Reporting is wired in through MCPs. Kit, Google Analytics, Microsoft Clarity. Clicks to leads to sales, plus a monthly P&L drop. 7. He uses Claude Cowork, not Claude Code. 90% of the features, and his work is mostly writing anyway. 8. Start with Jobs To Be Done. Every job becomes a folder. Every folder gets instructions. 9. Delivery is the product. The zip holds an ops manual and an installer.md that interviews the buyer and builds their system. Zero onboarding calls. 10. If they want to build it themselves, they're not your customer. Millionaires look at the system and say "can I just pay you?" His 2 key takeaways: 1. Every agent needs three things: memory, instructions, and tools. Get those right and you can replicate any process in any business. 2. Stop doing sales calls on the fly. Run a slide deck, tweak one slide per call, and by call five it's dialed in and trainable. That's how you get out of sales. Justin has been marketing for 20 years and this is the simplest AI operating system I've seen. Go follow Justin Brooke ❤️‍🔥 Full video below. (Also available on the Build With AI podcast wherever you get your pods)

Corey Ganim

13,753 次观看 • 1 个月前

Introducing: How Do You Use ChatGPT? 🚀 It's a weekly show where I interview the most interesting people in the world about how they use ChatGPT in their work and their lives—and show you every detail. The first episode is with Sahil Lavingia, CEO of Gumroad and Flexile. It's not theoretical: we screen-share through his actual prompts and responses, so you can see how ChatGPT helps him perform better at work and improve his life—one conversation at a time. We talk about how he's using ChatGPT to: Buy a building. He wants to buy a New York City hangout for Gumroad employees and customers, so he asked ChatGPT to research the history of real estate in NYC, suggest which neighborhoods might be best to target, generate questions for brokers, and even detail what the design of a particular property might look like. Write tweets. Sahil is a prolific Twitter/X user. He often uses ChatGPT to help him flesh out an idea. He says, “I [start] with a tweet, which is like a thesis, and then I just say, ‘Add three to four paragraphs to make the point compelling—also suggest more examples.’” We explore his precise process for using ChatGPT to help him brainstorm short tweets and longer essays in this episode. Pressure-test ideas. For Sahil, ChatGPT is like upgrading his peripheral vision. It lets him see around the corners, ask better questions of himself and other people, and avoid poor decisions. He told me, “I think a lot of people sort of delude themselves into thinking they have [good ideas]… I think that one of the most useful things about [ChatGPT] is it focuses your research on what actually matters.” It’s the ultimate tool to help him think better. Also in this episode: how ChatGPT could have helped Sahil save $70 million, how he thinks it will improve the most-talented creatives, and why he thinks—in the age of AI—people have no excuse for not knowing the answer to something anymore. Watch below! ---- Timestamps Intro 0:33 There’s no more excuse for not knowing anymore 2:00 He doesn’t spend as much time on bad ideas 2:50 How ChatGPT will make the top 1% of creative output better 6:15 How it turbocharges research 8:20 How he’s using ChatGPT to buy a building 11:00 How he uses ChatGPT to pressure-test ideas 17:43 How he uses DALL-E to help with interior design 20:50 How ChatGPT could have saved him $70 million 26:00 How he uses ChatGPT in his decision-making 29:50 How he uses ChatGPT for writing 38:00
47:00

Introducing: How Do You Use ChatGPT? 🚀 It's a weekly show where I interview the most interesting people in the world about how they use ChatGPT in their work and their lives—and show you every detail. The first episode is with Sahil Lavingia, CEO of Gumroad and Flexile. It's not theoretical: we screen-share through his actual prompts and responses, so you can see how ChatGPT helps him perform better at work and improve his life—one conversation at a time. We talk about how he's using ChatGPT to: Buy a building. He wants to buy a New York City hangout for Gumroad employees and customers, so he asked ChatGPT to research the history of real estate in NYC, suggest which neighborhoods might be best to target, generate questions for brokers, and even detail what the design of a particular property might look like. Write tweets. Sahil is a prolific Twitter/X user. He often uses ChatGPT to help him flesh out an idea. He says, “I [start] with a tweet, which is like a thesis, and then I just say, ‘Add three to four paragraphs to make the point compelling—also suggest more examples.’” We explore his precise process for using ChatGPT to help him brainstorm short tweets and longer essays in this episode. Pressure-test ideas. For Sahil, ChatGPT is like upgrading his peripheral vision. It lets him see around the corners, ask better questions of himself and other people, and avoid poor decisions. He told me, “I think a lot of people sort of delude themselves into thinking they have [good ideas]… I think that one of the most useful things about [ChatGPT] is it focuses your research on what actually matters.” It’s the ultimate tool to help him think better. Also in this episode: how ChatGPT could have helped Sahil save $70 million, how he thinks it will improve the most-talented creatives, and why he thinks—in the age of AI—people have no excuse for not knowing the answer to something anymore. Watch below! ---- Timestamps Intro 0:33 There’s no more excuse for not knowing anymore 2:00 He doesn’t spend as much time on bad ideas 2:50 How ChatGPT will make the top 1% of creative output better 6:15 How it turbocharges research 8:20 How he’s using ChatGPT to buy a building 11:00 How he uses ChatGPT to pressure-test ideas 17:43 How he uses DALL-E to help with interior design 20:50 How ChatGPT could have saved him $70 million 26:00 How he uses ChatGPT in his decision-making 29:50 How he uses ChatGPT for writing 38:00

