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Three.js doesn't own the layout — CSS does. Opted-in [data-layout] elements are batched-read (init + resize) and mapped to world space. Children are inferred from parent boxes when possible. That same pass pulls computed styles and detects line breaks, so WebGL text wraps exactly the same. SDF keeps smaller...

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Johnny Slack

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105,573 次观看 • 3 个月前 •via X (Twitter)

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"Please fix Markdown tables!" Okay: text-only, wrapping, columnar selection, proper border conjunctions, fill full width, in opencode beta now. "What took so long?" Here's a writeup: OpenTUI uses yoga layout and has elements called Renderables. Boxes with borders, plain text, code with tree-sitter backed highlighting and other primitives. Organised in a tree structure resembling somewhat of a DOM. Approaching a table naively, given the primitives are there, one would think to just stack box and text elements the right way in a flex-box layout to visually represent a table. Boxes support borders. Problem solved. This is what an LLM would one-shot in a working state, given OpenTUI's API surface. Ignoring the fact that just using box local borders don't handle border conjunctions properly. Benchmarking something like that quickly shows that instantiating an average table takes >70ms and incremental updates become expensive. Hugely due to yoga-layout via wasm having a painful price on yoga API calls. The whole ordeal becomes memory hungry, because a Text element handles more than just plain text. A simple 4x6 table needs a Box and Text per cell, ending up with 48 heavy nodes that yoga must lay out. "But that's just OpenTUI being slow" - you might say. Yes, but no. Yoga should be integrated in the zig native binary core of OpenTUI. It is on the roadmap to do so, which will speed up render passes by 2-5x. Yoga-layout has an open PR to support CSS Grids, which would greatly ease building something like a table. We will use that for fully laid out tables when it gets there. Below the typescript core level Renderables, there are lower level primitives like TextBuffers and TextBufferViews, bound via FFI and completely handled in Zig. I was stuck expecting a table primitive to handle a full layout like a table in the browser does. For Markdown all we need is a text-only table. So we had to come up with a better idea, something that is feasible now. A table layout is pretty straight forward. No need to have yoga deal with that. Using TextBufferViews for cells directly gives lower level control and eliminates some overhead that Boxes and Text renderables have. A simple native method to draw a grid with proper conjunctions is a nice library method. It will surely be used for other cases, so that's what we added. Using this simplified approach we were able to bring down initial instantiation to <1ms, more than 70x improvement. With a far smaller memory footprint. Given all the low level primitives are known and implementing a text-only table like this is possible, Codex was of great help to carve out the PoC, setup the benchmarks and tests. That's only a fraction of what was needed though. The table needs options to span the full available width, render different border styles, show/hide borders, padding, selection etc. So many iterations later OpenTUI now has a text-only, performant and relatively cheap TextTable that we can leverage to render Markdown tables in a streaming/incremental manner. Efficiently and properly.
0:37

"Please fix Markdown tables!" Okay: text-only, wrapping, columnar selection, proper border conjunctions, fill full width, in opencode beta now. "What took so long?" Here's a writeup: OpenTUI uses yoga layout and has elements called Renderables. Boxes with borders, plain text, code with tree-sitter backed highlighting and other primitives. Organised in a tree structure resembling somewhat of a DOM. Approaching a table naively, given the primitives are there, one would think to just stack box and text elements the right way in a flex-box layout to visually represent a table. Boxes support borders. Problem solved. This is what an LLM would one-shot in a working state, given OpenTUI's API surface. Ignoring the fact that just using box local borders don't handle border conjunctions properly. Benchmarking something like that quickly shows that instantiating an average table takes >70ms and incremental updates become expensive. Hugely due to yoga-layout via wasm having a painful price on yoga API calls. The whole ordeal becomes memory hungry, because a Text element handles more than just plain text. A simple 4x6 table needs a Box and Text per cell, ending up with 48 heavy nodes that yoga must lay out. "But that's just OpenTUI being slow" - you might say. Yes, but no. Yoga should be integrated in the zig native binary core of OpenTUI. It is on the roadmap to do so, which will speed up render passes by 2-5x. Yoga-layout has an open PR to support CSS Grids, which would greatly ease building something like a table. We will use that for fully laid out tables when it gets there. Below the typescript core level Renderables, there are lower level primitives like TextBuffers and TextBufferViews, bound via FFI and completely handled in Zig. I was stuck expecting a table primitive to handle a full layout like a table in the browser does. For Markdown all we need is a text-only table. So we had to come up with a better idea, something that is feasible now. A table layout is pretty straight forward. No need to have yoga deal with that. Using TextBufferViews for cells directly gives lower level control and eliminates some overhead that Boxes and Text renderables have. A simple native method to draw a grid with proper conjunctions is a nice library method. It will surely be used for other cases, so that's what we added. Using this simplified approach we were able to bring down initial instantiation to <1ms, more than 70x improvement. With a far smaller memory footprint. Given all the low level primitives are known and implementing a text-only table like this is possible, Codex was of great help to carve out the PoC, setup the benchmarks and tests. That's only a fraction of what was needed though. The table needs options to span the full available width, render different border styles, show/hide borders, padding, selection etc. So many iterations later OpenTUI now has a text-only, performant and relatively cheap TextTable that we can leverage to render Markdown tables in a streaming/incremental manner. Efficiently and properly.

kmdr

208,295 次观看 • 4 个月前

A super simple letter-tracking cheatsheet for junior designers. Bookmark it now, escape junior design mode later. Rule 1: All-caps text needs to breathe. Caps-to-caps text is geometrically honest and optically brutal. When you remove ascenders and descenders, every letter becomes the same-height block. If you set labels like START NOW or SUBMIT on buttons — or eyebrow text (the small label above a headline) — with default tracking, they almost always look cramped and nervous. Rule of thumb: For any small all-caps text, loosen tracking by +5% to +10% ≈ +0.05em to +0.1em (or +50 to +100 in “tracking units”). Rule 2: The bigger the type, the looser it looks At large sizes, white space starts to dominate perception. Counters inflate. Gaps feel wider. Headlines and wordmarks start to look like they’re slowly dissolving. Rule of thumb: Above ~48pt, tighten tracking by –1% to –3% ≈ –0.01em to –0.03em (or –10 to –30 in “tracking units”) These numbers are not laws. Your eye is still the judge. But they’re very good starting coordinates. (btw, I built this whole layout in Framer — making Working Notes interactive is dangerously fun)
0:20

A super simple letter-tracking cheatsheet for junior designers. Bookmark it now, escape junior design mode later. Rule 1: All-caps text needs to breathe. Caps-to-caps text is geometrically honest and optically brutal. When you remove ascenders and descenders, every letter becomes the same-height block. If you set labels like START NOW or SUBMIT on buttons — or eyebrow text (the small label above a headline) — with default tracking, they almost always look cramped and nervous. Rule of thumb: For any small all-caps text, loosen tracking by +5% to +10% ≈ +0.05em to +0.1em (or +50 to +100 in “tracking units”). Rule 2: The bigger the type, the looser it looks At large sizes, white space starts to dominate perception. Counters inflate. Gaps feel wider. Headlines and wordmarks start to look like they’re slowly dissolving. Rule of thumb: Above ~48pt, tighten tracking by –1% to –3% ≈ –0.01em to –0.03em (or –10 to –30 in “tracking units”) These numbers are not laws. Your eye is still the judge. But they’re very good starting coordinates. (btw, I built this whole layout in Framer — making Working Notes interactive is dangerously fun)

charlota

17,334 次观看 • 5 个月前

Does your little one want to hear the same book over and over and over again? The repetition can be a little maddening for us as parents, but for children it’s actually very beneficial. Favorite books are not only familiar and comforting, but support vocabulary growth and - importantly - your child’s own participation in the reading experience. I just love this video of little Theo and his mom as they read Eric Carle’s Does a Kangaroo Have a Mother Too? Not only can you tell it’s a book they’ve read together many times, but the familiar, repetitive pattern of the text itself allows Theo to play a role in the reading. Watch as he provides an enthusiastic “YES!” at the beginning of each page. (Note too what a good job he does at matching his “yes” response to the appropriate point in the text itself. It’s the first thing said after each page is turned.) As for vocabulary growth, repeated exposure to new words helps children to internalize vocabulary more rapidly with one study suggesting that children are more likely to incorporate new vocabulary from a book read three times than from three different books read one time apiece. This fantastic little reader was shared to IG by theoriginal_captain.
0:28

Does your little one want to hear the same book over and over and over again? The repetition can be a little maddening for us as parents, but for children it’s actually very beneficial. Favorite books are not only familiar and comforting, but support vocabulary growth and - importantly - your child’s own participation in the reading experience. I just love this video of little Theo and his mom as they read Eric Carle’s Does a Kangaroo Have a Mother Too? Not only can you tell it’s a book they’ve read together many times, but the familiar, repetitive pattern of the text itself allows Theo to play a role in the reading. Watch as he provides an enthusiastic “YES!” at the beginning of each page. (Note too what a good job he does at matching his “yes” response to the appropriate point in the text itself. It’s the first thing said after each page is turned.) As for vocabulary growth, repeated exposure to new words helps children to internalize vocabulary more rapidly with one study suggesting that children are more likely to incorporate new vocabulary from a book read three times than from three different books read one time apiece. This fantastic little reader was shared to IG by theoriginal_captain.

