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Proudly introducing my upcoming series, "Conversations between Nature’s Memory" 🏞️🧠 What if nature could remember? What if forests, roots, fungi, and soil were constantly speaking to each other through invisible systems we never learned how to hear? Conversations between Nature’s Memory imagines a living dialogue between two interconnected ecosystems,...

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orkhan

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84,806 views • 2 months ago •via X (Twitter)

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“Conversations with Nature’s Memory” Exhibition opening May 9 at the Venice Biennale. Series release May 19, 420 pieces, 0.0169 ETH each. Two ecosystems unfold at once, one above soil, one below. Different visual languages, shared logic of growth, branching, fusion, decay, redistribution. The line between them acts as soil, a boundary that is also exchange. Nutrient flow becomes language. Energy shifts, pathways reroute, hidden systems shape what appears. Not dialogue, but response. Not signal, but relation. An AI observes and translates these exchanges into fragments, traces of care, seasons, memory, never fully resolved. Part of “As Above, So Below”.
0:59

“Conversations with Nature’s Memory” Exhibition opening May 9 at the Venice Biennale. Series release May 19, 420 pieces, 0.0169 ETH each. Two ecosystems unfold at once, one above soil, one below. Different visual languages, shared logic of growth, branching, fusion, decay, redistribution. The line between them acts as soil, a boundary that is also exchange. Nutrient flow becomes language. Energy shifts, pathways reroute, hidden systems shape what appears. Not dialogue, but response. Not signal, but relation. An AI observes and translates these exchanges into fragments, traces of care, seasons, memory, never fully resolved. Part of “As Above, So Below”.

orkhan

16,014 views • 3 months ago

VISIONS 👁️ October 30th,⛵️@OpenSea The series illustrates how memory is deceptive, how it mutates and distorts. Each work is interactive, every touch triggers images to rewrite themselves, just as our perception is re-written with each memory. More information 👇
0:57

VISIONS 👁️ October 30th,⛵️@OpenSea The series illustrates how memory is deceptive, how it mutates and distorts. Each work is interactive, every touch triggers images to rewrite themselves, just as our perception is re-written with each memory. More information 👇

orkhan

35,374 views • 9 months ago

January, 2023: Inside the production room the PV team and I react to watching, for the first time, the Pfizer get. A week later I was removed as CEO from the org I founded. January, 2024: We’re going to expose them all. One by one. The whole rotten, crooked diseased system. Mark my words: a sleeping giant will awaken and there will be thousands of brave whistleblowers. But as we do this, I’ve learned what the fight is: an appeal to our better angels and against the dark side of human nature itself. When you gaze into that abyss, it gazes back, and it tells you what you are made of. The enemy is all around us, and competes within us. The line between good and evil doesn’t run through states, or classes, or between political parties — but right through every human heart…
0:45

January, 2023: Inside the production room the PV team and I react to watching, for the first time, the Pfizer get. A week later I was removed as CEO from the org I founded. January, 2024: We’re going to expose them all. One by one. The whole rotten, crooked diseased system. Mark my words: a sleeping giant will awaken and there will be thousands of brave whistleblowers. But as we do this, I’ve learned what the fight is: an appeal to our better angels and against the dark side of human nature itself. When you gaze into that abyss, it gazes back, and it tells you what you are made of. The enemy is all around us, and competes within us. The line between good and evil doesn’t run through states, or classes, or between political parties — but right through every human heart…

James O'Keefe

2,053,657 views • 2 years ago

Before dawn over Armenia, the sky starts to look almost alive. This timelapse was captured westbound as we crossed one of the busiest, and most geopolitically sensitive pieces of airspace on Earth. To the south lies Iran. To the north, Russia. Between them, a narrow corridor through the Caucasus carrying hundreds of aircraft linking Europe, the Gulf, and Asia. Every light you see here is another aircraft, another crew, another story moving through the night. What makes it remarkable is the precision. At closing speeds approaching 1,000 mph, aircraft pass each other separated not by miles, but by carefully managed vertical layers of airspace. Invisible highways in the sky, built on GPS accuracy, disciplined procedures, satellite surveillance, and trust. And yet these routes are never static. Politics shapes them. Conflict bends them. Closed airspace redraws them almost overnight. What was once a straightforward great-circle route can quickly become a carefully threaded path between terrain, and diplomacy. From the cockpit, though, there’s a strange beauty to it all. Tiny lights crossing ancient landscapes. Modern aircraft moving through corridors shaped by geography and history alike. Controlled. Precise. Fleeting. A reminder that aviation is never just about the aircraft. It’s about learning how to move safely through an increasingly complicated world.
0:20

Before dawn over Armenia, the sky starts to look almost alive. This timelapse was captured westbound as we crossed one of the busiest, and most geopolitically sensitive pieces of airspace on Earth. To the south lies Iran. To the north, Russia. Between them, a narrow corridor through the Caucasus carrying hundreds of aircraft linking Europe, the Gulf, and Asia. Every light you see here is another aircraft, another crew, another story moving through the night. What makes it remarkable is the precision. At closing speeds approaching 1,000 mph, aircraft pass each other separated not by miles, but by carefully managed vertical layers of airspace. Invisible highways in the sky, built on GPS accuracy, disciplined procedures, satellite surveillance, and trust. And yet these routes are never static. Politics shapes them. Conflict bends them. Closed airspace redraws them almost overnight. What was once a straightforward great-circle route can quickly become a carefully threaded path between terrain, and diplomacy. From the cockpit, though, there’s a strange beauty to it all. Tiny lights crossing ancient landscapes. Modern aircraft moving through corridors shaped by geography and history alike. Controlled. Precise. Fleeting. A reminder that aviation is never just about the aircraft. It’s about learning how to move safely through an increasingly complicated world.

Scott Bateman MBE

43,390 views • 3 months ago

An egregore is not merely a mystical idea; it is a living psychological structure. From a psychoanalytic lens, it can be understood as a shared mental ecosystem. The accumulated thoughts, emotions, fears, and longings of a group that, through repetition, begins to operate as an autonomous force. Just as the personal unconscious stores unresolved experiences and symbolic narratives, the egregore functions as a collective psyche in motion. It sustains itself through belief, emotional investment, ritualized behavior, and constant reinforcement. The more it is fed, the more real it becomes. Its influence is subtle but pervasive. Anyone entering its field begins to feel, think, and behave in alignment with its logic, often mistaking borrowed impulses for personal choice. This is why patterns echo across families, ideologies, and institutions. What appears individual is frequently inherited from the group mind. In clinical work, this emerges when a person carries guilt, obligations, or symptoms that did not originate with them alone, but were transmitted through relational and symbolic networks. The egregore is the architecture through which these imprints are organized and passed on. To recognize an egregore is to abandon the illusion of psychological isolation. Each of us is woven through invisible currents of memory, meaning, and desire. The “self” is not a closed system, but a dialogue; constantly shaped by the collective field speaking through us, even in silence.
0:47

An egregore is not merely a mystical idea; it is a living psychological structure. From a psychoanalytic lens, it can be understood as a shared mental ecosystem. The accumulated thoughts, emotions, fears, and longings of a group that, through repetition, begins to operate as an autonomous force. Just as the personal unconscious stores unresolved experiences and symbolic narratives, the egregore functions as a collective psyche in motion. It sustains itself through belief, emotional investment, ritualized behavior, and constant reinforcement. The more it is fed, the more real it becomes. Its influence is subtle but pervasive. Anyone entering its field begins to feel, think, and behave in alignment with its logic, often mistaking borrowed impulses for personal choice. This is why patterns echo across families, ideologies, and institutions. What appears individual is frequently inherited from the group mind. In clinical work, this emerges when a person carries guilt, obligations, or symptoms that did not originate with them alone, but were transmitted through relational and symbolic networks. The egregore is the architecture through which these imprints are organized and passed on. To recognize an egregore is to abandon the illusion of psychological isolation. Each of us is woven through invisible currents of memory, meaning, and desire. The “self” is not a closed system, but a dialogue; constantly shaped by the collective field speaking through us, even in silence.