Dan Shipper 📧

347,044 次观看 • 2 年前

Anthropic just changed how they think about Claude 5. Not with a new model. Not with a benchmark. With a completely different philosophy for building AI systems. Most people will miss it. They're still trying to write better prompts. Anthropic is optimizing something else entirely: Context. Here's what every AI builder should learn from it: 1. Stop telling AI exactly what to do. Start telling it what success looks like. Older models needed rigid instructions. Newer models perform better when you define the objective and let them make the decisions. The goal is no longer more control. It's more clarity. 2. Context is a budget, not a storage unit. Every extra sentence competes for the model's attention. A massive context file doesn't make AI smarter. It often makes reasoning worse. The best systems don't load everything. They load only what's relevant. 3. Great tools beat great prompts. Most people spend hours tweaking prompt wording. Anthropic is investing in better interfaces instead. Clear parameters. Structured inputs. Well-designed tools. If the interface removes ambiguity, the model makes better decisions before it even starts reasoning. 4. Show reality instead of describing it. Want a specific coding style? Provide the codebase. Want a certain design language? Share the mockup. Want consistent outputs? Give it tests to satisfy. Concrete references consistently outperform long written instructions. 5. Deliver information when it's needed. Not everything belongs in the initial context. Documentation. Style guides. Verification. Reviews. Load them only when they're relevant. Think of context like RAM, not a hard drive. 6. The real advantage is system design. Prompt engineering isn't disappearing. It's becoming one small part of a much bigger stack. Memory. Retrieval. Context architecture. Tool design. Evaluation. Every improvement compounds. The biggest takeaway from Anthropic's update? The next generation of AI won't be won by the people writing the longest prompts. It'll be won by the people building the smartest systems. Same models. Completely different results.
0:30

Anthropic just changed how they think about Claude 5. Not with a new model. Not with a benchmark. With a completely different philosophy for building AI systems. Most people will miss it. They're still trying to write better prompts. Anthropic is optimizing something else entirely: Context. Here's what every AI builder should learn from it: 1. Stop telling AI exactly what to do. Start telling it what success looks like. Older models needed rigid instructions. Newer models perform better when you define the objective and let them make the decisions. The goal is no longer more control. It's more clarity. 2. Context is a budget, not a storage unit. Every extra sentence competes for the model's attention. A massive context file doesn't make AI smarter. It often makes reasoning worse. The best systems don't load everything. They load only what's relevant. 3. Great tools beat great prompts. Most people spend hours tweaking prompt wording. Anthropic is investing in better interfaces instead. Clear parameters. Structured inputs. Well-designed tools. If the interface removes ambiguity, the model makes better decisions before it even starts reasoning. 4. Show reality instead of describing it. Want a specific coding style? Provide the codebase. Want a certain design language? Share the mockup. Want consistent outputs? Give it tests to satisfy. Concrete references consistently outperform long written instructions. 5. Deliver information when it's needed. Not everything belongs in the initial context. Documentation. Style guides. Verification. Reviews. Load them only when they're relevant. Think of context like RAM, not a hard drive. 6. The real advantage is system design. Prompt engineering isn't disappearing. It's becoming one small part of a much bigger stack. Memory. Retrieval. Context architecture. Tool design. Evaluation. Every improvement compounds. The biggest takeaway from Anthropic's update? The next generation of AI won't be won by the people writing the longest prompts. It'll be won by the people building the smartest systems. Same models. Completely different results.