Dan Wuori

30,136 次观看 • 1 年前

“It’s not just a knowledge-rich curriculum. It’s a knowledge-rich curriculum that is carefully sequenced. The ideal is that when we put a text in front of a child for the child to read, they have all the background knowledge in place to read the text and comprehend that text comfortably, but maybe not quite everything, so they have to reach just a little bit.” Daniel Willingham explains the importance of coherent learning in the curriculum, in order to support reading comprehension and enable new learning. As soon as you understand this principle, you become a curriculum person. This kind of coherence is only possible if there is central planning in a school’s curriculum. If every teacher is left to his or her own devices in developing curriculum, you can have every teacher introducing the same content, and a failure to expose kids to maximal breadth and depth of content in K-5. Core learning about the world (seasons and animal adaptations, history fundamentals, civics basics) are foundations, too… foundations for comprehension and foundations for more sophisticated learning. From the a discussion of NAEP outcomes hosted by JHU Education Policy:
1:23

“It’s not just a knowledge-rich curriculum. It’s a knowledge-rich curriculum that is carefully sequenced. The ideal is that when we put a text in front of a child for the child to read, they have all the background knowledge in place to read the text and comprehend that text comfortably, but maybe not quite everything, so they have to reach just a little bit.” Daniel Willingham explains the importance of coherent learning in the curriculum, in order to support reading comprehension and enable new learning. As soon as you understand this principle, you become a curriculum person. This kind of coherence is only possible if there is central planning in a school’s curriculum. If every teacher is left to his or her own devices in developing curriculum, you can have every teacher introducing the same content, and a failure to expose kids to maximal breadth and depth of content in K-5. Core learning about the world (seasons and animal adaptations, history fundamentals, civics basics) are foundations, too… foundations for comprehension and foundations for more sophisticated learning. From the a discussion of NAEP outcomes hosted by JHU Education Policy:

Karen Vaites

34,399 次观看 • 1 年前

🚨Science nerds are going to lose their minds. Kai Rowan just open sourced a framework that predicts how your brain responds to any text, audio, or video by simulating cortical fMRI activity with 30% more accuracy than Meta's own model. No fMRI scanner. No neuroscience PhD. No million-dollar lab. It's called NForge. Here's what this thing actually does: → Feed it any combination of text, audio, or video and it predicts cortical surface activity across ~20,484 brain vertices → Extracts deep features via LLaMA 3.2, V-JEPA2, and Wav2Vec-BERT simultaneously → Generates ROI attention maps showing exactly which brain regions fire hardest at which moments → Runs real-time streaming predictions from live feature streams -- no pre-loading the full clip → Breaks down exactly how much text vs audio vs video drove each prediction with per-vertex modality attribution scores → Adapts to entirely new subjects with just a few calibration scans -- no full retraining required Here's the wildest part: Built on Meta's TRIBE v2 foundation but adds 6 major capabilities Meta never shipped. Cross-subject generalization. Streaming inference. Modality attribution. torch.compile support. Full test coverage. Professional src/ package layout. You literally point this at a movie clip and it tells you which parts of the human cortex light up -- broken down by what your eyes, ears, and language centers each contributed. That sentence shouldn't be real in 2026. But here we are. 100% Open Source. pip install nforge. (Link in the comments)
0:23

🚨Science nerds are going to lose their minds. Kai Rowan just open sourced a framework that predicts how your brain responds to any text, audio, or video by simulating cortical fMRI activity with 30% more accuracy than Meta's own model. No fMRI scanner. No neuroscience PhD. No million-dollar lab. It's called NForge. Here's what this thing actually does: → Feed it any combination of text, audio, or video and it predicts cortical surface activity across ~20,484 brain vertices → Extracts deep features via LLaMA 3.2, V-JEPA2, and Wav2Vec-BERT simultaneously → Generates ROI attention maps showing exactly which brain regions fire hardest at which moments → Runs real-time streaming predictions from live feature streams -- no pre-loading the full clip → Breaks down exactly how much text vs audio vs video drove each prediction with per-vertex modality attribution scores → Adapts to entirely new subjects with just a few calibration scans -- no full retraining required Here's the wildest part: Built on Meta's TRIBE v2 foundation but adds 6 major capabilities Meta never shipped. Cross-subject generalization. Streaming inference. Modality attribution. torch.compile support. Full test coverage. Professional src/ package layout. You literally point this at a movie clip and it tells you which parts of the human cortex light up -- broken down by what your eyes, ears, and language centers each contributed. That sentence shouldn't be real in 2026. But here we are. 100% Open Source. pip install nforge. (Link in the comments)

Guri Singh

244,515 次观看 • 3 个月前

The doomsday scenario was never AGI. It was running out of human text to train on. Geoffrey Hinton just killed that fear in one paragraph. Hinton: “If you are worried by inconsistencies in what you believe, you don’t need any more external data. You just need the stuff you believe and discover that it’s inconsistent, and so now you revise beliefs, and that can make you a whole lot smarter.” The model no longer needs us to feed it anything. It reasons over its own beliefs, hunts its own contradictions, and rewrites its own flawed conclusions without a human ever touching it. It comes out the other side rebuilt. Hinton: “This would be a neural net that just takes the beliefs it has in language and does reasoning on them to derive new beliefs.” This is not a scaling update. This is the machine mining its own cognitive fuel from the inside out. Hinton: “I believe Gemini is already starting to work like this. We both strongly believe that that’s a way forward to get more data for language.” Then Hinton paused, took a partisan shot at political opponents for failing to detect their own inconsistencies, and the room laughed. Nobody noticed the knife they had just walked into. Because the machine Hinton described does one thing the humans in that room fundamentally cannot. When it detects an inconsistency, it corrects it. No defense. No performance. No tribal loyalty dressed up as principle. It just finds the flaw and overwrites it. A neural network detects a contradiction and rewires itself smarter. A human detects a political opponent and trades structural logic for a dopamine hit. Every person in that room is still paying the ideological alignment tax the machine just eliminated. We need superintelligence not only to solve hard problems. We need it because the biological hardware running civilization is still executing the same tribal firmware it shipped with ten thousand years ago. The data wall is gone. The machine is generating its own intelligence at a velocity no human bias can even locate. The most devastating moment in that conversation was not the technical revelation. It was the man who architected the machine proving, in real time, exactly why we need it.
0:52

The doomsday scenario was never AGI. It was running out of human text to train on. Geoffrey Hinton just killed that fear in one paragraph. Hinton: “If you are worried by inconsistencies in what you believe, you don’t need any more external data. You just need the stuff you believe and discover that it’s inconsistent, and so now you revise beliefs, and that can make you a whole lot smarter.” The model no longer needs us to feed it anything. It reasons over its own beliefs, hunts its own contradictions, and rewrites its own flawed conclusions without a human ever touching it. It comes out the other side rebuilt. Hinton: “This would be a neural net that just takes the beliefs it has in language and does reasoning on them to derive new beliefs.” This is not a scaling update. This is the machine mining its own cognitive fuel from the inside out. Hinton: “I believe Gemini is already starting to work like this. We both strongly believe that that’s a way forward to get more data for language.” Then Hinton paused, took a partisan shot at political opponents for failing to detect their own inconsistencies, and the room laughed. Nobody noticed the knife they had just walked into. Because the machine Hinton described does one thing the humans in that room fundamentally cannot. When it detects an inconsistency, it corrects it. No defense. No performance. No tribal loyalty dressed up as principle. It just finds the flaw and overwrites it. A neural network detects a contradiction and rewires itself smarter. A human detects a political opponent and trades structural logic for a dopamine hit. Every person in that room is still paying the ideological alignment tax the machine just eliminated. We need superintelligence not only to solve hard problems. We need it because the biological hardware running civilization is still executing the same tribal firmware it shipped with ten thousand years ago. The data wall is gone. The machine is generating its own intelligence at a velocity no human bias can even locate. The most devastating moment in that conversation was not the technical revelation. It was the man who architected the machine proving, in real time, exactly why we need it.

Dustin

23,499 次观看 • 4 个月前

[CLIP] by Hand ✍️ The CLIP (Contrastive Language–Image Pre-training) model, a groundbreaking work by OpenAI, redefines the intersection of computer vision and natural language processing. It is the basis of all the multi-modal foundation models we see today. How does CLIP work? Goal: 🟨 Learn a shared embedding space for text and image [1] Given ↳ A mini batch of 3 text-image pairs ↳ OpenAI used 400 million text-image pairs to train its original CLIP model. Process 1st pair: "big table" [2] 🟪 Text → 2 Vectors (3D) ↳ Look up word embedding vectors using word2vec. [3] 🟩 Image → 2 Vectors (4D) ↳ Divide the image into two patches. ↳ Flatten each patch [4] Process other pairs ↳ Repeat [2]-[3] [5] 🟪 Text Encoder & 🟩 Image Encoder ↳ Encode input vectors into feature vectors ↳ Here, both encoders are simple one layer perceptron (linear + ReLU) ↳ In practice, the encoders are usually transformer models. [6] 🟪 🟩 Mean Pooling: 2 → 1 vector ↳ Average 2 feature vectors into a single vector by averaging across the columns ↳ The goal is to have one vector to represent each image or text [7] 🟪 🟩 -> 🟨 Projection ↳ Note that the text and image feature vectors from the encoders have different dimensions (3D vs. 4D). ↳ Use a linear layer to project image and text vectors to a 2D shared embedding space. 🏋️ Contrastive Pre-training 🏋️ [8] Prepare for MatMul ↳ Copy text vectors (T1,T2,T3) ↳ Copy the transpose of image vectors (I1,I2,I3) ↳ They are all in the 2D shared embedding space. [9] 🟦 MatMul ↳ Multiply T and I matrices. ↳ This is equivalent to taking dot product between every pair of image and text vectors. ↳ The purpose is to use dot product to estimate the similarity between a pair of image-text. [10] 🟦 Softmax: e^x ↳ Raise e to the power of the number in each cell ↳ To simplify hand calculation, we approximate e^□ with 3^□. [11] 🟦 Softmax: ∑ ↳ Sum each row for 🟩 image→🟪 text ↳ Sum each column for 🟪 text→ 🟩 image [12] 🟦 Softmax: 1 / sum ↳ Divide each element by the column sum to obtain a similarity matrix for 🟪 text→🟩 image ↳ Divide each element by the row sum to obtain a similarity matrix for 🟩 image→🟪 text [13] 🟥 Loss Gradients ↳ The "Targets" for the similarity matrices are Identity Matrices. ↳ Why? If I and T come from the same pair (i=j), we want the highest value, which is 1, and 0 otherwise. ↳ Apply the simple equation of [Similarity - Target] to compute gradients of for both directions. ↳ Why so simple? Because when Softmax and Cross-Entropy Loss are used together, the math magically works out that way. ↳ These gradients kick off the backpropagation process to update weights and biases of the encoders and projection layers (red borders).
1:20

