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🧊 Drake’s “Ice Box” installation how long would it last at around 41°F (5°C)? That massive structure, like Drake’s “Ice Box” installation in Toronto, wouldn’t melt quickly in cooler temps. At 41°F (5°C), the process slows way down. ❄️ Why it melts so slowly •🧊 Thermal mass: Hundreds of...

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Richard Feynman on the one idea that explains almost everything around you: 1. heat isn't a substance. it's motion. when something feels hot, its atoms are jiggling fast. when it's cold, they're jiggling slow. that's all temperature is... speed. 2. when a warm cup heats your hand, nothing actually flows into you. the fast atoms just bump the slow ones and speed them up. heat "spreading" is only jiggling passed along by touch. 3. atoms never lose energy. a bouncing ball slows and stops... but the motion doesn't disappear. it leaked into the floor. the ball goes still, the floor gets a little warmer. the motion is still there. just scrambled. 4. hammer a piece of metal long enough and it gets hot. you added no heat. you just jiggled it harder. pounding is heating. 5. a drop of water pulls into a ball because the atoms want partners on every side. the ones at the surface only have them below. so they keep clawing inward, trying to get in. that's what rounds the drop. 6. that same drop is slowly vanishing. atoms at the surface keep breaking free into the air, one by one, until none are left. you're watching them leave. 7. ice, water, and steam are the same atoms at different speeds. slow, and they lock in place like oranges in a crate... ice. faster, and they roll over each other... water. faster still, and they fly apart... steam. 8. pump up a bike tire and the pump gets hot. squeezing the air speeds the atoms up. let the air expand and it cools, because the atoms hit a piston that gives way and lose speed. heat and cold are hiding in a bicycle pump.
7:06

Richard Feynman on the one idea that explains almost everything around you: 1. heat isn't a substance. it's motion. when something feels hot, its atoms are jiggling fast. when it's cold, they're jiggling slow. that's all temperature is... speed. 2. when a warm cup heats your hand, nothing actually flows into you. the fast atoms just bump the slow ones and speed them up. heat "spreading" is only jiggling passed along by touch. 3. atoms never lose energy. a bouncing ball slows and stops... but the motion doesn't disappear. it leaked into the floor. the ball goes still, the floor gets a little warmer. the motion is still there. just scrambled. 4. hammer a piece of metal long enough and it gets hot. you added no heat. you just jiggled it harder. pounding is heating. 5. a drop of water pulls into a ball because the atoms want partners on every side. the ones at the surface only have them below. so they keep clawing inward, trying to get in. that's what rounds the drop. 6. that same drop is slowly vanishing. atoms at the surface keep breaking free into the air, one by one, until none are left. you're watching them leave. 7. ice, water, and steam are the same atoms at different speeds. slow, and they lock in place like oranges in a crate... ice. faster, and they roll over each other... water. faster still, and they fly apart... steam. 8. pump up a bike tire and the pump gets hot. squeezing the air speeds the atoms up. let the air expand and it cools, because the atoms hit a piston that gives way and lose speed. heat and cold are hiding in a bicycle pump.

Naruto

112,459 Aufrufe • vor 1 Monat

Bitcoin hashrate dropped ~20% in just a few days. Because extreme winter weather across the US punished weak mining operations. Here’s what actually breaks miners during a polar vortex (and how operators prepare) 👇 HOW WINTER BREAKS MINERS Cold doesn’t usually kill miners while they’re running. It kills them when: • machines cool down too fast • airflow isn’t controlled • miners restart while still cold Most damage happens during restarts and boot-ups, not steady-state hashing. HOW OPERATORS PROTECT HASHRATE 1️⃣ Control temperature swings (not just temperature) Rapid changes are the enemy. • Insulate containers and buildings to slow heat loss • Limit cold air intake until miners are hashing and warming themselves • Keep container lids closed during startup • Use hot-air feedback to warm machines gradually Goal: warm up slowly, cool down slowly. 2️⃣ Don’t let airflow work against you Airflow that’s great in summer can kill you in winter. Watch out for: • External fans pushing freezing air inside • Chimney effects that pull heat out too fast • Wide-open intakes during startup Common fixes: • Slow or pause intake fans during warm-up • Disrupt chimney effects temporarily • Use finer dust filters to reduce cold airflow Once machines are stable, airflow can come back. 3️⃣ Be careful when rebooting hashing miners Reboots in freezing temperatures are where most costly mistakes happen. Safer approach: • Reboot during the warmest part of the day • Do it one rack at a time • Keep lower shelves running so rising heat protects upper ones Rushing reboots saves minutes now and costs days later. 4️⃣ Booting miners that were offline is different For curtailment, demand response, or long downtime: ⚠️ Do NOT boot if boards are below 0°C What to do instead: • Warm machines gradually • Monitor ambient temperature inside the facility • Physically check miners if possible Cold hardware doesn’t fail immediately. It fails later. 5️⃣ Use firmware to avoid cold-start mistakes With Braiins OS, operators rely on: • Pre-heat to bring chips closer to target temperature before hashing • Fan speed vs chip temperature to confirm miners are actually warming up If fans are low and chip temperature isn’t rising, stop and warm miners more. Booting too early is the fastest way to lose hardware. 6️⃣ Network outages can freeze miners fast When internet drops, miners stop hashing and cool down quickly. Two common safeguards: • Multiple pool fallback URLs • A drain pool as a last resort to keep miners hashing and warm Drain pools burn power, but they can prevent far more expensive cold restarts. ❄️ THE COLD TRUTH Winter punishes poor preparation and rushed decisions. We’ve been running 2+ GW of live hashpower on Braiins OS through harsh winters. The patterns repeat every year. For the full deep dive, read the complete Polar Vortex guide on our blog. Mining is hard. Braiins OS is built for this.
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Bitcoin hashrate dropped ~20% in just a few days. Because extreme winter weather across the US punished weak mining operations. Here’s what actually breaks miners during a polar vortex (and how operators prepare) 👇 HOW WINTER BREAKS MINERS Cold doesn’t usually kill miners while they’re running. It kills them when: • machines cool down too fast • airflow isn’t controlled • miners restart while still cold Most damage happens during restarts and boot-ups, not steady-state hashing. HOW OPERATORS PROTECT HASHRATE 1️⃣ Control temperature swings (not just temperature) Rapid changes are the enemy. • Insulate containers and buildings to slow heat loss • Limit cold air intake until miners are hashing and warming themselves • Keep container lids closed during startup • Use hot-air feedback to warm machines gradually Goal: warm up slowly, cool down slowly. 2️⃣ Don’t let airflow work against you Airflow that’s great in summer can kill you in winter. Watch out for: • External fans pushing freezing air inside • Chimney effects that pull heat out too fast • Wide-open intakes during startup Common fixes: • Slow or pause intake fans during warm-up • Disrupt chimney effects temporarily • Use finer dust filters to reduce cold airflow Once machines are stable, airflow can come back. 3️⃣ Be careful when rebooting hashing miners Reboots in freezing temperatures are where most costly mistakes happen. Safer approach: • Reboot during the warmest part of the day • Do it one rack at a time • Keep lower shelves running so rising heat protects upper ones Rushing reboots saves minutes now and costs days later. 4️⃣ Booting miners that were offline is different For curtailment, demand response, or long downtime: ⚠️ Do NOT boot if boards are below 0°C What to do instead: • Warm machines gradually • Monitor ambient temperature inside the facility • Physically check miners if possible Cold hardware doesn’t fail immediately. It fails later. 5️⃣ Use firmware to avoid cold-start mistakes With Braiins OS, operators rely on: • Pre-heat to bring chips closer to target temperature before hashing • Fan speed vs chip temperature to confirm miners are actually warming up If fans are low and chip temperature isn’t rising, stop and warm miners more. Booting too early is the fastest way to lose hardware. 6️⃣ Network outages can freeze miners fast When internet drops, miners stop hashing and cool down quickly. Two common safeguards: • Multiple pool fallback URLs • A drain pool as a last resort to keep miners hashing and warm Drain pools burn power, but they can prevent far more expensive cold restarts. ❄️ THE COLD TRUTH Winter punishes poor preparation and rushed decisions. We’ve been running 2+ GW of live hashpower on Braiins OS through harsh winters. The patterns repeat every year. For the full deep dive, read the complete Polar Vortex guide on our blog. Mining is hard. Braiins OS is built for this.

Braiins

12,141 Aufrufe • vor 6 Monaten

With Y Combinator demo day coming up this weekend, I wanted to reflect on what I learned from YC W14, 10 years on + another company in: 1. The 7 minute espresso rule. Our first meeting with Sam Altman lasted just 7 minutes. The batch hadn’t even officially started yet and my cofounder Ryan Rowe and I drove down from Mountain View to the tiny SF YC outpost to meet him. Sam was making an espresso when we walked in. He opened with – “have you launched”? We said no, too many bugs. Our product Kimono Labs made it easy to just point and click to build a web scraper. Our promise was to get you an API in 60 seconds, without code. But it only worked on a handful of sites at the time. Sam pushed us to launched in 2 weeks. We debated. The espresso finished brewing, he picked it up, looked at us and said, “well, you better get going then and fix those bugs”. We left, launched in 2 weeks and learned one of the most important lessons that day - speed matters. Ship something you’re embarrassed by. 2. There are no experts. Ryan and I were not prepared for the rapid influx of user on launch day. It got tons of traffic on day 1 and we didn’t sleep in the next 48 hours bc servers and database kept crashing. We realized we weren’t the experts and needed to hire one. We went over to Michael Seibel for advice who smiled and told us that in the early days at SocialCam (Twitch) experts thought the streaming video problem was impossible. The answer wasn’t hiring an expert, but hiring someone young, capable and naïve enough to give it an earnest try. So we opted to just figure it out ourselves. 3. Messages in Pizza Boxes. Startups win through incredible customer service, then through product, not the other way around. Seibel told us how SocialCam’s streaming infrastructure went down while a key teammate was unreachable off-grid in a Tahoe cabin for the weekend. Normal people would have waited until Monday. Not Michael. He called a local pizza delivery place and asked the delivery person to send a large pizza with an urgent message in the box to the cabin. Their infrastructure was back up in hours. 4. The Twinkle can matter more than the TAM. Ambition matters just as much as practicality. Before demo day, we were struggling with the end of our pitch. We knew the value of our product, and had a fanatical and fast-growing user base, but the market size we calculated either seemed so ridiculously big that it was not plausible, or so narrow that it was equally silly. Paul Graham and Geoff Ralston sat with us and showed that us that if we can really pull it off at scale, it would be bigger than Google. PG suggested not talking about market size, but just making sure people could see the twinkle in our eyes when we talked about what you might be able to do with a structured copy of the internet that’s larger than Google’s. 5. You’re always at the Origin. Our demo day was successful beyond our wildest beliefs. Afterwards, Geoff drew a chart for us on a whiteboard. It was a hockey stick. He asked us where we thought we were. It was a rhetorical question. He said we were at the origin. Sam doubled down. When he invested in Kimono, he gave us a Zimbabwean Trillion dollar note (the result of extreme hyperinflation in Zimbabwe), as a cautionary reminder that the fundraising and valuation mean nothing. We have channeled this into our culture @TheArenaAI with our ritual around neon shoelaces and a pair of neon track spikes hanging on the wall to remind us that we’re at the Olympic starting line, but we don’t have any medals yet. 6. High bandwidth discussions with users don’t happen over email. The “Collison installation” is part of YC lore. John Collison told us that talking to users live was essential because email and chat conversations were just not enough. We did 100s of Skype conversations with Kimono users + one power user Alex Chung, who I met this way, has become a close friend and even attended my wedding in India last year!
0:39

With Y Combinator demo day coming up this weekend, I wanted to reflect on what I learned from YC W14, 10 years on + another company in: 1. The 7 minute espresso rule. Our first meeting with Sam Altman lasted just 7 minutes. The batch hadn’t even officially started yet and my cofounder Ryan Rowe and I drove down from Mountain View to the tiny SF YC outpost to meet him. Sam was making an espresso when we walked in. He opened with – “have you launched”? We said no, too many bugs. Our product Kimono Labs made it easy to just point and click to build a web scraper. Our promise was to get you an API in 60 seconds, without code. But it only worked on a handful of sites at the time. Sam pushed us to launched in 2 weeks. We debated. The espresso finished brewing, he picked it up, looked at us and said, “well, you better get going then and fix those bugs”. We left, launched in 2 weeks and learned one of the most important lessons that day - speed matters. Ship something you’re embarrassed by. 2. There are no experts. Ryan and I were not prepared for the rapid influx of user on launch day. It got tons of traffic on day 1 and we didn’t sleep in the next 48 hours bc servers and database kept crashing. We realized we weren’t the experts and needed to hire one. We went over to Michael Seibel for advice who smiled and told us that in the early days at SocialCam (Twitch) experts thought the streaming video problem was impossible. The answer wasn’t hiring an expert, but hiring someone young, capable and naïve enough to give it an earnest try. So we opted to just figure it out ourselves. 3. Messages in Pizza Boxes. Startups win through incredible customer service, then through product, not the other way around. Seibel told us how SocialCam’s streaming infrastructure went down while a key teammate was unreachable off-grid in a Tahoe cabin for the weekend. Normal people would have waited until Monday. Not Michael. He called a local pizza delivery place and asked the delivery person to send a large pizza with an urgent message in the box to the cabin. Their infrastructure was back up in hours. 4. The Twinkle can matter more than the TAM. Ambition matters just as much as practicality. Before demo day, we were struggling with the end of our pitch. We knew the value of our product, and had a fanatical and fast-growing user base, but the market size we calculated either seemed so ridiculously big that it was not plausible, or so narrow that it was equally silly. Paul Graham and Geoff Ralston sat with us and showed that us that if we can really pull it off at scale, it would be bigger than Google. PG suggested not talking about market size, but just making sure people could see the twinkle in our eyes when we talked about what you might be able to do with a structured copy of the internet that’s larger than Google’s. 5. You’re always at the Origin. Our demo day was successful beyond our wildest beliefs. Afterwards, Geoff drew a chart for us on a whiteboard. It was a hockey stick. He asked us where we thought we were. It was a rhetorical question. He said we were at the origin. Sam doubled down. When he invested in Kimono, he gave us a Zimbabwean Trillion dollar note (the result of extreme hyperinflation in Zimbabwe), as a cautionary reminder that the fundraising and valuation mean nothing. We have channeled this into our culture @TheArenaAI with our ritual around neon shoelaces and a pair of neon track spikes hanging on the wall to remind us that we’re at the Olympic starting line, but we don’t have any medals yet. 6. High bandwidth discussions with users don’t happen over email. The “Collison installation” is part of YC lore. John Collison told us that talking to users live was essential because email and chat conversations were just not enough. We did 100s of Skype conversations with Kimono users + one power user Alex Chung, who I met this way, has become a close friend and even attended my wedding in India last year!

Pratap Ranade

143,089 Aufrufe • vor 2 Jahren

Sea Levels Not Surging Despite Years Of Climate Activists And Corporate Media Freaking Out, Study Finds | Audrey Streb, Daily Caller News Foundation One recent study compiling sea level rise data shows oceans are not surging as much as the scientific world previously projected and corporate media has repeatedly sounded the alarm over. The Journal of Marine Science and Engineering published the peer-reviewed study on Aug. 27, authored by Dutch engineering consultant Hessel G. Voortman and independent researcher Rob De Vos. The study concluded that the average rate of sea level rise in 2020 was well below other widely cited analyses, and that when projections were compared with local data, there was little evidence climate change was driving the acceleration seen in a few regions — a finding energy policy experts told the Daily Caller News Foundation challenges mainstream climate change orthodoxy. “Overall, this study indicates that in most places, sea levels are not rising unusually quickly. In the relatively few locations where sea levels are rising faster than average, the cause is almost certainly local factors such as land subsidence or ground compaction,” Sterling Burnett, director of the Arthur B. Robinson Center on Climate and Environmental Policy at The Heartland Institute, told the DCNF. “Global sea levels are currently rising more slowly than they have for much of the time since the last ice age ended — a period during which seas rose more than 400 feet. Any possible increase in the recent rate of rise compared with the past century is small, within the margin of error, and not outside historical patterns.” Though the report doesn’t account for sea level rise everywhere, Voortman told journalist Michael Shellenberger on Tuesday that he was surprised no one had compiled such a study before, noting it is the first to compare projections with recorded local data from the past century. The two data sets the researchers drew from did have some gaps, which meant that most sections in the world with usable data were in the Northern Hemisphere, with several of the “selected stations” spanning North America, Europe and Japan. Gaps in the data included regions around Australia, the northeast of Latin America, East Asia and most of Africa. “The average rate of sea level rise in 2020 is (only) around 1.5 mm/year (15 cm per century)” Voortman said Tuesday. “This is significantly lower than the 3 to 4 mm/year often reported by climate scientists in scientific literature and the media.” The report notes that in the data sets the researchers used, “approximately 95% of the suitable locations show no statistically significant acceleration of the rate of sea level rise,” and that regions that did see a spike in sea level likely “local, non-climatic phenomena are a plausible cause of the accelerated sea level rise observed at the remaining 5% of the suitable locations.” “It is crazy that it had not been done. … I started doing this research in 2021 by doing the literature review. ‘Who has done the comparison of the projections with the observations?’ And there were none,” Voortman said Tuesday. “I had to do a lot of programming and automate data imports and data management. I organized it by using databases so that I really knew what I was doing. It was very structured because I was dealing with 150,000 locations and, on average, 100 years of data. That made one and a half million lines of data. I found myself for days working on things that I felt, ‘This is more computer science than civil engineering.'” Steve Milloy, senior fellow at the Energy & Environment Legal Institute, told the DCNF that “there are a lot of additional factors that can affect tide gauge measurements including geological changes, groundwater withdrawal, and land use. But climate alarmists falsely chalk up all changes to polar ice melting caused by emissions-driven ‘global warming.'” A few other studies have been published of late that also challenge climate change hysteria, with one widely reported study showing that Arctic Sea ice melting has slowed in the last 20 years. Another recent report found that a 2024 climate change study — heavily cited by legacy media for projecting up to $38 trillion in global damages by 2050 — relied on inaccurate data.
4:31

Sea Levels Not Surging Despite Years Of Climate Activists And Corporate Media Freaking Out, Study Finds | Audrey Streb, Daily Caller News Foundation One recent study compiling sea level rise data shows oceans are not surging as much as the scientific world previously projected and corporate media has repeatedly sounded the alarm over. The Journal of Marine Science and Engineering published the peer-reviewed study on Aug. 27, authored by Dutch engineering consultant Hessel G. Voortman and independent researcher Rob De Vos. The study concluded that the average rate of sea level rise in 2020 was well below other widely cited analyses, and that when projections were compared with local data, there was little evidence climate change was driving the acceleration seen in a few regions — a finding energy policy experts told the Daily Caller News Foundation challenges mainstream climate change orthodoxy. “Overall, this study indicates that in most places, sea levels are not rising unusually quickly. In the relatively few locations where sea levels are rising faster than average, the cause is almost certainly local factors such as land subsidence or ground compaction,” Sterling Burnett, director of the Arthur B. Robinson Center on Climate and Environmental Policy at The Heartland Institute, told the DCNF. “Global sea levels are currently rising more slowly than they have for much of the time since the last ice age ended — a period during which seas rose more than 400 feet. Any possible increase in the recent rate of rise compared with the past century is small, within the margin of error, and not outside historical patterns.” Though the report doesn’t account for sea level rise everywhere, Voortman told journalist Michael Shellenberger on Tuesday that he was surprised no one had compiled such a study before, noting it is the first to compare projections with recorded local data from the past century. The two data sets the researchers drew from did have some gaps, which meant that most sections in the world with usable data were in the Northern Hemisphere, with several of the “selected stations” spanning North America, Europe and Japan. Gaps in the data included regions around Australia, the northeast of Latin America, East Asia and most of Africa. “The average rate of sea level rise in 2020 is (only) around 1.5 mm/year (15 cm per century)” Voortman said Tuesday. “This is significantly lower than the 3 to 4 mm/year often reported by climate scientists in scientific literature and the media.” The report notes that in the data sets the researchers used, “approximately 95% of the suitable locations show no statistically significant acceleration of the rate of sea level rise,” and that regions that did see a spike in sea level likely “local, non-climatic phenomena are a plausible cause of the accelerated sea level rise observed at the remaining 5% of the suitable locations.” “It is crazy that it had not been done. … I started doing this research in 2021 by doing the literature review. ‘Who has done the comparison of the projections with the observations?’ And there were none,” Voortman said Tuesday. “I had to do a lot of programming and automate data imports and data management. I organized it by using databases so that I really knew what I was doing. It was very structured because I was dealing with 150,000 locations and, on average, 100 years of data. That made one and a half million lines of data. I found myself for days working on things that I felt, ‘This is more computer science than civil engineering.'” Steve Milloy, senior fellow at the Energy & Environment Legal Institute, told the DCNF that “there are a lot of additional factors that can affect tide gauge measurements including geological changes, groundwater withdrawal, and land use. But climate alarmists falsely chalk up all changes to polar ice melting caused by emissions-driven ‘global warming.'” A few other studies have been published of late that also challenge climate change hysteria, with one widely reported study showing that Arctic Sea ice melting has slowed in the last 20 years. Another recent report found that a 2024 climate change study — heavily cited by legacy media for projecting up to $38 trillion in global damages by 2050 — relied on inaccurate data.