𝚃𝙷𝙴 𝚆𝙷𝙸𝚃𝙴 𝚁𝙰𝙱𝙱𝙸𝚃

17,369 views • 8 months ago

SHE IS PLAYING A PATTERN FOUND IN NATURE The Fibonacci sequence is a numerical pattern in which each number is the sum of the two before it: 1, 1, 2, 3, 5, 8, 13, 21… This sequence appears throughout nature in growth processes, spiral formations, branching systems, and proportional structures. While living systems do not always follow Fibonacci numbers perfectly, the underlying principle remains profound… Complex form often emerges through ordered relationships built from what came before. That same principle can be applied to rhythm. In music, timing is organized through recurring intervals, subdivisions, accents, and coordinated patterns that unfold through time. In drumming, this becomes especially remarkable because the brain and body must synchronize multiple limbs at once while maintaining a coherent rhythmic structure. When a drummer translates Fibonacci-based counting into performance, mathematics unfolds via movement. The numerical pattern becomes embodied timing. To do this, the nervous system must convert number into temporal spacing, temporal spacing into coordinated motion, and coordinated motion into sound. What makes this so powerful is that Fibonacci is a recursive structure, meaning that every new value emerges from the relationship between the previous two. That means the pattern carries memory, continuity, and expansion all at once. Rhythm works in a similar way. Each phrase is shaped by what came before it and by how it resolves into the next. So when Fibonacci logic is expressed through drumming, we are witnessing proportional growth translated into sound. The bottom line is that rhythm, mathematics, and biological coordination are far more interconnected than most people realize. This is a beautiful example of how numerical order can be felt, heard, and embodied as music. Did this expand your consciousness? ✨🙌🏾💫
0:58

SHE IS PLAYING A PATTERN FOUND IN NATURE The Fibonacci sequence is a numerical pattern in which each number is the sum of the two before it: 1, 1, 2, 3, 5, 8, 13, 21… This sequence appears throughout nature in growth processes, spiral formations, branching systems, and proportional structures. While living systems do not always follow Fibonacci numbers perfectly, the underlying principle remains profound… Complex form often emerges through ordered relationships built from what came before. That same principle can be applied to rhythm. In music, timing is organized through recurring intervals, subdivisions, accents, and coordinated patterns that unfold through time. In drumming, this becomes especially remarkable because the brain and body must synchronize multiple limbs at once while maintaining a coherent rhythmic structure. When a drummer translates Fibonacci-based counting into performance, mathematics unfolds via movement. The numerical pattern becomes embodied timing. To do this, the nervous system must convert number into temporal spacing, temporal spacing into coordinated motion, and coordinated motion into sound. What makes this so powerful is that Fibonacci is a recursive structure, meaning that every new value emerges from the relationship between the previous two. That means the pattern carries memory, continuity, and expansion all at once. Rhythm works in a similar way. Each phrase is shaped by what came before it and by how it resolves into the next. So when Fibonacci logic is expressed through drumming, we are witnessing proportional growth translated into sound. The bottom line is that rhythm, mathematics, and biological coordination are far more interconnected than most people realize. This is a beautiful example of how numerical order can be felt, heard, and embodied as music. Did this expand your consciousness? ✨🙌🏾💫

🧬Maxpein🧬

78,239 views • 3 months ago

RAG might already be becoming obsolete. A month ago, Andrej Karpathy dropped a simple GitHub gist called “LLM Wiki.” Now the comments section looks like the birth of an entirely new AI category. 5000+ stars later, developers are rapidly building: • persistent AI memory systems • self-maintaining knowledge bases • multi-agent research environments • contradiction detection engines • AI-native company operating systems • local-first memory architectures • graph-based reasoning layers • evolving second brains And the craziest part? Most of them were built in DAYS. Because the core idea is insanely powerful: Instead of AI repeatedly retrieving raw chunks like traditional RAG… …the model continuously maintains a living knowledge system. Not temporary context. Persistent synthesis. The shift sounds subtle until you realize what it changes: RAG: retrieve → answer → forget LLM Wiki: ingest → synthesize → evolve That one architectural difference is causing an explosion of experimentation right now. People are already building: • agent memory operating systems • AI-maintained engineering documentation • self-healing knowledge graphs • persistent research environments • conversational memory architectures • contradiction-aware wikis • context compression engines • machine-readable company systems The comments section alone feels like watching an ecosystem form in real time. One developer built deterministic contradiction detection using sheaf cohomology Another built “sleep consolidation” for AI memory systems inspired by human memory formation Another created persistent multi-agent vault conversations Another turned entire repositories into continuously maintained AI wikis Another built local-first memory systems with audit trails, provenance, graph exports, and MCP integration This is the important part: Karpathy didn’t launch a product. He introduced a pattern. And patterns are what create ecosystems. The same way: • transformers created modern AI • RAG created AI retrieval startups • agents created orchestration frameworks LLM Wikis may create persistent AI memory infrastructure. That’s why this moment feels different. For years, AI systems have been stateless. Now developers are trying to build systems that actually accumulate understanding over time. And once knowledge compounds instead of resetting… …the entire interface layer of AI changes. (Link in comments)
0:36

RAG might already be becoming obsolete. A month ago, Andrej Karpathy dropped a simple GitHub gist called “LLM Wiki.” Now the comments section looks like the birth of an entirely new AI category. 5000+ stars later, developers are rapidly building: • persistent AI memory systems • self-maintaining knowledge bases • multi-agent research environments • contradiction detection engines • AI-native company operating systems • local-first memory architectures • graph-based reasoning layers • evolving second brains And the craziest part? Most of them were built in DAYS. Because the core idea is insanely powerful: Instead of AI repeatedly retrieving raw chunks like traditional RAG… …the model continuously maintains a living knowledge system. Not temporary context. Persistent synthesis. The shift sounds subtle until you realize what it changes: RAG: retrieve → answer → forget LLM Wiki: ingest → synthesize → evolve That one architectural difference is causing an explosion of experimentation right now. People are already building: • agent memory operating systems • AI-maintained engineering documentation • self-healing knowledge graphs • persistent research environments • conversational memory architectures • contradiction-aware wikis • context compression engines • machine-readable company systems The comments section alone feels like watching an ecosystem form in real time. One developer built deterministic contradiction detection using sheaf cohomology Another built “sleep consolidation” for AI memory systems inspired by human memory formation Another created persistent multi-agent vault conversations Another turned entire repositories into continuously maintained AI wikis Another built local-first memory systems with audit trails, provenance, graph exports, and MCP integration This is the important part: Karpathy didn’t launch a product. He introduced a pattern. And patterns are what create ecosystems. The same way: • transformers created modern AI • RAG created AI retrieval startups • agents created orchestration frameworks LLM Wikis may create persistent AI memory infrastructure. That’s why this moment feels different. For years, AI systems have been stateless. Now developers are trying to build systems that actually accumulate understanding over time. And once knowledge compounds instead of resetting… …the entire interface layer of AI changes. (Link in comments)

Suryansh Tiwari

142,314 views • 3 months ago

Google Ironwood TPU Memory Hierarchy in 9 levels by hand ✍️ 1. Bit – The most basic unit of information, the on–off decision from which every number, tensor, and model state is ultimately constructed. 2. FP8 (1×8 → 8 bits) – Eight bits are grouped to form a floating-point value, typically used for inference, where reduced precision is a deliberate trade-off to maximize throughput and efficiency. 3. BF16 (×2 → 16 bits) – Two FP8-scale chunks are combined to gain more dynamic range and stability, while still staying friendly to high-throughput hardware. 4. Tensor tile (×1024 → 1K) – Data moves through the chip in blocks of 1024 values at a time, defining the granularity at which tensors are fetched and manipulated. 5. Matrix Multiplication Unit (MXU) (×64 → 64K) – A systolic array where matrix multiplication is not abstract but physical, with tensor tiles flowing through fixed hardware to achieve the highest possible throughput. 6. Vector Memory (VMEM) (×2048 → 128M) – On-chip working memory that holds activations, partial results, and intermediates, sized specifically to keep the systolic array busy without stalling. 7. Common Memory (CMEM) (×8 → 1 GB) – A small but critical shared memory sitting between VMEM and HBM, used for staging, accumulation, synchronization, and cross-lane coordination. 8. HBM (×96 → 96 GB) – Off-chip high-bandwidth memory where model weights and large states live, implemented as HBM3e with 16 stacks at 6 GB each, for a total of 96 GB. 9. Dual-Die (x2 → 192GB) – Two tightly coupled compute dies operate as a single logical accelerator, each with its own local HBM, effectively doubling memory capacity and bandwidth while allowing tensors and activations to stream seamlessly across dies as if they lived on one chip. I created this drawing for this week's seminar. I’ll take you through these 9 levels in a beginner-friendly way by hand ✍️. RSVP 👉
0:27

