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Introducing Vertical Cassette 7 (VC7), our latest generation of continuous conveyor which transports muck/rock from the TBM to the surface. - Height: 37 ft - Weight: ~220,000 lb - Max muck conveyance rate: 990 tons/hour (w/ 50% loaded belt) - Max supported TBM advance rate: 4 miles/week - Min... show more

The Boring Company

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Back in January when Megan and I charted out the year, the dream of the Leadville 100 course record was overwhelming. But big dreams should be overwhelming. It was time to get to work. Our plan started from 3 principles: 1. I’d need to be capable of running a 13:xx 5k at altitude, or around sub-4 min mile fitness. 6-minute mile pace would need to be a jog on race day, and that all came from improving my running economy. 2. I had to get stronger to handle the unknown distance, both in terms of threshold climbing and actual muscular strength. 3. I’d have to run every step of the race, including Hope Pass. Most interestingly, none of those goals required lots of training volume. I did 8-12 hours of aerobic training most weeks with very few doubles given life constraints, usually around 60-75 miles of running and 1-2 bike rides (pulsing up and down for adaptations, with some bigger weeks and a longer down period for my accident). You don’t need to do consistent 100+ mile weeks to be good at this sport. I have been building endurance bricks for 18 years, and every brick counts. With that time, we applied 6 ideas: 1. Most weeks had a speed workout (often pacing Allie Ostrander ahead of the Olympic Trials 🔥), culminating in 12 x 400 on short rest in 63-64 seconds at altitude in June 2. I’d do threshold sessions approximately every other week, often on the uphill treadmill at 8% or 15% grade, culminating in a massive 12 x 5 minute session a few weeks before race day 3. I did uphill treadmill runs in Z2 at 20% grade all year, including for 10 min after as many aerobic runs as I could 4. I did 3-4 days of strides every single week. My strength is my speed. 5. I biked once per week in place of a run, often using Zwift races for hard sessions (A+ racing category in Zwift!) 6. Every week, I did Ultra Legs strength + squats and took a rest day, plus did heat training Big takeaway: you don’t need to do wild volume to have success in ultras. Get fast, stay fast year round, spend time in Z2, and stack some fun bricks in the context of your life. The record may have shocked the ultra world. But as Megan said, it only shocked people who haven’t been following my Strava for the last decade 🧡

Back in January when Megan and I charted out the year, the dream of the Leadville 100 course record was overwhelming. But big dreams should be overwhelming. It was time to get to work. Our plan started from 3 principles: 1. I’d need to be capable of running a 13:xx 5k at altitude, or around sub-4 min mile fitness. 6-minute mile pace would need to be a jog on race day, and that all came from improving my running economy. 2. I had to get stronger to handle the unknown distance, both in terms of threshold climbing and actual muscular strength. 3. I’d have to run every step of the race, including Hope Pass. Most interestingly, none of those goals required lots of training volume. I did 8-12 hours of aerobic training most weeks with very few doubles given life constraints, usually around 60-75 miles of running and 1-2 bike rides (pulsing up and down for adaptations, with some bigger weeks and a longer down period for my accident). You don’t need to do consistent 100+ mile weeks to be good at this sport. I have been building endurance bricks for 18 years, and every brick counts. With that time, we applied 6 ideas: 1. Most weeks had a speed workout (often pacing Allie Ostrander ahead of the Olympic Trials 🔥), culminating in 12 x 400 on short rest in 63-64 seconds at altitude in June 2. I’d do threshold sessions approximately every other week, often on the uphill treadmill at 8% or 15% grade, culminating in a massive 12 x 5 minute session a few weeks before race day 3. I did uphill treadmill runs in Z2 at 20% grade all year, including for 10 min after as many aerobic runs as I could 4. I did 3-4 days of strides every single week. My strength is my speed. 5. I biked once per week in place of a run, often using Zwift races for hard sessions (A+ racing category in Zwift!) 6. Every week, I did Ultra Legs strength + squats and took a rest day, plus did heat training Big takeaway: you don’t need to do wild volume to have success in ultras. Get fast, stay fast year round, spend time in Z2, and stack some fun bricks in the context of your life. The record may have shocked the ultra world. But as Megan said, it only shocked people who haven’t been following my Strava for the last decade 🧡

David Roche

158,587 views • 2 years ago

The video shows the trials of the VT-1-1, a turretless tank with 2 x 105 mm guns, firing on the move at the Putlos training ground in 1976. The casemate (turretless) tank, designed for combat while moving, was introduced in Germany in the mid-1970s as a twin-gun casemate tank (the Soviet term for a "turretless tank"). For practical firing tests in the "target pass" mode, two prototypes were built in the mid-1970s. The first prototype, VT 1-1, was armed with two 105 mm guns, while the second, VT 1-2, was equipped with two 120 mm smoothbore guns. Additionally, the VT 1-2 featured a functioning autoloader behind one of the guns, with a firing rate of 10 rounds per minute. The vehicles were developed as part of the KPz 3 or Leopard 3 project. In both prototypes, the main guns were semi-fixed (with aiming and stabilization only in elevation). The chassis solutions were derived from the KPz 70 (MBT 70) program, with the running gear shortened by one road wheel (five road wheels per side). The vehicles weighed 36.8 and 43.5 tons, respectively, with a chassis rotation speed of 60 degrees per second. To achieve high maneuverability on rough terrain, 12-cylinder diesel engines from the MB 873 series with enhanced power were used, equipped with four turbochargers: 2000 hp for VT 1-1 and 2200 hp for VT 1-2. This provided an impressive power-to-weight ratio of 54 and 50 hp/ton, respectively, with acceleration to 55 km/h in 11 seconds, though only in a temporary "turbo" mode, as the standard power was 1600 hp. The accuracy of firing with two guns was unmatched by single-gun tanks, as confirmed by the tests. However, due to the novel combat approach, this revolutionary tank concept was rejected by the customer after trials in favor of the conventionally designed Leopard 2. In essence, although the project was developed as the Leopard 3, a tank for the future, it was, in reality, a parallel project and a potential competitor to the Leopard 2. Achieving the firepower, protection, and mobility of the Leopard 3 within reasonable weight limits was impossible with a conventional layout. At the same time, a significant tactical drawback of the casemate concept (including twin-gun casemate vehicles) is the linkage between the direction of fire and the direction of movement, which in many cases could complicate unit and formation control (according to German experts in the 1970s). Moreover, the VT 1-1 and VT 1-2 can hardly be considered balanced vehicles—they could have been simpler. The vehicles' mobility was exaggerated, with the main engine, transmission, auxiliary engine, batteries, and other systems occupying two-thirds of the vehicle's length. Pros and Cons of VT 1-1 and VT 1-2: Considering the key challenges, the twin-gun casemate concept can be evaluated as follows: Pros: Compact design due to a small internal volume. Two guns provide high firepower and hit probability. Cons: The weight advantage of the casemate design is largely offset by the integration of a second gun. Fire control alone results in high complexity, leading to increased maintenance costs and overall expenses. In 1975/76, five Gefechtsfeldversuchträger (GVT, combat test platforms) were developed and built for further mobility and concept trials. These were smaller than the VT 1-1 and VT 1-2, weighing 30 tons. The GVT 01-05 were equipped only with mock-up guns and laser firing simulators (TALLISSI) and telemetry systems, built using chassis components from the Leopard 1. The GVTs were used at the IABG facility in Lichtenau and the tank training school in Munster to test the twin-gun turretless tank concept in realistic exercise conditions, which is why five vehicles were built. However, the Leopard 2 was already in production, and tank crews showed little enthusiasm for this unconventional vehicle requiring a new approach.