Owen Gregorian

105,142 Aufrufe • vor 11 Monaten

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

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

Jamal ☯︎ 🔆🧘🏽🧠

13,149 Aufrufe • vor 4 Monaten

*** Test Your 9/11 Knowledge: The Explosive Evidence at the 3 WTC Towers The 50 Questions NIST Should Have Asked 20 Years Ago! WTC Building 7 Free-fall 1. How is it possible that 47-story Building 7 fell suddenly, symmetrically in free-fall acceleration, without any resistance from any of its 81 columns? 2. Why did NIST deny its free-fall for 7 years, only to be proven wrong and be forced to officially admit that it did collapse in free-fall? Symmetry 3. How, if Building 7 was damaged asymmetrically in the north-east corner on floor twelve, as per the NIST report, could it fall symmetrically downward? Shouldn’t the building have tilted toward its damaged side – and not fall straight down through the path of what was the greatest resistance? Fires 4. How could a few, small, and scattered ordinary office fires have brought this Type-1 fire-protected steel-frame skyscraper down, when several dozen examples of much hotter, much larger, and longer-lasting fires have never in history brought down such a building? 5. How could normal office fires take out all the columns in the building sequentially floor by floor, in 7 seconds? 6. Why did NIST claim that the fires were still burning, up until the time of the collapse, when the photos show that they were burnt out more than an hour before the collapse? 7. Why aren’t all the firefighters concerned, in the wake of the NIST report during the last 24 years, that such ordinary fightable fires can now bring skyscrapers down on top of them, and on top of the public who are told to “defend in place” in the building (and not obstruct access by firefighters)? 8. Why are many of these same firefighters calling for a new investigation of the NIST report itself? Controlled Demolition 9. Since the collapse of Building 7 looks exactly like a controlled demolition, why did NIST avoid any serious consideration of this hypothesis? 10. How could a 40,000-ton moment-resisting and X-braced structural steel frame collapse like a house of cards in 7 seconds, with most of its columns and beams severed – one from another? 11. Why does WTC 7 have all of the key features of typical controlled demolition, and none of the features of collapse by fire? Explosions 12. Why didn’t NIST include in its report on WTC 7 the half-dozen witnesses of explosions prior to its collapse, and even claim that there were no witnesses? 13. What could have caused an elevator cab to be “blown 30 feet out of its hoistway,” as Deputy Director of NY-Office of Emergency Management, Richard Rotanz, reported at Noon, when the building didn’t collapse for another 5 hours. 14. What caused Barry Jennings and Michael Hess to be injured by explosions and subsequently trapped in the building before either Twin Tower collapsed? Foreknowledge 15. Why did Fire Chief Nick Visconti declare, “We’re moving the command post over this way, that building’s coming down!”? 16. How could Fire Chief Hayden’s engineer declare, upon being asked, “how long until the building comes down?” – then accurately state, “In its current state you have about 5 hours,” when no steel-frame fire-protected high-rise had ever come down due to fire alone? 17. Why did construction workers, while walking away from Building 7 and upon hearing an explosion from the building, look straight into the CNN camera saying, “You hear that? Keep your eye on that building. That thing’s coming down. The building is about to blow up, flame and debris coming down”? 18. Why did former Air Force medic Kevin McPadden hear a “3-2-1” countdown on the radio, and subsequently hear explosions before Building 7 collapsed? 19. How could the BBC have announced, live on TV, the collapse of WTC 7 20 minutes before it collapsed? 20. Why did CNN announce, 7 hours early, the 10:45 AM collapse of a 50-story building (obviously referring to Building 7)? Expert Statements 21. Why have more than 3,600 Architects & Engineers signed onto the petition at demanding a new 9/11 WTC investigation? 22. Why are dozens of structural engineers making statements such as: “A localized failure in a steel-framed building like WTC 7 cannot cause a catastrophic collapse like a house of cards, without a simultaneous and patterned loss of several of its columns at key locations within the building”? 23. Why did the top European controlled demolition expert declare: “That is controlled demolition. It’s been imploded. It’s a hired job. A team of experts did this? 24. Why did top forensic structural engineer, Prof. Leroy Hulsey from the University of Alaska, following a 4-year study of WTC 7, declare: “The collapse of WTC 7 was a global failure involving the near-simultaneous failure of all columns in the building and not a progressive collapse, as claimed by NIST. Extreme Heat Molten Metal 25. What does it mean that FEMA, in its 2002 Report, including a metallurgical examination of the WTC 7 steel, revealed “a phenomenon never before observed in building fires….a liquid eutectic mixture containing primarily iron, oxygen, and sulfur formed during this hot corrosion attack on the steel...” Why did NIST eliminate this metallurgical report from their final report? 26. Did Fire Protection Engineer Jonathan Barnett know, when he said, “steel members in the debris pile that appear to have been partly evaporated,” that it takes 4,000°F to evaporate steel? And that jet fuel and office fires don’t even rise to a third of that temperature? 27. Why is there bright yellow molten steel or iron pouring out of the crab claw excavators in the WTC pit? And out of the South Tower just minutes before its collapse. 28. Why did the first responders in the pit report, “you get down in the pile, and you see molten steel – flowing down the channel rails, like lava from a volcano”? Did they know that it takes 3,000°F to melt steel, and that office fires and jet fuel can only achieve half of this temperature? 29. What can explain the well-documented 3,000°F temperatures that are well-documented in the WTC Twin Towers collapse aftermath? Why is there evidence of ignited thermite found by so many first responders in the WTC pile? Previously Molten Iron Microspheres 30. What does it mean that the US Geological Survey and RJ Lee Group independently documented billions of previously molten iron-rich microspheres in ALL of the WTC dust samples? Where would the required 3,000°F come from? Could the ignited thermite have created those molten iron microspheres? 31. Why is bright yellow molten steel or iron pouring out of the South Tower just minutes prior to its collapse? 32. What explains the 2009 peer-reviewed findings from the Niels Harrit research team of dual-layered red-gray chips of nano-thermite in all the independently-collected dust samples they analyzed? Why do they ignite at the same temperature as military grade “super-thermite”? Why do they produce molten iron-rich microspheres when ignited? 33. What does it mean that Harrit’s international research team found that the “red layer of the red/gray chips in all of their WTC dust samples is active unreacted thermitic material, incorporating nanotechnology, and is a highly energetic pyrotechnic or explosive material”? The Twin Towers Official Explanation 34. How can the official explanation of the Twin Towers’ collapse be true (that an intact top section drove down the rest of the building after weakening of some of the structural steel in the impact zone) when this top section had already been destroyed in the first 3 seconds of the collapse (telescoping in on itself) and so was not even available to drive anything down to the ground? NIST claims that the top part of the building drove the rest of the building down to the ground. Why then do none of the photos or videos show such a top part driving anything down? And why didn’t that top “pile driver” drive down the 800-foot-tall group of columns standing for 6 seconds after the overall collapse? 35. Why did Zdenek Bazant, in his calculations for his controversial paper submitted to the Journal of Engineering Mechanics on 9/13/01( only two days after 9/11) use twice the actual mass of the upper section of the North Tower above the impact floors and only one third of the actual column strength of the larger building section beneath it in his support for NIST collapse theory? a. Why is this paper still today the key theoretical basis of NIST’s column failure theory? 36. Why does the destruction of the towers look more like a volcanic eruption (than a straight-down gravitational collapse) with upward and outward arching streamers, a geometry of fireworks, freely flying solid molten objects trailing thick white smoke clouds? Witnesses of Explosions 37. Why are there 156 First Responder witnesses of explosions – seeing, hearing, and feeling explosions – many of them BEFORE the towers ever came down? 38. Why did NIST claim that there were “no witnesses of explosions” when there were as many as 200 publicly recorded testimonies – many before the collapse? What could explain Fire Chief Frank Cruthers’ testimony that, “… an explosion… appeared at the very top, simultaneously from all four sides, materials shot out horizontally. And then there seemed to be a momentary delay, before you could see the beginning of the collapse”? 39. Why did 36 reporters on the day of 9/11 report the WTC destruction as an explosion-based event, most of them actual witnesses of explosions? a. Why did the mainstream national media change the story the next day from explosion-based collapses to “fire-induced collapses”? 40. Why did the FBI, NYPD, and FDNY on the day of 9/11 all state that they suspected that explosives were used to bring down the towers, but change their story in the following week to fire-induced collapse? Seismic Evidence 41. Why did the Richter Scale recordings from Lamont Doherty Earth Observatory document significant seismic events for both towers, more than a dozen seconds before the planes hit either tower – corroborating the explosive testimony of William Rodriguez and others of massive explosions in the basement prior to the plane hitting the buildings? 42. Why did the seismic evidence from Lamont Doherty Earth Observatory document significant seismic events, in the North Tower, 5 seconds before the heaviest debris from each tower struck the ground? And in the South Tower, 7 seconds before any debris struck the ground? Wouldn’t this seismic evidence corroborate the testimony of the first responders that saw, heard, and/or felt explosions before the towers fell? 43. Why did at least 3 of the tripod-mounted cameras (two on the ground and one on the rooftop) “shake” 3 to 10 seconds before each of the towers fell? Would the camera evidence corroborate the seismic evidence and the first responder's explosive testimony? Explosive Evidence 44. Since the damage from the planes and fires was so asymmetrical, why was the destruction itself so precisely symmetrical – all the way down each face of each tower? Why do the videos show precise rows of individual explosions progressing down the towers – floor by floor? 45. Why do we see in the videos isolated pin-point explosive ejections occurring 20, 40, and even 60 stories down below the downward-traveling zone of destruction in each tower? Descent Profile and Speed 46. Why did the top sections of each tower descend suddenly, smoothly, down with no stoppage or “jolt” upon impact with the cold, hard, intact steel columns below the floors of the plane impacts? 47. How was it possible that the top section of each Tower descended without slowing at all, but instead accelerated, as if 80,000 tons of steel beneath wasn’t even there? What happened to the steel? Lateral Ejection of Steel 48. Why do we see in the videos the lateral ejection out of both of the Towers of hundreds of freely flying structural steel sections each weighing 4 to 8 tons, at 80mph, landing up to 600 feet in every direction, impaling all of the surrounding skyscrapers? Why are they trailing thick white smoke clouds when steel is not flammable in office fires, or under jet fuel conditions? Could this be due to the other byproduct of thermite – aluminum oxide ash? 49. Since FEMA officially documented a 1200-foot diameter zone of flying, fallen, and impaled structural steel beyond the footprints of both Towers, how could that steel, which comprised 1/3 of the weight of the falling section of each building, have still been available to crush the lower part as NIST claimed? Missing Floors 50. Since there were 110 concrete floors, each an acre in size, and since they were not stacked up in pile of “pancakes” at the bottom, and since a third of the WTC dust in the 3” thick blanket across Lower Manhattan from river to river is powdered concrete, then how could the concrete floors (also 1/3 of the weight of each Tower) be available to crush the building below? 51. What extreme-high temperature could have reduced 90,000 tons of concrete in each Tower back to its original aggregate, sand, and cement powder? Demolition Access 52. How could the perpetrators have gained access to the Towers to plant high energy explosives and incendiaries? Could a massive fireproofing upgrade project in the months and years prior to 9/11 have provided access to the underside of the floor systems to apply sprayed-on nano-thermite? Is it a coincidence that the WTC fireproofing upgrades occurred mostly on the floors that were hit by the planes on 9/11? Could the largest elevator modernization in the world in the 9 months prior to 9/11 have provided access to the core columns and beams? Is it just a coincidence that Ace Elevator employees were pulled out of the Towers on 9/11 for “union meeting”? Destruction of Evidence 53. Why was 99% of the WTC structural steel crime scene evidence loaded onto barges starting just 2 weeks after 9/11 and shipped to China for recycling before structural engineers and metallurgists could get their hands on it to do a proper forensic investigation? We encougage you to ask these questions of your elected representatives and the media. We address most of these questions in our presentations and podcast and radio interviews. So get is in front of them! Who do you know that might interview RichardGage911 about the explosive destruction of the 3 World Trade Center Skyscrapers on 9/11?
0:22

*** Test Your 9/11 Knowledge: The Explosive Evidence at the 3 WTC Towers The 50 Questions NIST Should Have Asked 20 Years Ago! WTC Building 7 Free-fall 1. How is it possible that 47-story Building 7 fell suddenly, symmetrically in free-fall acceleration, without any resistance from any of its 81 columns? 2. Why did NIST deny its free-fall for 7 years, only to be proven wrong and be forced to officially admit that it did collapse in free-fall? Symmetry 3. How, if Building 7 was damaged asymmetrically in the north-east corner on floor twelve, as per the NIST report, could it fall symmetrically downward? Shouldn’t the building have tilted toward its damaged side – and not fall straight down through the path of what was the greatest resistance? Fires 4. How could a few, small, and scattered ordinary office fires have brought this Type-1 fire-protected steel-frame skyscraper down, when several dozen examples of much hotter, much larger, and longer-lasting fires have never in history brought down such a building? 5. How could normal office fires take out all the columns in the building sequentially floor by floor, in 7 seconds? 6. Why did NIST claim that the fires were still burning, up until the time of the collapse, when the photos show that they were burnt out more than an hour before the collapse? 7. Why aren’t all the firefighters concerned, in the wake of the NIST report during the last 24 years, that such ordinary fightable fires can now bring skyscrapers down on top of them, and on top of the public who are told to “defend in place” in the building (and not obstruct access by firefighters)? 8. Why are many of these same firefighters calling for a new investigation of the NIST report itself? Controlled Demolition 9. Since the collapse of Building 7 looks exactly like a controlled demolition, why did NIST avoid any serious consideration of this hypothesis? 10. How could a 40,000-ton moment-resisting and X-braced structural steel frame collapse like a house of cards in 7 seconds, with most of its columns and beams severed – one from another? 11. Why does WTC 7 have all of the key features of typical controlled demolition, and none of the features of collapse by fire? Explosions 12. Why didn’t NIST include in its report on WTC 7 the half-dozen witnesses of explosions prior to its collapse, and even claim that there were no witnesses? 13. What could have caused an elevator cab to be “blown 30 feet out of its hoistway,” as Deputy Director of NY-Office of Emergency Management, Richard Rotanz, reported at Noon, when the building didn’t collapse for another 5 hours. 14. What caused Barry Jennings and Michael Hess to be injured by explosions and subsequently trapped in the building before either Twin Tower collapsed? Foreknowledge 15. Why did Fire Chief Nick Visconti declare, “We’re moving the command post over this way, that building’s coming down!”? 16. How could Fire Chief Hayden’s engineer declare, upon being asked, “how long until the building comes down?” – then accurately state, “In its current state you have about 5 hours,” when no steel-frame fire-protected high-rise had ever come down due to fire alone? 17. Why did construction workers, while walking away from Building 7 and upon hearing an explosion from the building, look straight into the CNN camera saying, “You hear that? Keep your eye on that building. That thing’s coming down. The building is about to blow up, flame and debris coming down”? 18. Why did former Air Force medic Kevin McPadden hear a “3-2-1” countdown on the radio, and subsequently hear explosions before Building 7 collapsed? 19. How could the BBC have announced, live on TV, the collapse of WTC 7 20 minutes before it collapsed? 20. Why did CNN announce, 7 hours early, the 10:45 AM collapse of a 50-story building (obviously referring to Building 7)? Expert Statements 21. Why have more than 3,600 Architects & Engineers signed onto the petition at demanding a new 9/11 WTC investigation? 22. Why are dozens of structural engineers making statements such as: “A localized failure in a steel-framed building like WTC 7 cannot cause a catastrophic collapse like a house of cards, without a simultaneous and patterned loss of several of its columns at key locations within the building”? 23. Why did the top European controlled demolition expert declare: “That is controlled demolition. It’s been imploded. It’s a hired job. A team of experts did this? 24. Why did top forensic structural engineer, Prof. Leroy Hulsey from the University of Alaska, following a 4-year study of WTC 7, declare: “The collapse of WTC 7 was a global failure involving the near-simultaneous failure of all columns in the building and not a progressive collapse, as claimed by NIST. Extreme Heat Molten Metal 25. What does it mean that FEMA, in its 2002 Report, including a metallurgical examination of the WTC 7 steel, revealed “a phenomenon never before observed in building fires….a liquid eutectic mixture containing primarily iron, oxygen, and sulfur formed during this hot corrosion attack on the steel...” Why did NIST eliminate this metallurgical report from their final report? 26. Did Fire Protection Engineer Jonathan Barnett know, when he said, “steel members in the debris pile that appear to have been partly evaporated,” that it takes 4,000°F to evaporate steel? And that jet fuel and office fires don’t even rise to a third of that temperature? 27. Why is there bright yellow molten steel or iron pouring out of the crab claw excavators in the WTC pit? And out of the South Tower just minutes before its collapse. 28. Why did the first responders in the pit report, “you get down in the pile, and you see molten steel – flowing down the channel rails, like lava from a volcano”? Did they know that it takes 3,000°F to melt steel, and that office fires and jet fuel can only achieve half of this temperature? 29. What can explain the well-documented 3,000°F temperatures that are well-documented in the WTC Twin Towers collapse aftermath? Why is there evidence of ignited thermite found by so many first responders in the WTC pile? Previously Molten Iron Microspheres 30. What does it mean that the US Geological Survey and RJ Lee Group independently documented billions of previously molten iron-rich microspheres in ALL of the WTC dust samples? Where would the required 3,000°F come from? Could the ignited thermite have created those molten iron microspheres? 31. Why is bright yellow molten steel or iron pouring out of the South Tower just minutes prior to its collapse? 32. What explains the 2009 peer-reviewed findings from the Niels Harrit research team of dual-layered red-gray chips of nano-thermite in all the independently-collected dust samples they analyzed? Why do they ignite at the same temperature as military grade “super-thermite”? Why do they produce molten iron-rich microspheres when ignited? 33. What does it mean that Harrit’s international research team found that the “red layer of the red/gray chips in all of their WTC dust samples is active unreacted thermitic material, incorporating nanotechnology, and is a highly energetic pyrotechnic or explosive material”? The Twin Towers Official Explanation 34. How can the official explanation of the Twin Towers’ collapse be true (that an intact top section drove down the rest of the building after weakening of some of the structural steel in the impact zone) when this top section had already been destroyed in the first 3 seconds of the collapse (telescoping in on itself) and so was not even available to drive anything down to the ground? NIST claims that the top part of the building drove the rest of the building down to the ground. Why then do none of the photos or videos show such a top part driving anything down? And why didn’t that top “pile driver” drive down the 800-foot-tall group of columns standing for 6 seconds after the overall collapse? 35. Why did Zdenek Bazant, in his calculations for his controversial paper submitted to the Journal of Engineering Mechanics on 9/13/01( only two days after 9/11) use twice the actual mass of the upper section of the North Tower above the impact floors and only one third of the actual column strength of the larger building section beneath it in his support for NIST collapse theory? a. Why is this paper still today the key theoretical basis of NIST’s column failure theory? 36. Why does the destruction of the towers look more like a volcanic eruption (than a straight-down gravitational collapse) with upward and outward arching streamers, a geometry of fireworks, freely flying solid molten objects trailing thick white smoke clouds? Witnesses of Explosions 37. Why are there 156 First Responder witnesses of explosions – seeing, hearing, and feeling explosions – many of them BEFORE the towers ever came down? 38. Why did NIST claim that there were “no witnesses of explosions” when there were as many as 200 publicly recorded testimonies – many before the collapse? What could explain Fire Chief Frank Cruthers’ testimony that, “… an explosion… appeared at the very top, simultaneously from all four sides, materials shot out horizontally. And then there seemed to be a momentary delay, before you could see the beginning of the collapse”? 39. Why did 36 reporters on the day of 9/11 report the WTC destruction as an explosion-based event, most of them actual witnesses of explosions? a. Why did the mainstream national media change the story the next day from explosion-based collapses to “fire-induced collapses”? 40. Why did the FBI, NYPD, and FDNY on the day of 9/11 all state that they suspected that explosives were used to bring down the towers, but change their story in the following week to fire-induced collapse? Seismic Evidence 41. Why did the Richter Scale recordings from Lamont Doherty Earth Observatory document significant seismic events for both towers, more than a dozen seconds before the planes hit either tower – corroborating the explosive testimony of William Rodriguez and others of massive explosions in the basement prior to the plane hitting the buildings? 42. Why did the seismic evidence from Lamont Doherty Earth Observatory document significant seismic events, in the North Tower, 5 seconds before the heaviest debris from each tower struck the ground? And in the South Tower, 7 seconds before any debris struck the ground? Wouldn’t this seismic evidence corroborate the testimony of the first responders that saw, heard, and/or felt explosions before the towers fell? 43. Why did at least 3 of the tripod-mounted cameras (two on the ground and one on the rooftop) “shake” 3 to 10 seconds before each of the towers fell? Would the camera evidence corroborate the seismic evidence and the first responder's explosive testimony? Explosive Evidence 44. Since the damage from the planes and fires was so asymmetrical, why was the destruction itself so precisely symmetrical – all the way down each face of each tower? Why do the videos show precise rows of individual explosions progressing down the towers – floor by floor? 45. Why do we see in the videos isolated pin-point explosive ejections occurring 20, 40, and even 60 stories down below the downward-traveling zone of destruction in each tower? Descent Profile and Speed 46. Why did the top sections of each tower descend suddenly, smoothly, down with no stoppage or “jolt” upon impact with the cold, hard, intact steel columns below the floors of the plane impacts? 47. How was it possible that the top section of each Tower descended without slowing at all, but instead accelerated, as if 80,000 tons of steel beneath wasn’t even there? What happened to the steel? Lateral Ejection of Steel 48. Why do we see in the videos the lateral ejection out of both of the Towers of hundreds of freely flying structural steel sections each weighing 4 to 8 tons, at 80mph, landing up to 600 feet in every direction, impaling all of the surrounding skyscrapers? Why are they trailing thick white smoke clouds when steel is not flammable in office fires, or under jet fuel conditions? Could this be due to the other byproduct of thermite – aluminum oxide ash? 49. Since FEMA officially documented a 1200-foot diameter zone of flying, fallen, and impaled structural steel beyond the footprints of both Towers, how could that steel, which comprised 1/3 of the weight of the falling section of each building, have still been available to crush the lower part as NIST claimed? Missing Floors 50. Since there were 110 concrete floors, each an acre in size, and since they were not stacked up in pile of “pancakes” at the bottom, and since a third of the WTC dust in the 3” thick blanket across Lower Manhattan from river to river is powdered concrete, then how could the concrete floors (also 1/3 of the weight of each Tower) be available to crush the building below? 51. What extreme-high temperature could have reduced 90,000 tons of concrete in each Tower back to its original aggregate, sand, and cement powder? Demolition Access 52. How could the perpetrators have gained access to the Towers to plant high energy explosives and incendiaries? Could a massive fireproofing upgrade project in the months and years prior to 9/11 have provided access to the underside of the floor systems to apply sprayed-on nano-thermite? Is it a coincidence that the WTC fireproofing upgrades occurred mostly on the floors that were hit by the planes on 9/11? Could the largest elevator modernization in the world in the 9 months prior to 9/11 have provided access to the core columns and beams? Is it just a coincidence that Ace Elevator employees were pulled out of the Towers on 9/11 for “union meeting”? Destruction of Evidence 53. Why was 99% of the WTC structural steel crime scene evidence loaded onto barges starting just 2 weeks after 9/11 and shipped to China for recycling before structural engineers and metallurgists could get their hands on it to do a proper forensic investigation? We encougage you to ask these questions of your elected representatives and the media. We address most of these questions in our presentations and podcast and radio interviews. So get is in front of them! Who do you know that might interview RichardGage911 about the explosive destruction of the 3 World Trade Center Skyscrapers on 9/11?