Google Ironwood TPU Memory Hierarchy in 9 levels by hand ✍️ 1. Bit – The most basic unit of information, the on–off decision from which every number, tensor, and model state is ultimately constructed. 2. FP8 (1×8 → 8 bits) – Eight bits are grouped to form a floating-point value, typically used for inference, where reduced precision is a deliberate trade-off to maximize throughput and efficiency. 3. BF16 (×2 → 16 bits) – Two FP8-scale chunks are combined to gain more dynamic range and stability, while still staying friendly to high-throughput hardware. 4. Tensor tile (×1024 → 1K) – Data moves through the chip in blocks of 1024 values at a time, defining the granularity at which tensors are fetched and manipulated. 5. Matrix Multiplication Unit (MXU) (×64 → 64K) – A systolic array where matrix multiplication is not abstract but physical, with tensor tiles flowing through fixed hardware to achieve the highest possible throughput. 6. Vector Memory (VMEM) (×2048 → 128M) – On-chip working memory that holds activations, partial results, and intermediates, sized specifically to keep the systolic array busy without stalling. 7. Common Memory (CMEM) (×8 → 1 GB) – A small but critical shared memory sitting between VMEM and HBM, used for staging, accumulation, synchronization, and cross-lane coordination. 8. HBM (×96 → 96 GB) – Off-chip high-bandwidth memory where model weights and large states live, implemented as HBM3e with 16 stacks at 6 GB each, for a total of 96 GB. 9. Dual-Die (x2 → 192GB) – Two tightly coupled compute dies operate as a single logical accelerator, each with its own local HBM, effectively doubling memory capacity and bandwidth while allowing tensors and activations to stream seamlessly across dies as if they lived on one chip. I created this drawing for this week's seminar. I’ll take you through these 9 levels in a beginner-friendly way by hand ✍️. RSVP 👉

Tom Yeh

30,489 views • 6 months ago

American nurse exposes a new change is coming, they will no longer be able to advise patients who call in over the phone 🚨 An artificial intelligence will listen in on all their calls and prompt them on what to say to patients going forward “This is a story about how AI is taking over health care. So I work as a nurse and I have several jobs. One of them, I work from home, as you can tell by my appearance today. And I work as a triage nurse where people will call in and say they're having certain symptoms. And I tell them, you know, you should go to the emergency room, you should call 9-1-1 you can take care of this at home, you know, whatever. And we've been informed that we are now going to be prompted by an AI chat function that listens to our conversations and prompts us with what it wants us to say” He goes on to explain how this AI tool is going to most likely be used to replace nurses
1:21

American nurse exposes a new change is coming, they will no longer be able to advise patients who call in over the phone 🚨 An artificial intelligence will listen in on all their calls and prompt them on what to say to patients going forward “This is a story about how AI is taking over health care. So I work as a nurse and I have several jobs. One of them, I work from home, as you can tell by my appearance today. And I work as a triage nurse where people will call in and say they're having certain symptoms. And I tell them, you know, you should go to the emergency room, you should call 9-1-1 you can take care of this at home, you know, whatever. And we've been informed that we are now going to be prompted by an AI chat function that listens to our conversations and prompts us with what it wants us to say” He goes on to explain how this AI tool is going to most likely be used to replace nurses

Wall Street Apes

240,343 views • 9 months ago

I use AI constantly. My 4 and 6 year olds even attend an AI-focused school. I am intensely optimistic about our AI future. But one thing I will fight to protect and never let AI automate: human autonomy. AI risks becoming the "autocomplete for life"—telling you the next action, job, relationship, identity. We who are living through this AI transition will be tempted, in a way that is unprecedented in human history, to let machines substitute for our self-direction. Most cognitive offloading is a genuine human triumph: writing reduced memory demands, mechanical calculation relieved us from arithmetic computation, and GPS navigation eliminated spatial reasoning from wayfinding. Each freed cognitive capacity for higher-order thinking. As Alfred North Whitehead observed, civilization advances by “extending the number of important operations which we can perform without thinking about them.” But as AI systems enter the realm of deliberation itself, something fundamentally different happens. Instead of freeing cognitive capacity for higher-order thinking, AI risks doing the choosing for us. Each small delegation of choice will seem harmless, even natural. But together, the micro-abdications of judgment could habituate you–choice by choice, day by day–to passivity and dependence. It could erode your ability to choose for yourself, from matters as trivial as what to have for breakfast, to fundamental choices about how to live well. Thankfully, AI can clearly do the opposite as well: By making learning more efficient AI can free time for self-directed exploration; as a Socratic interlocutor, it can strengthen your capacity to deliberate; through the right kind of personalization, it can help you discover and develop your unique gifts, and to use them to live autonomously and well. Let us choose to build for human autonomy...while we still remember what it means to choose. Full video with Johnathan Bi:
2:08:01

I use AI constantly. My 4 and 6 year olds even attend an AI-focused school. I am intensely optimistic about our AI future. But one thing I will fight to protect and never let AI automate: human autonomy. AI risks becoming the "autocomplete for life"—telling you the next action, job, relationship, identity. We who are living through this AI transition will be tempted, in a way that is unprecedented in human history, to let machines substitute for our self-direction. Most cognitive offloading is a genuine human triumph: writing reduced memory demands, mechanical calculation relieved us from arithmetic computation, and GPS navigation eliminated spatial reasoning from wayfinding. Each freed cognitive capacity for higher-order thinking. As Alfred North Whitehead observed, civilization advances by “extending the number of important operations which we can perform without thinking about them.” But as AI systems enter the realm of deliberation itself, something fundamentally different happens. Instead of freeing cognitive capacity for higher-order thinking, AI risks doing the choosing for us. Each small delegation of choice will seem harmless, even natural. But together, the micro-abdications of judgment could habituate you–choice by choice, day by day–to passivity and dependence. It could erode your ability to choose for yourself, from matters as trivial as what to have for breakfast, to fundamental choices about how to live well. Thankfully, AI can clearly do the opposite as well: By making learning more efficient AI can free time for self-directed exploration; as a Socratic interlocutor, it can strengthen your capacity to deliberate; through the right kind of personalization, it can help you discover and develop your unique gifts, and to use them to live autonomously and well. Let us choose to build for human autonomy...while we still remember what it means to choose. Full video with Johnathan Bi:

Brendan McCord 🏛️ x 🤖

16,456 views • 11 months ago

The AI boom just hit a wall nobody saw coming. And it's not software. It's not regulation. It's not even energy... It's memory chips. Right now, Dell is raising PC prices by 30%. Intel can't ship chips. Nvidia is slashing GPU production by 40%. And almost nobody understands why. Here's the "hidden" crisis the AI industry is trying to hide: AI data centers are hoarding memory. Not GPUs. Not processors. MEMORY. Every AI server needs massive amounts of high-bandwidth memory (HBM) to run those models everyone's hyping. One problem: There are only 3 companies in the world that can make it. Samsung. SK Hynix. Micron. That's it. And all 3 just diverted their entire production capacity away from normal RAM to feed AI data centers. The math that breaks everything: 1 gigabyte of HBM takes 4X the manufacturing capacity of regular DRAM. AI will consume 20% of global DRAM production in 2026. But the thing is, consumer demand for RAM didn't disappear. PCs still need memory. Phones still need memory. Cars still need memory. But there's no capacity left to make it. The price explosion: RAM prices are up 246% in the last 6 months. DDR5 contract prices jumped 100% month-over-month in some cases. Dell's CFO said he's "never witnessed costs escalating at this pace." SK Hynix and Micron? Sold out through all of 2026. Micron straight up EXITED the consumer memory market entirely to focus on AI customers. If you're not building an AI data center, you're not getting memory chips. AI data centers pay 3-5X margins compared to consumer products. So memory manufacturers are rationally choosing: Serve Microsoft and Google's AI buildout, or serve Dell's laptop business? Easy choice. Every wafer allocated to an Nvidia H100 GPU is a wafer DENIED to your next laptop. It's a zero-sum game. And consumers are losing. The dangerous cascade effect: Nvidia is cutting RTX 50-series GPU production by 30-40% because they can't get GDDR7 memory. Dell, Lenovo, HP are all raising PC prices 15-30% in early 2026. Xiaomi and other smartphone makers are cutting shipment targets. Even Intel's crash last week? Partially driven by memory shortages limiting chip production. This is a PERMANENT reallocation of the world's silicon capacity. Not a temporary supply hiccup. For decades, consumer electronics (phones, PCs, laptops) drove memory production. Now? AI data centers are the priority customer. And that priority shift is reshaping the entire tech economy. The timeline Is worse than you think: Industry analysts project shortages lasting through 2027, maybe 2028. Why? Because building new memory fabs takes 3-5 YEARS. Micron's new Idaho fab won't meaningfully impact supply until 2028. Samsung and SK Hynix are too busy ramping up HBM4 production to expand consumer DRAM. So we're stuck. AI companies need memory to scale. But producing that memory DESTROYS the supply chain for everything else. My question here: Everyone's betting on AI scaling infinitely. But what if the AI boom STALLS because there's not enough memory to support it? What if we're not in an "AI supercycle" but a "memory shortage that kills the AI buildout"? Intel crashed 17% because they can't manufacture enough chips. The root cause though? Memory shortages limiting what they can even produce. Nvidia is cutting GPU production by 40%. AMD is struggling to get GDDR6 for Radeon cards. This isn't just a consumer problem. It's an AI infrastructure problem. And if memory doesn't scale, AI doesn't scale. The AI industry sold you on infinite scaling. But they forgot to mention the part where there's only 3 companies making the memory chips that power everything. And all 3 just chose AI data centers over you. Even Nvidia can't make enough GPUs to meet demand. Not because of energy. Not because of regulation... But because the memory supply chain is BROKEN. And it won't be fixed until 2028.
0:58

The AI boom just hit a wall nobody saw coming. And it's not software. It's not regulation. It's not even energy... It's memory chips. Right now, Dell is raising PC prices by 30%. Intel can't ship chips. Nvidia is slashing GPU production by 40%. And almost nobody understands why. Here's the "hidden" crisis the AI industry is trying to hide: AI data centers are hoarding memory. Not GPUs. Not processors. MEMORY. Every AI server needs massive amounts of high-bandwidth memory (HBM) to run those models everyone's hyping. One problem: There are only 3 companies in the world that can make it. Samsung. SK Hynix. Micron. That's it. And all 3 just diverted their entire production capacity away from normal RAM to feed AI data centers. The math that breaks everything: 1 gigabyte of HBM takes 4X the manufacturing capacity of regular DRAM. AI will consume 20% of global DRAM production in 2026. But the thing is, consumer demand for RAM didn't disappear. PCs still need memory. Phones still need memory. Cars still need memory. But there's no capacity left to make it. The price explosion: RAM prices are up 246% in the last 6 months. DDR5 contract prices jumped 100% month-over-month in some cases. Dell's CFO said he's "never witnessed costs escalating at this pace." SK Hynix and Micron? Sold out through all of 2026. Micron straight up EXITED the consumer memory market entirely to focus on AI customers. If you're not building an AI data center, you're not getting memory chips. AI data centers pay 3-5X margins compared to consumer products. So memory manufacturers are rationally choosing: Serve Microsoft and Google's AI buildout, or serve Dell's laptop business? Easy choice. Every wafer allocated to an Nvidia H100 GPU is a wafer DENIED to your next laptop. It's a zero-sum game. And consumers are losing. The dangerous cascade effect: Nvidia is cutting RTX 50-series GPU production by 30-40% because they can't get GDDR7 memory. Dell, Lenovo, HP are all raising PC prices 15-30% in early 2026. Xiaomi and other smartphone makers are cutting shipment targets. Even Intel's crash last week? Partially driven by memory shortages limiting chip production. This is a PERMANENT reallocation of the world's silicon capacity. Not a temporary supply hiccup. For decades, consumer electronics (phones, PCs, laptops) drove memory production. Now? AI data centers are the priority customer. And that priority shift is reshaping the entire tech economy. The timeline Is worse than you think: Industry analysts project shortages lasting through 2027, maybe 2028. Why? Because building new memory fabs takes 3-5 YEARS. Micron's new Idaho fab won't meaningfully impact supply until 2028. Samsung and SK Hynix are too busy ramping up HBM4 production to expand consumer DRAM. So we're stuck. AI companies need memory to scale. But producing that memory DESTROYS the supply chain for everything else. My question here: Everyone's betting on AI scaling infinitely. But what if the AI boom STALLS because there's not enough memory to support it? What if we're not in an "AI supercycle" but a "memory shortage that kills the AI buildout"? Intel crashed 17% because they can't manufacture enough chips. The root cause though? Memory shortages limiting what they can even produce. Nvidia is cutting GPU production by 40%. AMD is struggling to get GDDR6 for Radeon cards. This isn't just a consumer problem. It's an AI infrastructure problem. And if memory doesn't scale, AI doesn't scale. The AI industry sold you on infinite scaling. But they forgot to mention the part where there's only 3 companies making the memory chips that power everything. And all 3 just chose AI data centers over you. Even Nvidia can't make enough GPUs to meet demand. Not because of energy. Not because of regulation... But because the memory supply chain is BROKEN. And it won't be fixed until 2028.

Ricardo

594,643 views • 6 months ago

Micron is going to $4,000 and once you understand what inference actually is, the number stops sounding crazy (Save this). Dylan Patel just said that by 2030, OpenAI and Anthropic alone will need over 100 gigawatts of compute combined and by 2040, we may not even be measuring AI infrastructure in gigawatts anymore. We may be talking about terawatts. Every single one of those gigawatts needs memory to function. Without it, the compute is worthless. Most people heard that and thought about Nvidia but they should be thinking about Micron. Every AI model generating a response has two phases. The first is prefill, processing your prompt which is compute-heavy and the second is decode generating each word one token at a time and that phase is almost entirely memory-bound, not compute-bound. During decode, the GPU's processing units sit idle more than 95% of the time, waiting for data to arrive from memory. Google confirmed it in a research paper that decode-phase bottlenecks are dominated by memory bandwidth and capacity not raw compute. The GPU is not the bottleneck but the memory feeding the GPU is. This matters because inference is now where all the money lives. Training a model happens once, Inference happens billions of times a day every ChatGPT response, every Claude output, every agentic workflow running in the background and every one of those token streams is a billing event tied directly to memory performance. Adding more GPUs does not fix this because GPUs are already underutilized in inference because they are sitting idle waiting on memory. Adding more memory bandwidth and capacity is what directly reduces token cost, reduces latency, and allows the same cluster to serve dramatically more users simultaneously. Longer context windows compound the problem further, a model running a 1 million token context window requires dramatically more memory per session than a 10,000 token window, and every new model generation pushes context longer. The market treats memory as a downstream beneficiary of Nvidia orders. The correct framework is the opposite, Micron is the upstream constraint on how much value every Nvidia GPU can actually generate at inference scale. Micron guided Q4 to $50 billion in revenue, has HBM4 ramping at twice the pace of the prior generation, and CEO Sanjay Mehrotra has said supply will not catch demand before the end of 2027. At 8x forward earnings on $112 projected FY2027 EPS, Micron is the most undervalued infrastructure company in the entire AI stack. Inference is memory. Memory is Micron and the inference ramp has barely started. Milk Road Pro members are already up massively on this position and we're just getting started. If you want the full breakdown of what we're buying and why, come join us for just a dollar using the link below!
0:39