The video shows the trials of the VT-1-1, a turretless tank with 2 x 105 mm guns, firing on the move at the Putlos training ground in 1976. The casemate (turretless) tank, designed for combat while moving, was introduced in Germany in the mid-1970s as a twin-gun casemate tank (the Soviet term for a "turretless tank"). For practical firing tests in the "target pass" mode, two prototypes were built in the mid-1970s. The first prototype, VT 1-1, was armed with two 105 mm guns, while the second, VT 1-2, was equipped with two 120 mm smoothbore guns. Additionally, the VT 1-2 featured a functioning autoloader behind one of the guns, with a firing rate of 10 rounds per minute. The vehicles were developed as part of the KPz 3 or Leopard 3 project. In both prototypes, the main guns were semi-fixed (with aiming and stabilization only in elevation). The chassis solutions were derived from the KPz 70 (MBT 70) program, with the running gear shortened by one road wheel (five road wheels per side). The vehicles weighed 36.8 and 43.5 tons, respectively, with a chassis rotation speed of 60 degrees per second. To achieve high maneuverability on rough terrain, 12-cylinder diesel engines from the MB 873 series with enhanced power were used, equipped with four turbochargers: 2000 hp for VT 1-1 and 2200 hp for VT 1-2. This provided an impressive power-to-weight ratio of 54 and 50 hp/ton, respectively, with acceleration to 55 km/h in 11 seconds, though only in a temporary "turbo" mode, as the standard power was 1600 hp. The accuracy of firing with two guns was unmatched by single-gun tanks, as confirmed by the tests. However, due to the novel combat approach, this revolutionary tank concept was rejected by the customer after trials in favor of the conventionally designed Leopard 2. In essence, although the project was developed as the Leopard 3, a tank for the future, it was, in reality, a parallel project and a potential competitor to the Leopard 2. Achieving the firepower, protection, and mobility of the Leopard 3 within reasonable weight limits was impossible with a conventional layout. At the same time, a significant tactical drawback of the casemate concept (including twin-gun casemate vehicles) is the linkage between the direction of fire and the direction of movement, which in many cases could complicate unit and formation control (according to German experts in the 1970s). Moreover, the VT 1-1 and VT 1-2 can hardly be considered balanced vehicles—they could have been simpler. The vehicles' mobility was exaggerated, with the main engine, transmission, auxiliary engine, batteries, and other systems occupying two-thirds of the vehicle's length. Pros and Cons of VT 1-1 and VT 1-2: Considering the key challenges, the twin-gun casemate concept can be evaluated as follows: Pros: Compact design due to a small internal volume. Two guns provide high firepower and hit probability. Cons: The weight advantage of the casemate design is largely offset by the integration of a second gun. Fire control alone results in high complexity, leading to increased maintenance costs and overall expenses. In 1975/76, five Gefechtsfeldversuchträger (GVT, combat test platforms) were developed and built for further mobility and concept trials. These were smaller than the VT 1-1 and VT 1-2, weighing 30 tons. The GVT 01-05 were equipped only with mock-up guns and laser firing simulators (TALLISSI) and telemetry systems, built using chassis components from the Leopard 1. The GVTs were used at the IABG facility in Lichtenau and the tank training school in Munster to test the twin-gun turretless tank concept in realistic exercise conditions, which is why five vehicles were built. However, the Leopard 2 was already in production, and tank crews showed little enthusiasm for this unconventional vehicle requiring a new approach.

Andrei_bt

81,636 views • 1 year ago

Nebius will be a TRILLION dollar company and here is exactly why (Save this). Brad Gerstner's Altimeter just said on camera that they are invested in ClickHouse, and explained exactly why in one sentence: "If you're in the data infrastructure layer, then token consumption is driving a lot more consumption of your basic services." The flip side of that point is equally important. Gerstner added that the closer you are to a point solution, a single use app built on top of AI, "that feels like you're on the front of the conveyor belt heading toward the guillotine." Models get better, apps get commoditized and the companies that own the foundational infrastructure that every AI application must run through keep compounding. ClickHouse is exactly that foundational layer. It is a real time analytical database engine originally built inside Yandex, optimized for the exact query patterns that AI agents, LLM observability pipelines, and machine learning infrastructure generate, massive write volumes, complex aggregations, and sub-second response at scale. It processes hundreds of billions of rows per second, serves over 2,000 enterprise customers including Cloudflare, Uber and ByteDance, and grew 300% in a single year. In January 2026, a $400 million Series D valued ClickHouse at $15 billion more than double its $6 billion valuation just eight months prior. Here is where Nebius comes in. Nebius holds a 28% stake in ClickHouse, an asset that traces back to its Yandex origins. At ClickHouse's current $15 billion valuation, that stake is worth approximately $4.2 billion, sitting largely unrecognized on Nebius's balance sheet while most market coverage focuses entirely on the AI cloud business. A ClickHouse IPO, which the company is actively positioning toward, would force the market to mark that position to full public market value for the first time and could alone reprice Nebius meaningfully. But that hidden asset is just one layer of the bull case. The core AI cloud business just printed 684% year over year revenue growth, $399 million in Q1 2026 against $50 million a year prior. AI specific revenue grew 841% and now represents 98% of total revenue. The moat underneath those numbers is 3.5 gigawatts of secured power capacity, a $27 billion five year contract with Meta, a $2 billion strategic investment from Nvidia, and a Microsoft partnership ramping to full run rate in 2027, all stacked on top of a ClickHouse stake that the market is still not fully pricing in. Milk Road Pro remains massively bullish on Nebius, we called it early, we are up huge on the position, and we continue to track every development across AI infrastructure before it becomes obvious to the rest of the market. Come join us to see our full Nebius thesis and every other position in the portfolio, link below!

Nebius will be a TRILLION dollar company and here is exactly why (Save this). Brad Gerstner's Altimeter just said on camera that they are invested in ClickHouse, and explained exactly why in one sentence: "If you're in the data infrastructure layer, then token consumption is driving a lot more consumption of your basic services." The flip side of that point is equally important. Gerstner added that the closer you are to a point solution, a single use app built on top of AI, "that feels like you're on the front of the conveyor belt heading toward the guillotine." Models get better, apps get commoditized and the companies that own the foundational infrastructure that every AI application must run through keep compounding. ClickHouse is exactly that foundational layer. It is a real time analytical database engine originally built inside Yandex, optimized for the exact query patterns that AI agents, LLM observability pipelines, and machine learning infrastructure generate, massive write volumes, complex aggregations, and sub-second response at scale. It processes hundreds of billions of rows per second, serves over 2,000 enterprise customers including Cloudflare, Uber and ByteDance, and grew 300% in a single year. In January 2026, a $400 million Series D valued ClickHouse at $15 billion more than double its $6 billion valuation just eight months prior. Here is where Nebius comes in. Nebius holds a 28% stake in ClickHouse, an asset that traces back to its Yandex origins. At ClickHouse's current $15 billion valuation, that stake is worth approximately $4.2 billion, sitting largely unrecognized on Nebius's balance sheet while most market coverage focuses entirely on the AI cloud business. A ClickHouse IPO, which the company is actively positioning toward, would force the market to mark that position to full public market value for the first time and could alone reprice Nebius meaningfully. But that hidden asset is just one layer of the bull case. The core AI cloud business just printed 684% year over year revenue growth, $399 million in Q1 2026 against $50 million a year prior. AI specific revenue grew 841% and now represents 98% of total revenue. The moat underneath those numbers is 3.5 gigawatts of secured power capacity, a $27 billion five year contract with Meta, a $2 billion strategic investment from Nvidia, and a Microsoft partnership ramping to full run rate in 2027, all stacked on top of a ClickHouse stake that the market is still not fully pricing in. Milk Road Pro remains massively bullish on Nebius, we called it early, we are up huge on the position, and we continue to track every development across AI infrastructure before it becomes obvious to the rest of the market. Come join us to see our full Nebius thesis and every other position in the portfolio, link below!