Richard Gage, AIA, Architect

44,559 Aufrufe • vor 1 Jahr

Why does ☣️ Pleb Kruse = BTC foundationalist in exile 🟩🔆 say there are 3 pillars that restore sleep cycles? Imagine those 3 pillars as a clock ⏰: 1️⃣ ☀️ Sunlight = the hour hand 2️⃣ 🌑 Darkness = the minute hand 3️⃣ ❄️ Temperature = the second hand If one hand is off, the whole clock is out of sync and sleep apnea occur. 🤔 How this works? 1️⃣☀️LIGHT Morning Sun Programs Our Brainstem. Sunrise light hits our retina and tells the SCN (suprachiasmatic nucleus, the master clock in our brain) to start the 24-hour program That program sets: ➡️ when cortisol should peak ➡️ when melatonin is built ➡️ when body temperature should rise or fall ➡️ when breathing rhythm should stabilize ➡️ how our autonomic nervous system fires,… If we miss sunrise… ⚠️ Our internal 24-hour clock becomes corrupted. This is a circadian software glitch. A glitch in this program = apnea later at night. And it’s not only about seeing the sunrise once. Living in sunlight during the day is the number one upgrade for almost everything, not just sleep apnea or “vitamin D” 2️⃣🌑DARKNESS Melatonin is the Breath-Stabilizing Molecule Darkness is not “the absence of light.” Darkness is a signal When the retina senses real darkness, the brain says: “Release the melatonin we built all day long.” Melatonin is the molecule of repair: 💚lowers inflammation 💚 increases mitochondrial efficiency 💚 stabilizes CO₂ sensing in the brainstem 💚 keeps airway muscles open 💚 synchronizes the breathing rhythm Without true darkness: ➡️ melatonin stays trapped = not released ➡️ the brainstem cannot hold a stable breathing pattern ➡️ apnea starts Blue light at night destroys CO₂ sensing; the primary signal our brainstem uses to trigger breathing and: ⚠️ circadian mismatch ⚠️ melatonin drops ⚠️ brainstem mitochondria lose energy ⚠️ they stop sensing CO₂ correctly 😳 When CO2, sensing fails, we stop breathing and wake up over and over without knowing Fix it: 🕯️candles at night 🧥Cover your skin from artificial light 😎Wear blue-blocking glasses 😴 Be asleep before 10pm Dr Jack says this often: “Melatonin is the CEO of nighttime repair” No CEO = chaos 3️⃣❄️TEMPERATURE The Final Switch That Lets Melatonin Work This is the part we heard on the podcast and very few people really understand. Even if: we get sunrise we have perfect darkness …melatonin still won’t release unless our core temperature drops at night. Why? Because we evolved with hot days and cool nights. Temperature is the ⏰ seconds hand. If we can’t cool down, melatonin won’t rise = sleep breaks and apnea shows up. ❄️ How to drop temperature at night? ✅ 🕯️ after sunset ✅ Open windows and let fresh air in ✅ AC? Ok, just keep it in another room. The bedroom should stay cool (16–19°C / 60–67°F) and free of electronics ✅ Use cotton, linen, bamboo, or wool bedsheets = breathe and don’t trap heat. ✅ Grounding reduces inflammation, makes it easier for the body to cool ✅ Cold face dunk: Fill a bowl with cold tap water (or a few ice cubes) Hold your breath, put the face in for 5–10 seconds. Repeat 2–3 times. This activates the mammalian diving reflex and lowers: heart rate body temperature stress hormones and prepares us for deep parasympathetic sleep. ✅ Shower at night? Only if keep the cool water on arms, legs, and face, NOT the chest. Cooling the chest at night = spike cortisol. Cortisol = “rise and shine” We need melatonin to sleep, not cortisol. ❌ No workouts after 5pm. It heats us deeply = delay nighttime cooling. ❌ No electronics in the bedroom ❌ No memory foam mattresses = trap heat and EMF ❌ No synthetic bedsheets = trap heat and create static ❌ Don’t cover feet or head. Warm extremities = warm core ❌ No alcohol / food after dark. Digestion = heat. ❌ No hot or cold bath right before bed. This one is a paradox and deserves its own post (the timing and environment after the bath decide whether we cool or overheat) ✅ REDOX is The Foundation Redox ? Go to my page, the 📌 post is a Redox Step-by-Step for beginners.
2:25

Why does ☣️ Pleb Kruse = BTC foundationalist in exile 🟩🔆 say there are 3 pillars that restore sleep cycles? Imagine those 3 pillars as a clock ⏰: 1️⃣ ☀️ Sunlight = the hour hand 2️⃣ 🌑 Darkness = the minute hand 3️⃣ ❄️ Temperature = the second hand If one hand is off, the whole clock is out of sync and sleep apnea occur. 🤔 How this works? 1️⃣☀️LIGHT Morning Sun Programs Our Brainstem. Sunrise light hits our retina and tells the SCN (suprachiasmatic nucleus, the master clock in our brain) to start the 24-hour program That program sets: ➡️ when cortisol should peak ➡️ when melatonin is built ➡️ when body temperature should rise or fall ➡️ when breathing rhythm should stabilize ➡️ how our autonomic nervous system fires,… If we miss sunrise… ⚠️ Our internal 24-hour clock becomes corrupted. This is a circadian software glitch. A glitch in this program = apnea later at night. And it’s not only about seeing the sunrise once. Living in sunlight during the day is the number one upgrade for almost everything, not just sleep apnea or “vitamin D” 2️⃣🌑DARKNESS Melatonin is the Breath-Stabilizing Molecule Darkness is not “the absence of light.” Darkness is a signal When the retina senses real darkness, the brain says: “Release the melatonin we built all day long.” Melatonin is the molecule of repair: 💚lowers inflammation 💚 increases mitochondrial efficiency 💚 stabilizes CO₂ sensing in the brainstem 💚 keeps airway muscles open 💚 synchronizes the breathing rhythm Without true darkness: ➡️ melatonin stays trapped = not released ➡️ the brainstem cannot hold a stable breathing pattern ➡️ apnea starts Blue light at night destroys CO₂ sensing; the primary signal our brainstem uses to trigger breathing and: ⚠️ circadian mismatch ⚠️ melatonin drops ⚠️ brainstem mitochondria lose energy ⚠️ they stop sensing CO₂ correctly 😳 When CO2, sensing fails, we stop breathing and wake up over and over without knowing Fix it: 🕯️candles at night 🧥Cover your skin from artificial light 😎Wear blue-blocking glasses 😴 Be asleep before 10pm Dr Jack says this often: “Melatonin is the CEO of nighttime repair” No CEO = chaos 3️⃣❄️TEMPERATURE The Final Switch That Lets Melatonin Work This is the part we heard on the podcast and very few people really understand. Even if: we get sunrise we have perfect darkness …melatonin still won’t release unless our core temperature drops at night. Why? Because we evolved with hot days and cool nights. Temperature is the ⏰ seconds hand. If we can’t cool down, melatonin won’t rise = sleep breaks and apnea shows up. ❄️ How to drop temperature at night? ✅ 🕯️ after sunset ✅ Open windows and let fresh air in ✅ AC? Ok, just keep it in another room. The bedroom should stay cool (16–19°C / 60–67°F) and free of electronics ✅ Use cotton, linen, bamboo, or wool bedsheets = breathe and don’t trap heat. ✅ Grounding reduces inflammation, makes it easier for the body to cool ✅ Cold face dunk: Fill a bowl with cold tap water (or a few ice cubes) Hold your breath, put the face in for 5–10 seconds. Repeat 2–3 times. This activates the mammalian diving reflex and lowers: heart rate body temperature stress hormones and prepares us for deep parasympathetic sleep. ✅ Shower at night? Only if keep the cool water on arms, legs, and face, NOT the chest. Cooling the chest at night = spike cortisol. Cortisol = “rise and shine” We need melatonin to sleep, not cortisol. ❌ No workouts after 5pm. It heats us deeply = delay nighttime cooling. ❌ No electronics in the bedroom ❌ No memory foam mattresses = trap heat and EMF ❌ No synthetic bedsheets = trap heat and create static ❌ Don’t cover feet or head. Warm extremities = warm core ❌ No alcohol / food after dark. Digestion = heat. ❌ No hot or cold bath right before bed. This one is a paradox and deserves its own post (the timing and environment after the bath decide whether we cool or overheat) ✅ REDOX is The Foundation Redox ? Go to my page, the 📌 post is a Redox Step-by-Step for beginners.

Light Me Away ☀️

23,862 Aufrufe • vor 8 Monaten

Here is some videos of the larger craft from 12-4-2024 and some of the UFO Spheres from 12-3-2024. The Spheres have been here every night this week. They have been down low by the house like normal but we have seen many of them flying all around the area very fast. They will blink very fast or keep the plasma field on but at a lower brightness than when they are just hovering beside the house. On 12-4-2024 we were watching the Spheres flying all around then we saw the larger UFO appear after a helicopter circled the area. It stayed beside the house for over an hour only moving about 800 meters in that amount of time. It was under 1000 feet in altitude and bigger than C-17 aircraft. It looked saucer shaped but wider in the center and tapered around the edges. It was showing up very hot on the thermal camera even though no visible light at all was coming from it. The Spheres showed up cold on the thermal camera when the plasma field was not glowing when we filmed them closer up before. This larger craft had the plasma field around it just like the Spheres but it was not glowing. We could not see the craft with our naked eyes or on the starlight camera or IR cameras. Only the thermal camera would pick it up. Can see how the air around it is shimmering and can see the field around it. It looked better in person but what I filmed is important because you all will see this again on some daytime close up videos! I have watched these craft up close many times and they have this plasma field around them and even when its not glowing its there. In the daytime you can see the air shimmering around the craft very good. We have seen them use this to bend the light around the craft someway and turn the craft almost completely invisible but you could still see the air shimmering around the craft. I had some cheap starlight cameras that would pick the larger crafts up when the Spheres were beside them glowing and the larger craft was almost invisible. The glow from the Spheres plasma would outline were the larger craft was and we could see it on those starlight cameras. We had a handheld digital starlight camera when this craft was over us and it would not pick it up at all. This is the real UFOs and we have seen them here for over twenty years and after the close encounters in 2012 they started staying very close to us and showing up very often. Even when we cant see them we have seen things to let us know they are near us. The Spheres can change shape and we have them on videos doing it. The larger craft we have seen have all looked different but the Spheres are a constant even though they can change shape. During the close encounter on 11-8-2012 there were many of the Spheres and two larger U shaped craft that the Beings came out of. On 2-16-2023 there was a larger craft here that was over 300 meters long and is the biggest craft we have seen the Spheres with. Then we saw this craft that we have seen before a few times including in the daytime. The Spheres have been with each of the larger craft every time. The Spheres act like they guard and protect the larger craft each time they are around. Sometimes they stay very close to them and on 2-16-2023 some of the Spheres were going in the bottom of the giant craft or attaching to the bottom of it and there was an electrical charge or something that would go all around the larger craft when the Spheres attached or went into it. When this larger craft on the 4th was sitting there we saw several of the Spheres flying around really fast with the plasma field on a constant low glow. We have seen them doing this all around the area this whole week. The other ones were down low beside the house and that's were they normally stay close to when they show themselves. I'm not sure what the ones flying all around were doing. Its really different activity the way they were acting. Monday there were so many doing this in every direction we could not count them all. It had to be in the hundreds. We watch the radars also and watch all the aircraft around us during these events and none of the craft we were seeing shows on any public radar sites. The military radar site does not show them either. Monday there was a large increase in commercial fights and some of these aircraft had to see all the smaller craft flying around really fast. Some had the plasma glowing constant but many had it flashing really fast. Way faster than the navigation lights of regular aircraft. Many of the so called drones being seen around the world and in America the last few weeks are these craft and there are some videos going around from New Jersey that shows these craft but some are showing something that looks more man made. On the ones that look more man made there has been sound that sounds like jet engines on many I have seen. The craft here make ZERO sound most of the time! We have only heard a few very strange sounds come from them over the years. One sound sounded like a giant transformer humming during the close encounter on 11-8-2012 and on 2-16-2023 we heard what sounded like very loud trumpets come from the sky! We were not the only ones that heard that. People up the road that can not see this area and that knows nothing about what's going on here told us they heard the trumpet sounds and thought it was the end of the world and told us they started praying! They also told us they saw a light floating through the trees back toward our land. There has been some other strange sounds also and something really strange is in the summer time there are hundreds of thousand of frogs in the swamps around our land when its raining more and when the UFOs show up all the frogs will stop singing at one time. This is on some of the videos I have. When you are out there beside the swamps and there are that many frogs singing then they all stop at one time its very strange! Its not just the frogs, all the animals and insects seem like they stop. Sometimes they do it right before the plasma field turns on and sometimes right after. Two of my male dogs would go crazy also right before the UFOs show themselves. I have other dogs but those two were super sensitive to whatever was coming from the UFOs. I think they all are hearing something from the UFOs that we cant hear or they are feeling what I feel from them when they are closer to me. Either way its probably something that scientist could pick up with the right equipment. #UFO #UAP #RealUFOs #Spheres #ufox #ua
4:40

Here is some videos of the larger craft from 12-4-2024 and some of the UFO Spheres from 12-3-2024. The Spheres have been here every night this week. They have been down low by the house like normal but we have seen many of them flying all around the area very fast. They will blink very fast or keep the plasma field on but at a lower brightness than when they are just hovering beside the house. On 12-4-2024 we were watching the Spheres flying all around then we saw the larger UFO appear after a helicopter circled the area. It stayed beside the house for over an hour only moving about 800 meters in that amount of time. It was under 1000 feet in altitude and bigger than C-17 aircraft. It looked saucer shaped but wider in the center and tapered around the edges. It was showing up very hot on the thermal camera even though no visible light at all was coming from it. The Spheres showed up cold on the thermal camera when the plasma field was not glowing when we filmed them closer up before. This larger craft had the plasma field around it just like the Spheres but it was not glowing. We could not see the craft with our naked eyes or on the starlight camera or IR cameras. Only the thermal camera would pick it up. Can see how the air around it is shimmering and can see the field around it. It looked better in person but what I filmed is important because you all will see this again on some daytime close up videos! I have watched these craft up close many times and they have this plasma field around them and even when its not glowing its there. In the daytime you can see the air shimmering around the craft very good. We have seen them use this to bend the light around the craft someway and turn the craft almost completely invisible but you could still see the air shimmering around the craft. I had some cheap starlight cameras that would pick the larger crafts up when the Spheres were beside them glowing and the larger craft was almost invisible. The glow from the Spheres plasma would outline were the larger craft was and we could see it on those starlight cameras. We had a handheld digital starlight camera when this craft was over us and it would not pick it up at all. This is the real UFOs and we have seen them here for over twenty years and after the close encounters in 2012 they started staying very close to us and showing up very often. Even when we cant see them we have seen things to let us know they are near us. The Spheres can change shape and we have them on videos doing it. The larger craft we have seen have all looked different but the Spheres are a constant even though they can change shape. During the close encounter on 11-8-2012 there were many of the Spheres and two larger U shaped craft that the Beings came out of. On 2-16-2023 there was a larger craft here that was over 300 meters long and is the biggest craft we have seen the Spheres with. Then we saw this craft that we have seen before a few times including in the daytime. The Spheres have been with each of the larger craft every time. The Spheres act like they guard and protect the larger craft each time they are around. Sometimes they stay very close to them and on 2-16-2023 some of the Spheres were going in the bottom of the giant craft or attaching to the bottom of it and there was an electrical charge or something that would go all around the larger craft when the Spheres attached or went into it. When this larger craft on the 4th was sitting there we saw several of the Spheres flying around really fast with the plasma field on a constant low glow. We have seen them doing this all around the area this whole week. The other ones were down low beside the house and that's were they normally stay close to when they show themselves. I'm not sure what the ones flying all around were doing. Its really different activity the way they were acting. Monday there were so many doing this in every direction we could not count them all. It had to be in the hundreds. We watch the radars also and watch all the aircraft around us during these events and none of the craft we were seeing shows on any public radar sites. The military radar site does not show them either. Monday there was a large increase in commercial fights and some of these aircraft had to see all the smaller craft flying around really fast. Some had the plasma glowing constant but many had it flashing really fast. Way faster than the navigation lights of regular aircraft. Many of the so called drones being seen around the world and in America the last few weeks are these craft and there are some videos going around from New Jersey that shows these craft but some are showing something that looks more man made. On the ones that look more man made there has been sound that sounds like jet engines on many I have seen. The craft here make ZERO sound most of the time! We have only heard a few very strange sounds come from them over the years. One sound sounded like a giant transformer humming during the close encounter on 11-8-2012 and on 2-16-2023 we heard what sounded like very loud trumpets come from the sky! We were not the only ones that heard that. People up the road that can not see this area and that knows nothing about what's going on here told us they heard the trumpet sounds and thought it was the end of the world and told us they started praying! They also told us they saw a light floating through the trees back toward our land. There has been some other strange sounds also and something really strange is in the summer time there are hundreds of thousand of frogs in the swamps around our land when its raining more and when the UFOs show up all the frogs will stop singing at one time. This is on some of the videos I have. When you are out there beside the swamps and there are that many frogs singing then they all stop at one time its very strange! Its not just the frogs, all the animals and insects seem like they stop. Sometimes they do it right before the plasma field turns on and sometimes right after. Two of my male dogs would go crazy also right before the UFOs show themselves. I have other dogs but those two were super sensitive to whatever was coming from the UFOs. I think they all are hearing something from the UFOs that we cant hear or they are feeling what I feel from them when they are closer to me. Either way its probably something that scientist could pick up with the right equipment. #UFO #UAP #RealUFOs #Spheres #ufox #ua

Ranger H

39,647 Aufrufe • vor 1 Jahr

🚨 OPERATIONAL UPDATE: ISRAEL–U.S. WAR WITH IRAN - Reporting Window: Last 24 Hours There’s a widening gap right now between the language being used publicly and what’s actually unfolding underneath it. You have ceasefire talk. You have negotiation chatter. But when you look at the mechanics, the system is still tightening. ━━━━━━━━━━━━━━━━━━ ⚓ AT SEA: PRESSURE BECOMES ENFORCEMENT The clearest development in the last 24 hours is at sea. The United States is now actively enforcing the blockade, not just signaling it. Thirteen commercial vessels have already been turned back after direct warnings from U.S. naval forces, backed by carrier strike groups and full air support overhead. That’s not symbolic pressure. That’s control being asserted in real time. ━━━━━━━━━━━━━━━━━━ 🏭 INSIDE IRAN: ADJUSTMENT UNDER STRAIN Iran, in turn, is no longer just absorbing that pressure. It’s adjusting to it. Tehran has halted petrochemical exports entirely, redirecting supply inward to stabilize domestic markets after sustained damage to industrial infrastructure. At the same time, the internal environment remains locked down. The nationwide internet blackout is still in place, each day setting a new record for the longest ever recorded, with measurable economic damage already mounting and day-to-day activity inside the country increasingly constrained. This is not collapse. But it is a regime shifting into a defensive footing internally. ━━━━━━━━━━━━━━━━━━ 🇱🇧 LEBANON: A PAUSE WITHOUT CONTROL Up north, the Lebanon front is sitting in a fragile and somewhat artificial pause. A 10-day ceasefire framework is technically holding, and civilians are already moving back toward southern areas. But the underlying conditions haven’t stabilized. Destroyed infrastructure is being rapidly repaired, including key bridges over the Litani, allowing movement back into areas that were deliberately restricted just days ago. At the same time, Israeli leadership continues to signal that operations will resume or expand if Hezbollah is not pushed back. And Hezbollah itself has not clearly aligned with the ceasefire structure. So what you have is not resolution. It’s a pause that neither side fully controls. ━━━━━━━━━━━━━━━━━━ 🔗 SUPPLY LINES: ACTIVE, BUT CONTESTED What’s happening around Lebanon reinforces that. A shipment of roughly 6,000 explosive detonators was intercepted in Syria en route to Lebanon, likely intended for Hezbollah. That’s a small detail, but it carries weight. Because it shows two things at once: *⃣ Hezbollah’s supply lines are still active. *⃣ And they are no longer moving freely. ━━━━━━━━━━━━━━━━━━ 🌍 REGIONAL POSTURE: SILENT ALIGNMENT And zooming out, that pattern holds across the region. There is no meaningful external relief for Iran right now. Gulf financial channels are tightening under U.S. pressure. Shipping lanes are being enforced, not contested. Supply corridors are being disrupted, even if unevenly. Even countries that are publicly critical of U.S. and Israeli actions are not stepping in to offset the pressure in any material way. That silence is not neutral. It’s alignment without exposure. ━━━━━━━━━━━━━━━━━━ 🕊️ DIPLOMACY: ACTIVE, BUT UNCHANGED Diplomatically, nothing has simplified. There is still discussion of extending the ceasefire window with Iran. There are still backchannel efforts moving through intermediaries. But the actual points that matter haven’t moved: Nuclear capability. Missile systems. Proxy networks. Hormuz access. Sanctions relief. And now there’s an added complication. Iran continues trying to tie the Lebanon front into the broader negotiation, while Israel is treating it as a separate problem entirely. That disconnect is still unresolved. ━━━━━━━━━━━━━━━━━━ 🧠 WHAT ACTUALLY MATTERS If you strip out the headlines, what changed in the last 24 hours is straightforward: 1. The U.S. moved from pressure to enforcement at sea. 2. Iran shifted further into internal economic stabilization mode. 3. The Lebanon front held, but only in a way that looks temporary. 4. And across the region, no one stepped in to relieve the pressure on Iran. ━━━━━━━━━━━━━━━━━━ BOTTOM LINE Nothing decisive happened today. But the structure tightened again. More enforcement. More constraint. More pressure being applied from multiple directions at once. And until something gives on the issues that actually matter, there will be no resolution.
0:32