Micron is going to $4,000 and once you understand what inference actually is, the number stops sounding crazy (Save this). Dylan Patel just said that by 2030, OpenAI and Anthropic alone will need over 100 gigawatts of compute combined and by 2040, we may not even be measuring AI infrastructure in gigawatts anymore. We may be talking about terawatts. Every single one of those gigawatts needs memory to function. Without it, the compute is worthless. Most people heard that and thought about Nvidia but they should be thinking about Micron. Every AI model generating a response has two phases. The first is prefill, processing your prompt which is compute-heavy and the second is decode generating each word one token at a time and that phase is almost entirely memory-bound, not compute-bound. During decode, the GPU's processing units sit idle more than 95% of the time, waiting for data to arrive from memory. Google confirmed it in a research paper that decode-phase bottlenecks are dominated by memory bandwidth and capacity not raw compute. The GPU is not the bottleneck but the memory feeding the GPU is. This matters because inference is now where all the money lives. Training a model happens once, Inference happens billions of times a day every ChatGPT response, every Claude output, every agentic workflow running in the background and every one of those token streams is a billing event tied directly to memory performance. Adding more GPUs does not fix this because GPUs are already underutilized in inference because they are sitting idle waiting on memory. Adding more memory bandwidth and capacity is what directly reduces token cost, reduces latency, and allows the same cluster to serve dramatically more users simultaneously. Longer context windows compound the problem further, a model running a 1 million token context window requires dramatically more memory per session than a 10,000 token window, and every new model generation pushes context longer. The market treats memory as a downstream beneficiary of Nvidia orders. The correct framework is the opposite, Micron is the upstream constraint on how much value every Nvidia GPU can actually generate at inference scale. Micron guided Q4 to $50 billion in revenue, has HBM4 ramping at twice the pace of the prior generation, and CEO Sanjay Mehrotra has said supply will not catch demand before the end of 2027. At 8x forward earnings on $112 projected FY2027 EPS, Micron is the most undervalued infrastructure company in the entire AI stack. Inference is memory. Memory is Micron and the inference ramp has barely started. Milk Road Pro members are already up massively on this position and we're just getting started. If you want the full breakdown of what we're buying and why, come join us for just a dollar using the link below!

Milk Road AI

128,678 views • 1 month ago

A woman sits between two men. One is old and wealthy, and he is offering her a fortune. The other is young with nothing to give but himself. And Bouguereau painted her in the exact moment before she chooses... The painting is called Entre la richesse et l'amour, "Between Wealth and Love," made in 1869 by the French master William-Adolphe Bouguereau. At its center sits a young woman in a soft pink dress, her expression caught somewhere between thought and sorrow. On one side of her leans an old man, richly dressed, holding out an ornate casket, a small chest of treasure. He is wealth. He is offering comfort, security, a life without want, in exchange for her hand. On her other side is a young man in simpler clothes, earnest, leaning toward her, his hand pressed over his heart. That gesture is all he offers. He holds out no gold and no gift, only himself, the sincerity of his feeling, and the promise of a life that may be poor but will be warm. He is love. Bouguereau does not tell us what she decides. That is the genius of the painting: he freezes her in the one instant every human being recognizes, the moment when two futures stand on either side of you, and you understand that to choose one is to lose the other forever. Everyone, sooner or later, sits in that chair. Everyone is asked, in one form or another, to choose between the safe life and the true one. And the painting does not pretend the choice is easy. It never tells us what she should do. It only asks us to look, and to notice what we find ourselves wishing for... I started my newsletter because the past is full of masterpieces like this one, and fewer and fewer people are helping us truly see them anymore. Every week I try to. If that is something you'd like to be part of, you can join through the link in my bio, and if you'd like to support my work, a paid subscription is what makes it possible. Thanks for reading.
0:31

A woman sits between two men. One is old and wealthy, and he is offering her a fortune. The other is young with nothing to give but himself. And Bouguereau painted her in the exact moment before she chooses... The painting is called Entre la richesse et l'amour, "Between Wealth and Love," made in 1869 by the French master William-Adolphe Bouguereau. At its center sits a young woman in a soft pink dress, her expression caught somewhere between thought and sorrow. On one side of her leans an old man, richly dressed, holding out an ornate casket, a small chest of treasure. He is wealth. He is offering comfort, security, a life without want, in exchange for her hand. On her other side is a young man in simpler clothes, earnest, leaning toward her, his hand pressed over his heart. That gesture is all he offers. He holds out no gold and no gift, only himself, the sincerity of his feeling, and the promise of a life that may be poor but will be warm. He is love. Bouguereau does not tell us what she decides. That is the genius of the painting: he freezes her in the one instant every human being recognizes, the moment when two futures stand on either side of you, and you understand that to choose one is to lose the other forever. Everyone, sooner or later, sits in that chair. Everyone is asked, in one form or another, to choose between the safe life and the true one. And the painting does not pretend the choice is easy. It never tells us what she should do. It only asks us to look, and to notice what we find ourselves wishing for... I started my newsletter because the past is full of masterpieces like this one, and fewer and fewer people are helping us truly see them anymore. Every week I try to. If that is something you'd like to be part of, you can join through the link in my bio, and if you'd like to support my work, a paid subscription is what makes it possible. Thanks for reading.

James Lucas

110,698 views • 1 month ago

A new way of working. And a scary one at that. Memory Store is one of a group of new kinds of AI-first companies that can turn you into a Fast Company. I’m using several of them on my desktop and they are a dramatically new way to work. It builds a memory for: 1. Your AI agents. 2. Any employee using it. 3. The company itself. I sit down with founder Diwank Singh Tomer, Diwank Singh Tomer, who both freaks me out as well as shows how AI can radically help workers as well as managers. First, why does it freak me out? Well, his AI watches nearly everything a worker does and keeps a “memory” of it. It watches your email. Your calendar. Your Slack. And a whole lot of other things. This can really freak out workers if “forced” on them. And leads to a whole new set of security issues companies need to consider before adopting these things. Such data about a company could give a competitor a HUGE advantage, if leaked. They would know how a company “thinks.” It really is a surveillance system for employees and the company itself. OK, now why would anyone ever use such a thing? Because it gives employees super powers. It makes them more productive. Shows workers a lot of things about themselves, and helps them work and stay on task. It also gives the company super powers. Institutional memory stays with the AI now, even if an employee dies or leaves. As companies move to “AI First” approaches, they will increasingly see the value in companies like Memory Store. It prepares employees for meetings. It helps them remember things. It shows them what they should be working on, and helps them do it. Memory Store builds a memory for: 1. Your agents. 2. Your company. 3. Yourself, or any employee on it. This helps all three work better together. Diwank Singh Tomer and I go in depth about what it does and how deeply it improves working at a company that deploys it. But to get the ultimate benefits you gotta convince your coworkers to use it. And your managers to approve it. Which means you have to get over your fears and get everyone you work with over theirs too. Which will be the challenge for Diwank. Luckily for him his first customers are raving about how good it is and how much his platform helped their companies. Increases sales. Makes teams more productive. Decreases errors and unnecessary costs. Which tells me everyone soon will be using systems like this. This is what the new way of working looks like. Once I got over my fears it sure is an amazing way to work. Will you try working this way?
52:08

A new way of working. And a scary one at that. Memory Store is one of a group of new kinds of AI-first companies that can turn you into a Fast Company. I’m using several of them on my desktop and they are a dramatically new way to work. It builds a memory for: 1. Your AI agents. 2. Any employee using it. 3. The company itself. I sit down with founder Diwank Singh Tomer, Diwank Singh Tomer, who both freaks me out as well as shows how AI can radically help workers as well as managers. First, why does it freak me out? Well, his AI watches nearly everything a worker does and keeps a “memory” of it. It watches your email. Your calendar. Your Slack. And a whole lot of other things. This can really freak out workers if “forced” on them. And leads to a whole new set of security issues companies need to consider before adopting these things. Such data about a company could give a competitor a HUGE advantage, if leaked. They would know how a company “thinks.” It really is a surveillance system for employees and the company itself. OK, now why would anyone ever use such a thing? Because it gives employees super powers. It makes them more productive. Shows workers a lot of things about themselves, and helps them work and stay on task. It also gives the company super powers. Institutional memory stays with the AI now, even if an employee dies or leaves. As companies move to “AI First” approaches, they will increasingly see the value in companies like Memory Store. It prepares employees for meetings. It helps them remember things. It shows them what they should be working on, and helps them do it. Memory Store builds a memory for: 1. Your agents. 2. Your company. 3. Yourself, or any employee on it. This helps all three work better together. Diwank Singh Tomer and I go in depth about what it does and how deeply it improves working at a company that deploys it. But to get the ultimate benefits you gotta convince your coworkers to use it. And your managers to approve it. Which means you have to get over your fears and get everyone you work with over theirs too. Which will be the challenge for Diwank. Luckily for him his first customers are raving about how good it is and how much his platform helped their companies. Increases sales. Makes teams more productive. Decreases errors and unnecessary costs. Which tells me everyone soon will be using systems like this. This is what the new way of working looks like. Once I got over my fears it sure is an amazing way to work. Will you try working this way?