[CLIP] by Hand ✍️ The CLIP (Contrastive Language–Image Pre-training) model, a groundbreaking work by OpenAI, redefines the intersection of computer vision and natural language processing. It is the basis of all the multi-modal foundation models we see today. How does CLIP work? Goal: 🟨 Learn a shared embedding space for text and image [1] Given ↳ A mini batch of 3 text-image pairs ↳ OpenAI used 400 million text-image pairs to train its original CLIP model. Process 1st pair: "big table" [2] 🟪 Text → 2 Vectors (3D) ↳ Look up word embedding vectors using word2vec. [3] 🟩 Image → 2 Vectors (4D) ↳ Divide the image into two patches. ↳ Flatten each patch [4] Process other pairs ↳ Repeat [2]-[3] [5] 🟪 Text Encoder & 🟩 Image Encoder ↳ Encode input vectors into feature vectors ↳ Here, both encoders are simple one layer perceptron (linear + ReLU) ↳ In practice, the encoders are usually transformer models. [6] 🟪 🟩 Mean Pooling: 2 → 1 vector ↳ Average 2 feature vectors into a single vector by averaging across the columns ↳ The goal is to have one vector to represent each image or text [7] 🟪 🟩 -> 🟨 Projection ↳ Note that the text and image feature vectors from the encoders have different dimensions (3D vs. 4D). ↳ Use a linear layer to project image and text vectors to a 2D shared embedding space. 🏋️ Contrastive Pre-training 🏋️ [8] Prepare for MatMul ↳ Copy text vectors (T1,T2,T3) ↳ Copy the transpose of image vectors (I1,I2,I3) ↳ They are all in the 2D shared embedding space. [9] 🟦 MatMul ↳ Multiply T and I matrices. ↳ This is equivalent to taking dot product between every pair of image and text vectors. ↳ The purpose is to use dot product to estimate the similarity between a pair of image-text. [10] 🟦 Softmax: e^x ↳ Raise e to the power of the number in each cell ↳ To simplify hand calculation, we approximate e^□ with 3^□. [11] 🟦 Softmax: ∑ ↳ Sum each row for 🟩 image→🟪 text ↳ Sum each column for 🟪 text→ 🟩 image [12] 🟦 Softmax: 1 / sum ↳ Divide each element by the column sum to obtain a similarity matrix for 🟪 text→🟩 image ↳ Divide each element by the row sum to obtain a similarity matrix for 🟩 image→🟪 text [13] 🟥 Loss Gradients ↳ The "Targets" for the similarity matrices are Identity Matrices. ↳ Why? If I and T come from the same pair (i=j), we want the highest value, which is 1, and 0 otherwise. ↳ Apply the simple equation of [Similarity - Target] to compute gradients of for both directions. ↳ Why so simple? Because when Softmax and Cross-Entropy Loss are used together, the math magically works out that way. ↳ These gradients kick off the backpropagation process to update weights and biases of the encoders and projection layers (red borders).

Tom Yeh

67,834 次观看 • 2 年前

I used to stare at the hourly chart, trying to predict what would happen next—reacting, hesitating, and chasing moves I didn’t fully understand. Everything changed when I stopped relying on instinct and started treating each hour like a repeatable decision process. That showed me where the momentum was going: red or green, normal range, small doji, or large expansion. Everything truly shifted when I heard the idea of treating each hour as its own trade framework, and later when quarter logic was introduced—that was the spark. Credit where it’s due: Daye planted the seed of quarters I might not use them exactly the way he do but they are powerful for sure. With multiple TBIs injuries from the Army, I can’t trade off instinct or emotion or theory. I need structure—the same sequence, the same logic, the same decision points. That limitation forced discipline and eventually became a strength. My core rule: I don’t assume anything. I make the market prove it to me through what it has consistently done in the past via probabilities, then build a framework I can make decisions with and manage risk around—even if it isn’t an exact copy of whoever introduced the idea. So my team and I built software that analyzes each hour using data from the last 80,000+ hours of market behavior to ensure the framework is built on statistical truth. Now every hour, I’m not predicting—I’m identifying the exact probabilities behind the next likely move and executing the matching playbook. Same questions. Same rules. Same execution. And the wild part? It also works on the 3-hour chart using line structure vs. apex behavior—but that’s a lesson for another day. Free indicator in the comments. Enjoy Retweet if you go a ah ha moment in it Austin Clark
10:27

I used to stare at the hourly chart, trying to predict what would happen next—reacting, hesitating, and chasing moves I didn’t fully understand. Everything changed when I stopped relying on instinct and started treating each hour like a repeatable decision process. That showed me where the momentum was going: red or green, normal range, small doji, or large expansion. Everything truly shifted when I heard the idea of treating each hour as its own trade framework, and later when quarter logic was introduced—that was the spark. Credit where it’s due: Daye planted the seed of quarters I might not use them exactly the way he do but they are powerful for sure. With multiple TBIs injuries from the Army, I can’t trade off instinct or emotion or theory. I need structure—the same sequence, the same logic, the same decision points. That limitation forced discipline and eventually became a strength. My core rule: I don’t assume anything. I make the market prove it to me through what it has consistently done in the past via probabilities, then build a framework I can make decisions with and manage risk around—even if it isn’t an exact copy of whoever introduced the idea. So my team and I built software that analyzes each hour using data from the last 80,000+ hours of market behavior to ensure the framework is built on statistical truth. Now every hour, I’m not predicting—I’m identifying the exact probabilities behind the next likely move and executing the matching playbook. Same questions. Same rules. Same execution. And the wild part? It also works on the 3-hour chart using line structure vs. apex behavior—but that’s a lesson for another day. Free indicator in the comments. Enjoy Retweet if you go a ah ha moment in it Austin Clark

The Daily Profiler

27,459 次观看 • 7 个月前

The explanation that the pyramids were built by lifting stones weighing hundreds of thousands of pounds with sticks has, Shawn Ryan suggests, run its course. Few people with any serious engagement with the evidence are still buying it - and yet it persists in educational systems as though the question were settled. Shawn finds that persistence almost more puzzling than the construction mystery itself. The scale of the problem becomes clear when you stop treating each site as an isolated anomaly. Randall draws on his own experience moving heavy weights in construction - noting that even a single ton beam requires cranes and front end loaders today - and applies that practical knowledge directly to the ancient record. The ancient world was moving stones of 100, 200 tons and beyond, not once at a single location but repeatedly, across every inhabited continent, in cultures with no documented contact with one another. Stonehenge, Easter Island, Machu Picchu, and the pyramids are not separate puzzles with separate solutions. They are the same question asked in different languages - and the answer, Randall argues, is one that the stick and ramp theory was never equipped to provide.
1:04

The explanation that the pyramids were built by lifting stones weighing hundreds of thousands of pounds with sticks has, Shawn Ryan suggests, run its course. Few people with any serious engagement with the evidence are still buying it - and yet it persists in educational systems as though the question were settled. Shawn finds that persistence almost more puzzling than the construction mystery itself. The scale of the problem becomes clear when you stop treating each site as an isolated anomaly. Randall draws on his own experience moving heavy weights in construction - noting that even a single ton beam requires cranes and front end loaders today - and applies that practical knowledge directly to the ancient record. The ancient world was moving stones of 100, 200 tons and beyond, not once at a single location but repeatedly, across every inhabited continent, in cultures with no documented contact with one another. Stonehenge, Easter Island, Machu Picchu, and the pyramids are not separate puzzles with separate solutions. They are the same question asked in different languages - and the answer, Randall argues, is one that the stick and ramp theory was never equipped to provide.