🚨 OPERATIONAL UPDATE: ISRAEL–U.S. WAR WITH IRAN - Reporting Window: Last 24 Hours There’s a widening gap right now between the language being used publicly and what’s actually unfolding underneath it. You have ceasefire talk. You have negotiation chatter. But when you look at the mechanics, the system is still tightening. ━━━━━━━━━━━━━━━━━━ ⚓ AT SEA: PRESSURE BECOMES ENFORCEMENT The clearest development in the last 24 hours is at sea. The United States is now actively enforcing the blockade, not just signaling it. Thirteen commercial vessels have already been turned back after direct warnings from U.S. naval forces, backed by carrier strike groups and full air support overhead. That’s not symbolic pressure. That’s control being asserted in real time. ━━━━━━━━━━━━━━━━━━ 🏭 INSIDE IRAN: ADJUSTMENT UNDER STRAIN Iran, in turn, is no longer just absorbing that pressure. It’s adjusting to it. Tehran has halted petrochemical exports entirely, redirecting supply inward to stabilize domestic markets after sustained damage to industrial infrastructure. At the same time, the internal environment remains locked down. The nationwide internet blackout is still in place, each day setting a new record for the longest ever recorded, with measurable economic damage already mounting and day-to-day activity inside the country increasingly constrained. This is not collapse. But it is a regime shifting into a defensive footing internally. ━━━━━━━━━━━━━━━━━━ 🇱🇧 LEBANON: A PAUSE WITHOUT CONTROL Up north, the Lebanon front is sitting in a fragile and somewhat artificial pause. A 10-day ceasefire framework is technically holding, and civilians are already moving back toward southern areas. But the underlying conditions haven’t stabilized. Destroyed infrastructure is being rapidly repaired, including key bridges over the Litani, allowing movement back into areas that were deliberately restricted just days ago. At the same time, Israeli leadership continues to signal that operations will resume or expand if Hezbollah is not pushed back. And Hezbollah itself has not clearly aligned with the ceasefire structure. So what you have is not resolution. It’s a pause that neither side fully controls. ━━━━━━━━━━━━━━━━━━ 🔗 SUPPLY LINES: ACTIVE, BUT CONTESTED What’s happening around Lebanon reinforces that. A shipment of roughly 6,000 explosive detonators was intercepted in Syria en route to Lebanon, likely intended for Hezbollah. That’s a small detail, but it carries weight. Because it shows two things at once: *⃣ Hezbollah’s supply lines are still active. *⃣ And they are no longer moving freely. ━━━━━━━━━━━━━━━━━━ 🌍 REGIONAL POSTURE: SILENT ALIGNMENT And zooming out, that pattern holds across the region. There is no meaningful external relief for Iran right now. Gulf financial channels are tightening under U.S. pressure. Shipping lanes are being enforced, not contested. Supply corridors are being disrupted, even if unevenly. Even countries that are publicly critical of U.S. and Israeli actions are not stepping in to offset the pressure in any material way. That silence is not neutral. It’s alignment without exposure. ━━━━━━━━━━━━━━━━━━ 🕊️ DIPLOMACY: ACTIVE, BUT UNCHANGED Diplomatically, nothing has simplified. There is still discussion of extending the ceasefire window with Iran. There are still backchannel efforts moving through intermediaries. But the actual points that matter haven’t moved: Nuclear capability. Missile systems. Proxy networks. Hormuz access. Sanctions relief. And now there’s an added complication. Iran continues trying to tie the Lebanon front into the broader negotiation, while Israel is treating it as a separate problem entirely. That disconnect is still unresolved. ━━━━━━━━━━━━━━━━━━ 🧠 WHAT ACTUALLY MATTERS If you strip out the headlines, what changed in the last 24 hours is straightforward: 1. The U.S. moved from pressure to enforcement at sea. 2. Iran shifted further into internal economic stabilization mode. 3. The Lebanon front held, but only in a way that looks temporary. 4. And across the region, no one stepped in to relieve the pressure on Iran. ━━━━━━━━━━━━━━━━━━ BOTTOM LINE Nothing decisive happened today. But the structure tightened again. More enforcement. More constraint. More pressure being applied from multiple directions at once. And until something gives on the issues that actually matter, there will be no resolution.

Inside_Israel_Intel

13,913 Aufrufe • vor 4 Monaten

In honor of Y Combinator's 20th anniversary, here are my 6 favorite lessons: 1. The 7 minute espresso rule: Our first meeting with Sam Altman lasted just seven minutes. The batch hadn’t even officially started yet and my cofounder Ryan Rowe and I drove down from Mountain View to the tiny SF YC outpost to meet him. Sam was making an espresso when we walked in. He opened with – “have you launched”? We said no, too many bugs. Our product, kimono, made it easy to just point and click to build a web scraper. Our promise was to get you an API in 60 seconds, without writing a single line of code. But, the web is vast, and websites are very different. We needed it to work on a large enough spectrum of sites so that our first users would have a good experience. It worked on just a handful of sites at the time. Sam pushed us to make sure we were launched in less than 2 weeks. We debated, highlighting the complexity of the bugs and the limits of underlying headless browsing technology. The espresso finished brewing, he picked it up, looked at us and said, “well, you better get going then and fix those bugs”. We left, launched within those next two weeks and learned one of the most important lessons that day - speed matters. Ship something you’re embarrassed by. 2. There are no experts. Ryan and I were not prepared for the rapid influx of user on launch day. We did 88 user interviews to validate our product idea, and everyone basically said they wouldn’t use it. We thought they were wrong and built it anyway. It got tons of traffic on day 1. We didn’t sleep in the 48 hours following because our servers and database kept crashing. We hadn’t indexed it properly. We realized we weren’t the experts and needed to hire one. We went over to Michael Seibel's apartment for advice. Michael smiled and told us about the early days at SocialCam, that eventually became Twitch – experts thought the streaming video problem was too hard to be possible. The answer wasn’t hiring an expert, but hiring someone young, capable and naïve enough to give it an earnest try. So we opted to just figure it out ourselves. 3. Messages in Pizza Boxes. Startups win through incredible customer service, then through product, not the other way around. Michael Seibel told us how SocialCam’s streaming infrastructure went down while a key teammate was unreachable off-grid in a Tahoe cabin for the weekend. Normal people would have waited until Monday. Not Michael. He called a local pizza delivery place and asked the delivery person to send a large pizza with an urgent message in the box to the cabin. Their infrastructure was back up in hours. 4. The Twinkle can matter more than the TAM. Ambition matters just as much as practicality. Before demo day, we were struggling with the end note of our pitch. We knew the value of our product, and had a fanatical and fast-growing user base, but the market size we calculated either seemed so ridiculously big that it was not plausible, or so narrow that it was equally silly. Paul Graham and Geoff Ralston sat with us and showed us that if we can really pull it off at scale, it would be bigger than Google. So, we could skip the market size, if we really believed it. PG suggested not talking about market size, but just making sure people could see the twinkle in our eyes when we talked about what you might be able to do with a structured copy of the internet that’s larger than Google’s. 5. You’re always at the Origin. Our demo day was successful beyond our wildest beliefs. Afterwards, Geoff Ralston drew a chart for us on a whiteboard. It was a hockey stick. He asked us where we thought we were. It was a rhetorical question. He said we were at the origin, and that the most important thing is to remember that. Sam Altman doubled down. When he invested in Kimono, he gave us a Zimbabwean Trillion dollar note (the result of extreme hyperinflation in Zimbabwe), as a cautionary reminder that the fundraising and valuation mean nothing. We have channeled this into our culture at @TheArenaAI with our ritual around neon shoelaces and a pair of neon track spikes hanging on the wall to remind us that we’re at the Olympic starting line, but we don’t have any medals yet. 6. High bandwidth discussions with users don’t happen over email. The Collison brothers, who founded Stripe, took user engagement to the next level – so much so that “Collison installation” is part of YC lore. John Collison told us that talking to users was essential because email and chat conversations were not “high bandwidth” enough. He emphasized the importance of what you can learn being with users in person or talking to them over Skype (remember, this was early 2014!). We took this to heart, and spoke to hundreds of users at Kimono regularly over Skype. One power user Alexander Chung, who I met this way, has become a close friend and even attended my wedding in India. At Arena, we now go a step further and regularly fly to our users. We over-invest to a degree that is almost crazy in order to be more than a supplier — a true partner to our early customers. YC taught us it’s the only way to understand their problems on the ground and really make sure our products work for them. YC taught us to outsource very little. Own the problem. Own the outcome. And if you do, you get to be stupidly ambitious. If you’re curious about Kimono Labs (which we later sold to Palantir ), here was our launch demo. No-code web scraping before “no code” was even a term: Here's our demo day pitch, from 2014. Forever grateful to those above + Garry Tan Jessica Livingston Trevor Blackwell
2:37

In honor of Y Combinator's 20th anniversary, here are my 6 favorite lessons: 1. The 7 minute espresso rule: Our first meeting with Sam Altman lasted just seven minutes. The batch hadn’t even officially started yet and my cofounder Ryan Rowe and I drove down from Mountain View to the tiny SF YC outpost to meet him. Sam was making an espresso when we walked in. He opened with – “have you launched”? We said no, too many bugs. Our product, kimono, made it easy to just point and click to build a web scraper. Our promise was to get you an API in 60 seconds, without writing a single line of code. But, the web is vast, and websites are very different. We needed it to work on a large enough spectrum of sites so that our first users would have a good experience. It worked on just a handful of sites at the time. Sam pushed us to make sure we were launched in less than 2 weeks. We debated, highlighting the complexity of the bugs and the limits of underlying headless browsing technology. The espresso finished brewing, he picked it up, looked at us and said, “well, you better get going then and fix those bugs”. We left, launched within those next two weeks and learned one of the most important lessons that day - speed matters. Ship something you’re embarrassed by. 2. There are no experts. Ryan and I were not prepared for the rapid influx of user on launch day. We did 88 user interviews to validate our product idea, and everyone basically said they wouldn’t use it. We thought they were wrong and built it anyway. It got tons of traffic on day 1. We didn’t sleep in the 48 hours following because our servers and database kept crashing. We hadn’t indexed it properly. We realized we weren’t the experts and needed to hire one. We went over to Michael Seibel's apartment for advice. Michael smiled and told us about the early days at SocialCam, that eventually became Twitch – experts thought the streaming video problem was too hard to be possible. The answer wasn’t hiring an expert, but hiring someone young, capable and naïve enough to give it an earnest try. So we opted to just figure it out ourselves. 3. Messages in Pizza Boxes. Startups win through incredible customer service, then through product, not the other way around. Michael Seibel told us how SocialCam’s streaming infrastructure went down while a key teammate was unreachable off-grid in a Tahoe cabin for the weekend. Normal people would have waited until Monday. Not Michael. He called a local pizza delivery place and asked the delivery person to send a large pizza with an urgent message in the box to the cabin. Their infrastructure was back up in hours. 4. The Twinkle can matter more than the TAM. Ambition matters just as much as practicality. Before demo day, we were struggling with the end note of our pitch. We knew the value of our product, and had a fanatical and fast-growing user base, but the market size we calculated either seemed so ridiculously big that it was not plausible, or so narrow that it was equally silly. Paul Graham and Geoff Ralston sat with us and showed us that if we can really pull it off at scale, it would be bigger than Google. So, we could skip the market size, if we really believed it. PG suggested not talking about market size, but just making sure people could see the twinkle in our eyes when we talked about what you might be able to do with a structured copy of the internet that’s larger than Google’s. 5. You’re always at the Origin. Our demo day was successful beyond our wildest beliefs. Afterwards, Geoff Ralston drew a chart for us on a whiteboard. It was a hockey stick. He asked us where we thought we were. It was a rhetorical question. He said we were at the origin, and that the most important thing is to remember that. Sam Altman doubled down. When he invested in Kimono, he gave us a Zimbabwean Trillion dollar note (the result of extreme hyperinflation in Zimbabwe), as a cautionary reminder that the fundraising and valuation mean nothing. We have channeled this into our culture at @TheArenaAI with our ritual around neon shoelaces and a pair of neon track spikes hanging on the wall to remind us that we’re at the Olympic starting line, but we don’t have any medals yet. 6. High bandwidth discussions with users don’t happen over email. The Collison brothers, who founded Stripe, took user engagement to the next level – so much so that “Collison installation” is part of YC lore. John Collison told us that talking to users was essential because email and chat conversations were not “high bandwidth” enough. He emphasized the importance of what you can learn being with users in person or talking to them over Skype (remember, this was early 2014!). We took this to heart, and spoke to hundreds of users at Kimono regularly over Skype. One power user Alexander Chung, who I met this way, has become a close friend and even attended my wedding in India. At Arena, we now go a step further and regularly fly to our users. We over-invest to a degree that is almost crazy in order to be more than a supplier — a true partner to our early customers. YC taught us it’s the only way to understand their problems on the ground and really make sure our products work for them. YC taught us to outsource very little. Own the problem. Own the outcome. And if you do, you get to be stupidly ambitious. If you’re curious about Kimono Labs (which we later sold to Palantir ), here was our launch demo. No-code web scraping before “no code” was even a term: Here's our demo day pitch, from 2014. Forever grateful to those above + Garry Tan Jessica Livingston Trevor Blackwell

Pratap Ranade

177,308 Aufrufe • vor 1 Jahr

Dyson Sphere by 2040? > Paul Christiano gave a 15% probability a Dyson Sphere by 2040 on Dwarkesh Patel > So I invited Jason Wright , astronomer and astrophysicist at Penn State, a specialist on detecting alien technological signatures to figure out whether it would be possible Highlights: On The Physics of Dyson Spheres > On the Ultimate Limiter - Cooling: "The only thing you can do is put out radiators and let the heat radiate away... you don't have a river nearby, you can't do water cooling, you can't blow fans across it. There's no air." > On Building Them Hot, Not Cold: "I actually expect the Dyson spheres to be as hot as possible if they're maximizing computation... I think these things will be room temperature or warmer." On Building A Dyson Sphere > On Disassembling Jupiter: "You could disassemble Jupiter and build a Dyson sphere... He [Freeman Dyson] worked out the details like, it's totally possible to disassemble a planet." > On the Impossibility of a Quick Dyson Sphere: "It is literally impossible to build a Dyson sphere around the sun that quickly [in 50 years]... It's not that I'm not being imaginative. It's that there's not enough mass. The mass is in Jupiter." > On the Sheer Energy Required: "If you took all the energy the sun puts out for 50 years and with perfect efficiency used it to lift mass off of Jupiter, you would still not have enough time." > On the Key Technology Required: "I think you have to invoke exponential growth. You need some way to have a runaway exponential that mines so much material, builds so many things... I think it's self-replicating machines, Von Neumann machines." On Detecting Alien Civilizations > On the Impossibility of Hiding: "It's very hard to hide the fact that you're using energy... you can't keep it, you'll melt everything. And when you get rid of it, it'll be obvious." > On How Easy They Are to Find: "Such a thing would be extremely detectable... Even if it only captured 1 or 2% of a star's light altogether, that would still be quite obvious. It would look quite anomalous." > On Finding a Hidden Civilization: "You can put on infrared goggles and you'll see who's got the heat on. Like the houses that that are warm on the inside... you can see the heat coming off of those houses." > On Ruling Out Super-Civilizations: "We were able to show that out of the 100,000 closest galaxies to the Milky Way, there aren't any of those [Type III civilizations]." On The Space Industry Wrecking Science > On the Perfect Spot for Alien Hunting: "Putting a radio telescope on the far side of the moon would be amazing... the moon acts as a shield and there's no radio frequency interference." > On the Threat to the Lunar Far Side: "As soon as you say, 'Alright, we want to build something on the far side of the moon,' everyone's like, 'Great, let's build the infrastructure'... and they set up all this wireless communication across the moon. And you're like, 'That defeats the entire purpose.'" > On the Impact of Starlink on Science: "Starlink went up and just completely wrecked all of our plans for astronomy... And now every time it takes a picture, it gets 'Starlinked.' That's what we call it. Big old streaks through every image." > On the Race to Study Mars: "We kind of got to hurry up and do all of our life detection experiments before boots hit the ground there... As soon as people go to Mars, we will contaminate it." This was by far the best pod I've done so far. Links below:
1:07

Dyson Sphere by 2040? > Paul Christiano gave a 15% probability a Dyson Sphere by 2040 on Dwarkesh Patel > So I invited Jason Wright , astronomer and astrophysicist at Penn State, a specialist on detecting alien technological signatures to figure out whether it would be possible Highlights: On The Physics of Dyson Spheres > On the Ultimate Limiter - Cooling: "The only thing you can do is put out radiators and let the heat radiate away... you don't have a river nearby, you can't do water cooling, you can't blow fans across it. There's no air." > On Building Them Hot, Not Cold: "I actually expect the Dyson spheres to be as hot as possible if they're maximizing computation... I think these things will be room temperature or warmer." On Building A Dyson Sphere > On Disassembling Jupiter: "You could disassemble Jupiter and build a Dyson sphere... He [Freeman Dyson] worked out the details like, it's totally possible to disassemble a planet." > On the Impossibility of a Quick Dyson Sphere: "It is literally impossible to build a Dyson sphere around the sun that quickly [in 50 years]... It's not that I'm not being imaginative. It's that there's not enough mass. The mass is in Jupiter." > On the Sheer Energy Required: "If you took all the energy the sun puts out for 50 years and with perfect efficiency used it to lift mass off of Jupiter, you would still not have enough time." > On the Key Technology Required: "I think you have to invoke exponential growth. You need some way to have a runaway exponential that mines so much material, builds so many things... I think it's self-replicating machines, Von Neumann machines." On Detecting Alien Civilizations > On the Impossibility of Hiding: "It's very hard to hide the fact that you're using energy... you can't keep it, you'll melt everything. And when you get rid of it, it'll be obvious." > On How Easy They Are to Find: "Such a thing would be extremely detectable... Even if it only captured 1 or 2% of a star's light altogether, that would still be quite obvious. It would look quite anomalous." > On Finding a Hidden Civilization: "You can put on infrared goggles and you'll see who's got the heat on. Like the houses that that are warm on the inside... you can see the heat coming off of those houses." > On Ruling Out Super-Civilizations: "We were able to show that out of the 100,000 closest galaxies to the Milky Way, there aren't any of those [Type III civilizations]." On The Space Industry Wrecking Science > On the Perfect Spot for Alien Hunting: "Putting a radio telescope on the far side of the moon would be amazing... the moon acts as a shield and there's no radio frequency interference." > On the Threat to the Lunar Far Side: "As soon as you say, 'Alright, we want to build something on the far side of the moon,' everyone's like, 'Great, let's build the infrastructure'... and they set up all this wireless communication across the moon. And you're like, 'That defeats the entire purpose.'" > On the Impact of Starlink on Science: "Starlink went up and just completely wrecked all of our plans for astronomy... And now every time it takes a picture, it gets 'Starlinked.' That's what we call it. Big old streaks through every image." > On the Race to Study Mars: "We kind of got to hurry up and do all of our life detection experiments before boots hit the ground there... As soon as people go to Mars, we will contaminate it." This was by far the best pod I've done so far. Links below:

Prakash

43,295 Aufrufe • vor 9 Monaten

Roy makes a very fair point here, I was torn on Green + 100k vs Merrett all week. I literally swapped them back and forth 3-4 times from Fri night to Sat night. He is right, you don’t get that many opportunities to get a top-liner like that and I possibly should’ve taken the chance when it presented, especially with the Ess run. I think Merrett was priced at around 116. If he goes at 125 over the next 5-6 weeks, after adding the Captaincy benefit, it would’ve probably been a mistake to not go him. One thing I was v.surprised with last week was how low down Merrett was on the trade-ins list. It is not often you get an Uber-premium like that who has a juicy upcoming schedule AND is a POD. That’s a very unusual set of circumstances. If your POD premium then goes ham it really can separate your team from the pack. We saw last yr with Zorko what a boost it can be to have a POD premium go large for a big stretch (granted a little diff in the FWDs last yr). That, along with my previously outlined thesis about teams potentially being completed earlier than ever this yr was my bull-case for going Merrett. The reason I went with Green, or rather, why I opted for another option that left me with 100k in the bank is that my bench is a thin. Stone won’t make us much, FOS not looking like he’ll make us much, Berry now been sub 2 games in a row and is dropping cash, Hastie getting subbed each week etc. Other teams that have Moraes instead of Hastie and Hall instead of Berry may have an extra few hundred k to play with in terms of making upgrades. So in light of my weaker bench I felt that the 100k would help facilitate a better subsequent trade (ie this week). Whilst I could downgrade a Prior this week to make a nice upgrade, it wouldn’t be too long before my bench is not sufficient enough to facilitate the next upgrade. So ultimately I was looking at it as a 2 week decision. Ie, would I rather a Merrett + Tom Stewart type this week or would I rather Tom Green + Nick Daicos and ultimately I opted for the latter, though I do feel like I need to fix one of Hastie or Berry this week so we’ll see what I end up doing. But that was the thought process. As stupid as it now sounds, if Dees didn’t tag Holmes the previous week I prob would’ve gone Merrett, but the faint possibility of that happening to Merrett was prob the deciding factor in letting him go. I was also close to trading in Zorko the prev week and seeing Zork do 79 against Richmond spooked me. Didn’t want to run the risk of paying a huge price and then Zerrett underperforming as Zorko did. Can’t say I was thrilled seeing Merrett go nuclear Sat night, esp after the TDK C decision on Sat arvo. But, I can potentially make up the points this week if I’m able to put that 100k to good use.
0:27

Roy makes a very fair point here, I was torn on Green + 100k vs Merrett all week. I literally swapped them back and forth 3-4 times from Fri night to Sat night. He is right, you don’t get that many opportunities to get a top-liner like that and I possibly should’ve taken the chance when it presented, especially with the Ess run. I think Merrett was priced at around 116. If he goes at 125 over the next 5-6 weeks, after adding the Captaincy benefit, it would’ve probably been a mistake to not go him. One thing I was v.surprised with last week was how low down Merrett was on the trade-ins list. It is not often you get an Uber-premium like that who has a juicy upcoming schedule AND is a POD. That’s a very unusual set of circumstances. If your POD premium then goes ham it really can separate your team from the pack. We saw last yr with Zorko what a boost it can be to have a POD premium go large for a big stretch (granted a little diff in the FWDs last yr). That, along with my previously outlined thesis about teams potentially being completed earlier than ever this yr was my bull-case for going Merrett. The reason I went with Green, or rather, why I opted for another option that left me with 100k in the bank is that my bench is a thin. Stone won’t make us much, FOS not looking like he’ll make us much, Berry now been sub 2 games in a row and is dropping cash, Hastie getting subbed each week etc. Other teams that have Moraes instead of Hastie and Hall instead of Berry may have an extra few hundred k to play with in terms of making upgrades. So in light of my weaker bench I felt that the 100k would help facilitate a better subsequent trade (ie this week). Whilst I could downgrade a Prior this week to make a nice upgrade, it wouldn’t be too long before my bench is not sufficient enough to facilitate the next upgrade. So ultimately I was looking at it as a 2 week decision. Ie, would I rather a Merrett + Tom Stewart type this week or would I rather Tom Green + Nick Daicos and ultimately I opted for the latter, though I do feel like I need to fix one of Hastie or Berry this week so we’ll see what I end up doing. But that was the thought process. As stupid as it now sounds, if Dees didn’t tag Holmes the previous week I prob would’ve gone Merrett, but the faint possibility of that happening to Merrett was prob the deciding factor in letting him go. I was also close to trading in Zorko the prev week and seeing Zork do 79 against Richmond spooked me. Didn’t want to run the risk of paying a huge price and then Zerrett underperforming as Zorko did. Can’t say I was thrilled seeing Merrett go nuclear Sat night, esp after the TDK C decision on Sat arvo. But, I can potentially make up the points this week if I’m able to put that 100k to good use.

Vams

10,940 Aufrufe • vor 1 Jahr

Leaking my biggest winners part 2: My most OFFENSIVE ad of all time? This was for Ecom Affiliate, a Security Camera offer. We were the only ones that managed to compete with a Melania and Elon deepfake affiliate, without crossing that line. This "dark lord" angle was the reason why. When selling ecom shit, the angle is everything. Finding one subset of the market that would buy the offer for a specific reason, and hyper targeting that ad. I was selling a security camera. Meaning at the core I was selling: Security. For me to sell security, I need to find a fear to sell security against. The ICE protests were all over the news at the time, and tne massive fear amongst conservatives at the time was ILLEGAL IMMIGRANTS. It was also a touchy topic so I knew engagement would be crazy. So like the disgusting vultures that we affiliates are, we decided to turn the chaos and political divide another country is going through into cold hard cash for ourselves. Here's how we did it: 1. I made the actor a military dude with camo face paint. Why? Because conservatives LOVE the military and believe everything they say. It also plain stops the scroll. 2. I included an absurdly fake statistics about how many homes are affected by "illegals" breaking in. 3. I said "the government is urging all patriots..." this is to provide authority, since the target market loves Trump. Patriots is self identification (us vs them). I ended up having to change this due to ad rejections and I instead said: "One of the top military commanders is urging", etc. 4. I reframed the product as a "military" camera (because these people love the military, camo etc.) designed to protect them until the "wall is built". 5. A little product demo section highlighting the benefits. 6. Note the language I'm using: "Patriots", "Great Nation", "Wall", etc. The entire ad appeals to a very specific avatar and speaks their language verbatim. 6. Price justification: "steep discount because that's how much they cost to produce" 7. Final little dagger to their fear bone to push them over the edge: "These animals could strike at any moment". Fear, division, familiar language. The most extreme close I could come up with. Last thing: The landing page was a quiz asking them if they're legal residents making them feel like they've earned the deal they're getting. That's all for today, I plan to share one more ad for this offer (super different angle), and 2 loan ads that make this ad look PC. Any preference around which one I should break down first?
0:58

Leaking my biggest winners part 2: My most OFFENSIVE ad of all time? This was for Ecom Affiliate, a Security Camera offer. We were the only ones that managed to compete with a Melania and Elon deepfake affiliate, without crossing that line. This "dark lord" angle was the reason why. When selling ecom shit, the angle is everything. Finding one subset of the market that would buy the offer for a specific reason, and hyper targeting that ad. I was selling a security camera. Meaning at the core I was selling: Security. For me to sell security, I need to find a fear to sell security against. The ICE protests were all over the news at the time, and tne massive fear amongst conservatives at the time was ILLEGAL IMMIGRANTS. It was also a touchy topic so I knew engagement would be crazy. So like the disgusting vultures that we affiliates are, we decided to turn the chaos and political divide another country is going through into cold hard cash for ourselves. Here's how we did it: 1. I made the actor a military dude with camo face paint. Why? Because conservatives LOVE the military and believe everything they say. It also plain stops the scroll. 2. I included an absurdly fake statistics about how many homes are affected by "illegals" breaking in. 3. I said "the government is urging all patriots..." this is to provide authority, since the target market loves Trump. Patriots is self identification (us vs them). I ended up having to change this due to ad rejections and I instead said: "One of the top military commanders is urging", etc. 4. I reframed the product as a "military" camera (because these people love the military, camo etc.) designed to protect them until the "wall is built". 5. A little product demo section highlighting the benefits. 6. Note the language I'm using: "Patriots", "Great Nation", "Wall", etc. The entire ad appeals to a very specific avatar and speaks their language verbatim. 6. Price justification: "steep discount because that's how much they cost to produce" 7. Final little dagger to their fear bone to push them over the edge: "These animals could strike at any moment". Fear, division, familiar language. The most extreme close I could come up with. Last thing: The landing page was a quiz asking them if they're legal residents making them feel like they've earned the deal they're getting. That's all for today, I plan to share one more ad for this offer (super different angle), and 2 loan ads that make this ad look PC. Any preference around which one I should break down first?

Jordan D

105,356 Aufrufe • vor 7 Monaten

My best friend & fellow January 6 Political Prisoner is gone 😓 Barton Wade Shively has been called home by our Father in Heaven ❤️ (March 29th 1967 - June 22nd 2025) Bart was a tall, handsome US Marine & All American Football Player that become terminally ill from drinking contaminated water at Marine Training Camp Lejeune 😡 He developed serious & fatal non-Hodgkin’s lymphoma I first met Bart Shively (or as I called him Bartley!) in the winter of 2023 inside the Washington DC Jail The Weaponized DOJ & Merrick Garland threw Bart into the Washington DC Gulag for January 6, They tried to murder him with barbaric cancer treatment including chemotherapy and radiation inside a prison cell (they cooked him alive) ☢️ Bart fought everyday without complaint and always kept his big goofy smile (Jack-o’-lantern!) 🎃 Bart and I instantly became best friends & would pal around with Peter Stager inside my cell all day, reading the Bible, listening to music, beating each other up, AND EATING SNACKS: Peter’s homemade prison hookups! (Barts favorite!!) When the Chemo treatment didn’t have him bedridden, Bart was as outgoing and lovable as any person I’ve ever met - even while being persecuted and suffering tremendous physical pain: Bart was a Godly, “happy warrior” as we call him Bart had always known the Lord but after re-surrendering his life to Jesus - he asked me to Baptize him in the Gulag (The greatest honor of my life, I cry everytime I think about it and how much I miss him) We filled up a big bucket of water in my cell, and just before we said the prayer, Ronnie Sandlin, one of Barts best friends put some ice cubes in the bucket, and Bart had an ICE COLD Baptism!! 🤣😇 He had never been so surprised in his life!!!! 😅 Bart was a regular on Don & Donna’s show ‘Cowboy Logic’ from inside the Gulag and quickly became a national icon of resilience and ultimate patriotism, Barts story even reached President Trump - who HAND WROTE Bart a letter last week when he was in the hospital!!! 🇺🇸 Barts final days were full of raw determination and instinctual manly grit - he just wouldn’t give in! He went over 30 days without eating a nearly a week without being able to keep down a cup of water for more then 5 minutes 😲 Bart was a survivor, a patriot, a friend - Bart was an AMERICAN. I am broken and grieving like I lost my mother or father over Barts passing, I never imagined I would miss him this much, the gentle giant he was… But my heart is so full knowing his suffering finally over, and the arms of Jesus have finally wrapped around him ✝️❤️🙏 Please join me & dozens of other J6 patriots in laying this hero to rest on July 2nd, in Dillsburg PA We are prepared to have hundreds of people from across the Country at his Wake: ALL patriots & J6 supporters are welcome: 10 AM: Cocklin Funeral Home (30 N Chestnut St, Dillsburg PA) Then a procession of HUNDREDS of Veteran Bikers will be escorting the hero’s hearse to Indiantown Gap National Military Cemetery for a 21 Gun Salute & Burial at 1:30pm 🇺🇸 Send flowers to Cocklin Funeral Home & please help us pay for the funeral, it’s costing over $10,000+ & we have only raised half!! Link to donate is in the comments below 👇🏻 Godspeed Bart Shively, your legacy will always be remembered forever⚜️
0:46

My best friend & fellow January 6 Political Prisoner is gone 😓 Barton Wade Shively has been called home by our Father in Heaven ❤️ (March 29th 1967 - June 22nd 2025) Bart was a tall, handsome US Marine & All American Football Player that become terminally ill from drinking contaminated water at Marine Training Camp Lejeune 😡 He developed serious & fatal non-Hodgkin’s lymphoma I first met Bart Shively (or as I called him Bartley!) in the winter of 2023 inside the Washington DC Jail The Weaponized DOJ & Merrick Garland threw Bart into the Washington DC Gulag for January 6, They tried to murder him with barbaric cancer treatment including chemotherapy and radiation inside a prison cell (they cooked him alive) ☢️ Bart fought everyday without complaint and always kept his big goofy smile (Jack-o’-lantern!) 🎃 Bart and I instantly became best friends & would pal around with Peter Stager inside my cell all day, reading the Bible, listening to music, beating each other up, AND EATING SNACKS: Peter’s homemade prison hookups! (Barts favorite!!) When the Chemo treatment didn’t have him bedridden, Bart was as outgoing and lovable as any person I’ve ever met - even while being persecuted and suffering tremendous physical pain: Bart was a Godly, “happy warrior” as we call him Bart had always known the Lord but after re-surrendering his life to Jesus - he asked me to Baptize him in the Gulag (The greatest honor of my life, I cry everytime I think about it and how much I miss him) We filled up a big bucket of water in my cell, and just before we said the prayer, Ronnie Sandlin, one of Barts best friends put some ice cubes in the bucket, and Bart had an ICE COLD Baptism!! 🤣😇 He had never been so surprised in his life!!!! 😅 Bart was a regular on Don & Donna’s show ‘Cowboy Logic’ from inside the Gulag and quickly became a national icon of resilience and ultimate patriotism, Barts story even reached President Trump - who HAND WROTE Bart a letter last week when he was in the hospital!!! 🇺🇸 Barts final days were full of raw determination and instinctual manly grit - he just wouldn’t give in! He went over 30 days without eating a nearly a week without being able to keep down a cup of water for more then 5 minutes 😲 Bart was a survivor, a patriot, a friend - Bart was an AMERICAN. I am broken and grieving like I lost my mother or father over Barts passing, I never imagined I would miss him this much, the gentle giant he was… But my heart is so full knowing his suffering finally over, and the arms of Jesus have finally wrapped around him ✝️❤️🙏 Please join me & dozens of other J6 patriots in laying this hero to rest on July 2nd, in Dillsburg PA We are prepared to have hundreds of people from across the Country at his Wake: ALL patriots & J6 supporters are welcome: 10 AM: Cocklin Funeral Home (30 N Chestnut St, Dillsburg PA) Then a procession of HUNDREDS of Veteran Bikers will be escorting the hero’s hearse to Indiantown Gap National Military Cemetery for a 21 Gun Salute & Burial at 1:30pm 🇺🇸 Send flowers to Cocklin Funeral Home & please help us pay for the funeral, it’s costing over $10,000+ & we have only raised half!! Link to donate is in the comments below 👇🏻 Godspeed Bart Shively, your legacy will always be remembered forever⚜️

Jake Lang - January 6 Political Prisoner 🇺🇸

101,930 Aufrufe • vor 1 Jahr

Three days ago I asked myself a dumb question. It was so stupid I was actually ashamed to Google it. Can AI earn money while I sleep? Not saving time. Not automating routine. I mean putting real money into my account while I am not looking at the screen. Everyone says ClawdBot will change how we work. Automation. Task management. Smart replies. But I was sitting in my kitchen thinking about something else entirely. You know that feeling when you look at a tool and realize everyone is using only 1% of its potential? It is like being given a race car and only using it to drive to the store for bread. I decided to test it. I started a notebook. I record everything. > Day One I started with something simple. I gave Clawdbot a task. Find wallets on Polymarket where the numbers do not add up. Where the profit is too high for the win rate. Where the result smells like a system rather than luck. It thought for 14 minutes. I had time to pour a coffee and forget about it. Then the screen flashed. 4 addresses. I scrolled through the first three in a minute. Big bets on politics. They guessed the election. Classic. On the fourth one I stopped. Not because it was the most profitable but because I did not understand what I was looking at. The wallet was not trading politics or sports or anything people write reviews about. It was trading the weather. I read it three times. Weather. Will it be 9 degrees in London tomorrow? Will it rain in Tokyo? These are markets I would not even click on by accident. Then I looked at the numbers. > It started with $27. It is now at $63,853. $27 is two trips to McDonald's. It is nothing. $63,853 is a new car or a down payment on an apartment. It is two years of someone's salary. Between those two numbers was only one thing. Thousands of bets on rain. I closed the tab. Opened it again. Checked if it was a glitch. Real dollars. On markets that look like a bad joke. > Day Two I could not get that wallet out of my head. I went to look at its transaction history. I expected to find one big win that explained everything. A lucky hurricane forecast. Instead I saw thousands of small bets. Boring. "Will the temperature in New York be above 15 degrees?" Then I noticed the detail that finally broke my brain. Its win rate: 33%. It loses more often than it wins. 2 out of 3 bets go to zero. Any normal person with that result would be posting about how the market is unfair. Yet this wallet is sitting on $63,000 in profit. How? I started deconstructing the trades. After an hour I got it. When it loses, it loses 10 or 20 cents. When it wins, it takes $1.00. Loses 9 times in a row? Lost $1.80. Wins 1 time? Got $10.00. > This is not trading. It is math that works as long as you do not interfere with your emotions. Here is how it works. Weather is one of the most predictable things on the planet. Governments invest billions in satellites. Data is updated every 2 or 3 hours. Precision to a tenth of a degree. This data is public. But Polymarket is not a weather station. It updates its markets with a delay of 6 or 8 hours. Imagine the situation. 6 AM. The weather service updated the forecast. The probability that London reaches 9 degrees tomorrow rose to 80%. Algorithms everywhere already recalculated the data. But on Polymarket the YES button is still sitting there for 10 cents. Because the market has not woken up yet. This bot sees the difference. It buys YES for 10 cents when the real probability is already 80%. It is not guessing. It is buying what is essentially already known. It just waits a day and collects the dollar. 10 cents turn into a dollar. On information available to anyone who can read weather APIs. That evening I called a friend. He has been trading for 3 years. He sits in analytical chats. Draws support levels. I asked him: "How was the last month?" "I broke even. The market is tough right now. Too much noise." I looked at the screen. A bot betting on rain with a 33% win rate. Profit: $63,853. My friend with 3 years of experience and hundreds of hours of analysis. Profit: $0. Who is doing it wrong? I am not asking you to take my word for it. The blockchain does not lie: > Day Three I decided to dig deeper. I looked at the wallet description. I expected something complex. A hedge fund. A team of developers. Secret data sources. I found one line: Claude plus public weather APIs. Ordinary Claude. The one on your phone. Connected to free weather services. No secret stations. No insiders. No millions for infrastructure. Just an AI doing what any of us could do. But we are too lazy. Or bored. Or we think it is too simple to work. If someone already built this with basic Claude and free APIs... What happens when Clawdbot gets direct access to trading? > Day Four I watched the wallet in real time. First bet: loss. Second bet: loss. Third bet: loss. I thought: this is it. The statistics are collapsing. Fourth bet: loss. Fifth bet: loss. Down $12 in an hour. I was ready to write a post about how I overestimated this. Sixth bet: Temperature in Chicago. Win. +$87. Seventh bet: Win. +$94. By evening: 9 losses. 5 wins. Daily total: +$385. No emotions. No posts about injustice. No strategy changes after a loss. Just the next bet. I wrote to my friend. The one who has been trading for 3 years. "How was your day?" "Down $200. Market makers caught my stop loss again." I looked at the screen. A bot with no posts and no loud claims. +$385 for the day on rain bets. My friend with 3 years of experience and dozens of books. Minus $200 and a post about how the system is against him. > Day Five I woke up with a thought that kept me up all night. It finally hit me. It is not about the weather. It is not about APIs. It is not that the bot is "smarter". > It is about what the bot does NOT have: an ego that hates being wrong. No urge to revenge-trade. No boredom from repetition. My friend trades against the market. He tries to be smarter than the crowd. This bot trades against human nature. And nature loses every day. Clawdbot found me this wallet in 14 minutes. The weather bot turned $27 into $63,000 on markets everyone else thinks are trash. Both use the same principle. Do something simple. Remove emotions. Repeat. I do not know when Clawdbot will start trading on its own. Maybe in a month. Maybe in a year. But I know one thing. While we discuss if it is possible... Someone already set up their bot and went to live their life. Right now as you read this. Somewhere a weather service updated a forecast. Polymarket is sleeping. The bot is already entering a position. And my friend is writing a post about how market makers do not let honest people earn. Guess who wakes up tomorrow with money in their account?
0:25