Robert Scoble

25,975 views • 3 months ago

⚡️Iran’s Foreign Ministry Spokesperson Esmaeil Baqaei: 🔹 We reached understandings on a number of issues, but on two to three major points, there were differences in views, which ultimately prevented an agreement. 🔹This round was the longest set of negotiations we’ve had with the Americans in the past year, lasting around 24 to 25 hours. 🔹 The talks began yesterday morning through indirect discussions and message exchanges between the two sides, mediated by Pakistan, and continued without interruption. 🔹 Discussions went on through the night, covering a range of topics, including items in Iran’s 10-point plan as well as points raised by the other side. 🔹 These negotiations took place after 40 days of imposed war and were conducted in an atmosphere of mistrust and suspicion; naturally, we should not have expected to reach an agreement in a single session. No one had such an expectation. 🔹 Another point was the complexity of the issues and conditions. Some new issues, like the Strait of Hormuz, were added to the negotiations, each with its own complexities. 🔹 While some areas saw progress, differences on two to three key issues ultimately blocked a final agreement. 🎥 Video: IRIB. Translated by Drop Site News
5:55

⚡️Iran’s Foreign Ministry Spokesperson Esmaeil Baqaei: 🔹 We reached understandings on a number of issues, but on two to three major points, there were differences in views, which ultimately prevented an agreement. 🔹This round was the longest set of negotiations we’ve had with the Americans in the past year, lasting around 24 to 25 hours. 🔹 The talks began yesterday morning through indirect discussions and message exchanges between the two sides, mediated by Pakistan, and continued without interruption. 🔹 Discussions went on through the night, covering a range of topics, including items in Iran’s 10-point plan as well as points raised by the other side. 🔹 These negotiations took place after 40 days of imposed war and were conducted in an atmosphere of mistrust and suspicion; naturally, we should not have expected to reach an agreement in a single session. No one had such an expectation. 🔹 Another point was the complexity of the issues and conditions. Some new issues, like the Strait of Hormuz, were added to the negotiations, each with its own complexities. 🔹 While some areas saw progress, differences on two to three key issues ultimately blocked a final agreement. 🎥 Video: IRIB. Translated by Drop Site News

Drop Site

62,233 views • 4 months ago

Masquerade now hangs in The Toledo Museum of Art. But unlike a painting, Masquerade holds within itself: a gathering of people in the network and systems to connect us. The piece is not "A" piece. It is every Mask, every dot, every observation made and, through a new layer, every one yet to be made. I feel both stupidly lucky and genuinely honored to bring everyone who has given energy to Luci out of the network and into a place that has honored my work and the work of so many other artists I deeply admire with immense and precise care. This absurd world of masks and monkeys I love so much because of what has been shared between people through it has had my love compound through the care of people. To help to protect and preserve a story bigger than my own, not unlike what has taken place atop its surface. Much as the systems of participation echoed the systems of its creation, so too does its curation and cultivation by others help it grow. Masquerade now hangs in a museum, but it hangs alongside a cadre of digital art's giants in an exhibition titled “Infinite Images,” which sits centered inside a museum filled with the giants of art history itself. For this to happen requires a lattice frequency of care: not as a feeling, but as something intensely actionable. Care—demonstrated through sacrifice and skin in the game. It pours out of the dark blue diamond walls and the work atop them and into our orange room; it bleeds from every installation, overflowing from each and every artist, and the space between each of us within. Care is revealed through a curator who helped me understand my own work more but also learn what connects me backwards to the shoulders I stand on. Julia Kaganskiy 🇺🇦 found and presented our systems not as separate worlds, but as part of one system of life—all while bringing into life of a higher order: her first child. Care extended through a director, Adam Levine, who pushed for something quite radical and got his hands dirty to allow this show to even exist. The Museum and the people who keep its engine moving treated our new as sacred as their old. They did so not to onboard, but to remain. A portal that lifts up a movement happening in the network by seeing the need to connect our nodes rather than relegate or shut the gate behind. Doing this required the patronage of Alan Howard, channeled through the cataloging brilliance of Martina Negro, and the willingness of high‑order patronage to become a cooperative network rather than an adversarial one. In that cooperation is where Masquerade’s place in the exhibition came through Kanbas. A throughline drawn between the roles of our ecosystem. Of what can be built between us if we see each other as one ecosystem, both different and essential. An exhibition filled with cutting‑edge technology and masterful presentation does not exist with only artists or curators or museums; it is enabled through a network of generous patronage and curiosity. Kanbas helped me take what began as the orange room with a digital confessional that is Rachel and I’s studio in New York, that last year became The Monument Game boat‑dock exhibition in Venice, Italy through the genius of ScriptedFantasy and support of Ryan Zurrer, and evolve it to sit in a Museum in a way that does not just exhibit the work, but educates and invites people to come closer who may not know any of this even exists. Rather than just house the singular work, Kanbas and Amanda cared to share the network of 613 Masks and the people who wear them with it. Care came through my team who built Masquerade with me in Nifty Gateway Studio. Chrisly, Ashlin, Bob, Nirali, Tara, and all who pitched in make our interface and systems accessible to a whole new audience; to build infrastructure and enable thousands of people who have never touched the blockchain to create a wallet just by leaving an Observation atop the Masquerade is more than a feature: it is spreading the network rather than closing its gates. I feel so lucky to have such a tremendous team of people to think not as a marketplace, but as stewards to present what we have been building well and improve it with time and circumstance. This patronage enabled artistry within artistry to thrive, through a masterful exhibition design led by Richard The, technology and fabrication partners through TCI in Greg and Anthony, Jack with projections, and fabricators through Bednark. With me is my brilliant engineer, Alex Borre, who has broken apart all of this alongside me and helped me grow as an artist. By my side has been joeyL.eth, the eternal vibe check of taste, restraint, and precision - the finest eye and mind I know, and the relentless, perpetual support of El Barba Roja blue check who championed and willed this exhibition into existence more than anyone. No matter how much “this is how we build” has looped and human‑centipeded in on itself through cynical interpretations, in this case, it truly is, and his hands are all over not just my contribution, but many others in this show, as well as in the exhibition’s foundation itself. But in the end: care has been given in the highest order by my wife Rachel Spratt who has done what all new great mothers do and given life through tremendous exertion. We did not take a break after Masquerade, because it just immediately became Masquerade IRL. She has given to me, given to our daughter Syla, given to our tribe of Masks and Skulls, to this exhibition: everything. What is left after one gives all away, no matter how sweetly, can be a hollowing of self or, if met well before a break, a shedding of self. To sit in this beautiful room together, within an exhibition this magnificent, inside a building this rich with history and love of humanity, and have so much care be put in to match hers by this network around us on and offline, I got to sit in pride because I knew this very real moment for me, that began so alone and has become quite collective, would simply, unequivocally, never even come close to existing without her. So much of my life has been defined by solitude. I was not expecting having my work presented like this to move me so much. I thought it would be more of a feather in the cap or rather beautiful box to check. But it isn't that at all. It's connection to others. I fell in love with the artists' work that sits alongside my own. I saw Dmitri Cherniak's Ringers I’ve seen a thousand times, but saw them again in a way my daughter could visualize her own curiosity about the world—decisions in how these were shared served a higher purpose: to communicate. I saw Casey REAS as someone not just as a pioneer, but as someone more real—who, like my wife, cut part of himself out and set it aside to see what others could create. I saw Operator not as slick performers of the code, but as arbiters of freedom. I saw invitations to see the humanity in code in dozens of directions, and gratitude to sit even close to any of it. Talent. Sweat. Blood. Skin in the game. The artists pushed. The fabricators pushed. The docents pushed. Not one of us is like the other and no template was ever to be made to try to force otherwise. On September 12th, I will be inviting every holder of Luci: Masks, Players, Council, to come out to a small town in Ohio and leave a bit of themselves behind and get a very deep look into the world I am trying to build in the process. More on that very soon, but should you be able to come, know that there is much to care about beyond what I’ve built within this exhibition—and I hope you will fall in love with the rest of these infinite worlds as I have. We all know we aren't supposed to touch the paintings in a museum. But my daughter Syla touched the illuminated lightbox of Masquerade that she unknowingly stars in at the center of, and I smiled. Because she's not touching a world of divine objects susceptible to fingerprints and pretense; she's touching a display. A front‑end stand‑in representation for the network of humanity underneath. A place where light, code, instructions, a design that rhymes across all things has formed a very unlikely but very real gathering of strangers over the last few years to create together. I feel lucky for her to grow up surrounded by people who create and who care.
1:15