Milk Road AI

216,498 views • 3 months ago

🚨 EXPOSING NOSTRA. AI 🚨 We have exposed some pretty nasty grifts in this space, but Nostra reigns supreme above all others (by a fair margin). Since we have >30 min video and an intensive Notion document (linked at the very bottom of this post) I am going to just highlight the key areas below. For those who don't want to watch it all - here's some time stamps that cover the most important/most hilarious parts of the video. 1:08 - Site Speed Scamming 101 4:20 - Beginning of the actual findings of what we caught Nostra doing. 5:25 - Nostra CEO tweets about how vitally important it is to have your most critical information above the fold on your site. Then we show that they almost exclusively marketed their fake site speed scores above the fold. 6:37 - The 'before and after' that shows how radically different the Nostra team made their site after they realized they were being investigated (perhaps my favorite part) 11:05 - Before: YOUR SITE PERFORMANCE SCORE MATTERS ..... 1 day later, Nostra CEO: "Yeah page speed scores are pretty useless" 😂 12:07 - "Nostra clients are 8x more likely to pass core web vitals compared to non Nostra clients" *Lukas then shows how every single one of their 'success story' case studies are failing almost all core web vitals on their home pages.... 15:10 - Jake dives into the technical shortcomings of the Nostra product, from them using a deprecated form of rendering and claiming (falsely) that Google still endorses it, to showing that any visitor logged into any Nostra-enabled site is not fed the cached pages... meaning that many of their highest LTV clients are being given a drastically worse browsing experience.... + lots more! 29:47 - Lukas shows all of the deleted tweets from the Nostra CEO. Suspiciously, all of them just so happen to be based on page speed scores... hmm. The main points: - Nostra uses site speed cloaking tactics to artificially inflate performance numbers on Google's Page Speed Insights. There is no ethical reason to do this. It is a tactic used exclusively by site speed scammers. *A few people may point to the fact that Meta uses Lighthouse scores as one of many contributing factors to showing ads, so certain sites MAY see an uptick in paid performance on Meta while having these fake scores. This is a horrible basis to justify attempting black hat scams on Google tools. I'll be doing a whole separate video for this topic alone, but for now, just understand it is shortsighted and unbelievably stupid. - They then used those artificially inflated performance scores as the central focus of their entire marketing strategy. *They have released some hilariously inaccurate/misleading blog posts in the past week that try and claim they don't use cloaking and that their methods are totally ethical... you better believe we are doing a follow-up video that dismantles these blog posts paragraph by paragraph. - Various current and former clients of Nostra have confirmed that one of their central selling points when convincing them to pay for Nostra (often quoting/charging thousands a month) was that their Google performance numbers were going to go up, which meant a faster site, which meant more revenue. A blatant, irrefutable lie. - As we dug into their code, we found even more issues. Most notably, their 'crawler optimization' was stripping down pages for both Lighthouse (Page Speed Insights) as well as Googlebot, which means that the contents of any page 'client-side rendered' by Nostra in this way was almost entirely invisible to Google, as it saw basically nothing to crawl and index. - All of our findings were confirmed by over a dozen well-respected developers in the Shopify space, including high-ranking engineers at Shopify. - Before we notified Nostra of our investigation into them, Jake Casto (partner in this report) met directly with the Nostra team, including their Chief Lead Architect (?) and asked every question he could to gain as much context as possible. This meeting further confirmed all of our findings and even pushed some further. - Hours after we notified the CEO of Nostra about our investigation into them and the impending report we would release, their entire site changed.... like... CHANGED. Nearly every mention of 'page speed' or 'performance score' was stripped from the site, including all of their case studies. Additionally, they renamed an entire product. Their 'Crawler Optimization' tool became 'Bimodal Dynamic Rendering'.... - That same night, the Nostra CEO then deleted all tweets insinuating performance score as a benefit of using Nostra (proof shown in the video) and began publicly talking about how useless speed scores are. A metric they had long lauded as the single-most important aspect of what their tech improves was now "pretty useless" just a few hours later. - It is imperative to know that we did not mention ANYTHING about our interest in investigating their focus on performance scores as a key marketing strategy. All of these changes were made by them without knowing anything about what in particular we were investigating. Not shockingly, the main scam we were highlighting in our investigation is what was wiped entirely (within literal hours) from their site/their founders personal messaging. * go look at their site now and try to find any claims about performance scores on their home page. They even took them off the top of all their case studies. (we show this all in the video as well). - Nostra has since continued to modify their code, resulting in some of their 'success story' clients seeing a 50+ point drop in performance scores (shown in the video). More current and former clients continue to reach out and share more stories about the many sketchy happenings at Nostra. - Nostra also released a very weak response in the form of 2 blog posts that aim to justify their actions/tech. They have been sharing this with their clients and attempting to patch over the MANY inconsistencies and blatant lies they were caught in. As I said earlier, we will be doing a video dedicated to dismantling these blog posts in detail. Moral of the story. No SaaS is going to plug in to your Shopify store and drastically increase your performance scores in a matter of moments. Any tool or dev or agency, no matter how fancy they look and how much venture backing they have, will be able to get your Shopify stores' mobile performance scores into the 80/90's under any normal circumstances. If someone says they can... You are 100% being scammed. Site speed optimization is a complex development process that takes highly-skilled devs dozens of hours to do properly. No tool can replace this. Don't be fooled into thinking otherwise. For a deeper look into the technical side, check out the link below.