Three days ago I asked myself a dumb question. It was so stupid I was actually ashamed to Google it. Can AI earn money while I sleep? Not saving time. Not automating routine. I mean putting real money into my account while I am not looking at the screen. Everyone says ClawdBot will change how we work. Automation. Task management. Smart replies. But I was sitting in my kitchen thinking about something else entirely. You know that feeling when you look at a tool and realize everyone is using only 1% of its potential? It is like being given a race car and only using it to drive to the store for bread. I decided to test it. I started a notebook. I record everything. > Day One I started with something simple. I gave Clawdbot a task. Find wallets on Polymarket where the numbers do not add up. Where the profit is too high for the win rate. Where the result smells like a system rather than luck. It thought for 14 minutes. I had time to pour a coffee and forget about it. Then the screen flashed. 4 addresses. I scrolled through the first three in a minute. Big bets on politics. They guessed the election. Classic. On the fourth one I stopped. Not because it was the most profitable but because I did not understand what I was looking at. The wallet was not trading politics or sports or anything people write reviews about. It was trading the weather. I read it three times. Weather. Will it be 9 degrees in London tomorrow? Will it rain in Tokyo? These are markets I would not even click on by accident. Then I looked at the numbers. > It started with $27. It is now at $63,853. $27 is two trips to McDonald's. It is nothing. $63,853 is a new car or a down payment on an apartment. It is two years of someone's salary. Between those two numbers was only one thing. Thousands of bets on rain. I closed the tab. Opened it again. Checked if it was a glitch. Real dollars. On markets that look like a bad joke. > Day Two I could not get that wallet out of my head. I went to look at its transaction history. I expected to find one big win that explained everything. A lucky hurricane forecast. Instead I saw thousands of small bets. Boring. "Will the temperature in New York be above 15 degrees?" Then I noticed the detail that finally broke my brain. Its win rate: 33%. It loses more often than it wins. 2 out of 3 bets go to zero. Any normal person with that result would be posting about how the market is unfair. Yet this wallet is sitting on $63,000 in profit. How? I started deconstructing the trades. After an hour I got it. When it loses, it loses 10 or 20 cents. When it wins, it takes $1.00. Loses 9 times in a row? Lost $1.80. Wins 1 time? Got $10.00. > This is not trading. It is math that works as long as you do not interfere with your emotions. Here is how it works. Weather is one of the most predictable things on the planet. Governments invest billions in satellites. Data is updated every 2 or 3 hours. Precision to a tenth of a degree. This data is public. But Polymarket is not a weather station. It updates its markets with a delay of 6 or 8 hours. Imagine the situation. 6 AM. The weather service updated the forecast. The probability that London reaches 9 degrees tomorrow rose to 80%. Algorithms everywhere already recalculated the data. But on Polymarket the YES button is still sitting there for 10 cents. Because the market has not woken up yet. This bot sees the difference. It buys YES for 10 cents when the real probability is already 80%. It is not guessing. It is buying what is essentially already known. It just waits a day and collects the dollar. 10 cents turn into a dollar. On information available to anyone who can read weather APIs. That evening I called a friend. He has been trading for 3 years. He sits in analytical chats. Draws support levels. I asked him: "How was the last month?" "I broke even. The market is tough right now. Too much noise." I looked at the screen. A bot betting on rain with a 33% win rate. Profit: $63,853. My friend with 3 years of experience and hundreds of hours of analysis. Profit: $0. Who is doing it wrong? I am not asking you to take my word for it. The blockchain does not lie: > Day Three I decided to dig deeper. I looked at the wallet description. I expected something complex. A hedge fund. A team of developers. Secret data sources. I found one line: Claude plus public weather APIs. Ordinary Claude. The one on your phone. Connected to free weather services. No secret stations. No insiders. No millions for infrastructure. Just an AI doing what any of us could do. But we are too lazy. Or bored. Or we think it is too simple to work. If someone already built this with basic Claude and free APIs... What happens when Clawdbot gets direct access to trading? > Day Four I watched the wallet in real time. First bet: loss. Second bet: loss. Third bet: loss. I thought: this is it. The statistics are collapsing. Fourth bet: loss. Fifth bet: loss. Down $12 in an hour. I was ready to write a post about how I overestimated this. Sixth bet: Temperature in Chicago. Win. +$87. Seventh bet: Win. +$94. By evening: 9 losses. 5 wins. Daily total: +$385. No emotions. No posts about injustice. No strategy changes after a loss. Just the next bet. I wrote to my friend. The one who has been trading for 3 years. "How was your day?" "Down $200. Market makers caught my stop loss again." I looked at the screen. A bot with no posts and no loud claims. +$385 for the day on rain bets. My friend with 3 years of experience and dozens of books. Minus $200 and a post about how the system is against him. > Day Five I woke up with a thought that kept me up all night. It finally hit me. It is not about the weather. It is not about APIs. It is not that the bot is "smarter". > It is about what the bot does NOT have: an ego that hates being wrong. No urge to revenge-trade. No boredom from repetition. My friend trades against the market. He tries to be smarter than the crowd. This bot trades against human nature. And nature loses every day. Clawdbot found me this wallet in 14 minutes. The weather bot turned $27 into $63,000 on markets everyone else thinks are trash. Both use the same principle. Do something simple. Remove emotions. Repeat. I do not know when Clawdbot will start trading on its own. Maybe in a month. Maybe in a year. But I know one thing. While we discuss if it is possible... Someone already set up their bot and went to live their life. Right now as you read this. Somewhere a weather service updated a forecast. Polymarket is sleeping. The bot is already entering a position. And my friend is writing a post about how market makers do not let honest people earn. Guess who wakes up tomorrow with money in their account?

Blaze

29,808 Aufrufe • vor 6 Monaten

Just in $AMD Anush "Speed is the moat"|ROCm🎙️ In the race to define the future of AI, what's the one advantage that truly lasts? It's not proprietary tech, argues Anush Elangovan Elangovan, VP of AI Software at AMD , but the sustainable speed of innovation. He explains why AMD is rejecting the "walled garden" model for its open source ROCm stack, betting that an open community flywheel is the key to victory. Listen to understand how this open strategy is designed to out-innovate closed systems by empowering developers to solve everything from frontier-model challenges to the mundane, everyday problems that define the "last mile" of AI. AMD ROCm Software: Part 1 Transcript [00:00:00] Andrew Zigler: Joining me is Anush Elangovan, VP of AI software at AMD. And when people talk about AI compute, the conversation often stops at hardware specs, but it's more than just physical chips that win the game. It's also the software ecosystems supporting them. [00:00:18] Andrew Zigler: The prevailing strategy in the industry has been to build something like a walled garden. You know, something closed, proprietary locks, developers in. But AMD is betting on an entirely different play, open source acceleration, and with rock, their open source AI software stack. AMD is building not just hardware parity, but an innovation flywheel that's powered by the community with interoperability and the freedom to scale without all of that pesky lockin. [00:00:48] Andrew Zigler: And in this world, speed is your moat and how fast you can innovate while your platform remains open, flexible, and standardize across all of its applications. That's what we're gonna explore [00:01:00] today. So Anush, I'm really excited to have you here. Welcome to Dev Interrupted. [00:01:04] Anush Elangovan: Thanks for having me. Uh, super excited to chat about it. [00:01:07] Andrew Zigler: Amazing. Well, let's go ahead and dive right in with kind of what I laid it out with in the beginning, the idea of the moat and it being about speed. I wanna unpack that a bit because that came from you when you and I first spoke. And I, and I want to know, you know, how do you define speed inside of AMD beyond just things like hardware, benchmarks. [00:01:27] Anush Elangovan: Yeah, that's a very good question. So when we typically talk about speed, everyone's like, Hey, hardware benchmark specs, right? Like, uh, memory bandwidth or, or flops. And that is one important part of it, uh, AMD does very well. With that, we do have, a, a very good history of executing on that axis. [00:01:47] Anush Elangovan: But when I say speed is the moat, it is about, uh, how we prepare, how we build the muscle to run the race for a long time and run it fast. And it is [00:02:00] not about a single point in time that you've, you've beat some you know, benchmark and, and you declare victory. It's about building the ability to consistently develop and deliver. [00:02:13] Anush Elangovan: Both hardware and software innovation at scale and do it fast, right? Like, you know, we we're increasingly getting to a point where models come out and they're, uh, you know, a year or two ago it was like, Hey, they work on AMD on day zero, which is great, but now they are performing on AMD the day it releases, right? [00:02:32] Anush Elangovan: So, what does it take to Prefetch where the industry is going? Be prepared to intercept. At that point is what you know, I, I refer to as you know, the, the speed factor in, in creating this mode, right? And the mode is just shed all things that hold you back and run as fast as you can. [00:02:53] Anush Elangovan: Uh, because the pace of innovation that is, uh, being seen in, in AI [00:03:00] industries is just. Amazing. Right? And it's like, it's transformational at at how you generate electricity. It's transformational as at how you build data centers. It's transformational at how you deploy compute, networking. It's transformational at what kind of use cases you, you know, uh, use AI for. [00:03:17] Anush Elangovan: Uh, and for that, you need to be prepared to, see what comes tomorrow and be prepared to run the race tomorrow. [00:03:23] Andrew Zigler: Yeah, it's a really great perspective because it highlights that it's not just like a checkpoint that you run through. I like how you called out, like it's not just hitting that benchmark or being the best in class at that moment, in that snapshot, it's about having a. The throughput and about having that dedication to the idea and continuing to deliver on it. [00:03:43] Andrew Zigler: It's not just crossing the threshold, but it's also being the engine. And that's what, that's what protects a business. That is the moat, because the moat is that innovation layer, the faster and more, uh, future forward. That you can work and think, [00:04:00] you know, the better. Uh, we, we talk a lot about like future forward work styles. [00:04:04] Andrew Zigler: Like what are the things I could be doing right now today that are gonna be like, way more useful tomorrow? Let, let's abandon those, workflows that are older and that kind of like, that translates into. An advantage when you work that way. You know, what kind of things have you learned working with, uh, like across all spectrums of people who would use ROCm, right? [00:04:23] Andrew Zigler: You have like the developers, but then you also have the enterprises and you have this large span of adoptees, right? So what is the, what does that look like that you learn? [00:04:32] Anush Elangovan: Yeah, so, so the way I look at it is there are gonna be pockets of different, uh, you know, cadences, right? Like, so people who are deploying in enterprises, for example, right? The validation and how long it takes for them to deploy an LLM that's secure. It's, with guardrails, et cetera, maybe longer. [00:04:52] Anush Elangovan: but you still have to go through the process and you have to be prepared to like, walk that walk to deploy an enterprises. That doesn't mean it's [00:05:00] not fast, that's as fast as you can do for that industry, right? And if you are deploying AI in healthcare, right, it's, it's got its own, uh, cycle. [00:05:07] Anush Elangovan: but in each one of these, you want to see how, like, go down to the essence of what is it that you actually have to do. And, you know, I, I, I like how you framed it. It's like it's, you shed your prior assumptions of how things are done, right. And, and you kind of build up from a, uh, first principles, uh, approach to say, this is how I could use AI to unlock, whatever I'm doing. [00:05:33] Anush Elangovan: And, and, some of it, you know, it's good to really step back and look at. Just question every part of it, right? Like right now you're getting chat GPT and, Gemini competing for like, math, olympiads and, and, uh, college, uh, reasoning, uh, tests. Right? And, and those are like that, that is amazing and increasingly like complex tasks that they're trying to do. [00:05:58] Anush Elangovan: But there may also be like. [00:06:00] More mundane things that AI could, could get applied to. Right? And, and so when we think about shedding old ways, you wanna shed it not just in like the tip of the spear. It's like, you know, I'm gonna see what's the frontier model. It's also, it could be something as simple as. [00:06:18] Anush Elangovan: How do you choose a, a movie, uh, you know, like a recommendation system, right? Or, or, uh, an automated, uh, flight, uh, rebooking system. So the moment, you know, your flight is late, uh, right now it's a notification, right? It's like, oh, you got a text message saying your flight's late. And I got that like three times this week. [00:06:38] Anush Elangovan: But anyway, uh, and, and, and, and, I was just like, okay, so if I were to rethink this. All this MCPs that we have that should be hooked up into an MCP that says, your flight's delayed. Here are your options. If you want, you know, these are the paid options. Yeah. Here are the free options. This will get you back into your you know, Toronto airport [00:07:00] tonight. [00:07:00] Anush Elangovan: Or if you stay, here's a hotel plus this, plus this, plus. It's just like, go ahead is all I should say. Versus now I'm like, okay, can someone, you know, can I call a travel agent? Can I do this? Can I go online and log into And you know, so we gotta fundamentally rethink even those like small, nuances of, things that we do that can be automated out and AI is really, really good at doing something like this, right? Maybe I just explained an AI startup idea right now. Somebody should just start that. [00:07:29] Andrew Zigler: I think you did. Yeah, you definitely did. Someone, one of our listeners is definitely going to lift that off of you. I, I, I, you know, I hate being on the receiving end of those. You feel a little helpless and then you have to like, follow the whole flow. So I know what you mean. Like I, I like how you called out that the build and this like. [00:07:45] Andrew Zigler: Where speed is your moat and the innovation layer is protecting you, is what makes you better than your competitors. How you scale that and you bring that to market. So by understanding the problems that you're solving, uh, throwing away those older assumptions, but also [00:08:00] recognizing that like. We're building every single day, new things and new ways of using stuff that we're still figuring out the implications of. [00:08:08] Andrew Zigler: And so when you have a lot of velocity and you're introducing a lot of new ideas, and maybe you have that workflow now that automatically rebook your flight off of your late flight text message, and uh, I know I would certainly use it, but you know, what kind of philosophies guide the way that y'all think about building this ecosystem to manage that stability while letting folks. [00:08:29] Andrew Zigler: Play with the speed and the assumptions and the airplane re bookings. [00:08:34] Anush Elangovan: so, so I think, you know, we need to peel one layer down, right? and the philosophy is, Hey, we, we just discovered electricity, right? And you know what we're gonna do? We are gonna make motors, uh, or dynamos, right? Like engines. Uh, sure. We don't know if it's gonna be a Ferrari that you're gonna make, or it's a a a a dump truck. [00:08:57] Anush Elangovan: That's good for doing this. But let's [00:09:00] let, which is also required, right? You need a dump truck. You need a garbage truck. And, [00:09:04] Andrew Zigler: Yeah. You need the [00:09:04] Anush Elangovan: course you need, uh, a Ferrari for a midlife crisis, right? So, [00:09:09] Andrew Zigler: precisely. [00:09:10] Anush Elangovan: But, but my, uh, point is what do we build next? And, uh, and this is what I meant by like, okay, let's, let's take those baby steps to build the. [00:09:20] Anush Elangovan: Infrastructure that's required that we know we'll have to use, right? So, so if I just discovered electricity, okay, great. Now one, how do I save this electricity and how do I use it? So there's battery technology, so you need to do something like that, right? Like so. But then you also want to make it into an actionable thing. [00:09:37] Anush Elangovan: You want to make it for like automobiles, or you wanna use it for, you know, powering, uh, entire cities. So it is that transformational. So, uh, AI is that transformational. So, if you distill down, it'll, it'll come down to how do we think about, what we can do with this this fundamental technology that, We may not be aware of what it [00:10:00] is gonna unlock next, but at least you know the next step is clear, right? It's like a dense fog, you know, it's gonna be like, it, it's the right path. You see the light, but it's kind of like out there and, and the steps you're taking are concrete and you're like, okay, this is good. [00:10:16] Anush Elangovan: I, this is better than where I was or where we were. So we are moving forward. So you can build with the. Intuition from what you see in the short term and a tactical view, but towards what you think the future is gonna be. [00:10:28] Andrew Zigler: Right. You almost like we're all in this like fog of war, right? And like you said, you're reaching out and you're trying to step through it. You could think of it too, as like you're in the dark and your hands are up in front of you and you know that. You're, you're not gonna run your face into a wall because your hands are out in front of you, but you're not gonna maybe do much better than that. [00:10:45] Andrew Zigler: So that's kind of like, I think the eco, the, the industry, the world that we find ourselves in, uh, and we all have to, then this becomes the power of an ecosystem, of a group of people working together to create that layer of, [00:11:00] uh, of establishing the [00:11:01] Anush Elangovan: exactly. And I, I, I just, instead of, you know, saying fog of war I describe it as like, you're in this. Beautiful valley with like a morning, uh, fog that's in. You can smell the flowers. You, you hear the birds. You are like, okay, it's, we are in like, uh, utopian paradise and yes, I just need to like, continue the walk, right? [00:11:24] Anush Elangovan: and then move forward with that, conviction that you're in the right spot. [00:11:27] Andrew Zigler: Yeah. So let's talk about that ecosystem world. This nice, I love how you describe it, this grassy side of a hill in the morning that's covered in some mist and maybe we can't see 30 feet in one direction, but it sure is a beautiful hill and it smells nice. And so we're all here. And why is, in that world, why is. [00:11:44] Andrew Zigler: You know, open source, their strategic advantage that y'all are going for in the AI hardware market. And, and then how does like ROCm turn that into wins for people within that ecosystem? [00:11:56] Anush Elangovan: you know, the, the way we look at it is this, is kind of like how I view [00:12:00] AI and the ecosystem, right? But, but it is for everyone to enjoy. Uh, and so we do want to make sure that. You know, it is, uh, beneficial for everyone. [00:12:09] Anush Elangovan: The ecosystem can come in and, and innovate. It's an open innovation engine. and uh, it is very different from, you know, having a walled garden with, Hey, only I know how to do this and I'm gonna do it and throw it over the fence and you can use it or keep walking, right? So we'd like to be good citizens that way, but also. [00:12:30] Anush Elangovan: Uh, it is self-fulfilling in a way, right? Like it, the, the pace at which we innovate with open source is unmatched. Like, you know, our serving engines are like VLLM and, and sg l. Those things, uh, those frameworks are like super, super aggressive in terms of how fast they come out with features and how fast they can you know, get performant models out. [00:12:52] Anush Elangovan: And that compared with what, uh, you'd get from, you know, the likes of like T-R-T-L-L-M or something is always lagging, right? Because you [00:13:00] just can't keep up with you know, 200 commits a week just on one particular model to get that model really performant [00:13:06] Andrew Zigler: And, and, and in that world where, you know, everyone can enjoy the winds of this, what kind of customer stories or innovation stories have really stood out to you and excite you about building and creating this place for developers? [00:13:19] Anush Elangovan: Yeah. So I think the parts that are super exciting for me are when when we get to see a customer that is first skeptical. Then they start a little like, okay, fine, we'll give you a chance. Uh, we do a simple, uh, POC and then they're like, huh, this seems to work. Yeah, we told you it works. [00:13:42] Anush Elangovan: You don't have to change one line of code. Really? Yes, no need to change one line of code. Okay, let's try a production workload. So then they try it. Oh, you're more performant than the competition. Yes. We're more performant than, than the competition. So how much does it cost? And we're like, oh, it's your TCO is better with, uh, [00:14:00] AMD. [00:14:00] Anush Elangovan: So again, they're like, wow, okay, good. So now how do we deploy at scale? And then we go deploy it at scale. And when they give a thumbs up on that and they say, this is good, right? That's when you know, you, you see it go full circle from like, oh, we, we've never heard about AMD to like actually deploy to tens of thousands of GPUs In the order of a few months, right? It, it, it really is fascinating to see and very exciting and invigorating to [00:14:28] Andrew Zigler: Yeah. At like a great exposure to a lot of interesting problems. And, and then people using the infrastructure, the, the technology available to solve those problems. Really specific problems by the way, that's often why they're bringing their data and AI to it, uh, is because it is really specific and important for them. [00:14:45] Andrew Zigler: And there's a, a lot I think that other engineering orgs can learn and even emulate from AMD's success and, and having this open source ecosystem and it causing this acceleration within. You [00:15:00] know, uh, customers and enterprises that use and adopt the tools and, and, and that creates an advantage. And that goes back to why we're talking and like the real thesis of our conversation today. [00:15:10] Andrew Zigler: So how do you think engineering leaders that are listening to this and obviously tapping into this great success AMD has from an open source flywheel, how do you think other, other folks building in the same space can foster that open, first, that open source oriented culture in order to, you know, accelerate their innovation goals? [00:15:29] Anush Elangovan: Yeah, that's a very good question. So the startup that um, was acquired by AMD we, we built, I mean, we started off doing iot stuff and you know, smart ring and all that, right? But in the, the end of like, uh, and not the end, the last six years of the company was building ML compilers. [00:15:47] Anush Elangovan: And ml, ML compilers are like super, uh, complicated, sophisticated, advanced algorithms, dah, dah, dah. but it was all open source, right? So our VCs were like, wait, what do you mean your core [00:16:00] IP is open source? And um, the speed is the moat applied even then, right? It was just like, yes, if you have an idea that. [00:16:08] Anush Elangovan: Because someone saw this idea that you are, they're gonna be able to catch up, then you probably have the wrong idea anyway. But if they are, you know, you execute and they're gonna catch up, that you should assume they're gonna catch up. Right? So you gotta move forward. So keeping it open source is super important. [00:16:25] Anush Elangovan: But also to your question on like, you know, the learnings from an AMD standpoint, right? If there are, hard problems, I'd say dig in and work through it, right? Like there's no way but through it, right? That should be the simple mentality. And more, uh, frequently than not. you'll see that you'll just make it through in a, in, in good form. [00:16:52] Anush Elangovan: But if you doubt it and you're like, oh, I don't know if I should commit, if I'm, I, you know, what should just commit to do the right thing [00:17:00] every step, right? Every step, and just keep taking one step in front of the other. And in no time you'll see that you'll be running. Right. And, and yes, the first few steps will be like, yeah, everyone's complaining about your software quality. [00:17:15] Anush Elangovan: Everyone's complaining about this and that, and it doesn't work. And, and a few steps in, you know, you get, you get the hang of all the complaints that are coming in. You get the feedback loop. You're like, okay, what, what are you prioritizing again? One step in front of the other, right? You just keep knocking that out and then you get to a point where you're, it just becomes second nature, right? To do the, to do the right thing. And, and then yes, if someone gives you two options, you'll be like, fine. This is, uh, you know, there's always the resource trade off. There's always a human capital trade off, but what's the right thing to do? of course, I, I'm pragmatic about what we choose, but, but if the right thing for your long-term success is dig in, go first, principles, make it [00:18:00] happen. [00:18:00] Anush Elangovan: Well. Then just go for that. There's, there is no shortcut to [00:18:04] Andrew Zigler: acknowledging, you know, how it aligns with your mission, your core company goals, and what you're looking to achieve. And, and I, I love how you rightfully called out that in the open source world and you know, you have your technology that you've built, what you think is your moat upon, right? [00:18:22] Andrew Zigler: It's your code and, and to open source that, or to just make it where anyone could peer in is, you know. Scary in one regard, but two, it just kind of feels like you're handing away your throne room in some kind of sense, a very direct feeling sense. But the ultimately, you were really right to call out, and this is something I think about all the time, that the real power there is still the speed This the speed. [00:18:42] Andrew Zigler: That was the moat at the beginning of our conversation. It's the speed in combination with your. Very specific domain understanding of what you're building and what you're creating, and your new role as the steward of that world and how people plug into it, which [00:19:00] has frankly, a lot more influence and power than lording over a closed. [00:19:04] Andrew Zigler: You know, repository or an ecosystem, and like you said, like throwing things over the wall. Sure. There, there might be people always on the other side of that wall, but you're not gonna have a great connection with them. You're not gonna be able to really clearly understand them. I, I like your metaphor of the side of the field of the mountain a lot more. [00:19:23] Andrew Zigler: But, but in the, in this world, you know, where. That speed is, is the power and, and open source is just one way that you can harness that speed to get really far ahead and to innovate. , There's other parts of this equation that you can be experimenting with too, and I'd love to pick your brain about them as a software leader and, and, and one of them is about looking forward and kind of understanding that future that we're all building towards and beyond today's models and hardware. [00:19:48] Andrew Zigler: You know, what do you see as the next major bottleneck or opportunity in the AI compute space? As, as you know, enterprises and folks start to get a little more mature about what's available to [00:20:00] them. [00:20:00] Anush Elangovan: Yeah, I think, the bottleneck and opportunity is, uh, what I'd call, call walking the last mile of ai. Right. Uh, and like I I, I gave you an example, uh, previously, but, but it's similar to that. It's like there are cases where Humans have so many, uh, things to do in your day. You know, like the, if we sit down and actually had a customer focus like, okay, these customers lives, I'm gonna save four hours of this customer's life. And if you actually sit down and look at all of that, it'll be. Easily automatable, easily you know, uh, applicable, uh, for ai, right? [00:20:39] Anush Elangovan: Like, but then making it happen is gonna take a little bit, right? It's like maybe it's, uh, paying your utility bill, right? Or something like that, right? Or, or, your healthcare explanation of benefits. Uh, like, I'm sure you get an explanation of benefits, and I'm like, I, I don't even know what that thing is. [00:20:55] Anush Elangovan: It's just like EOB and like. [00:20:57] Andrew Zigler: it's a big, a big old PDF. Yeah, [00:21:00] exactly. [00:21:01] Anush Elangovan: Like, like, I'm like great straight to the, uh, shredder, right? And but that could be, you know, automated with the ai, right? It, it, it'd be like, Hey, the summary of this thing is you went and visited this day. Everything is okay. Everything is paid for, so don't worry, it's not a bill. [00:21:17] Anush Elangovan: That again, the same, uh, thing, but the sense of what that information overload is could be. Digested by ai, uh, accumulated over time and retrieved when you need it. Like, I don't, I actually don't even need to know this EOB right now, unless of course, whenever I need to know it, that maybe, you know, like for some benefits I need to figure out what do, what did I do over the past year and how do I apply it? Source:
31:35