Masquerade now hangs in The Toledo Museum of Art. But unlike a painting, Masquerade holds within itself: a gathering of people in the network and systems to connect us. The piece is not "A" piece. It is every Mask, every dot, every observation made and, through a new layer, every one yet to be made. I feel both stupidly lucky and genuinely honored to bring everyone who has given energy to Luci out of the network and into a place that has honored my work and the work of so many other artists I deeply admire with immense and precise care. This absurd world of masks and monkeys I love so much because of what has been shared between people through it has had my love compound through the care of people. To help to protect and preserve a story bigger than my own, not unlike what has taken place atop its surface. Much as the systems of participation echoed the systems of its creation, so too does its curation and cultivation by others help it grow. Masquerade now hangs in a museum, but it hangs alongside a cadre of digital art's giants in an exhibition titled “Infinite Images,” which sits centered inside a museum filled with the giants of art history itself. For this to happen requires a lattice frequency of care: not as a feeling, but as something intensely actionable. Care—demonstrated through sacrifice and skin in the game. It pours out of the dark blue diamond walls and the work atop them and into our orange room; it bleeds from every installation, overflowing from each and every artist, and the space between each of us within. Care is revealed through a curator who helped me understand my own work more but also learn what connects me backwards to the shoulders I stand on. Julia Kaganskiy 🇺🇦 found and presented our systems not as separate worlds, but as part of one system of life—all while bringing into life of a higher order: her first child. Care extended through a director, Adam Levine, who pushed for something quite radical and got his hands dirty to allow this show to even exist. The Museum and the people who keep its engine moving treated our new as sacred as their old. They did so not to onboard, but to remain. A portal that lifts up a movement happening in the network by seeing the need to connect our nodes rather than relegate or shut the gate behind. Doing this required the patronage of Alan Howard, channeled through the cataloging brilliance of Martina Negro, and the willingness of high‑order patronage to become a cooperative network rather than an adversarial one. In that cooperation is where Masquerade’s place in the exhibition came through Kanbas. A throughline drawn between the roles of our ecosystem. Of what can be built between us if we see each other as one ecosystem, both different and essential. An exhibition filled with cutting‑edge technology and masterful presentation does not exist with only artists or curators or museums; it is enabled through a network of generous patronage and curiosity. Kanbas helped me take what began as the orange room with a digital confessional that is Rachel and I’s studio in New York, that last year became The Monument Game boat‑dock exhibition in Venice, Italy through the genius of ScriptedFantasy and support of Ryan Zurrer, and evolve it to sit in a Museum in a way that does not just exhibit the work, but educates and invites people to come closer who may not know any of this even exists. Rather than just house the singular work, Kanbas and Amanda cared to share the network of 613 Masks and the people who wear them with it. Care came through my team who built Masquerade with me in Nifty Gateway Studio. Chrisly, Ashlin, Bob, Nirali, Tara, and all who pitched in make our interface and systems accessible to a whole new audience; to build infrastructure and enable thousands of people who have never touched the blockchain to create a wallet just by leaving an Observation atop the Masquerade is more than a feature: it is spreading the network rather than closing its gates. I feel so lucky to have such a tremendous team of people to think not as a marketplace, but as stewards to present what we have been building well and improve it with time and circumstance. This patronage enabled artistry within artistry to thrive, through a masterful exhibition design led by Richard The, technology and fabrication partners through TCI in Greg and Anthony, Jack with projections, and fabricators through Bednark. With me is my brilliant engineer, Alex Borre, who has broken apart all of this alongside me and helped me grow as an artist. By my side has been joeyL.eth, the eternal vibe check of taste, restraint, and precision - the finest eye and mind I know, and the relentless, perpetual support of El Barba Roja blue check who championed and willed this exhibition into existence more than anyone. No matter how much “this is how we build” has looped and human‑centipeded in on itself through cynical interpretations, in this case, it truly is, and his hands are all over not just my contribution, but many others in this show, as well as in the exhibition’s foundation itself. But in the end: care has been given in the highest order by my wife Rachel Spratt who has done what all new great mothers do and given life through tremendous exertion. We did not take a break after Masquerade, because it just immediately became Masquerade IRL. She has given to me, given to our daughter Syla, given to our tribe of Masks and Skulls, to this exhibition: everything. What is left after one gives all away, no matter how sweetly, can be a hollowing of self or, if met well before a break, a shedding of self. To sit in this beautiful room together, within an exhibition this magnificent, inside a building this rich with history and love of humanity, and have so much care be put in to match hers by this network around us on and offline, I got to sit in pride because I knew this very real moment for me, that began so alone and has become quite collective, would simply, unequivocally, never even come close to existing without her. So much of my life has been defined by solitude. I was not expecting having my work presented like this to move me so much. I thought it would be more of a feather in the cap or rather beautiful box to check. But it isn't that at all. It's connection to others. I fell in love with the artists' work that sits alongside my own. I saw Dmitri Cherniak's Ringers I’ve seen a thousand times, but saw them again in a way my daughter could visualize her own curiosity about the world—decisions in how these were shared served a higher purpose: to communicate. I saw Casey REAS as someone not just as a pioneer, but as someone more real—who, like my wife, cut part of himself out and set it aside to see what others could create. I saw Operator not as slick performers of the code, but as arbiters of freedom. I saw invitations to see the humanity in code in dozens of directions, and gratitude to sit even close to any of it. Talent. Sweat. Blood. Skin in the game. The artists pushed. The fabricators pushed. The docents pushed. Not one of us is like the other and no template was ever to be made to try to force otherwise. On September 12th, I will be inviting every holder of Luci: Masks, Players, Council, to come out to a small town in Ohio and leave a bit of themselves behind and get a very deep look into the world I am trying to build in the process. More on that very soon, but should you be able to come, know that there is much to care about beyond what I’ve built within this exhibition—and I hope you will fall in love with the rest of these infinite worlds as I have. We all know we aren't supposed to touch the paintings in a museum. But my daughter Syla touched the illuminated lightbox of Masquerade that she unknowingly stars in at the center of, and I smiled. Because she's not touching a world of divine objects susceptible to fingerprints and pretense; she's touching a display. A front‑end stand‑in representation for the network of humanity underneath. A place where light, code, instructions, a design that rhymes across all things has formed a very unlikely but very real gathering of strangers over the last few years to create together. I feel lucky for her to grow up surrounded by people who create and who care.