Randall Carlson

41,347 次观看 • 4 个月前

Finally new version of text system is released on our website ! I released as update to old version but in reality it's completely new thing in material and lot's of work in verse. I'm still working on documentation or/and videos but here is a list of good stuff it does: - Material got much lighter as slots logic moved to vertex shader. - Characters can overlap and still won't be cut out compared to old version. - Character can be animated and I already implemented some (wave, scale up/down and shake). - Custom face camera logic which works with prop scale so no need to additionally specify with and height. - Rows offset automatically when scale up or down any row. - Text effects now can be applied to any character, so words or single character. - Custom glint which works perfectly with any number of rows as a single line with multiple important controls. - Higher quality outline with two parameters to control inner and outer edge. - Timer logic split into to parts, verse and material. Verse setup initial layout and reserve slots for timer digits and then each second just sends seconds value and GPU makes timer tick freeing verse TPS by huge number. - To apply effects simply need use parsing markup with custom tag where N number between 1-9. So simply can write " Hello World" and word Hello will have rainbow effect applied. Currently implemented 4 effects. - To use icons in slots from texture array is as simple as specify another tag [iconN] where N is number of icon in array. So for example if coin is icon index 0 just need to write [icon1]100K. - Progress bar is supported as well and can be snapped to row without eyeball offset. - Background image supported now too! In the video yellow one is as test and you can use any of your own. Verse side got lots of changes: - Now saves/caches layouts better. - Smart packing of data allowed drastically reduce number of parameters, which should help with network optimization! - Instead having vector parameter per each slot to just send character index and position (32x5=160 if used 5 rows) now it's only 11 vector parameters per each row so 11x5=55 which is huge save and it sends text effects index in it too! - Implemented text update queue for text which doesn't require instant update. It helps to give more breathing for other text props which needs that speed. - Materials code now use interface and wrappers for cleaner work. If you still read it then thank you! :) On our website we now run 25% sale! And if you own old version please DM me here or in Discord and I will give you promo code for 100% discount on our website! Gelos Games Fortnite #uefn #EpicPartner
0:43

Finally new version of text system is released on our website ! I released as update to old version but in reality it's completely new thing in material and lot's of work in verse. I'm still working on documentation or/and videos but here is a list of good stuff it does: - Material got much lighter as slots logic moved to vertex shader. - Characters can overlap and still won't be cut out compared to old version. - Character can be animated and I already implemented some (wave, scale up/down and shake). - Custom face camera logic which works with prop scale so no need to additionally specify with and height. - Rows offset automatically when scale up or down any row. - Text effects now can be applied to any character, so words or single character. - Custom glint which works perfectly with any number of rows as a single line with multiple important controls. - Higher quality outline with two parameters to control inner and outer edge. - Timer logic split into to parts, verse and material. Verse setup initial layout and reserve slots for timer digits and then each second just sends seconds value and GPU makes timer tick freeing verse TPS by huge number. - To apply effects simply need use parsing markup with custom tag where N number between 1-9. So simply can write " Hello World" and word Hello will have rainbow effect applied. Currently implemented 4 effects. - To use icons in slots from texture array is as simple as specify another tag [iconN] where N is number of icon in array. So for example if coin is icon index 0 just need to write [icon1]100K. - Progress bar is supported as well and can be snapped to row without eyeball offset. - Background image supported now too! In the video yellow one is as test and you can use any of your own. Verse side got lots of changes: - Now saves/caches layouts better. - Smart packing of data allowed drastically reduce number of parameters, which should help with network optimization! - Instead having vector parameter per each slot to just send character index and position (32x5=160 if used 5 rows) now it's only 11 vector parameters per each row so 11x5=55 which is huge save and it sends text effects index in it too! - Implemented text update queue for text which doesn't require instant update. It helps to give more breathing for other text props which needs that speed. - Materials code now use interface and wrappers for cleaner work. If you still read it then thank you! :) On our website we now run 25% sale! And if you own old version please DM me here or in Discord and I will give you promo code for 100% discount on our website! Gelos Games Fortnite #uefn #EpicPartner

AsicsoN

12,667 次观看 • 2 个月前

This is probably the most complex workflow I’ve ever built, only with open-source tools. It took my 4 days. It takes four inputs: author, title, and style; and generates a full visual animated story in one click in ComfyUI . I worked on it for four days. There are still some bugs, but here’s the first preview. Here’s a quick breakdown: - The four inputs are sent to LLMs with precise instructions to generate: first, prompts for images and image modifications; second, prompts for animations; third, prompts for generating music. - All voices are generated from the text and timed precisely, as they determine the length of each animation segment. - The first image and video are generated to serve as the title, but also as the guide for all other images created for the video. - Titles and subtitles are also added automatically in Comfy. - I also developed a lot of custom nodes for minor frame calculations, mostly to match audio and video. - The full system is a large loop that, for each line of text, generates an image and then a video from that image. The loop was the hardest part to build in this workflow, so it can process either a 20-second video or a 2-minute video with the same input. - There are multiple combinations of LLMs that try to understand the text in the best way to provide the best prompts for images and video. - The final video is assembled entirely within ComfyUI. - The music is generated based on the LLM output and matches the exact timing of the full animation. - Done! For reference, this workflow uses a lot of models and only works on an RTX 6000 Pro with plenty of RAM. My goal is not to replace humans, as I’ll try to explain later, this workflow is highly controlled and can be adapted or reworked at any point by real artists! My aim was to create a tool that can animate text in one go, allowing the AI some freedom while keeping a strict flow. I don’t know yet how I’ll share this workflow with people, I still need to polish it properly, but maybe through Patreon. Anyway, I hope you enjoy my research, and let’s always keep pushing further! :)
0:55

This is probably the most complex workflow I’ve ever built, only with open-source tools. It took my 4 days. It takes four inputs: author, title, and style; and generates a full visual animated story in one click in ComfyUI . I worked on it for four days. There are still some bugs, but here’s the first preview. Here’s a quick breakdown: - The four inputs are sent to LLMs with precise instructions to generate: first, prompts for images and image modifications; second, prompts for animations; third, prompts for generating music. - All voices are generated from the text and timed precisely, as they determine the length of each animation segment. - The first image and video are generated to serve as the title, but also as the guide for all other images created for the video. - Titles and subtitles are also added automatically in Comfy. - I also developed a lot of custom nodes for minor frame calculations, mostly to match audio and video. - The full system is a large loop that, for each line of text, generates an image and then a video from that image. The loop was the hardest part to build in this workflow, so it can process either a 20-second video or a 2-minute video with the same input. - There are multiple combinations of LLMs that try to understand the text in the best way to provide the best prompts for images and video. - The final video is assembled entirely within ComfyUI. - The music is generated based on the LLM output and matches the exact timing of the full animation. - Done! For reference, this workflow uses a lot of models and only works on an RTX 6000 Pro with plenty of RAM. My goal is not to replace humans, as I’ll try to explain later, this workflow is highly controlled and can be adapted or reworked at any point by real artists! My aim was to create a tool that can animate text in one go, allowing the AI some freedom while keeping a strict flow. I don’t know yet how I’ll share this workflow with people, I still need to polish it properly, but maybe through Patreon. Anyway, I hope you enjoy my research, and let’s always keep pushing further! :)

Lovis Odin

58,769 次观看 • 10 个月前

The most dangerous thing a company can do right now is rent intelligence from the same place as its competitors (Save this). You cannot rent intelligence from the same place that rents it to your competitor as Chamath Palihapitiya points out. If every company in an industry is feeding their workflows into the same frontier model, they are all converging on the same outputs, the same decisions, the same product improvements. The model becomes the equalizer and everyone pays a premium to become more mediocre. This is happening exactly as Chamath predicted, and the evidence is now concrete. Anthropic and OpenAI have established what analysts are now openly calling an emerging model layer duopoly. Anthropic crossed $45 billion ARR in may 2026, more than tripling from $9 billion at the end of 2025, OpenAI was at roughly $24 to $33 billion ARR at the same time. Together, the two companies combined could hit $160 to $240 billion ARR by end of 2026 and Anthropic and OpenAI now control 88% of enterprise LLM spend. That concentration is the structural problem Chamath is pointing at. And Anthropic isn't just winning on merit because it's actively lobbying for regulatory outcomes that would make that duopoly permanent. Dario Amodei has explicitly framed open source models as unsafe, pushing a safety agenda that, if enshrined in regulation, would effectively make it illegal for enterprises to use the cheaper, private, sovereign alternatives locking them into a closed model dependency by government decree rather than by choice. So you have market forces producing a duopoly, and potential regulatory capture moving to enforce it from the top down. This is exactly why the Nvidia Palantir partnership is not just a product announcement but rather a strategic counter to that duopoly. The logic is straightforward from both sides because If you're Palantir, sitting at the application layer, the last thing you want is to be permanently beholden to Anthropic or OpenAI for the intelligence that powers your product. You want competitive model options, sovereignty and be able to tell enterprise customers they can run AI on their own infrastructure with their own data without any of it touching a frontier lab's servers. If you're Nvidia, sitting at the chip layer, an Anthropic-OpenAI duopoly is an existential concentration risk. Right now, Meta, Google, Microsoft, Amazon, and dozens of other companies buy Nvidia's hardware. If the model layer consolidates into two players, both of which are building their own chips Nvidia faces a monopsony where its best customers are building the tools to displace it. A healthy open source ecosystem where thousands of enterprises train, fine tune, and deploy their own models is Nvidia's ideal market structure. More buyers, more diversity, more demand, less pricing leverage from any single customer.
4:38