🚨 EXPOSING NOSTRA. AI 🚨 We have exposed some pretty nasty grifts in this space, but Nostra reigns supreme above all others (by a fair margin). Since we have >30 min video and an intensive Notion document (linked at the very bottom of this post) I am going to just highlight the key areas below. For those who don't want to watch it all - here's some time stamps that cover the most important/most hilarious parts of the video. 1:08 - Site Speed Scamming 101 4:20 - Beginning of the actual findings of what we caught Nostra doing. 5:25 - Nostra CEO tweets about how vitally important it is to have your most critical information above the fold on your site. Then we show that they almost exclusively marketed their fake site speed scores above the fold. 6:37 - The 'before and after' that shows how radically different the Nostra team made their site after they realized they were being investigated (perhaps my favorite part) 11:05 - Before: YOUR SITE PERFORMANCE SCORE MATTERS ..... 1 day later, Nostra CEO: "Yeah page speed scores are pretty useless" 😂 12:07 - "Nostra clients are 8x more likely to pass core web vitals compared to non Nostra clients" Lukas then shows how every single one of their 'success story' case studies are failing almost all core web vitals on their home pages.... 15:10 - Jake dives into the technical shortcomings of the Nostra product, from them using a deprecated form of rendering and claiming (falsely) that Google still endorses it, to showing that any visitor logged into any Nostra-enabled site is not fed the cached pages... meaning that many of their highest LTV clients are being given a drastically worse browsing experience.... + lots more! 29:47 - Lukas shows all of the deleted tweets from the Nostra CEO. Suspiciously, all of them just so happen to be based on page speed scores... hmm. The main points: - Nostra uses site speed cloaking tactics to artificially inflate performance numbers on Google's Page Speed Insights. There is no ethical reason to do this. It is a tactic used exclusively by site speed scammers. A few people may point to the fact that Meta uses Lighthouse scores as one of many contributing factors to showing ads, so certain sites MAY see an uptick in paid performance on Meta while having these fake scores. This is a horrible basis to justify attempting black hat scams on Google tools. I'll be doing a whole separate video for this topic alone, but for now, just understand it is shortsighted and unbelievably stupid. - They then used those artificially inflated performance scores as the central focus of their entire marketing strategy. They have released some hilariously inaccurate/misleading blog posts in the past week that try and claim they don't use cloaking and that their methods are totally ethical... you better believe we are doing a follow-up video that dismantles these blog posts paragraph by paragraph. - Various current and former clients of Nostra have confirmed that one of their central selling points when convincing them to pay for Nostra (often quoting/charging thousands a month) was that their Google performance numbers were going to go up, which meant a faster site, which meant more revenue. A blatant, irrefutable lie. - As we dug into their code, we found even more issues. Most notably, their 'crawler optimization' was stripping down pages for both Lighthouse (Page Speed Insights) as well as Googlebot, which means that the contents of any page 'client-side rendered' by Nostra in this way was almost entirely invisible to Google, as it saw basically nothing to crawl and index. - All of our findings were confirmed by over a dozen well-respected developers in the Shopify space, including high-ranking engineers at Shopify. - Before we notified Nostra of our investigation into them, Jake Casto (partner in this report) met directly with the Nostra team, including their Chief Lead Architect (?) and asked every question he could to gain as much context as possible. This meeting further confirmed all of our findings and even pushed some further. - Hours after we notified the CEO of Nostra about our investigation into them and the impending report we would release, their entire site changed.... like... CHANGED. Nearly every mention of 'page speed' or 'performance score' was stripped from the site, including all of their case studies. Additionally, they renamed an entire product. Their 'Crawler Optimization' tool became 'Bimodal Dynamic Rendering'.... - That same night, the Nostra CEO then deleted all tweets insinuating performance score as a benefit of using Nostra (proof shown in the video) and began publicly talking about how useless speed scores are. A metric they had long lauded as the single-most important aspect of what their tech improves was now "pretty useless" just a few hours later. - It is imperative to know that we did not mention ANYTHING about our interest in investigating their focus on performance scores as a key marketing strategy. All of these changes were made by them without knowing anything about what in particular we were investigating. Not shockingly, the main scam we were highlighting in our investigation is what was wiped entirely (within literal hours) from their site/their founders personal messaging. go look at their site now and try to find any claims about performance scores on their home page. They even took them off the top of all their case studies. (we show this all in the video as well). - Nostra has since continued to modify their code, resulting in some of their 'success story' clients seeing a 50+ point drop in performance scores (shown in the video). More current and former clients continue to reach out and share more stories about the many sketchy happenings at Nostra. - Nostra also released a very weak response in the form of 2 blog posts that aim to justify their actions/tech. They have been sharing this with their clients and attempting to patch over the MANY inconsistencies and blatant lies they were caught in. As I said earlier, we will be doing a video dedicated to dismantling these blog posts in detail. Moral of the story. No SaaS is going to plug in to your Shopify store and drastically increase your performance scores in a matter of moments. Any tool or dev or agency, no matter how fancy they look and how much venture backing they have, will be able to get your Shopify stores' mobile performance scores into the 80/90's under any normal circumstances. If someone says they can... You are 100% being scammed. Site speed optimization is a complex development process that takes highly-skilled devs dozens of hours to do properly. No tool can replace this. Don't be fooled into thinking otherwise. For a deeper look into the technical side, check out the link below.

Lukas Tanasiuk

77,517 views • 2 years ago

The Cost of Intelligence is Heading to Zero | Hyperspace P2P Distributed Cache We present to you our breakthrough cross-domain work across AI, distributed systems, cryptography, game theory to solve the primary structural inefficiency at the heart of AI infrastructure: most inference is redundant. Google has reported that only 15% of daily searches are truly novel. The rest are repeats or close variants. LLM inference inherits this same power-law distribution. Enterprise chatbots see 70-80% of queries fall into a handful of intent categories. System prompts are identical across 100% of requests within an application. The KV attention state for "You are a helpful assistant" has been computed billions of times, on millions of GPUs, identically. And yet every AI lab, every startup, every self-hosted deployment - computes and caches these results independently. There is no shared layer. No global memory. Every provider pays the full compute cost for every query, even when the answer already exists somewhere in the network. This is the problem Hyperspace solves where distributed cache operates at three levels, each catching a different class of redundancy: 1. Response cache Same prompt, same model, same parameters - instant cached response from any node in the network. SHA-256 hash lookup via DHT, with cryptographic cache proofs linking every response to its original inference execution. No trust required. Fetchers re-announce as providers, so popular responses replicate naturally across more nodes. 2. KV prefix cache Same system prompt tokens - skip the most expensive part of inference entirely. Prefill (computing Key-Value attention states) is deterministic: same model plus same tokens always produces identical KV state. The network caches these states using erasure coding and distributes them via the routing network. New questions that share a common prefix resume generation from cached state instead of recomputing from scratch. 3. Routing to cached nodes Instead of transferring KV state across the network for every request, Hyperspace routes the request to the node that already has the state loaded in VRAM. The request goes to the cache, not the cache to the request. Together, these three layers mean that 70-90% of inference requests at network scale never require full GPU computation. This work doesn't exist in isolation. It builds on research from across the industry: SGLang's RadixAttention demonstrated that automatic prefix sharing can yield up to 5x speedup on structured LLM workloads. Moonshot AI's Mooncake built an entire KV-cache-centric disaggregated architecture for production serving at Kimi. Anthropic, OpenAI, and Google all launched prompt caching products in 2024 - priced at 50-90% discounts - because system prompt reuse is so pervasive that it changes the economics of inference. What all of these systems share is a common limitation: they operate within a single organization's infrastructure. SGLang caches prefixes within one server. Mooncake disaggregates KV cache within one datacenter. Anthropic's prompt caching works within one API provider's fleet. None of them can share cached state across organizational boundaries. Hyperspace removes this boundary. The cache is global. A response computed by a node in Tokyo is immediately available to a node in Berlin. A KV prefix state generated for Qwen-32B on one machine is verifiable and reusable by any other machine running the same model. The routing network provides the delivery guarantees, the erasure coding provides the redundancy, and the cache proofs provide the trust. What this means for the cost of intelligence Big AI labs scale linearly: twice the users means twice the GPU spend. Every query is a cost center. Their internal caching helps, but it's siloed - Lab A's cache can't serve Lab B's users, and neither can serve a self-hosted Llama deployment. Hyperspace scales sub-linearly. Every new node that joins the network adds to the global cache. Every inference result enriches the cache for all future requests. The cache hit rate rises with network size because query distributions follow a power law - the most common questions are asked exponentially more often than rare ones. The implication is simple: as the network grows, the effective cost per inference drops. Not linearly. Logarithmically. At 10 million nodes, we estimate 75-90% of all inference requests can be served from cache, eliminating 400,000+ MWh of energy consumption per year and avoiding over 200,000 tons of CO2 emissions. The first person to ask a question pays the compute cost. Everyone after them gets the answer for free, with cryptographic proof that it's authentic. Training is competitive. Inference is shared Open-weight models are converging on quality with closed models. Labs will continue to differentiate on training - data curation, architecture innovation, RLHF tuning. That's where the real intellectual property lives. But inference is a commodity. Two copies of Qwen-32B running the same prompt produce the same KV state and the same response, byte for byte, regardless of whose GPU runs the matrix multiplication. There is no moat in multiplying matrices. The moat is in training the weights. A global distributed cache makes this separation explicit. It doesn't matter who trained the model. Once the weights are open, the inference cost approaches zero at scale - because the network remembers every answer and can prove it's correct. No lab, no matter how well-funded, can match this. They cannot share caches across competitors. They scale linearly. The network scales logarithmically. The marginal cost of intelligence approaches zero. That's the endgame.