Evan Luthra

38,751 次观看 • 27 天前

I charge $999 to ask a business owner questions for 45 minutes. Then Claude does the analysis in 5 minutes. I call it the AI Tools Assessment. It finds 3 to 7 off-the-shelf tools that reclaim 5 to 10 hours a week, and it's the front door to upsells from $3,500 projects to $2,000/month retainers. Here's the entire model: 1) The discovery call is questions only. "Walk me through yesterday." "What tasks do you dread?" "Where does work pile up?" No pitching. A free AI notetaker captures the transcript. 2) Claude runs the entire analysis. Paste the transcript, run one skill, and it pulls the pain points and prescribes the tools in about 5 minutes. It catches patterns you missed on the call. 3) When Claude whiffs on a tool, and fill the gaps. Thousands of tools, grouped by industry. 4) The report is 9 slides. Executive summary, effort vs impact matrix, tool recommendations, a 4-day quick win plan, and the financial impact. I open sourced the template free at 5) I go for the close on the review call. Three questions: which of these is most urgent, do you want to DIY or get help, and what's your timeline? 50 to 60% of clients ask you to implement it for them. 6) Process redesign sells for $3,500 with zero automation. One e-commerce client had an 18-step ad workflow. We cut it to 9 steps. Fixed the process, didn't touch AI, charged $3,500. 7) Knowledge systems are $3K builds. A business broker got 400 emails per listing. We trained a custom GPT on the marketing package, and buyers called it the best broker experience they've had. 8) You don't need an audience to sell this. One guy walked into 30 local businesses offering a free 15-minute mini assessment. 5 meetings, 2 clients. The free mini assessment is the hook for every channel. 9) Co-working spaces are the cheat code. Dennis in our community hosted his first free AI office hours this week. 9 people showed up, 2 became warm leads. 10) AI Concierge is the best upsell of all. Two 45-minute calls a month at $1,200 to $2,000. I have 5 clients and my blended rate is about $1,100 an hour at 99.9% net margin. Two things that make this work: 1) Sell the diagnosis before the cure. The $999 assessment is a paid discovery call that qualifies the buyer and tees up every upsell on the menu. 2) High perceived value can cost you nothing. Unlimited Voxer access sells the retainer. In 3 months across 5 clients I've gotten 4 messages. Full breakdown below. watch, implement, make money. (also available on the Build With AI podcast wherever you get your pods)
25:36

I charge $999 to ask a business owner questions for 45 minutes. Then Claude does the analysis in 5 minutes. I call it the AI Tools Assessment. It finds 3 to 7 off-the-shelf tools that reclaim 5 to 10 hours a week, and it's the front door to upsells from $3,500 projects to $2,000/month retainers. Here's the entire model: 1) The discovery call is questions only. "Walk me through yesterday." "What tasks do you dread?" "Where does work pile up?" No pitching. A free AI notetaker captures the transcript. 2) Claude runs the entire analysis. Paste the transcript, run one skill, and it pulls the pain points and prescribes the tools in about 5 minutes. It catches patterns you missed on the call. 3) When Claude whiffs on a tool, and fill the gaps. Thousands of tools, grouped by industry. 4) The report is 9 slides. Executive summary, effort vs impact matrix, tool recommendations, a 4-day quick win plan, and the financial impact. I open sourced the template free at 5) I go for the close on the review call. Three questions: which of these is most urgent, do you want to DIY or get help, and what's your timeline? 50 to 60% of clients ask you to implement it for them. 6) Process redesign sells for $3,500 with zero automation. One e-commerce client had an 18-step ad workflow. We cut it to 9 steps. Fixed the process, didn't touch AI, charged $3,500. 7) Knowledge systems are $3K builds. A business broker got 400 emails per listing. We trained a custom GPT on the marketing package, and buyers called it the best broker experience they've had. 8) You don't need an audience to sell this. One guy walked into 30 local businesses offering a free 15-minute mini assessment. 5 meetings, 2 clients. The free mini assessment is the hook for every channel. 9) Co-working spaces are the cheat code. Dennis in our community hosted his first free AI office hours this week. 9 people showed up, 2 became warm leads. 10) AI Concierge is the best upsell of all. Two 45-minute calls a month at $1,200 to $2,000. I have 5 clients and my blended rate is about $1,100 an hour at 99.9% net margin. Two things that make this work: 1) Sell the diagnosis before the cure. The $999 assessment is a paid discovery call that qualifies the buyer and tees up every upsell on the menu. 2) High perceived value can cost you nothing. Unlimited Voxer access sells the retainer. In 3 months across 5 clients I've gotten 4 messages. Full breakdown below. watch, implement, make money. (also available on the Build With AI podcast wherever you get your pods)