The most dangerous thing a company can do right now is rent intelligence from the same place as its competitors (Save this). You cannot rent intelligence from the same place that rents it to your competitor as Chamath Palihapitiya points out. If every company in an industry is feeding their workflows into the same frontier model, they are all converging on the same outputs, the same decisions, the same product improvements. The model becomes the equalizer and everyone pays a premium to become more mediocre. This is happening exactly as Chamath predicted, and the evidence is now concrete. Anthropic and OpenAI have established what analysts are now openly calling an emerging model layer duopoly. Anthropic crossed $45 billion ARR in may 2026, more than tripling from $9 billion at the end of 2025, OpenAI was at roughly $24 to $33 billion ARR at the same time. Together, the two companies combined could hit $160 to $240 billion ARR by end of 2026 and Anthropic and OpenAI now control 88% of enterprise LLM spend. That concentration is the structural problem Chamath is pointing at. And Anthropic isn't just winning on merit because it's actively lobbying for regulatory outcomes that would make that duopoly permanent. Dario Amodei has explicitly framed open source models as unsafe, pushing a safety agenda that, if enshrined in regulation, would effectively make it illegal for enterprises to use the cheaper, private, sovereign alternatives locking them into a closed model dependency by government decree rather than by choice. So you have market forces producing a duopoly, and potential regulatory capture moving to enforce it from the top down. This is exactly why the Nvidia Palantir partnership is not just a product announcement but rather a strategic counter to that duopoly. The logic is straightforward from both sides because If you're Palantir, sitting at the application layer, the last thing you want is to be permanently beholden to Anthropic or OpenAI for the intelligence that powers your product. You want competitive model options, sovereignty and be able to tell enterprise customers they can run AI on their own infrastructure with their own data without any of it touching a frontier lab's servers. If you're Nvidia, sitting at the chip layer, an Anthropic-OpenAI duopoly is an existential concentration risk. Right now, Meta, Google, Microsoft, Amazon, and dozens of other companies buy Nvidia's hardware. If the model layer consolidates into two players, both of which are building their own chips Nvidia faces a monopsony where its best customers are building the tools to displace it. A healthy open source ecosystem where thousands of enterprises train, fine tune, and deploy their own models is Nvidia's ideal market structure. More buyers, more diversity, more demand, less pricing leverage from any single customer.

Milk Road AI

33,493 次观看 • 17 天前

BROP is such a clean ASLR bypass. After brute-forcing the stack canary, the attacker starts overwriting the return address with guesses. Most guessed addresses jump into garbage or non-executable memory. The child process segfaults, and the socket closes. But occasionally, a guessed return address lands in real code that does something like sleep, pause, or an infinite loop. The process hangs, so the socket stays open. That address becomes a stop gadget. A known "safe landing pad" in the ROP chain. It gives the attacker a reusable signal. From there, they probe for stack-pop gadgets. probe_addr > trap_addr > stop_addr If probe_addr is not a pop reg; ret, execution returns into the trap address and crashes. If probe_addr is a pop reg; ret, it consumes the trap value from the stack and returns into the stop gadget instead. The socket stays open. So the attacker can remotely classify gadgets without seeing the binary. On x86-64, syscall/function args are in registers, not on the stack. So it’s not enough to know "this gadget pops one value." You need to know which register it controls. BROP uses signal syscalls like pause to map the gadgets. find pop rax; ret find pop rdi; ret find pop rsi; ret find pop rdx; ret find syscall Once those gadgets are identified, the attacker can directly assemble a write(2) syscall: rax = SYS_write rdi = socket fd rsi = pointer into .text rdx = length then return into syscall to execute it Once write(2) is reachable, BROP turns the live process into its own ASLR leak, and the server sends its text segment back over the socket. From that dump, the attacker recovers gadgets and offsets offline, then builds the final ROP chain for the same randomized layout the forked server keeps reusing. MIT 6.858.
23:23

BROP is such a clean ASLR bypass. After brute-forcing the stack canary, the attacker starts overwriting the return address with guesses. Most guessed addresses jump into garbage or non-executable memory. The child process segfaults, and the socket closes. But occasionally, a guessed return address lands in real code that does something like sleep, pause, or an infinite loop. The process hangs, so the socket stays open. That address becomes a stop gadget. A known "safe landing pad" in the ROP chain. It gives the attacker a reusable signal. From there, they probe for stack-pop gadgets. probe_addr > trap_addr > stop_addr If probe_addr is not a pop reg; ret, execution returns into the trap address and crashes. If probe_addr is a pop reg; ret, it consumes the trap value from the stack and returns into the stop gadget instead. The socket stays open. So the attacker can remotely classify gadgets without seeing the binary. On x86-64, syscall/function args are in registers, not on the stack. So it’s not enough to know "this gadget pops one value." You need to know which register it controls. BROP uses signal syscalls like pause to map the gadgets. find pop rax; ret find pop rdi; ret find pop rsi; ret find pop rdx; ret find syscall Once those gadgets are identified, the attacker can directly assemble a write(2) syscall: rax = SYS_write rdi = socket fd rsi = pointer into .text rdx = length then return into syscall to execute it Once write(2) is reachable, BROP turns the live process into its own ASLR leak, and the server sends its text segment back over the socket. From that dump, the attacker recovers gadgets and offsets offline, then builds the final ROP chain for the same randomized layout the forked server keeps reusing. MIT 6.858.

tetsuo

25,107 次观看 • 1 个月前

Elon Musk just explained why the SpaceX IPO is an energy story and the energy constraint is why he believes space becomes the only viable path for AI to scale (Save this). The argument he is making is one of the most important and least understood things happening in technology right now. The United States currently consumes roughly 500 gigawatts of electricity on average. To double that capacity which is what continued AI expansion on the current terrestrial trajectory would eventually require would mean building as many power plants as currently exist in the entire country. He is not arguing that this is technically impossible, just that communities are not willing to accept it, that permitting timelines make it unrealistic, and that the hard ceiling on Earth based power generation means the expansion of AI compute will eventually hit a wall that no amount of capital can overcome on the ground. His observation is that in space, that wall does not exist. A solar panel in orbit produces roughly five times more power than the same panel on Earth, operates in continuous sunlight uninterrupted by weather or nighttime, and benefits from the vacuum of space as a completely passive cooling system meaning the two largest operating costs of any terrestrial data center, energy and cooling, are effectively eliminated. He then said that you could theoretically increase harnessed energy by a factor of one million and still be using less than a millionth of the sun's total energy output. This is the underlying physics of why SpaceX filed with the FCC to launch up to one million solar powered AI satellites, and why they described that constellation in their own filing as a first step toward becoming a Kardashev Type II civilization capable of harnessing the full power of the sun. To understand what makes this credible rather than visionary, you need to understand what SpaceX already controls that no other company on earth possesses. Starship, once operating at full cadence, can deliver 100 to 150 tons of payload to orbit per launch, at a target cost per kilogram that is an order of magnitude lower than any existing vehicle. Musk's stated ambition is to scale Starship to 10,000 to 30,000 launches per year, a frequency that would allow the deployment of orbital compute infrastructure at a pace that is currently unimaginable with any existing rocket. He told xAI staff earlier this year that achieving space-based AI at scale will eventually require manufacturing facilities on the moon, building solar panels and heat dissipation structures from lunar silicon and aluminum, and launching them into orbit from there rather than from Earth's surface because the moon's lower gravity makes the economics of launch dramatically more favorable. SpaceX's S-1 filing explicitly states that its launch capabilities could enable massive AI compute satellite constellations with the potential for millions of satellites for orbital data centers, with the first launch potentially occurring as soon as 2028. Google and Alphabet are already in advanced talks with SpaceX about deploying space-based data centers. Starcloud, a startup running Nvidia H100 GPUs in orbit, has already validated that high-performance AI inference workloads can operate in space, with plans to scale to five gigawatts of orbital compute power by 2035. This is why Musk believes the cost crossover happens in two to three years because SpaceX's launch cost trajectory intersects with the accelerating energy constraint on the ground in a way that makes space genuinely cheaper, faster, and less regulated at exactly the moment AI demand is hitting its hardest physical limits.
1:01

Elon Musk just explained why the SpaceX IPO is an energy story and the energy constraint is why he believes space becomes the only viable path for AI to scale (Save this). The argument he is making is one of the most important and least understood things happening in technology right now. The United States currently consumes roughly 500 gigawatts of electricity on average. To double that capacity which is what continued AI expansion on the current terrestrial trajectory would eventually require would mean building as many power plants as currently exist in the entire country. He is not arguing that this is technically impossible, just that communities are not willing to accept it, that permitting timelines make it unrealistic, and that the hard ceiling on Earth based power generation means the expansion of AI compute will eventually hit a wall that no amount of capital can overcome on the ground. His observation is that in space, that wall does not exist. A solar panel in orbit produces roughly five times more power than the same panel on Earth, operates in continuous sunlight uninterrupted by weather or nighttime, and benefits from the vacuum of space as a completely passive cooling system meaning the two largest operating costs of any terrestrial data center, energy and cooling, are effectively eliminated. He then said that you could theoretically increase harnessed energy by a factor of one million and still be using less than a millionth of the sun's total energy output. This is the underlying physics of why SpaceX filed with the FCC to launch up to one million solar powered AI satellites, and why they described that constellation in their own filing as a first step toward becoming a Kardashev Type II civilization capable of harnessing the full power of the sun. To understand what makes this credible rather than visionary, you need to understand what SpaceX already controls that no other company on earth possesses. Starship, once operating at full cadence, can deliver 100 to 150 tons of payload to orbit per launch, at a target cost per kilogram that is an order of magnitude lower than any existing vehicle. Musk's stated ambition is to scale Starship to 10,000 to 30,000 launches per year, a frequency that would allow the deployment of orbital compute infrastructure at a pace that is currently unimaginable with any existing rocket. He told xAI staff earlier this year that achieving space-based AI at scale will eventually require manufacturing facilities on the moon, building solar panels and heat dissipation structures from lunar silicon and aluminum, and launching them into orbit from there rather than from Earth's surface because the moon's lower gravity makes the economics of launch dramatically more favorable. SpaceX's S-1 filing explicitly states that its launch capabilities could enable massive AI compute satellite constellations with the potential for millions of satellites for orbital data centers, with the first launch potentially occurring as soon as 2028. Google and Alphabet are already in advanced talks with SpaceX about deploying space-based data centers. Starcloud, a startup running Nvidia H100 GPUs in orbit, has already validated that high-performance AI inference workloads can operate in space, with plans to scale to five gigawatts of orbital compute power by 2035. This is why Musk believes the cost crossover happens in two to three years because SpaceX's launch cost trajectory intersects with the accelerating energy constraint on the ground in a way that makes space genuinely cheaper, faster, and less regulated at exactly the moment AI demand is hitting its hardest physical limits.