The Cost of Intelligence is Heading to Zero | Hyperspace P2P Distributed Cache We present to you our breakthrough cross-domain work across AI, distributed systems, cryptography, game theory to solve the primary structural inefficiency at the heart of AI infrastructure: most inference is redundant. Google has reported that only 15% of daily searches are truly novel. The rest are repeats or close variants. LLM inference inherits this same power-law distribution. Enterprise chatbots see 70-80% of queries fall into a handful of intent categories. System prompts are identical across 100% of requests within an application. The KV attention state for "You are a helpful assistant" has been computed billions of times, on millions of GPUs, identically. And yet every AI lab, every startup, every self-hosted deployment - computes and caches these results independently. There is no shared layer. No global memory. Every provider pays the full compute cost for every query, even when the answer already exists somewhere in the network. This is the problem Hyperspace solves where distributed cache operates at three levels, each catching a different class of redundancy: 1. Response cache Same prompt, same model, same parameters - instant cached response from any node in the network. SHA-256 hash lookup via DHT, with cryptographic cache proofs linking every response to its original inference execution. No trust required. Fetchers re-announce as providers, so popular responses replicate naturally across more nodes. 2. KV prefix cache Same system prompt tokens - skip the most expensive part of inference entirely. Prefill (computing Key-Value attention states) is deterministic: same model plus same tokens always produces identical KV state. The network caches these states using erasure coding and distributes them via the routing network. New questions that share a common prefix resume generation from cached state instead of recomputing from scratch. 3. Routing to cached nodes Instead of transferring KV state across the network for every request, Hyperspace routes the request to the node that already has the state loaded in VRAM. The request goes to the cache, not the cache to the request. Together, these three layers mean that 70-90% of inference requests at network scale never require full GPU computation. This work doesn't exist in isolation. It builds on research from across the industry: SGLang's RadixAttention demonstrated that automatic prefix sharing can yield up to 5x speedup on structured LLM workloads. Moonshot AI's Mooncake built an entire KV-cache-centric disaggregated architecture for production serving at Kimi. Anthropic, OpenAI, and Google all launched prompt caching products in 2024 - priced at 50-90% discounts - because system prompt reuse is so pervasive that it changes the economics of inference. What all of these systems share is a common limitation: they operate within a single organization's infrastructure. SGLang caches prefixes within one server. Mooncake disaggregates KV cache within one datacenter. Anthropic's prompt caching works within one API provider's fleet. None of them can share cached state across organizational boundaries. Hyperspace removes this boundary. The cache is global. A response computed by a node in Tokyo is immediately available to a node in Berlin. A KV prefix state generated for Qwen-32B on one machine is verifiable and reusable by any other machine running the same model. The routing network provides the delivery guarantees, the erasure coding provides the redundancy, and the cache proofs provide the trust. What this means for the cost of intelligence Big AI labs scale linearly: twice the users means twice the GPU spend. Every query is a cost center. Their internal caching helps, but it's siloed - Lab A's cache can't serve Lab B's users, and neither can serve a self-hosted Llama deployment. Hyperspace scales sub-linearly. Every new node that joins the network adds to the global cache. Every inference result enriches the cache for all future requests. The cache hit rate rises with network size because query distributions follow a power law - the most common questions are asked exponentially more often than rare ones. The implication is simple: as the network grows, the effective cost per inference drops. Not linearly. Logarithmically. At 10 million nodes, we estimate 75-90% of all inference requests can be served from cache, eliminating 400,000+ MWh of energy consumption per year and avoiding over 200,000 tons of CO2 emissions. The first person to ask a question pays the compute cost. Everyone after them gets the answer for free, with cryptographic proof that it's authentic. Training is competitive. Inference is shared Open-weight models are converging on quality with closed models. Labs will continue to differentiate on training - data curation, architecture innovation, RLHF tuning. That's where the real intellectual property lives. But inference is a commodity. Two copies of Qwen-32B running the same prompt produce the same KV state and the same response, byte for byte, regardless of whose GPU runs the matrix multiplication. There is no moat in multiplying matrices. The moat is in training the weights. A global distributed cache makes this separation explicit. It doesn't matter who trained the model. Once the weights are open, the inference cost approaches zero at scale - because the network remembers every answer and can prove it's correct. No lab, no matter how well-funded, can match this. They cannot share caches across competitors. They scale linearly. The network scales logarithmically. The marginal cost of intelligence approaches zero. That's the endgame.