Corey Ganim

49,064 次观看 • 1 个月前

25 million people, every single year, download Shaan Puri’s podcast. What has Shaan figured out about storytelling? He spilled the beans in our new chat. Here's how he does it: 1. A story is a five second moment of change. A story is not a sequence of events; it's about transformation. Weave in U-turns and unexpected flips. 2. Write like you talk. Natural, conversational, led by stories. 3. A formula for a great story: Intention + Obstacle. At all moments, the listener should know what the hero wants and what's stopping them from getting there. This one's from Aaron Sorkin, who wrote The Social Network. 4. Work backwards from the emotion you're trying to create in the reader. Then let the structure follow. 5. Aim for strong reactions. If you can get the reader to widen their eyes, raise their eyebrows, and/or burst out laughing, they will share your work. 6. Don't write to the faceless masses. Write to one specific person. BuzzFeed writers used to write to "Debbie at her Desk," the bored woman at her desk who wanted a 5-minute distraction. 7. “Likeability” is downstream of vulnerability. The more honestly you share your challenges, the more invested your reader gets. Write your heart out. 8. Don't be the 9,000 IQ guy. Stop competing in imaginary intelligence contests and start telling stories. Big words alienate but tight narratives pull people in. 9. Forget resumes and portfolios. Create a “binge bank” instead. A binge bank is a set of videos or essays that people can binge on. Stack up material so that when people do go down your rabbit hole, they come out the other side a fan. 10. Mere practice gets you nowhere. But intentional practice leads to exponential progress. Always learn from your attempts and make intelligent tweaks on the next try. 11. Comedy is great, but definitely don't make every sentence a joke. 12. Comedy is a pretty easy way to improve your writing. The essence of all comedy is surprise. Study your favorite comedians. Read books like How to Write Funny and The Hidden Tools of Comedy. 13. For better storytelling, Shaan recommends two books: Storyworthy and Building a Storybrand. 14. How to make headlines juicy: use specific and odd numbers, focus on the first three words and the last three words, use "you" whenever possible, and know that longer is typically better than shorter. 15. Your writing should only be as long as it is interesting. An uninteresting 20 second reel will fail; an interesting 30-minute essay will win. But you must be honest while gauging how objectively interesting your piece is…in a world with infinite content. 16. Most people think writing is about transferring information but writing is just as much about transferring emotion. Emotion gets people to take action (like, share, buy…). The first 40-minutes of this conversation with Shaan Puri is all about storytelling. Then there's an hour more about the genius of Dave Chapelle and how to write with zest. If you'd rather listen on YouTube, Spotify, or Apple, check out the replies…
1:48:30

25 million people, every single year, download Shaan Puri’s podcast. What has Shaan figured out about storytelling? He spilled the beans in our new chat. Here's how he does it: 1. A story is a five second moment of change. A story is not a sequence of events; it's about transformation. Weave in U-turns and unexpected flips. 2. Write like you talk. Natural, conversational, led by stories. 3. A formula for a great story: Intention + Obstacle. At all moments, the listener should know what the hero wants and what's stopping them from getting there. This one's from Aaron Sorkin, who wrote The Social Network. 4. Work backwards from the emotion you're trying to create in the reader. Then let the structure follow. 5. Aim for strong reactions. If you can get the reader to widen their eyes, raise their eyebrows, and/or burst out laughing, they will share your work. 6. Don't write to the faceless masses. Write to one specific person. BuzzFeed writers used to write to "Debbie at her Desk," the bored woman at her desk who wanted a 5-minute distraction. 7. “Likeability” is downstream of vulnerability. The more honestly you share your challenges, the more invested your reader gets. Write your heart out. 8. Don't be the 9,000 IQ guy. Stop competing in imaginary intelligence contests and start telling stories. Big words alienate but tight narratives pull people in. 9. Forget resumes and portfolios. Create a “binge bank” instead. A binge bank is a set of videos or essays that people can binge on. Stack up material so that when people do go down your rabbit hole, they come out the other side a fan. 10. Mere practice gets you nowhere. But intentional practice leads to exponential progress. Always learn from your attempts and make intelligent tweaks on the next try. 11. Comedy is great, but definitely don't make every sentence a joke. 12. Comedy is a pretty easy way to improve your writing. The essence of all comedy is surprise. Study your favorite comedians. Read books like How to Write Funny and The Hidden Tools of Comedy. 13. For better storytelling, Shaan recommends two books: Storyworthy and Building a Storybrand. 14. How to make headlines juicy: use specific and odd numbers, focus on the first three words and the last three words, use "you" whenever possible, and know that longer is typically better than shorter. 15. Your writing should only be as long as it is interesting. An uninteresting 20 second reel will fail; an interesting 30-minute essay will win. But you must be honest while gauging how objectively interesting your piece is…in a world with infinite content. 16. Most people think writing is about transferring information but writing is just as much about transferring emotion. Emotion gets people to take action (like, share, buy…). The first 40-minutes of this conversation with Shaan Puri is all about storytelling. Then there's an hour more about the genius of Dave Chapelle and how to write with zest. If you'd rather listen on YouTube, Spotify, or Apple, check out the replies…

David Perell

386,591 次观看 • 2 年前