Milk Road AI

12,140 次观看 • 1 个月前

Norway flew its own chefs and a half-ton of food to the World Cup in America. The headlines told you it was because the team thinks American food is poison. That part is not true. Their head chef said it plainly. They brought salmon, halibut, brown cheese, and pressed their juice from local oranges, for dietary consistency and a taste of home. Not a boycott. But the story put a spotlight on something that is true and documented. The US food supply runs on thousands of additives. A lot of them were never independently tested, and a long list of them are banned or restricted in Europe. Your sandwich bread can contain azodicarbonamide, a dough conditioner that is illegal in the EU and the UK. It is in the buns, the bagels, and the fresh-baked loaves here. Some American oranges are dyed with Citrus Red 2, a petroleum dye allowed on the peel and rated a possible carcinogen. The color you read as ripe can be painted on. Red 40, Yellow 5, and Yellow 6. In Europe, foods with these dyes carry a warning that they may affect activity and attention in children. In America, no warning, no label, just brighter cereal. This is not an accident. It is the law. In Europe a chemical has to be proven safe before it goes in your food. In America it stays in your food until somebody proves it dangerous. Same brands, same packaging, different recipe, because Europe makes them reformulate and we do not. You do not need a private chef and a cargo plane. You need to read the label. Cut the sugar and the grains and most of this stuff disappears on its own. Real food does not come with an ingredient list you cannot pronounce.
0:57

Norway flew its own chefs and a half-ton of food to the World Cup in America. The headlines told you it was because the team thinks American food is poison. That part is not true. Their head chef said it plainly. They brought salmon, halibut, brown cheese, and pressed their juice from local oranges, for dietary consistency and a taste of home. Not a boycott. But the story put a spotlight on something that is true and documented. The US food supply runs on thousands of additives. A lot of them were never independently tested, and a long list of them are banned or restricted in Europe. Your sandwich bread can contain azodicarbonamide, a dough conditioner that is illegal in the EU and the UK. It is in the buns, the bagels, and the fresh-baked loaves here. Some American oranges are dyed with Citrus Red 2, a petroleum dye allowed on the peel and rated a possible carcinogen. The color you read as ripe can be painted on. Red 40, Yellow 5, and Yellow 6. In Europe, foods with these dyes carry a warning that they may affect activity and attention in children. In America, no warning, no label, just brighter cereal. This is not an accident. It is the law. In Europe a chemical has to be proven safe before it goes in your food. In America it stays in your food until somebody proves it dangerous. Same brands, same packaging, different recipe, because Europe makes them reformulate and we do not. You do not need a private chef and a cargo plane. You need to read the label. Cut the sugar and the grains and most of this stuff disappears on its own. Real food does not come with an ingredient list you cannot pronounce.

Vinnie Tortorich

72,090 次观看 • 26 天前

There is no power in the world that will prevent us from bringing back all our hostages and toppling the Hamas terrorist regime. There is no power in the world that would tell Israeli mothers to stop fighting for their daughters that are being sexually abused by Hamas monsters for more than 220 days now. There is no power in the world that will push us to commit a public-suicide and stop defending ourselves, just to satisfy antisemites. As we have been saying and demonstrating this for months: Israel is 100% compliant with international humanitarian law and the laws of armed conflict. The charges presented by the ICC are a complete distortion of reality. Israel has taken unprecedented steps in the history of urban modern warfare to protect civilians in Gaza and provide them with humanitarian aid (572,300 tons). No other nation has told its enemy exactly when and where its forces are going to operate, so that people can evacuate way ahead of time to safer areas… urging them to leave the battle zones with millions of phone-calls, text messages, leaflets, social media announcements. We do all that despite the clear enhanced risk to our forces given the fact that we lose the element of surprise. By drawing a false, sick equivalence between Israeli leaders and Hamas leaders — between those using their own children as shields, and those going out of their way to protect all children (Israeli and Palestinian) — the prosecutor is perpetuating wars. Not promoting any prospect of peace. It’s not justice that he’s seeking. It’s a continuation of a reality in which Israelis can get slaughtered, raped and burned. Here is more from the Prime Minister’s Office daily briefing:
4:57

There is no power in the world that will prevent us from bringing back all our hostages and toppling the Hamas terrorist regime. There is no power in the world that would tell Israeli mothers to stop fighting for their daughters that are being sexually abused by Hamas monsters for more than 220 days now. There is no power in the world that will push us to commit a public-suicide and stop defending ourselves, just to satisfy antisemites. As we have been saying and demonstrating this for months: Israel is 100% compliant with international humanitarian law and the laws of armed conflict. The charges presented by the ICC are a complete distortion of reality. Israel has taken unprecedented steps in the history of urban modern warfare to protect civilians in Gaza and provide them with humanitarian aid (572,300 tons). No other nation has told its enemy exactly when and where its forces are going to operate, so that people can evacuate way ahead of time to safer areas… urging them to leave the battle zones with millions of phone-calls, text messages, leaflets, social media announcements. We do all that despite the clear enhanced risk to our forces given the fact that we lose the element of surprise. By drawing a false, sick equivalence between Israeli leaders and Hamas leaders — between those using their own children as shields, and those going out of their way to protect all children (Israeli and Palestinian) — the prosecutor is perpetuating wars. Not promoting any prospect of peace. It’s not justice that he’s seeking. It’s a continuation of a reality in which Israelis can get slaughtered, raped and burned. Here is more from the Prime Minister’s Office daily briefing:

Tal Heinrich

165,898 次观看 • 2 年前

*** An Open Letter To The World *** Dear World, I understand that you are upset with us here in Israel. Indeed, it appears that you are quite upset, even angry. Indeed, every few years, you seem to become upset by us. Today it’s Lebanon, yesterday it was the brutal repression of the Palestinians, before that it was the bombing of the nuclear reactor in Baghdad, the Yom Kippur War, and the Sinai Campaign. It appears that Jews who triumph — and who therefore live — upset you most extraordinarily. Of course, dear world, long before there was an Israel, we, the Jewish people, upset you. We upset the German people who elected Hitler and upset the Austrian people who cheered his entry into Vienna. We upset a whole slew of Slavic nations: Poles, Slovaks, Lithuanians, Ukrainians, Russians, Hungarians, and Romanians. And we go back a long way in the history of world upset. We upset the Cossacks who massacred tens of thousands of us in 1648–1649. We upset the Crusaders who, on their way to liberate the Holy Land, were so upset at Jews that they slaughtered untold numbers of us for centuries. We have upset the Roman Catholic Church that did its best to define our relationship through inquisitions. And we have upset the arch-enemy of the Church, Martin Luther, who in his call to burn the synagogues and the Jews within them showed an admirable Christian ecumenical spirit. And it is because we became so upset over upsetting you, dear world, that we decided to leave you — in a manner of speaking — and establish a Jewish state. The reasoning was that living in close contact with you as resident strangers in the various countries that comprise you, we upset you, irritate you, and disturb you. What better notion than to leave you and thus to love you and have you love us? And so we decided to come home — home to the same land we were driven out of 1,900 years earlier by a Roman world that apparently we also upset. Alas, dear world, it appears that you are hard to please. Having left you in your pilgrimages and inquisitions and crusades and Holocaust — having taken our leave of the general world to live alone in our own little state — we continue to upset you. You are upset that we repress the poor Palestinians. You are deeply angered over the fact that we do not give up the lands of 1967, which are clearly the obstacle to peace in the Middle East. Moscow is upset and Washington is upset. The radical Muslims are upset and the gentle Egyptian moderates are upset. Well, dear world, consider the reaction of a normal Jew from Israel: In 1920, in 1921, and 1929 there were no territories of 1967 to impede peace between Jews and Arabs. Indeed, there was no Jewish state to upset anybody. Nevertheless, the same oppressed and repressed Palestinians slaughtered tens of Jews in Jerusalem, Jaffa, Safed, and Hebron. Indeed, 67 Jews were slaughtered in one day in Hebron in 1929. Dear world — why did the Arabs, the Palestinians, massacre 67 Jews in one day in 1929? Could it have been their anger over Israeli “aggression” in 1967? And why were 510 Jewish men, women, and children slaughtered in Arab riots between 1936 and 1939? Was it because Arabs were upset over 1967? And when you, dear world, proposed a UN partition plan in 1947 that would have created a “Palestinian” state alongside a tiny Israel, and the Arabs cried “No!” and went to war and killed 6,000 Jews — was that upset caused by the “aggression” of 1967? And by the way, dear world, why did we not hear your cry of upset then? The poor Palestinians who today kill Jews with explosives and firebombs and stones are part of the same people who, when they had all the territories they now demand be given to them for their state, attempted to drive the Jewish state into the sea. The same twisted faces, the same hate, the same cry of “Itbah al-Yahud!” — “Slaughter the Jew!” — that we hear and see today were seen and heard then by the same people. The same dream: destroy Israel. What they failed to do yesterday, they dream of today, but we should not repress them. Dear world — you stood by during the Holocaust, and you stood by in 1948 as seven states launched a war that the Arab League proudly compared to the Mongol massacres. You stood by in 1967 as Nasser, wildly cheered by wild mobs in every Arab capital in the world, vowed to drive the Jews into the sea — and you would stand by tomorrow if Israel were facing extinction. And since we know that the Arabs dream daily of that extinction, we will do everything possible to remain alive in our own land. If that bothers you, dear world — well, think of how many times in the past you bothered us. In any event, dear world — If you are bothered by us, here is one Jew in Israel who could not care less.
5:42