Just in $AMD Anush "Speed is the moat"|ROCm🎙️ In the race to define the future of AI, what's the one advantage that truly lasts? It's not proprietary tech, argues Anush Elangovan Elangovan, VP of AI Software at AMD , but the sustainable speed of innovation. He explains why AMD is rejecting the "walled garden" model for its open source ROCm stack, betting that an open community flywheel is the key to victory. Listen to understand how this open strategy is designed to out-innovate closed systems by empowering developers to solve everything from frontier-model challenges to the mundane, everyday problems that define the "last mile" of AI. AMD ROCm Software: Part 1 Transcript [00:00:00] Andrew Zigler: Joining me is Anush Elangovan, VP of AI software at AMD. And when people talk about AI compute, the conversation often stops at hardware specs, but it's more than just physical chips that win the game. It's also the software ecosystems supporting them. [00:00:18] Andrew Zigler: The prevailing strategy in the industry has been to build something like a walled garden. You know, something closed, proprietary locks, developers in. But AMD is betting on an entirely different play, open source acceleration, and with rock, their open source AI software stack. AMD is building not just hardware parity, but an innovation flywheel that's powered by the community with interoperability and the freedom to scale without all of that pesky lockin. [00:00:48] Andrew Zigler: And in this world, speed is your moat and how fast you can innovate while your platform remains open, flexible, and standardize across all of its applications. That's what we're gonna explore [00:01:00] today. So Anush, I'm really excited to have you here. Welcome to Dev Interrupted. [00:01:04] Anush Elangovan: Thanks for having me. Uh, super excited to chat about it. [00:01:07] Andrew Zigler: Amazing. Well, let's go ahead and dive right in with kind of what I laid it out with in the beginning, the idea of the moat and it being about speed. I wanna unpack that a bit because that came from you when you and I first spoke. And I, and I want to know, you know, how do you define speed inside of AMD beyond just things like hardware, benchmarks. [00:01:27] Anush Elangovan: Yeah, that's a very good question. So when we typically talk about speed, everyone's like, Hey, hardware benchmark specs, right? Like, uh, memory bandwidth or, or flops. And that is one important part of it, uh, AMD does very well. With that, we do have, a, a very good history of executing on that axis. [00:01:47] Anush Elangovan: But when I say speed is the moat, it is about, uh, how we prepare, how we build the muscle to run the race for a long time and run it fast. And it is [00:02:00] not about a single point in time that you've, you've beat some you know, benchmark and, and you declare victory. It's about building the ability to consistently develop and deliver. [00:02:13] Anush Elangovan: Both hardware and software innovation at scale and do it fast, right? Like, you know, we we're increasingly getting to a point where models come out and they're, uh, you know, a year or two ago it was like, Hey, they work on AMD on day zero, which is great, but now they are performing on AMD the day it releases, right? [00:02:32] Anush Elangovan: So, what does it take to Prefetch where the industry is going? Be prepared to intercept. At that point is what you know, I, I refer to as you know, the, the speed factor in, in creating this mode, right? And the mode is just shed all things that hold you back and run as fast as you can. [00:02:53] Anush Elangovan: Uh, because the pace of innovation that is, uh, being seen in, in AI [00:03:00] industries is just. Amazing. Right? And it's like, it's transformational at at how you generate electricity. It's transformational as at how you build data centers. It's transformational at how you deploy compute, networking. It's transformational at what kind of use cases you, you know, uh, use AI for. [00:03:17] Anush Elangovan: Uh, and for that, you need to be prepared to, see what comes tomorrow and be prepared to run the race tomorrow. [00:03:23] Andrew Zigler: Yeah, it's a really great perspective because it highlights that it's not just like a checkpoint that you run through. I like how you called out, like it's not just hitting that benchmark or being the best in class at that moment, in that snapshot, it's about having a. The throughput and about having that dedication to the idea and continuing to deliver on it. [00:03:43] Andrew Zigler: It's not just crossing the threshold, but it's also being the engine. And that's what, that's what protects a business. That is the moat, because the moat is that innovation layer, the faster and more, uh, future forward. That you can work and think, [00:04:00] you know, the better. Uh, we, we talk a lot about like future forward work styles. [00:04:04] Andrew Zigler: Like what are the things I could be doing right now today that are gonna be like, way more useful tomorrow? Let, let's abandon those, workflows that are older and that kind of like, that translates into. An advantage when you work that way. You know, what kind of things have you learned working with, uh, like across all spectrums of people who would use ROCm, right? [00:04:23] Andrew Zigler: You have like the developers, but then you also have the enterprises and you have this large span of adoptees, right? So what is the, what does that look like that you learn? [00:04:32] Anush Elangovan: Yeah, so, so the way I look at it is there are gonna be pockets of different, uh, you know, cadences, right? Like, so people who are deploying in enterprises, for example, right? The validation and how long it takes for them to deploy an LLM that's secure. It's, with guardrails, et cetera, maybe longer. [00:04:52] Anush Elangovan: but you still have to go through the process and you have to be prepared to like, walk that walk to deploy an enterprises. That doesn't mean it's [00:05:00] not fast, that's as fast as you can do for that industry, right? And if you are deploying AI in healthcare, right, it's, it's got its own, uh, cycle. [00:05:07] Anush Elangovan: but in each one of these, you want to see how, like, go down to the essence of what is it that you actually have to do. And, you know, I, I, I like how you framed it. It's like it's, you shed your prior assumptions of how things are done, right. And, and you kind of build up from a, uh, first principles, uh, approach to say, this is how I could use AI to unlock, whatever I'm doing. [00:05:33] Anush Elangovan: And, and, some of it, you know, it's good to really step back and look at. Just question every part of it, right? Like right now you're getting chat GPT and, Gemini competing for like, math, olympiads and, and, uh, college, uh, reasoning, uh, tests. Right? And, and those are like that, that is amazing and increasingly like complex tasks that they're trying to do. [00:05:58] Anush Elangovan: But there may also be like. [00:06:00] More mundane things that AI could, could get applied to. Right? And, and so when we think about shedding old ways, you wanna shed it not just in like the tip of the spear. It's like, you know, I'm gonna see what's the frontier model. It's also, it could be something as simple as. [00:06:18] Anush Elangovan: How do you choose a, a movie, uh, you know, like a recommendation system, right? Or, or, uh, an automated, uh, flight, uh, rebooking system. So the moment, you know, your flight is late, uh, right now it's a notification, right? It's like, oh, you got a text message saying your flight's late. And I got that like three times this week. [00:06:38] Anush Elangovan: But anyway, uh, and, and, and, and, I was just like, okay, so if I were to rethink this. All this MCPs that we have that should be hooked up into an MCP that says, your flight's delayed. Here are your options. If you want, you know, these are the paid options. Yeah. Here are the free options. This will get you back into your you know, Toronto airport [00:07:00] tonight. [00:07:00] Anush Elangovan: Or if you stay, here's a hotel plus this, plus this, plus. It's just like, go ahead is all I should say. Versus now I'm like, okay, can someone, you know, can I call a travel agent? Can I do this? Can I go online and log into And you know, so we gotta fundamentally rethink even those like small, nuances of, things that we do that can be automated out and AI is really, really good at doing something like this, right? Maybe I just explained an AI startup idea right now. Somebody should just start that. [00:07:29] Andrew Zigler: I think you did. Yeah, you definitely did. Someone, one of our listeners is definitely going to lift that off of you. I, I, I, you know, I hate being on the receiving end of those. You feel a little helpless and then you have to like, follow the whole flow. So I know what you mean. Like I, I like how you called out that the build and this like. [00:07:45] Andrew Zigler: Where speed is your moat and the innovation layer is protecting you, is what makes you better than your competitors. How you scale that and you bring that to market. So by understanding the problems that you're solving, uh, throwing away those older assumptions, but also [00:08:00] recognizing that like. We're building every single day, new things and new ways of using stuff that we're still figuring out the implications of. [00:08:08] Andrew Zigler: And so when you have a lot of velocity and you're introducing a lot of new ideas, and maybe you have that workflow now that automatically rebook your flight off of your late flight text message, and uh, I know I would certainly use it, but you know, what kind of philosophies guide the way that y'all think about building this ecosystem to manage that stability while letting folks. [00:08:29] Andrew Zigler: Play with the speed and the assumptions and the airplane re bookings. [00:08:34] Anush Elangovan: so, so I think, you know, we need to peel one layer down, right? and the philosophy is, Hey, we, we just discovered electricity, right? And you know what we're gonna do? We are gonna make motors, uh, or dynamos, right? Like engines. Uh, sure. We don't know if it's gonna be a Ferrari that you're gonna make, or it's a a a a dump truck. [00:08:57] Anush Elangovan: That's good for doing this. But let's [00:09:00] let, which is also required, right? You need a dump truck. You need a garbage truck. And, [00:09:04] Andrew Zigler: Yeah. You need the [00:09:04] Anush Elangovan: course you need, uh, a Ferrari for a midlife crisis, right? So, [00:09:09] Andrew Zigler: precisely. [00:09:10] Anush Elangovan: But, but my, uh, point is what do we build next? And, uh, and this is what I meant by like, okay, let's, let's take those baby steps to build the. [00:09:20] Anush Elangovan: Infrastructure that's required that we know we'll have to use, right? So, so if I just discovered electricity, okay, great. Now one, how do I save this electricity and how do I use it? So there's battery technology, so you need to do something like that, right? Like so. But then you also want to make it into an actionable thing. [00:09:37] Anush Elangovan: You want to make it for like automobiles, or you wanna use it for, you know, powering, uh, entire cities. So it is that transformational. So, uh, AI is that transformational. So, if you distill down, it'll, it'll come down to how do we think about, what we can do with this this fundamental technology that, We may not be aware of what it [00:10:00] is gonna unlock next, but at least you know the next step is clear, right? It's like a dense fog, you know, it's gonna be like, it, it's the right path. You see the light, but it's kind of like out there and, and the steps you're taking are concrete and you're like, okay, this is good. [00:10:16] Anush Elangovan: I, this is better than where I was or where we were. So we are moving forward. So you can build with the. Intuition from what you see in the short term and a tactical view, but towards what you think the future is gonna be. [00:10:28] Andrew Zigler: Right. You almost like we're all in this like fog of war, right? And like you said, you're reaching out and you're trying to step through it. You could think of it too, as like you're in the dark and your hands are up in front of you and you know that. You're, you're not gonna run your face into a wall because your hands are out in front of you, but you're not gonna maybe do much better than that. [00:10:45] Andrew Zigler: So that's kind of like, I think the eco, the, the industry, the world that we find ourselves in, uh, and we all have to, then this becomes the power of an ecosystem, of a group of people working together to create that layer of, [00:11:00] uh, of establishing the [00:11:01] Anush Elangovan: exactly. And I, I, I just, instead of, you know, saying fog of war I describe it as like, you're in this. Beautiful valley with like a morning, uh, fog that's in. You can smell the flowers. You, you hear the birds. You are like, okay, it's, we are in like, uh, utopian paradise and yes, I just need to like, continue the walk, right? [00:11:24] Anush Elangovan: and then move forward with that, conviction that you're in the right spot. [00:11:27] Andrew Zigler: Yeah. So let's talk about that ecosystem world. This nice, I love how you describe it, this grassy side of a hill in the morning that's covered in some mist and maybe we can't see 30 feet in one direction, but it sure is a beautiful hill and it smells nice. And so we're all here. And why is, in that world, why is. [00:11:44] Andrew Zigler: You know, open source, their strategic advantage that y'all are going for in the AI hardware market. And, and then how does like ROCm turn that into wins for people within that ecosystem? [00:11:56] Anush Elangovan: you know, the, the way we look at it is this, is kind of like how I view [00:12:00] AI and the ecosystem, right? But, but it is for everyone to enjoy. Uh, and so we do want to make sure that. You know, it is, uh, beneficial for everyone. [00:12:09] Anush Elangovan: The ecosystem can come in and, and innovate. It's an open innovation engine. and uh, it is very different from, you know, having a walled garden with, Hey, only I know how to do this and I'm gonna do it and throw it over the fence and you can use it or keep walking, right? So we'd like to be good citizens that way, but also. [00:12:30] Anush Elangovan: Uh, it is self-fulfilling in a way, right? Like it, the, the pace at which we innovate with open source is unmatched. Like, you know, our serving engines are like VLLM and, and sg l. Those things, uh, those frameworks are like super, super aggressive in terms of how fast they come out with features and how fast they can you know, get performant models out. [00:12:52] Anush Elangovan: And that compared with what, uh, you'd get from, you know, the likes of like T-R-T-L-L-M or something is always lagging, right? Because you [00:13:00] just can't keep up with you know, 200 commits a week just on one particular model to get that model really performant [00:13:06] Andrew Zigler: And, and, and in that world where, you know, everyone can enjoy the winds of this, what kind of customer stories or innovation stories have really stood out to you and excite you about building and creating this place for developers? [00:13:19] Anush Elangovan: Yeah. So I think the parts that are super exciting for me are when when we get to see a customer that is first skeptical. Then they start a little like, okay, fine, we'll give you a chance. Uh, we do a simple, uh, POC and then they're like, huh, this seems to work. Yeah, we told you it works. [00:13:42] Anush Elangovan: You don't have to change one line of code. Really? Yes, no need to change one line of code. Okay, let's try a production workload. So then they try it. Oh, you're more performant than the competition. Yes. We're more performant than, than the competition. So how much does it cost? And we're like, oh, it's your TCO is better with, uh, [00:14:00] AMD. [00:14:00] Anush Elangovan: So again, they're like, wow, okay, good. So now how do we deploy at scale? And then we go deploy it at scale. And when they give a thumbs up on that and they say, this is good, right? That's when you know, you, you see it go full circle from like, oh, we, we've never heard about AMD to like actually deploy to tens of thousands of GPUs In the order of a few months, right? It, it, it really is fascinating to see and very exciting and invigorating to [00:14:28] Andrew Zigler: Yeah. At like a great exposure to a lot of interesting problems. And, and then people using the infrastructure, the, the technology available to solve those problems. Really specific problems by the way, that's often why they're bringing their data and AI to it, uh, is because it is really specific and important for them. [00:14:45] Andrew Zigler: And there's a, a lot I think that other engineering orgs can learn and even emulate from AMD's success and, and having this open source ecosystem and it causing this acceleration within. You [00:15:00] know, uh, customers and enterprises that use and adopt the tools and, and, and that creates an advantage. And that goes back to why we're talking and like the real thesis of our conversation today. [00:15:10] Andrew Zigler: So how do you think engineering leaders that are listening to this and obviously tapping into this great success AMD has from an open source flywheel, how do you think other, other folks building in the same space can foster that open, first, that open source oriented culture in order to, you know, accelerate their innovation goals? [00:15:29] Anush Elangovan: Yeah, that's a very good question. So the startup that um, was acquired by AMD we, we built, I mean, we started off doing iot stuff and you know, smart ring and all that, right? But in the, the end of like, uh, and not the end, the last six years of the company was building ML compilers. [00:15:47] Anush Elangovan: And ml, ML compilers are like super, uh, complicated, sophisticated, advanced algorithms, dah, dah, dah. but it was all open source, right? So our VCs were like, wait, what do you mean your core [00:16:00] IP is open source? And um, the speed is the moat applied even then, right? It was just like, yes, if you have an idea that. [00:16:08] Anush Elangovan: Because someone saw this idea that you are, they're gonna be able to catch up, then you probably have the wrong idea anyway. But if they are, you know, you execute and they're gonna catch up, that you should assume they're gonna catch up. Right? So you gotta move forward. So keeping it open source is super important. [00:16:25] Anush Elangovan: But also to your question on like, you know, the learnings from an AMD standpoint, right? If there are, hard problems, I'd say dig in and work through it, right? Like there's no way but through it, right? That should be the simple mentality. And more, uh, frequently than not. you'll see that you'll just make it through in a, in, in good form. [00:16:52] Anush Elangovan: But if you doubt it and you're like, oh, I don't know if I should commit, if I'm, I, you know, what should just commit to do the right thing [00:17:00] every step, right? Every step, and just keep taking one step in front of the other. And in no time you'll see that you'll be running. Right. And, and yes, the first few steps will be like, yeah, everyone's complaining about your software quality. [00:17:15] Anush Elangovan: Everyone's complaining about this and that, and it doesn't work. And, and a few steps in, you know, you get, you get the hang of all the complaints that are coming in. You get the feedback loop. You're like, okay, what, what are you prioritizing again? One step in front of the other, right? You just keep knocking that out and then you get to a point where you're, it just becomes second nature, right? To do the, to do the right thing. And, and then yes, if someone gives you two options, you'll be like, fine. This is, uh, you know, there's always the resource trade off. There's always a human capital trade off, but what's the right thing to do? of course, I, I'm pragmatic about what we choose, but, but if the right thing for your long-term success is dig in, go first, principles, make it [00:18:00] happen. [00:18:00] Anush Elangovan: Well. Then just go for that. There's, there is no shortcut to [00:18:04] Andrew Zigler: acknowledging, you know, how it aligns with your mission, your core company goals, and what you're looking to achieve. And, and I, I love how you rightfully called out that in the open source world and you know, you have your technology that you've built, what you think is your moat upon, right? [00:18:22] Andrew Zigler: It's your code and, and to open source that, or to just make it where anyone could peer in is, you know. Scary in one regard, but two, it just kind of feels like you're handing away your throne room in some kind of sense, a very direct feeling sense. But the ultimately, you were really right to call out, and this is something I think about all the time, that the real power there is still the speed This the speed. [00:18:42] Andrew Zigler: That was the moat at the beginning of our conversation. It's the speed in combination with your. Very specific domain understanding of what you're building and what you're creating, and your new role as the steward of that world and how people plug into it, which [00:19:00] has frankly, a lot more influence and power than lording over a closed. [00:19:04] Andrew Zigler: You know, repository or an ecosystem, and like you said, like throwing things over the wall. Sure. There, there might be people always on the other side of that wall, but you're not gonna have a great connection with them. You're not gonna be able to really clearly understand them. I, I like your metaphor of the side of the field of the mountain a lot more. [00:19:23] Andrew Zigler: But, but in the, in this world, you know, where. That speed is, is the power and, and open source is just one way that you can harness that speed to get really far ahead and to innovate. , There's other parts of this equation that you can be experimenting with too, and I'd love to pick your brain about them as a software leader and, and, and one of them is about looking forward and kind of understanding that future that we're all building towards and beyond today's models and hardware. [00:19:48] Andrew Zigler: You know, what do you see as the next major bottleneck or opportunity in the AI compute space? As, as you know, enterprises and folks start to get a little more mature about what's available to [00:20:00] them. [00:20:00] Anush Elangovan: Yeah, I think, the bottleneck and opportunity is, uh, what I'd call, call walking the last mile of ai. Right. Uh, and like I I, I gave you an example, uh, previously, but, but it's similar to that. It's like there are cases where Humans have so many, uh, things to do in your day. You know, like the, if we sit down and actually had a customer focus like, okay, these customers lives, I'm gonna save four hours of this customer's life. And if you actually sit down and look at all of that, it'll be. Easily automatable, easily you know, uh, applicable, uh, for ai, right? [00:20:39] Anush Elangovan: Like, but then making it happen is gonna take a little bit, right? It's like maybe it's, uh, paying your utility bill, right? Or something like that, right? Or, or, your healthcare explanation of benefits. Uh, like, I'm sure you get an explanation of benefits, and I'm like, I, I don't even know what that thing is. [00:20:55] Anush Elangovan: It's just like EOB and like. [00:20:57] Andrew Zigler: it's a big, a big old PDF. Yeah, [00:21:00] exactly. [00:21:01] Anush Elangovan: Like, like, I'm like great straight to the, uh, shredder, right? And but that could be, you know, automated with the ai, right? It, it, it'd be like, Hey, the summary of this thing is you went and visited this day. Everything is okay. Everything is paid for, so don't worry, it's not a bill. [00:21:17] Anush Elangovan: That again, the same, uh, thing, but the sense of what that information overload is could be. Digested by ai, uh, accumulated over time and retrieved when you need it. Like, I don't, I actually don't even need to know this EOB right now, unless of course, whenever I need to know it, that maybe, you know, like for some benefits I need to figure out what do, what did I do over the past year and how do I apply it? Source:

Mike

14,195 Aufrufe • vor 8 Monaten

You round the bend of that forgotten mountain trail, boots crunching on pine needles, the air thick with the scent of damp earth and rushing water. The stream’s been whispering to you for miles—clear, cold, cutting through granite like it’s been waiting centuries just for this moment. Then you see it: a small carved-out alcove tucked right against the bank, maybe three feet deep and wide enough for one person to kneel, the rock smoothed by who-knows-how-many old-timers before you. Sunlight slants in like a spotlight, turning the water’s edge into liquid gold. Your pulse kicks up. This isn’t just a pretty spot. This is a natural sluice box, a hidden trap where the current slows, drops its heavy cargo, and leaves the black sand shimmering like midnight promises. You drop your pack, heart hammering. Modern panning isn’t the back-breaking pick-and-shovel grind of 1849 anymore—you’ve got the kit: a lightweight 22-inch ceramic pan with riffles that catch every speck, a classifier screen to sort out the big stuff in seconds, a snuffer bottle the size of your thumb, and maybe a tiny hand sluice you can prop in the alcove’s curve if the mood strikes. Gold’s sitting at over $5,100 an ounce right now—prices that would’ve made those Forty-Niners weep. One decent pinch of flakes and you’re already ahead of your coffee money. You scoop a panful of that dark, promising gravel right from the alcove’s lip, plunge it into the stream, and start the dance: tilt, swirl, dip, let the river do the work. Lighter sands and pebbles wash away in lazy spirals while the heavies—magnetite, hematite, and yes, maybe that telltale flash of yellow—sink to the bottom like they belong there. The excitement builds with every swirl. You see the first “color”—tiny glittering threads hugging the riffles. Another pan. More. The alcove’s geometry is perfect; the stream bends just enough here to drop the good stuff century after century. You’re grinning like a kid, knees wet, sun warm on your neck, imagining the nugget that’s been waiting since the last ice age. A full vial after an hour? Two? The daydream races: sell it raw, melt it into a ring, or just keep it in a jar on the shelf as proof you outsmarted the mountains. Modern tools make it feel almost too easy—apps on your phone showing public claim maps, lightweight gear that fits in a daypack, even a cheap UV light to spot fluorescent tracers if you get fancy. This alcove could be your lucky strike. The thrill is electric. Here’s the straight truth, though, whispered like the stream itself: no, modern hand-panning isn’t a lucrative feat. Not in the “quit your job and buy a yacht” way. The average recreational panner pulls about 0.041 grams an hour in decent ground—that’s roughly twenty bucks at today’s prices, before gas, food, and the sheer sweat equity. A banner day in a rich pocket might net you a gram or two (maybe $160–$170), enough to cover your weekend and leave a little sparkle in your pocket. But most folks walk away with beautiful memories, a few flakes, and the quiet satisfaction of having chased the dream instead of scrolling on the couch. The real gold? The peace, the exercise, the story you’ll tell when you get home with wet boots and a grin you can’t wipe off. So yeah… that carved-out alcove next to the stream? It’s calling. Grab your pan, let the excitement build, and go see what the river’s been hiding. Just don’t quit your day job—let the mountains pay you in wonder instead.
0:53

You round the bend of that forgotten mountain trail, boots crunching on pine needles, the air thick with the scent of damp earth and rushing water. The stream’s been whispering to you for miles—clear, cold, cutting through granite like it’s been waiting centuries just for this moment. Then you see it: a small carved-out alcove tucked right against the bank, maybe three feet deep and wide enough for one person to kneel, the rock smoothed by who-knows-how-many old-timers before you. Sunlight slants in like a spotlight, turning the water’s edge into liquid gold. Your pulse kicks up. This isn’t just a pretty spot. This is a natural sluice box, a hidden trap where the current slows, drops its heavy cargo, and leaves the black sand shimmering like midnight promises. You drop your pack, heart hammering. Modern panning isn’t the back-breaking pick-and-shovel grind of 1849 anymore—you’ve got the kit: a lightweight 22-inch ceramic pan with riffles that catch every speck, a classifier screen to sort out the big stuff in seconds, a snuffer bottle the size of your thumb, and maybe a tiny hand sluice you can prop in the alcove’s curve if the mood strikes. Gold’s sitting at over $5,100 an ounce right now—prices that would’ve made those Forty-Niners weep. One decent pinch of flakes and you’re already ahead of your coffee money. You scoop a panful of that dark, promising gravel right from the alcove’s lip, plunge it into the stream, and start the dance: tilt, swirl, dip, let the river do the work. Lighter sands and pebbles wash away in lazy spirals while the heavies—magnetite, hematite, and yes, maybe that telltale flash of yellow—sink to the bottom like they belong there. The excitement builds with every swirl. You see the first “color”—tiny glittering threads hugging the riffles. Another pan. More. The alcove’s geometry is perfect; the stream bends just enough here to drop the good stuff century after century. You’re grinning like a kid, knees wet, sun warm on your neck, imagining the nugget that’s been waiting since the last ice age. A full vial after an hour? Two? The daydream races: sell it raw, melt it into a ring, or just keep it in a jar on the shelf as proof you outsmarted the mountains. Modern tools make it feel almost too easy—apps on your phone showing public claim maps, lightweight gear that fits in a daypack, even a cheap UV light to spot fluorescent tracers if you get fancy. This alcove could be your lucky strike. The thrill is electric. Here’s the straight truth, though, whispered like the stream itself: no, modern hand-panning isn’t a lucrative feat. Not in the “quit your job and buy a yacht” way. The average recreational panner pulls about 0.041 grams an hour in decent ground—that’s roughly twenty bucks at today’s prices, before gas, food, and the sheer sweat equity. A banner day in a rich pocket might net you a gram or two (maybe $160–$170), enough to cover your weekend and leave a little sparkle in your pocket. But most folks walk away with beautiful memories, a few flakes, and the quiet satisfaction of having chased the dream instead of scrolling on the couch. The real gold? The peace, the exercise, the story you’ll tell when you get home with wet boots and a grin you can’t wipe off. So yeah… that carved-out alcove next to the stream? It’s calling. Grab your pan, let the excitement build, and go see what the river’s been hiding. Just don’t quit your day job—let the mountains pay you in wonder instead.

🚫👁️Drinks on Saturday🇺🇸

1,322,206 Aufrufe • vor 5 Monaten

It's not about what we have or don't have that drives our trading decisions—it's what we're afraid of losing. This fear of loss has often led me down the false path of perfectionism. Yet true mastery and profitability in trading, like in art, comes from embracing the craft's imperfections. ✉️ At the recent Mumbai traders' meetup, Chhirag Kedia spoke a line that has resonated with me all week— वो आदमी सफल होने से कोई रोक नहीं सकता जो अपनी कश्ती जला कर आया हो! It made me reflect on my trading journey and how my risk-taking appetite has evolved over the years. I'm inherently risk-conservative as an individual, though my career decisions and trajectory paint a completely opposite picture. I've never gone bust or even had a significant drawdown in my trading life—initially because I was too cautious, and now because my skills have improved. When I look back at my interactions with other traders and analyse my own performance graph, I am noticing a pattern: traders who started recklessly or faced major drawdowns—but persistently improved their execution—often developed better and faster learning curves than those who began cautiously with small positions. Even Quallamaggie (Q) and Zanger (Z) demonstrated similar patterns—their initial failures didn't reduce their risk appetite or aggression. Rather, they increased their risk appetite as their accounts grew larger. When ordinary traders dismiss Q and Z as exceptions in the trading world, they're likely rationalizing their own fears—fear of bouncing back from setbacks beyond their risk comfort zone, and fear of not having enough skin in the game. After all, as Taleb says, courage is the only virtue you cannot fake. I wonder if I would have been a better trader today had I started more aggressively—even borderline recklessly—and then learned to control that aggression, rather than the other way around. Has my obsession with perfection (or trying to get close to it) actually slowed down my learning curve as a trader? Perfectionism and Self-Abuse In every trade—even with flawless setups and meticulously calculated risks—there are countless ways to feel wrong, whether you make money or not: You buy and it goes down You don't buy and it goes up You buy, it falls, you sell—then it goes up It goes up, you sell, and it keeps going up It goes up, you buy, it goes up further—then drops You buy with half size and it moves up; you pyramid with full size and it goes down You buy with double size and it drops; you buy with half size and it doesn't go up as much . . . and the list can go on But there is only one way you'll feel right: When you buy and it immediately goes up, and when you sell and it immediately goes down. And this is a very very rare instance. But the pursuit of perfect trade—trying to capture both the first and last eighth of every trade—is where much self-belief and confidence is needlessly lost. The search for the perfect chart, perfect market conditions, and perfect mindset was probably the most paralyzing form of self-abuse in trading I had done. It led me to the comfort of inaction rather than risk the ego to scrape the imperfect rewards on offer. Lets take up an example that was discussed in the last Mumbai meetup - PDMJE Paper - Trade Objective An Episodic Pivot setup, gapping out of a big base, to be held as a longer positional play. Entry (Orange lines) 29th October 2024 Entry 113.95, Stop Loss 2% - 111.7 (~Day low) Risk on Trade 0.75% of portfolio, Size - 35% Sells (Blue Lines) 50% Sell at 6R - 128.6 - This was not a planned sell, but I observed weak market depth with sporadic volumes over the next 3-4 days. As a precaution, I reduced size in this illiquid counter. = 3R 50% sell at ~12R - 143 - The swing move had become overextended, moving far from the 10/21 EMA. The position was sold when price broke below the opening range lows in weakness. = 6R Impact of portfolio - 6.75% Analyzing this trade up to this point, it was executed well with little room for improvement—almost perfect. This was also a very obvious EP trade, and many others had executed it similarly. In the group discussion of this trade, even though everyone had profited, regret about the price movements after exit overshadowed the satisfaction from actual gains. If you had missed the pullback entries near the 21 EMA on November 13th (which wasn't actually setup-ready) or the breakout entry that triggered on November 29th (when markets were strong and many stocks were breaking out), you would have likely missed the 80% move that happened in less than a month, which I did. The traders in the group spent much of their emotional energy obsessing over this missed opportunity, ultimately accumulating emotional debt from the markets. The paradox of trading is that while realized losses may dent our account, missing potential gains often dent our confidence. Each time we let the fear of missed opportunities overshadow our actual successes, we unconsciously train ourselves to trade smaller, not bigger - precisely when our proven profitability should be empowering us to scale up. It took me years to understand that successful trading doesn't require feeling happy. I can make sound decisions and evaluate my performance objectively, even when I feel frustrated about missed opportunities. The only true nobility in this business is making money, not chasing dopamine highs. The Adjustment Taking a loss is straightforward—we simply follow our stop loss. The real challenge—and greatest potential for regret—lies in managing profitable positions, particularly when a stock has made big moves in a short period. This is particularly common in magnitude trades like an EP or IPO where our objective is to hold for a longer duration and sell into weakness, but often have moments when the stock is overextended in the short term with a high probability of pulling back. However, we hesitate to sell either because it conflicts with our original trade objective or because we fear missing the chance to buy back during the pullback. This is where many professional traders actively manage their core positions. Rather than passively waiting for a deeper trailing stop loss to trigger during weakness, they sell a portion when the price becomes extended and buy back the same amount at a lower price. This strategy proves more effective than enduring drawdowns while waiting for a formal pullback setup at support levels or moving averages. Let's take a recent example of IGIL (5 min chart) - Trade Objective An early-stage IPO setup displaying a typical volatility contraction pattern (VCP) on intraday charts. The plan is to hold this as a longer-term position, treating it as an All-or-Nothing trade. Entry (Orange line) 24th December 2024 Entry 504, Stop Loss 2% - 493.95 Risk on Trade 0.50% of portfolio, Size - 23% of pf Adjustment Context - 27th December 2024 The stock had surged powerfully over the previous two days, hitting Upper Circuits. On the third day, despite gapping up at open, it immediately broke down during the opening range - like a typical parabolic short setup. This was a point with a high probability of a short-term pullback or consolidation. Sells (Blue Line) 27th December - 585 - 50% size Buyback (Orange line 2) 27th December - 565 - 50% size - Gained 1R Rationale - My anchor bias was for the price to cool off for a bit. - At the 27th open, the price was already at ~8R+ for me. Even if the stock rose further after I sold my partial position, I wouldn't regret it much—I had already secured 4R with half my position still pending, well above my journal averages. This served as an important emotional anchor point for this adjustment. - When buying back, I was simply looking to average down my costs without a specific target or a perfect setup in mind. In this case, I bought back at 565, as the buyback itself presented a good psychological point to cover (10 Rs initial stop loss, 20 Rs points averaged ~1R at half size). It could have been lower too if the breakdown was slower. This was more intuitive and intentionally imperfect. - I close most of these adjustments on the same day since they are just short-term pullbacks and my overall bias remains bullish. A magnitude trade can also be looked at as a combination of several intraday trades around a core position. - This adjustment method applies specifically to magnitude trades like EP and IPO positions, where the trade objective aligns with pyramiding or averaging costs. Caveat You might think this is a cherry-picked example—and you'd be partly right, since it's one of my better and more recent trades (you can see similar patterns in Care or TI). However, I urge you to stay open to the concept. Look back at your previous trades where you held positions too long passively—you'll often find that temporary extensions and pullbacks were easily visible, offering opportunities to capture additional R’s along the way. Traders commonly face similar emotions and dilemmas when deciding how to act in these situations. End Note
0:45

It's not about what we have or don't have that drives our trading decisions—it's what we're afraid of losing. This fear of loss has often led me down the false path of perfectionism. Yet true mastery and profitability in trading, like in art, comes from embracing the craft's imperfections. ✉️ At the recent Mumbai traders' meetup, Chhirag Kedia spoke a line that has resonated with me all week— वो आदमी सफल होने से कोई रोक नहीं सकता जो अपनी कश्ती जला कर आया हो! It made me reflect on my trading journey and how my risk-taking appetite has evolved over the years. I'm inherently risk-conservative as an individual, though my career decisions and trajectory paint a completely opposite picture. I've never gone bust or even had a significant drawdown in my trading life—initially because I was too cautious, and now because my skills have improved. When I look back at my interactions with other traders and analyse my own performance graph, I am noticing a pattern: traders who started recklessly or faced major drawdowns—but persistently improved their execution—often developed better and faster learning curves than those who began cautiously with small positions. Even Quallamaggie (Q) and Zanger (Z) demonstrated similar patterns—their initial failures didn't reduce their risk appetite or aggression. Rather, they increased their risk appetite as their accounts grew larger. When ordinary traders dismiss Q and Z as exceptions in the trading world, they're likely rationalizing their own fears—fear of bouncing back from setbacks beyond their risk comfort zone, and fear of not having enough skin in the game. After all, as Taleb says, courage is the only virtue you cannot fake. I wonder if I would have been a better trader today had I started more aggressively—even borderline recklessly—and then learned to control that aggression, rather than the other way around. Has my obsession with perfection (or trying to get close to it) actually slowed down my learning curve as a trader? Perfectionism and Self-Abuse In every trade—even with flawless setups and meticulously calculated risks—there are countless ways to feel wrong, whether you make money or not: You buy and it goes down You don't buy and it goes up You buy, it falls, you sell—then it goes up It goes up, you sell, and it keeps going up It goes up, you buy, it goes up further—then drops You buy with half size and it moves up; you pyramid with full size and it goes down You buy with double size and it drops; you buy with half size and it doesn't go up as much . . . and the list can go on But there is only one way you'll feel right: When you buy and it immediately goes up, and when you sell and it immediately goes down. And this is a very very rare instance. But the pursuit of perfect trade—trying to capture both the first and last eighth of every trade—is where much self-belief and confidence is needlessly lost. The search for the perfect chart, perfect market conditions, and perfect mindset was probably the most paralyzing form of self-abuse in trading I had done. It led me to the comfort of inaction rather than risk the ego to scrape the imperfect rewards on offer. Lets take up an example that was discussed in the last Mumbai meetup - PDMJE Paper - Trade Objective An Episodic Pivot setup, gapping out of a big base, to be held as a longer positional play. Entry (Orange lines) 29th October 2024 Entry 113.95, Stop Loss 2% - 111.7 (~Day low) Risk on Trade 0.75% of portfolio, Size - 35% Sells (Blue Lines) 50% Sell at 6R - 128.6 - This was not a planned sell, but I observed weak market depth with sporadic volumes over the next 3-4 days. As a precaution, I reduced size in this illiquid counter. = 3R 50% sell at ~12R - 143 - The swing move had become overextended, moving far from the 10/21 EMA. The position was sold when price broke below the opening range lows in weakness. = 6R Impact of portfolio - 6.75% Analyzing this trade up to this point, it was executed well with little room for improvement—almost perfect. This was also a very obvious EP trade, and many others had executed it similarly. In the group discussion of this trade, even though everyone had profited, regret about the price movements after exit overshadowed the satisfaction from actual gains. If you had missed the pullback entries near the 21 EMA on November 13th (which wasn't actually setup-ready) or the breakout entry that triggered on November 29th (when markets were strong and many stocks were breaking out), you would have likely missed the 80% move that happened in less than a month, which I did. The traders in the group spent much of their emotional energy obsessing over this missed opportunity, ultimately accumulating emotional debt from the markets. The paradox of trading is that while realized losses may dent our account, missing potential gains often dent our confidence. Each time we let the fear of missed opportunities overshadow our actual successes, we unconsciously train ourselves to trade smaller, not bigger - precisely when our proven profitability should be empowering us to scale up. It took me years to understand that successful trading doesn't require feeling happy. I can make sound decisions and evaluate my performance objectively, even when I feel frustrated about missed opportunities. The only true nobility in this business is making money, not chasing dopamine highs. The Adjustment Taking a loss is straightforward—we simply follow our stop loss. The real challenge—and greatest potential for regret—lies in managing profitable positions, particularly when a stock has made big moves in a short period. This is particularly common in magnitude trades like an EP or IPO where our objective is to hold for a longer duration and sell into weakness, but often have moments when the stock is overextended in the short term with a high probability of pulling back. However, we hesitate to sell either because it conflicts with our original trade objective or because we fear missing the chance to buy back during the pullback. This is where many professional traders actively manage their core positions. Rather than passively waiting for a deeper trailing stop loss to trigger during weakness, they sell a portion when the price becomes extended and buy back the same amount at a lower price. This strategy proves more effective than enduring drawdowns while waiting for a formal pullback setup at support levels or moving averages. Let's take a recent example of IGIL (5 min chart) - Trade Objective An early-stage IPO setup displaying a typical volatility contraction pattern (VCP) on intraday charts. The plan is to hold this as a longer-term position, treating it as an All-or-Nothing trade. Entry (Orange line) 24th December 2024 Entry 504, Stop Loss 2% - 493.95 Risk on Trade 0.50% of portfolio, Size - 23% of pf Adjustment Context - 27th December 2024 The stock had surged powerfully over the previous two days, hitting Upper Circuits. On the third day, despite gapping up at open, it immediately broke down during the opening range - like a typical parabolic short setup. This was a point with a high probability of a short-term pullback or consolidation. Sells (Blue Line) 27th December - 585 - 50% size Buyback (Orange line 2) 27th December - 565 - 50% size - Gained 1R Rationale - My anchor bias was for the price to cool off for a bit. - At the 27th open, the price was already at ~8R+ for me. Even if the stock rose further after I sold my partial position, I wouldn't regret it much—I had already secured 4R with half my position still pending, well above my journal averages. This served as an important emotional anchor point for this adjustment. - When buying back, I was simply looking to average down my costs without a specific target or a perfect setup in mind. In this case, I bought back at 565, as the buyback itself presented a good psychological point to cover (10 Rs initial stop loss, 20 Rs points averaged ~1R at half size). It could have been lower too if the breakdown was slower. This was more intuitive and intentionally imperfect. - I close most of these adjustments on the same day since they are just short-term pullbacks and my overall bias remains bullish. A magnitude trade can also be looked at as a combination of several intraday trades around a core position. - This adjustment method applies specifically to magnitude trades like EP and IPO positions, where the trade objective aligns with pyramiding or averaging costs. Caveat You might think this is a cherry-picked example—and you'd be partly right, since it's one of my better and more recent trades (you can see similar patterns in Care or TI). However, I urge you to stay open to the concept. Look back at your previous trades where you held positions too long passively—you'll often find that temporary extensions and pullbacks were easily visible, offering opportunities to capture additional R’s along the way. Traders commonly face similar emotions and dilemmas when deciding how to act in these situations. End Note

Anuragg Venkatakrishnan

24,423 Aufrufe • vor 1 Jahr