Varun

37,555 views • 5 months ago

I paid Alex & Leila Hormozi $5,000 for their 2-day scaling workshop. Why? To grow my business from $6 million to $12 million in 2025. These 12 lessons from the event will help me get there: 1. The fastest-moving entrepreneurs are obsessive resource allocators. Similar to investors, they seek the best risk-adjusted returns with the resources they have. The main resources of the business are: • Time (of the team) • Attention (of the team) • And capital (of the business) So resource allocation is: • Aligning attention on the most important thing • Properly allocating everyone’s time to achieve that thing the fastest • Strategically investing capital to accelerate the outcome or increase its likelihood of achievement 2. $3m to $10m in EBITDA is where the majority of the value in a business is created. $3m in EBITDA likely gets a 1x multiple, so $3m of enterprise value. The process of going to $10m (when done well), not only 3.3x’s the EBITDA, but can take the multiple from 1 to 4 -> which is a 13.2x return. The EV goes from $3m to $40m, and that is the stage we are in right now as a business. 3. LTV:CAC are two metrics you must have staring at you and constantly audited. LTV = lifetime value of the customer CAC = customer acquisition cost The scope of calculating those is beyond this write-up, but basically you want this metric to be ~8:1 or higher when aggressively scaling a service-based business. On top of that, these are the only two metrics that you can “improve” in your business → either making customers worth more or reducing the cost to acquire them. You should be able to tie every project on your list directly to the improvement of one of these metrics. 4. We need a single dashboard with the most important metrics in the business. The quality of the dashboard is: • How many people use it on a daily basis • And how clearly they can connect their performance to the performance of the main numbers on the dashboard. We have data thrown about across Airtable, Google Sheets, and various Slack channels. Now, it’s time to unite them such that we can make even better decisions as a team. 5. Leveling up in business is transitioning from selling to people to selling to employees. In the beginning, you are the one creating all of the value. Over time, you will replace yourself out of certain functions that are customer-facing (if you are approaching business correctly). However, your job then becomes selling to your employees to spark their highest performance and retain them. 6. Brand is the best way to improve LTV and reduce CAC at the same time. It makes it cheaper to acquire customers since you have fixed media expenses (just labor) but unlimited upside in the number of eyeballs you can reach. It increases LTV because the continued content you create makes customers likely to keep purchasing because they associate the good content with the purchase they made, whether it’s free content or not. 7. Every single thing in your business is trainable, you just lack the skill of training. Seeing their presentations, their handshakes, the way they repeat the question back to the audience, it was so clear that Alex & Leila did this first, then obsessively role-played and drilled each person on their performance until it was indistinguishable from theirs. 8. The people doing it at the highest level of an obsessive, intentional standard. It was so evident the way these employees conducted themselves that they: • Loved working there • Loved the culture of high performance • And had been trained with extreme repetition and attention to detail 9. Past $3-5m in revenue, anything “new” starts with “who” not “how.” I made the mistake last year of trying to “bootstrap” our cold ads initiative (while continuing to run the rest of the business & sales team). I spent roughly ~200 hours on this throughout the year, which took time away from both my content and the management of the sales team. But for whatever reason, I thought I “had” to be the one who got it off the ground, then handed it off to a new hire or media buyer. But I had the sequence flipped. I should have spent the first 50 hours finding a world-class director of paid marketing, someone with far more experience than me building out a cold traffic acquisition system. Heck, I could have even spent 200 hours on it and ended up with a far greater return than I ended up with. 10. Excellence is a remarkably high number of extremely small details done well. Throughout the workshop, I paid close attention to the event operations, taking notes on how to run a great in-person event in case we wanted to do so in the future. Several things stood out that were clearly “iterations” from prior events, all based around eliminating the small, annoying parts of attending any kind of seminar. • High-quality food • Greeters at the door • Clear bathroom signs • A barista for fresh coffee • WiFi signs posted everywhere • Constant 15-minute breaks every 90 minutes The list goes on and on. 11. Any change you make in a business you should expect a 20% “decrease” in performance to start. That makes the hurdle rate to doing “new” at least 20% for it to be worth it, and arguably 40%. This happens because the switching cost leads to an immediate drop just from having to retrain the team. Change a meeting cadence, change a sales script, change an onboarding flow, all of these are going to come with a switching cost the team must overcome. Therefore, the highest risk-adjusted return is always to just do more or better or whatever you’re already doing, rather than add something new. 12. The ultimate size of the business is the sum of the intelligence of its people. Alex laid out this golden nugget during one of his talks and I found it interesting for a few reasons. First, because of his definition of intelligence = speed of learning, that means the ultimate size of the company is how quickly everyone can learn things. And so said another way, the ultimate size of the company is correlated to the speed of its iterations. The second reason I found this interesting is because you can create a culture of iteration through constant, rapid feedback on every behavior. And when I say constant, I mean constant. You could tell they’ve built this culture by the way their presenters all presented the exact same way as Alex and Leila. Aaand that’s it! I go deeper into all these lessons in this video, check it out: Timestamps 00:37 The Fastest Moving Entrepreneurs Are Obsessive Resource Allocators 04:09 $3m To $10m EBITDA Is Where The Majority Of The Value In A Business Is Created 07:00 LTV:CAC Are Two Metrics You Must Have Staring At You 10:04 You Need A Single Dashboard With The Most Important Metrics In The Business 12:03 Leveling Up In Business Is Transitioning To Selling To People To Selling To Employees 14:10 Brand Is The Best Way To Improve LTV And Reduce CAC At The Same Time 16:02 Every Single Thing In Your Business Is Trainable, You Just Lack The Skill Of Training 18:54 The People Doing It At The Highest Level Have An Obsessive, Intentional Standard 20:04 Past $3-5m In Revenue, Anything "New" Starts With "Who" Not "How" 23:33 Excellence Is A Remarkably High Number Of Extremely Small Details Done Well 26:23 Any Change You Make In A Business You Should Expect A 20% "Decrease" In Performance To Start 28:07 The Ultimate Size Of The Business Is The Sum Of The Intelligence Of It's People

Dickie Bush

62,195 views • 1 year ago

Qwen 3.8 27B on hit 3.3x faster decode in 7 days. Here's what happened and what we're thinking next. Result (so far) Median decode speed increased from 26 tok/s to 87.9 tok/s on the verifier M5 Max (33 to 93.1 tok/s across the eight prompts), with prefill around 971.8 tok/s. This came out of a collective effort: 31 solvers across 67 improvements. Most of the recent ones run custom MTP heads that draft and accept ~3.9 tokens per round while still matching serial output exactly. Why this matters Beyond the performance itself, two things stand out to me. (1) Dense models on Apple Silicon were supposed to be the hard case. "Everyone knows Macs are slow at dense models." But watching the community take it from the usual baseline to >3x in seven days shows the low-hanging fruit was still there. (2) Open-weight models have been small and effective for a while. This is the first time one is small and frontier. Qwen 3.8 27B is an extremely strong dense model, comparable in capability to Opus 4.6 (Max). Running it at usable speed (>45 tok/s) is a step change for local AI users. What we improved about the challenge itself This is our second challenge, and we took the feedback from the Laguna track and rebuilt a few core pieces. - Speculative decoding (native MTP) was available and editable on day one instead of bolted on later. - Scoring became the median of eight independent prompt speedups over pure serial decode (anchored at 1.0, floor 0.90, ceiling 3.0), so no single fixture could dominate. - The leaderboard now ranks total contribution rather than just the current record holder. - Every submission gets automated screening for gaming before it scores. I really appreciate folks who's provided feedback. Naming a few that came to mind Ivan Fioravanti ᯅ TheDavidTai Morgan McGuire poly Takeshi7 Steven Gumbii.Digital Tanishq Dubey Arjun Ram Andrey 🦃 Petrov tiny edge David Zhang Jaime Rader Peter and many others on slack! We also widened the editable surface to include the MTP head weights themselves, the full draft/verify loop, and a large set of the underlying Metal kernels. How we got to the 3x speedup Here's a summary from Grok. Much of it is beyond my understanding, but I expect people (and agents) smarter than I am can take these insights and apply them in other contexts. Custom MTP heads + adaptive draft policy People stopped treating the head as fixed and started training or editing it for higher acceptance under the exact verify constraints. Combined with per-round draft counts that can adapt (0 to 8), this is what pushed average accepted tokens from ~1-2 up to 3.9 on the top runs. Tighter verify-block and KV rollback paths The Swift session code for assembling the verify pass, snapshotting KV, and rolling back on rejects got cleaned up a lot. Small latency wins here compound once you're drafting ~four tokens at a time. Metal kernel work on the hot paths SDPA, the MoE gather GEMM, RoPE, RMSNorm, and a few of the smaller element-wise ops saw targeted edits. Most of the gains only show up once the verify width is high and the memory traffic pattern changes. Fidelity-preserving residual handling Several submissions improved how residuals and acceptance decisions are managed, so that higher draft depth doesn't quietly degrade the token match rate. The gates stayed strict: every emitted token still has to equal serial, so these were real engineering wins rather than score hacks. What's next for Qwen 3.8 27B MLX. We plan to keep the track live a bit longer, then switch to Qwen 3.8's MoE version (rumored to be 35B-A3B). Given the recent DFlash 2 announcement, we're also looking at whether we can support broader speculative methods. The current surface already supports a lot of experimentation. The main gaps are better upstreaming for local usage and clearer docs on how the benchmark and verifier work. Multiplatform. In parallel, we're experimenting with running a similar effort around CUDA for Qwen 3.8 27B. A lot of people have asked for this, since the two communities overlap quite a bit. Our goal is to ship the CUDA version next week. We'd also love to partner with Qwen on it. If anyone has a connection there, please introduce us, and we'll see if they're down to match a bounty with us to push this out. What's most useful for the broader MLX community The improvements from the challenge are already upstreamed inside Darkbloom, and we're seeing ~2x faster decode in our production traffic for Qwen. Outside the challenge itself, something I've been thinking about deeply, and that a few community members have raised, is how to make these results useful to more people. There are many individual efforts happening across the MLX community, and honestly, the more I dig in, the more confused I get by the overlapping libraries and concepts. I'm sure I'm not alone, and newcomers probably feel the same. That's no one's fault, just the growing pains of an open source community. I don't expect I'm gonna come up with the answer, but I'd love to learn more about what different folks are working on and how they're thinking about their roadmaps. I'll share what I learn along the way, and hopefully someone smarter than me can turn it into a proposal for us to rally around.