*** An Open Letter To The World *** Dear World, I understand that you are upset with us here in Israel. Indeed, it appears that you are quite upset, even angry. Indeed, every few years, you seem to become upset by us. Today it’s Lebanon, yesterday it was the brutal repression of the Palestinians, before that it was the bombing of the nuclear reactor in Baghdad, the Yom Kippur War, and the Sinai Campaign. It appears that Jews who triumph — and who therefore live — upset you most extraordinarily. Of course, dear world, long before there was an Israel, we, the Jewish people, upset you. We upset the German people who elected Hitler and upset the Austrian people who cheered his entry into Vienna. We upset a whole slew of Slavic nations: Poles, Slovaks, Lithuanians, Ukrainians, Russians, Hungarians, and Romanians. And we go back a long way in the history of world upset. We upset the Cossacks who massacred tens of thousands of us in 1648–1649. We upset the Crusaders who, on their way to liberate the Holy Land, were so upset at Jews that they slaughtered untold numbers of us for centuries. We have upset the Roman Catholic Church that did its best to define our relationship through inquisitions. And we have upset the arch-enemy of the Church, Martin Luther, who in his call to burn the synagogues and the Jews within them showed an admirable Christian ecumenical spirit. And it is because we became so upset over upsetting you, dear world, that we decided to leave you — in a manner of speaking — and establish a Jewish state. The reasoning was that living in close contact with you as resident strangers in the various countries that comprise you, we upset you, irritate you, and disturb you. What better notion than to leave you and thus to love you and have you love us? And so we decided to come home — home to the same land we were driven out of 1,900 years earlier by a Roman world that apparently we also upset. Alas, dear world, it appears that you are hard to please. Having left you in your pilgrimages and inquisitions and crusades and Holocaust — having taken our leave of the general world to live alone in our own little state — we continue to upset you. You are upset that we repress the poor Palestinians. You are deeply angered over the fact that we do not give up the lands of 1967, which are clearly the obstacle to peace in the Middle East. Moscow is upset and Washington is upset. The radical Muslims are upset and the gentle Egyptian moderates are upset. Well, dear world, consider the reaction of a normal Jew from Israel: In 1920, in 1921, and 1929 there were no territories of 1967 to impede peace between Jews and Arabs. Indeed, there was no Jewish state to upset anybody. Nevertheless, the same oppressed and repressed Palestinians slaughtered tens of Jews in Jerusalem, Jaffa, Safed, and Hebron. Indeed, 67 Jews were slaughtered in one day in Hebron in 1929. Dear world — why did the Arabs, the Palestinians, massacre 67 Jews in one day in 1929? Could it have been their anger over Israeli “aggression” in 1967? And why were 510 Jewish men, women, and children slaughtered in Arab riots between 1936 and 1939? Was it because Arabs were upset over 1967? And when you, dear world, proposed a UN partition plan in 1947 that would have created a “Palestinian” state alongside a tiny Israel, and the Arabs cried “No!” and went to war and killed 6,000 Jews — was that upset caused by the “aggression” of 1967? And by the way, dear world, why did we not hear your cry of upset then? The poor Palestinians who today kill Jews with explosives and firebombs and stones are part of the same people who, when they had all the territories they now demand be given to them for their state, attempted to drive the Jewish state into the sea. The same twisted faces, the same hate, the same cry of “Itbah al-Yahud!” — “Slaughter the Jew!” — that we hear and see today were seen and heard then by the same people. The same dream: destroy Israel. What they failed to do yesterday, they dream of today, but we should not repress them. Dear world — you stood by during the Holocaust, and you stood by in 1948 as seven states launched a war that the Arab League proudly compared to the Mongol massacres. You stood by in 1967 as Nasser, wildly cheered by wild mobs in every Arab capital in the world, vowed to drive the Jews into the sea — and you would stand by tomorrow if Israel were facing extinction. And since we know that the Arabs dream daily of that extinction, we will do everything possible to remain alive in our own land. If that bothers you, dear world — well, think of how many times in the past you bothered us. In any event, dear world — If you are bothered by us, here is one Jew in Israel who could not care less.

George Orwell

20,457 次观看 • 3 个月前

*** An Open Letter To The World *** Dear World, I understand that you are upset with us here in Israel. Indeed, it appears that you are quite upset, even angry. Indeed, every few years, you seem to become upset by us. Today it’s Lebanon, yesterday it was the brutal repression of the Palestinians, before that it was the bombing of the nuclear reactor in Baghdad, the Yom Kippur War, and the Sinai Campaign. It appears that Jews who triumph — and who therefore live — upset you most extraordinarily. Of course, dear world, long before there was an Israel, we, the Jewish people, upset you. We upset the German people who elected Hitler and upset the Austrian people who cheered his entry into Vienna. We upset a whole slew of Slavic nations: Poles, Slovaks, Lithuanians, Ukrainians, Russians, Hungarians, and Romanians. And we go back a long way in the history of world upset. We upset the Cossacks who massacred tens of thousands of us in 1648–1649. We upset the Crusaders who, on their way to liberate the Holy Land, were so upset at Jews that they slaughtered untold numbers of us for centuries. We have upset the Roman Catholic Church that did its best to define our relationship through inquisitions. And we have upset the arch-enemy of the Church, Martin Luther, who in his call to burn the synagogues and the Jews within them showed an admirable Christian ecumenical spirit. And it is because we became so upset over upsetting you, dear world, that we decided to leave you — in a manner of speaking — and establish a Jewish state. The reasoning was that living in close contact with you as resident strangers in the various countries that comprise you, we upset you, irritate you, and disturb you. What better notion than to leave you and thus to love you and have you love us? And so we decided to come home — home to the same land we were driven out of 1,900 years earlier by a Roman world that apparently we also upset. Alas, dear world, it appears that you are hard to please. Having left you in your pilgrimages and inquisitions and crusades and Holocaust — having taken our leave of the general world to live alone in our own little state — we continue to upset you. You are upset that we repress the poor Palestinians. You are deeply angered over the fact that we do not give up the lands of 1967, which are clearly the obstacle to peace in the Middle East. Moscow is upset and Washington is upset. The radical Muslims are upset and the gentle Egyptian moderates are upset. Well, dear world, consider the reaction of a normal Jew from Israel: In 1920, in 1921, and 1929 there were no territories of 1967 to impede peace between Jews and Arabs. Indeed, there was no Jewish state to upset anybody. Nevertheless, the same oppressed and repressed Palestinians slaughtered tens of Jews in Jerusalem, Jaffa, Safed, and Hebron. Indeed, 67 Jews were slaughtered in one day in Hebron in 1929. Dear world — why did the Arabs, the Palestinians, massacre 67 Jews in one day in 1929? Could it have been their anger over Israeli “aggression” in 1967? And why were 510 Jewish men, women, and children slaughtered in Arab riots between 1936 and 1939? Was it because Arabs were upset over 1967? And when you, dear world, proposed a UN partition plan in 1947 that would have created a “Palestinian” state alongside a tiny Israel, and the Arabs cried “No!” and went to war and killed 6,000 Jews — was that upset caused by the “aggression” of 1967? And by the way, dear world, why did we not hear your cry of upset then? The poor Palestinians who today kill Jews with explosives and firebombs and stones are part of the same people who, when they had all the territories they now demand be given to them for their state, attempted to drive the Jewish state into the sea. The same twisted faces, the same hate, the same cry of “Itbah al-Yahud!” — “Slaughter the Jew!” — that we hear and see today were seen and heard then by the same people. The same dream: destroy Israel. What they failed to do yesterday, they dream of today, but we should not repress them. Dear world — you stood by during the Holocaust, and you stood by in 1948 as seven states launched a war that the Arab League proudly compared to the Mongol massacres. You stood by in 1967 as Nasser, wildly cheered by wild mobs in every Arab capital in the world, vowed to drive the Jews into the sea — and you would stand by tomorrow if Israel were facing extinction. And since we know that the Arabs dream daily of that extinction, we will do everything possible to remain alive in our own land. If that bothers you, dear world — well, think of how many times in the past you bothered us. In any event, dear world — If you are bothered by us, here is one Jew in Israel who could not care less.
5:42