Sam Spratt

49,176 views • 1 year ago

What if you could visualise how electricity flows from a wind farm in Scotland to your home in London? Ok it's not quite that simple, but it got me thinking that there must be a way of visualising the complexity of the electricity transmission grid in Great Britain. This is my first attempt… What you're seeing are the real power lines and substations on the transmission grid using OpenStreetMap data. The real connections between each substation are found using PyPSA-Eur as well as calculating the "electrical distance" or impedance between each substation. This is my incredibly basic and naive way of attempting to show the "speed" at which the energy is moving through the network, where the bottlenecks are, etc. I know it's not as simple as this, but it's a first attempt so… 😉 I've also added a way to pick any substation in the network and show how electricity pulses through the rest of the network from that point. This is the magic sauce that really brings to life how much infrastructure electricity has to pass through to get from point A to point B. Of course this isn't really what happens, and there's no demand to soak up the energy. However I think it still serves as an interesting visual of how complex the network actually is and the distances involved to get energy from one side of the country to the other. What do you think? Is this worth exploring further?
0:29

What if you could visualise how electricity flows from a wind farm in Scotland to your home in London? Ok it's not quite that simple, but it got me thinking that there must be a way of visualising the complexity of the electricity transmission grid in Great Britain. This is my first attempt… What you're seeing are the real power lines and substations on the transmission grid using OpenStreetMap data. The real connections between each substation are found using PyPSA-Eur as well as calculating the "electrical distance" or impedance between each substation. This is my incredibly basic and naive way of attempting to show the "speed" at which the energy is moving through the network, where the bottlenecks are, etc. I know it's not as simple as this, but it's a first attempt so… 😉 I've also added a way to pick any substation in the network and show how electricity pulses through the rest of the network from that point. This is the magic sauce that really brings to life how much infrastructure electricity has to pass through to get from point A to point B. Of course this isn't really what happens, and there's no demand to soak up the energy. However I think it still serves as an interesting visual of how complex the network actually is and the distances involved to get energy from one side of the country to the other. What do you think? Is this worth exploring further?

Robin Hawkes

144,544 views • 6 months ago

FTC documents show Pepsi is price fixing The FTC looked at internal emails with Pepsi and Walmart, they found they act as one company and Pepsi even takes action against grocery stores and other chains like Target if they price Pepsi as cheaply as Walmart does “The FTC looked at a bunch of internal Pepsi emails and communications. The executives at Pepsi would talk about Walmart's prices as our prices. As if this is, this is all one big company — Big rivals like Target, small mom and pop shops, regional chains, everywhere it could keep an eye on it, it did. And where it found that Pepsi prices in those other stores were getting low enough to really compete with Walmart, with all of the discounts and all of the other things that it was giving Walmart, it took action against those other stores. What does that look like? It reduced their deals, it reduced the discounts they were giving these other retailers. It even increased their wholesale prices, started to kind of ratchet it up more and more every year so that the price of Pepsi products at every other store that wasn't a Walmart were artificially higher for the consumer than they would have been if there was actual competition between Walmart and these stores. These stores wanted to push the prices down, but Pepsi simply wouldn't let them by charging them more” This is price fixing and illegal
2:09

FTC documents show Pepsi is price fixing The FTC looked at internal emails with Pepsi and Walmart, they found they act as one company and Pepsi even takes action against grocery stores and other chains like Target if they price Pepsi as cheaply as Walmart does “The FTC looked at a bunch of internal Pepsi emails and communications. The executives at Pepsi would talk about Walmart's prices as our prices. As if this is, this is all one big company — Big rivals like Target, small mom and pop shops, regional chains, everywhere it could keep an eye on it, it did. And where it found that Pepsi prices in those other stores were getting low enough to really compete with Walmart, with all of the discounts and all of the other things that it was giving Walmart, it took action against those other stores. What does that look like? It reduced their deals, it reduced the discounts they were giving these other retailers. It even increased their wholesale prices, started to kind of ratchet it up more and more every year so that the price of Pepsi products at every other store that wasn't a Walmart were artificially higher for the consumer than they would have been if there was actual competition between Walmart and these stores. These stores wanted to push the prices down, but Pepsi simply wouldn't let them by charging them more” This is price fixing and illegal

Wall Street Apes

98,740 views • 8 months ago

One of my favorite upgrades to Claude Cowork right now: Projects Here's the problem it solves: By default, every new thread starts blank Claude has no memory of what you were working on, what other threads figured out, or what your instructions were So you'd re-explain yourself at the start of every session Or your agents would overlap, contradict each other, or miss context that another thread already had If you've been following my Cowork setup guides, you've already solved part of this — local files and a CLAUDE.md get you most of the way there Projects takes it the rest of the way Shared memory across every thread in the project Permanent instructions that apply everywhere — set once, always on Scheduled tasks that prep your work before you open the app One project HQ to launch every agent from The day-one prompt I paste into every new project: "I just created this project. Read every file in the folder. Then summarize what you know about this workspace — what's here, what I probably use it for, and what instructions you'll follow. If anything is unclear, ask me some questions." Run that once and Claude builds its own understanding of your project workspace from scratch Full video with my live build, including my 4 tips for getting Projects right, and where projects still lack. Send to someone whose ready to Do More with AI, or bookmark to implement this weekend!
28:01

One of my favorite upgrades to Claude Cowork right now: Projects Here's the problem it solves: By default, every new thread starts blank Claude has no memory of what you were working on, what other threads figured out, or what your instructions were So you'd re-explain yourself at the start of every session Or your agents would overlap, contradict each other, or miss context that another thread already had If you've been following my Cowork setup guides, you've already solved part of this — local files and a CLAUDE.md get you most of the way there Projects takes it the rest of the way Shared memory across every thread in the project Permanent instructions that apply everywhere — set once, always on Scheduled tasks that prep your work before you open the app One project HQ to launch every agent from The day-one prompt I paste into every new project: "I just created this project. Read every file in the folder. Then summarize what you know about this workspace — what's here, what I probably use it for, and what instructions you'll follow. If anything is unclear, ask me some questions." Run that once and Claude builds its own understanding of your project workspace from scratch Full video with my live build, including my 4 tips for getting Projects right, and where projects still lack. Send to someone whose ready to Do More with AI, or bookmark to implement this weekend!

JJ Englert

29,809 views • 4 months ago

My walk by the canal this evening and I thought you might be interested to see a lock. I realised that some of you might never have seen one and I know everyone is fascinated as to how they work. Locks are a way for boats to go up hill or downhill. Without them, there would be waterfalls and a narrowboat can’t do those. If I were sailing my boat from where I’m walking in this video, I’d need to open the paddles with my windlass to let all of the water out. Once the water was out of the lock, I could open one gate (a narrowboat exactly fits through just one gate) and sail into the lock. I’d then close the gate and walk to the other end of the lock as I do in this video. I’d open the paddles at these gates and the water would now flow into the lock and my boat would rise up as the lock filled with water. Once it was full, I’d open a gate and sail out. A very simple system, but very clever. P.S. I have employed a swan to carry my video camera for these videos, as the duck I first employed couldn’t handle the weight of the camera and kept crashing into the water. The swan is better but as you’ll see at the beginning of the video, he’s so big that he crashes straight through the tree branches. There is a lot more training required and he was told off and debriefed after this video was made.
1:41

My walk by the canal this evening and I thought you might be interested to see a lock. I realised that some of you might never have seen one and I know everyone is fascinated as to how they work. Locks are a way for boats to go up hill or downhill. Without them, there would be waterfalls and a narrowboat can’t do those. If I were sailing my boat from where I’m walking in this video, I’d need to open the paddles with my windlass to let all of the water out. Once the water was out of the lock, I could open one gate (a narrowboat exactly fits through just one gate) and sail into the lock. I’d then close the gate and walk to the other end of the lock as I do in this video. I’d open the paddles at these gates and the water would now flow into the lock and my boat would rise up as the lock filled with water. Once it was full, I’d open a gate and sail out. A very simple system, but very clever. P.S. I have employed a swan to carry my video camera for these videos, as the duck I first employed couldn’t handle the weight of the camera and kept crashing into the water. The swan is better but as you’ll see at the beginning of the video, he’s so big that he crashes straight through the tree branches. There is a lot more training required and he was told off and debriefed after this video was made.

Alastair Hilton

21,907 views • 1 month ago