Qwen 3.8 27B on hit 3.3x faster decode in 7 days. Here's what happened and what we're thinking next. Result (so far) Median decode speed increased from 26 tok/s to 87.9 tok/s on the verifier M5 Max (33 to 93.1 tok/s across the eight prompts), with prefill around 971.8 tok/s. This came out of a collective effort: 31 solvers across 67 improvements. Most of the recent ones run custom MTP heads that draft and accept ~3.9 tokens per round while still matching serial output exactly. Why this matters Beyond the performance itself, two things stand out to me. (1) Dense models on Apple Silicon were supposed to be the hard case. "Everyone knows Macs are slow at dense models." But watching the community take it from the usual baseline to >3x in seven days shows the low-hanging fruit was still there. (2) Open-weight models have been small and effective for a while. This is the first time one is small and frontier. Qwen 3.8 27B is an extremely strong dense model, comparable in capability to Opus 4.6 (Max). Running it at usable speed (>45 tok/s) is a step change for local AI users. What we improved about the challenge itself This is our second challenge, and we took the feedback from the Laguna track and rebuilt a few core pieces. - Speculative decoding (native MTP) was available and editable on day one instead of bolted on later. - Scoring became the median of eight independent prompt speedups over pure serial decode (anchored at 1.0, floor 0.90, ceiling 3.0), so no single fixture could dominate. - The leaderboard now ranks total contribution rather than just the current record holder. - Every submission gets automated screening for gaming before it scores. I really appreciate folks who's provided feedback. Naming a few that came to mind Ivan Fioravanti ᯅ TheDavidTai Morgan McGuire poly Takeshi7 Steven Gumbii.Digital Tanishq Dubey Arjun Ram Andrey 🦃 Petrov tiny edge David Zhang Jaime Rader Peter and many others on slack! We also widened the editable surface to include the MTP head weights themselves, the full draft/verify loop, and a large set of the underlying Metal kernels. How we got to the 3x speedup Here's a summary from Grok. Much of it is beyond my understanding, but I expect people (and agents) smarter than I am can take these insights and apply them in other contexts. Custom MTP heads + adaptive draft policy People stopped treating the head as fixed and started training or editing it for higher acceptance under the exact verify constraints. Combined with per-round draft counts that can adapt (0 to 8), this is what pushed average accepted tokens from ~1-2 up to 3.9 on the top runs. Tighter verify-block and KV rollback paths The Swift session code for assembling the verify pass, snapshotting KV, and rolling back on rejects got cleaned up a lot. Small latency wins here compound once you're drafting ~four tokens at a time. Metal kernel work on the hot paths SDPA, the MoE gather GEMM, RoPE, RMSNorm, and a few of the smaller element-wise ops saw targeted edits. Most of the gains only show up once the verify width is high and the memory traffic pattern changes. Fidelity-preserving residual handling Several submissions improved how residuals and acceptance decisions are managed, so that higher draft depth doesn't quietly degrade the token match rate. The gates stayed strict: every emitted token still has to equal serial, so these were real engineering wins rather than score hacks. What's next for Qwen 3.8 27B MLX. We plan to keep the track live a bit longer, then switch to Qwen 3.8's MoE version (rumored to be 35B-A3B). Given the recent DFlash 2 announcement, we're also looking at whether we can support broader speculative methods. The current surface already supports a lot of experimentation. The main gaps are better upstreaming for local usage and clearer docs on how the benchmark and verifier work. Multiplatform. In parallel, we're experimenting with running a similar effort around CUDA for Qwen 3.8 27B. A lot of people have asked for this, since the two communities overlap quite a bit. Our goal is to ship the CUDA version next week. We'd also love to partner with Qwen on it. If anyone has a connection there, please introduce us, and we'll see if they're down to match a bounty with us to push this out. What's most useful for the broader MLX community The improvements from the challenge are already upstreamed inside Darkbloom, and we're seeing ~2x faster decode in our production traffic for Qwen. Outside the challenge itself, something I've been thinking about deeply, and that a few community members have raised, is how to make these results useful to more people. There are many individual efforts happening across the MLX community, and honestly, the more I dig in, the more confused I get by the overlapping libraries and concepts. I'm sure I'm not alone, and newcomers probably feel the same. That's no one's fault, just the growing pains of an open source community. I don't expect I'm gonna come up with the answer, but I'd love to learn more about what different folks are working on and how they're thinking about their roadmaps. I'll share what I learn along the way, and hopefully someone smarter than me can turn it into a proposal for us to rally around.