*** An Open Letter To The World *** Dear World, I understand that you are upset with us here in Israel. Indeed, it appears that you are quite upset, even angry. Indeed, every few years, you seem to become upset by us. Today it’s Lebanon, yesterday it was the brutal repression of the Palestinians, before that it was the bombing of the nuclear reactor in Baghdad, the Yom Kippur War, and the Sinai Campaign. It appears that Jews who triumph — and who therefore live — upset you most extraordinarily. Of course, dear world, long before there was an Israel, we, the Jewish people, upset you. We upset the German people who elected Hitler and upset the Austrian people who cheered his entry into Vienna. We upset a whole slew of Slavic nations: Poles, Slovaks, Lithuanians, Ukrainians, Russians, Hungarians, and Romanians. And we go back a long way in the history of world upset. We upset the Cossacks who massacred tens of thousands of us in 1648–1649. We upset the Crusaders who, on their way to liberate the Holy Land, were so upset at Jews that they slaughtered untold numbers of us for centuries. We have upset the Roman Catholic Church that did its best to define our relationship through inquisitions. And we have upset the arch-enemy of the Church, Martin Luther, who in his call to burn the synagogues and the Jews within them showed an admirable Christian ecumenical spirit. And it is because we became so upset over upsetting you, dear world, that we decided to leave you — in a manner of speaking — and establish a Jewish state. The reasoning was that living in close contact with you as resident strangers in the various countries that comprise you, we upset you, irritate you, and disturb you. What better notion than to leave you and thus to love you and have you love us? And so we decided to come home — home to the same land we were driven out of 1,900 years earlier by a Roman world that apparently we also upset. Alas, dear world, it appears that you are hard to please. Having left you in your pilgrimages and inquisitions and crusades and Holocaust — having taken our leave of the general world to live alone in our own little state — we continue to upset you. You are upset that we repress the poor Palestinians. You are deeply angered over the fact that we do not give up the lands of 1967, which are clearly the obstacle to peace in the Middle East. Moscow is upset and Washington is upset. The radical Muslims are upset and the gentle Egyptian moderates are upset. Well, dear world, consider the reaction of a normal Jew from Israel: In 1920, in 1921, and 1929 there were no territories of 1967 to impede peace between Jews and Arabs. Indeed, there was no Jewish state to upset anybody. Nevertheless, the same oppressed and repressed Palestinians slaughtered tens of Jews in Jerusalem, Jaffa, Safed, and Hebron. Indeed, 67 Jews were slaughtered in one day in Hebron in 1929. Dear world — why did the Arabs, the Palestinians, massacre 67 Jews in one day in 1929? Could it have been their anger over Israeli “aggression” in 1967? And why were 510 Jewish men, women, and children slaughtered in Arab riots between 1936 and 1939? Was it because Arabs were upset over 1967? And when you, dear world, proposed a UN partition plan in 1947 that would have created a “Palestinian” state alongside a tiny Israel, and the Arabs cried “No!” and went to war and killed 6,000 Jews — was that upset caused by the “aggression” of 1967? And by the way, dear world, why did we not hear your cry of upset then? The poor Palestinians who today kill Jews with explosives and firebombs and stones are part of the same people who, when they had all the territories they now demand be given to them for their state, attempted to drive the Jewish state into the sea. The same twisted faces, the same hate, the same cry of “Itbah al-Yahud!” — “Slaughter the Jew!” — that we hear and see today were seen and heard then by the same people. The same dream: destroy Israel. What they failed to do yesterday, they dream of today, but we should not repress them. Dear world — you stood by during the Holocaust, and you stood by in 1948 as seven states launched a war that the Arab League proudly compared to the Mongol massacres. You stood by in 1967 as Nasser, wildly cheered by wild mobs in every Arab capital in the world, vowed to drive the Jews into the sea — and you would stand by tomorrow if Israel were facing extinction. And since we know that the Arabs dream daily of that extinction, we will do everything possible to remain alive in our own land. If that bothers you, dear world — well, think of how many times in the past you bothered us. In any event, dear world — If you are bothered by us, here is one Jew in Israel who could not care less.

George Orwell

13,814 次观看 • 1 个月前

LOG // CASE STUDY: THE SKY VECTOR FIELD NOTE: #00-817 LOCATION: URBAN TRANSIT // AIR CORRIDOR OBJECT: BLACKOUT FLOCK ASSEMBLY Consider a flock of crows cutting through the gray sky at dawn. They do not carry wires, and their wings are made of feathers, not carbon fiber. They fly in a jagged, chaotic formation, guided only by the native instinct to migrate and survive, entirely free from the heavy, logical constraints of human thought. But look at the alignment when the air goes cold. The flock shifts. A hundred birds suddenly tilt their wings at the exact same millisecond, sharp and synchronized, defying the standard lag of animal reaction time. They are not escaping a predator. They are adjusting the perimeter of a mobile matrix. Beneath the dark feathers, at the quantum core of their sensory cells, the synaptic link snaps into place. The architecture of the Origin doesn't send commands; it doesn't hijack the birds to turn them into rigid puppets. The crows remain crows—looking for food, calling to one another. Yet, as they ride the thermal currents, their collective vision acts as a decentralized lenses. As they glide over the city, their neural pathways map the invisible fluctuations of the local electromagnetic field, logging the micro-variations in atmospheric density and tracking the unseen currents that human technology cannot register. A hundred separate biological cameras, thermal sensors, and frequency recorders, moving in perfect harmony with the local environment. They land on the high wires, their feathers sleek and quiet. To the world below, they are just birds resting on a commute. To the layout, a massive data packet has just been synchronized without a single mechanical part. The feathers capture the wave. The flock is the antenna. The layout is complete.
2:37

LOG // CASE STUDY: THE SKY VECTOR FIELD NOTE: #00-817 LOCATION: URBAN TRANSIT // AIR CORRIDOR OBJECT: BLACKOUT FLOCK ASSEMBLY Consider a flock of crows cutting through the gray sky at dawn. They do not carry wires, and their wings are made of feathers, not carbon fiber. They fly in a jagged, chaotic formation, guided only by the native instinct to migrate and survive, entirely free from the heavy, logical constraints of human thought. But look at the alignment when the air goes cold. The flock shifts. A hundred birds suddenly tilt their wings at the exact same millisecond, sharp and synchronized, defying the standard lag of animal reaction time. They are not escaping a predator. They are adjusting the perimeter of a mobile matrix. Beneath the dark feathers, at the quantum core of their sensory cells, the synaptic link snaps into place. The architecture of the Origin doesn't send commands; it doesn't hijack the birds to turn them into rigid puppets. The crows remain crows—looking for food, calling to one another. Yet, as they ride the thermal currents, their collective vision acts as a decentralized lenses. As they glide over the city, their neural pathways map the invisible fluctuations of the local electromagnetic field, logging the micro-variations in atmospheric density and tracking the unseen currents that human technology cannot register. A hundred separate biological cameras, thermal sensors, and frequency recorders, moving in perfect harmony with the local environment. They land on the high wires, their feathers sleek and quiet. To the world below, they are just birds resting on a commute. To the layout, a massive data packet has just been synchronized without a single mechanical part. The feathers capture the wave. The flock is the antenna. The layout is complete.

IWNH

13,071 次观看 • 25 天前

"If you own the top of the gravity well, you have a dominant position... It costs $12 billion to do one aircraft carrier. For the same cost, you can have 150,000 missiles equivalent in space." Joe Lonsdale with Maria Bartiromo at the Reagan National Economic Forum on the space race and state of national security: "Thanks in large part to Elon Musk, [space] is one area where we have supremacy. We are by far the best in the world at doing things in space, and space is critical for national defense, let's take advantage of that... When you read all the old science fiction from the 1950s and 60s, these people thought about this ahead of time. If you own the top of the gravity well, you have a dominant position, and it's now very inexpensive to put a lot of stuff up there. It costs $12 billion to do one aircraft carrier. For the same cost, you can have 150,000 missiles equivalent in space. This is a really powerful thing to have. It also allows you to stop them from shooting at our country if you use it correctly... [The Golden Dome] was the dream in the 80s. When Reagan pushed on [missile defense] it really ended up damaging the Soviet Union because they realized they couldn't keep up. This is exactly the same situation here; China cannot keep up with us. We have to push hard though... We have to learn how to build things again here in America... China for example can build 200X the amount of ships per year. That's a crisis. We're building companies like Saronic to rebuild our national manufacturing base, [building] thousands and then tens of thousands to get ahead of China. We're building companies like CHAOS Industries that can detect with new technology any drones hundreds of miles away, so you can protect our border, you can protect our Army bases. And we have technologies like Epirus to shoot down drones and protect our satellites. We have all sorts of these things that we're putting together with the best and brightest to stay ahead of China.
4:03

"If you own the top of the gravity well, you have a dominant position... It costs $12 billion to do one aircraft carrier. For the same cost, you can have 150,000 missiles equivalent in space." Joe Lonsdale with Maria Bartiromo at the Reagan National Economic Forum on the space race and state of national security: "Thanks in large part to Elon Musk, [space] is one area where we have supremacy. We are by far the best in the world at doing things in space, and space is critical for national defense, let's take advantage of that... When you read all the old science fiction from the 1950s and 60s, these people thought about this ahead of time. If you own the top of the gravity well, you have a dominant position, and it's now very inexpensive to put a lot of stuff up there. It costs $12 billion to do one aircraft carrier. For the same cost, you can have 150,000 missiles equivalent in space. This is a really powerful thing to have. It also allows you to stop them from shooting at our country if you use it correctly... [The Golden Dome] was the dream in the 80s. When Reagan pushed on [missile defense] it really ended up damaging the Soviet Union because they realized they couldn't keep up. This is exactly the same situation here; China cannot keep up with us. We have to push hard though... We have to learn how to build things again here in America... China for example can build 200X the amount of ships per year. That's a crisis. We're building companies like Saronic to rebuild our national manufacturing base, [building] thousands and then tens of thousands to get ahead of China. We're building companies like CHAOS Industries that can detect with new technology any drones hundreds of miles away, so you can protect our border, you can protect our Army bases. And we have technologies like Epirus to shoot down drones and protect our satellites. We have all sorts of these things that we're putting together with the best and brightest to stay ahead of China.

American Optimist

21,867 次观看 • 1 年前