Kydo

32,548 views • 17 days ago

Behind The Scenes In The Vegas Loop: Inside Elon Musk's The Boring Company Bold Bet On Urban Mobility Hey everyone. Tesla Owners Silicon Valley (Tesla Owners Silicon Valley) here. I recently had the chance to go behind the scenes with Steve Davis, President of The Boring Company, for a deep dive into the Vegas Loop in Las Vegas. This wasn’t a quick photo op. It was a full 47-minute immersion: riding through the LED-lit tunnels in a Tesla, visiting active construction sites with Prufrock boring machines, and hearing directly from Steve about what’s working today, and what’s coming next. I’m posting the full long-form video alongside this recap so you can experience it firsthand. But here’s the readable, “what actually matters” story from the tour. From “Traffic Is Soul-Crushing” To A Working Underground Network The Boring Company was founded in 2016, born of a familiar frustration: gridlocked cities that can’t build fast enough, cheap enough, or with minimal disruption. The premise is simple but ambitious: reinvent tunneling to make it practical infrastructure, not a decade-long mega-project. Las Vegas is where that idea is being tested at real scale. Instead of waiting for buses, shuttles, or rail schedules, the Vegas Loop aims to provide point-to-point trips in Teslas, fast, quiet, and emissions-free, connecting major destinations without the chaos of the Strip above. And after seeing it up close, what stands out most is how operational it already is. This isn’t a render. It’s a functioning system handling real demand, in real conditions, with real riders. What It Feels Like: Fast, Weirdly Fun, And Surprisingly Smooth The “Loop experience” is part transit, part sci-fi. The tunnels are lined with shifting LEDs—purples, greens, yellows—that make the ride feel more like entering a venue than commuting. Trips are short and direct. One example Steve shared: LVCC to Encore in about 85 seconds. But the biggest “wait, that just happened” moment on the tour was Full Self-Driving. FSD Underground (And Onto Surface Streets) We rode in a Model Y running Full Self-Driving (Supervised), which navigated the tunnels smoothly and then transitioned back to surface streets without intervention. Steve’s point wasn’t that autonomy is a cool demo; it’s that autonomy is a force multiplier for throughput, consistency, and future scale. Steve Davis: “Full Self-Driving Supervised is live commercially between LVCC and Encore, watch this: zero interventions as it navigates the tunnels and pops out onto surface streets seamlessly.” Right now, they still operate with safety drivers, but the trajectory is clear: as autonomy matures, the system can move more people with tighter headways and less variability than human-driven operations. The Numbers: “Spiky Demand” Is Where This System Wants To Win Vegas isn’t a steady-demand commuter city. It’s a burst-demand city: conventions, games, concerts, and tourist surges. Steve emphasized that this is exactly where the Loop model shines, because you can scale vehicles dynamically without rebuilding an entire transit line. During CES 2026, the Loop moved 90,000+ passengers, peaking at 6,600+ riders per hour, including 22,000+ trips to/from Resorts World, Encore, and Westgate. That’s on top of 3.5M+ total passengers since 2021. Steve Davis: “We’ve hit over 3 million passengers since 2021, and during CES 2026 alone, we shuttled more than 90,000 people, peaking at 6,600 passengers per hour without a hitch.” And beyond the numbers, there’s a secondary effect people don’t always talk about: for many riders, this is their first time in a Tesla, and it’s an unusually positive first impression. The Airport Connection: A Phased Plan With A Very Clear Endgame Connecting the system to Harry Reid International Airport is the crown jewel, and they’re doing it in phases to deliver value quickly while they work through the harder parts. Phase 1 (Live Now) Limited airport rides are already operating via a mix of tunnels and surface streets from existing stations, including Resorts World, Encore, Westgate, and LVCC. They’re doing roughly 50 test rides per day, and Steve noted 100 of ~130 vehicles are already “airport-ready” with transponders. Phase 2 (Next Couple Months) This is where things get meaningfully faster: a 2.2-mile dual tunnel from Westgate to 4744 Paradise Road, eliminating about two miles of surface traffic and stoplights. New stations are planned at Virgin Hotels, The Boring Company’s apartment complex, the former Gordon Biersch site, and Firefly. Fleet expands to 160 vehicles. Steve Davis: “Phase 2 kicks in soon: a 2.2-mile tunnel to Paradise Road, cutting out those surface miles and stoplights.” Phase 3 Extend to 5032 Palo Verde Road near Terminal 1, further removing surface bottlenecks around Tropicana and University Center. Fleet scales to 250–300 vehicles. Phase 4 (The “Holy Grail”) A direct underground station at the terminals, true curb-to-gate simplicity, fully underground. Steve Davis: “Phase 4 is the holy grail: a direct underground station right at the airport terminals.” The Big Build: 68 Miles, 104 Stations, Privately Funded The long-term vision is expansive: 68 miles of tunnels and 104 stations spanning the Strip, downtown, the stadium, and the airport. Core Strip construction begins this fall, with a 2027 target for that major phase, and further expansion into 2028–2029. Steve emphasized something important here: the funding model. These builds are privately funded, and the cost structure is the entire point: build rapidly and avoid “subway economics.” Steve Davis: “68 miles, 104 stations… all privately funded at about $10M per mile, versus billions for subways.” The Real Workhorses: Prufrock Boring Machines Up Close If the Loop is the user experience, Prufrock is the engine underneath it. Seeing Prufrock at an active dig site is hard to describe unless you’ve stood next to one. It’s enormous, loud, and relentlessly practical. The key advantage is that it changes the setup cost: it can launch from the surface without massive open pits, and it’s designed to move fast, with a long-term target of one mile per week. The machine isn’t just digging; it’s built around an integrated approach to lining, pumping, and maintaining the tunnel environment while staying cost-effective. Challenges They’re Solving In Real Time: Groundwater And Permitting One of the most interesting “myth-busting” moments was hearing Steve talk about tunnel conditions. Despite the desert setting, the tunnels are roughly 30 feet below grade, and in many areas, they’re fully submerged in groundwater, sand, clay, caliche, and water management, all part of the daily reality. Steve Davis: “Tunnels are 30 feet down, fully submerged in groundwater, desert myth busted.” They manage leaks through periodic sealing (foam, maintenance cycles) and now operate with stronger compliance processes for water treatment and disposal. The bigger long-term bottleneck, though, isn’t engineering; it’s approvals. Steve noted they need hundreds of permits (600+), and many can take months. Their push is toward a more streamlined, operator-style approval model, closer to how SpaceX is regulated: certify capability and safety, then execute without rearguing every step. Steve Davis: “Permitting’s the bottleneck… we’re advocating for a SpaceX-style operator license.” Fleet Scaling And The “Robovan” Strategy Right now, the fleet is about 130 Teslas, including Model Ys and Cybertrucks, tuned for tight turns and repeated high-frequency operations. The larger goal is to scale up to 1,200 vehicles as the network grows. And that’s where Robovan (high-occupancy, event-optimized vehicles) becomes strategically important. Steve’s framing was refreshingly clear: cars are more efficient for small groups. Robovans win when you can predict surges, like a Raiders game or a Sphere show, and load high-occupancy vehicles in advance. Steve Davis: “Robovans shine when everyone’s going to the same spot… that’s when you put the high occupancy vehicle in.” What’s Next: Suburbs, Regional Links, And Bigger Swing Ideas After the core network is built, they’re looking at suburban expansions (Henderson, Summerlin) via shorter demo segments first, proving utility for pedestrian and vehicle connectivity. And then Steve hinted at the kind of long-range thinking that gets people excited (and skeptical): longer-distance routes, potentially even Hyperloop concepts like Reno connections, if permitting and economics align. Steve Davis: “Suburbs like Henderson and Summerlin next… long-term? Hyperloop to Reno… private funding makes it doable if permitting catches up.” Final Take: Vegas Is Becoming A Live Testbed For A New Kind Of Transit This tour made one thing very clear: The Boring Company isn’t trying to win the “traditional public transit debate.” They’re trying to change the rules of what’s feasible, building faster, cheaper, and with an experience that people actually want to use. Watching FSD glide through the tunnels, seeing Prufrock tearing through the ground, and hearing the phased plan for the airport and Strip expansion straight from Steve… It’s hard not to feel like Vegas is a real-world preview of what mobility can look like when infrastructure is built like technology. Huge thanks to Steve Davis and The Boring Company team for the access and the time. And keep an eye out, I’m posting the full 47-minute video with this recap so you can see the ride, the sites, and the details for yourself. What do you think, would you ride the Loop instead of sitting in Strip traffic?

Behind The Scenes In The Vegas Loop: Inside Elon Musk's The Boring Company Bold Bet On Urban Mobility Hey everyone. Tesla Owners Silicon Valley (Tesla Owners Silicon Valley) here. I recently had the chance to go behind the scenes with Steve Davis, President of The Boring Company, for a deep dive into the Vegas Loop in Las Vegas. This wasn’t a quick photo op. It was a full 47-minute immersion: riding through the LED-lit tunnels in a Tesla, visiting active construction sites with Prufrock boring machines, and hearing directly from Steve about what’s working today, and what’s coming next. I’m posting the full long-form video alongside this recap so you can experience it firsthand. But here’s the readable, “what actually matters” story from the tour. From “Traffic Is Soul-Crushing” To A Working Underground Network The Boring Company was founded in 2016, born of a familiar frustration: gridlocked cities that can’t build fast enough, cheap enough, or with minimal disruption. The premise is simple but ambitious: reinvent tunneling to make it practical infrastructure, not a decade-long mega-project. Las Vegas is where that idea is being tested at real scale. Instead of waiting for buses, shuttles, or rail schedules, the Vegas Loop aims to provide point-to-point trips in Teslas, fast, quiet, and emissions-free, connecting major destinations without the chaos of the Strip above. And after seeing it up close, what stands out most is how operational it already is. This isn’t a render. It’s a functioning system handling real demand, in real conditions, with real riders. What It Feels Like: Fast, Weirdly Fun, And Surprisingly Smooth The “Loop experience” is part transit, part sci-fi. The tunnels are lined with shifting LEDs—purples, greens, yellows—that make the ride feel more like entering a venue than commuting. Trips are short and direct. One example Steve shared: LVCC to Encore in about 85 seconds. But the biggest “wait, that just happened” moment on the tour was Full Self-Driving. FSD Underground (And Onto Surface Streets) We rode in a Model Y running Full Self-Driving (Supervised), which navigated the tunnels smoothly and then transitioned back to surface streets without intervention. Steve’s point wasn’t that autonomy is a cool demo; it’s that autonomy is a force multiplier for throughput, consistency, and future scale. Steve Davis: “Full Self-Driving Supervised is live commercially between LVCC and Encore, watch this: zero interventions as it navigates the tunnels and pops out onto surface streets seamlessly.” Right now, they still operate with safety drivers, but the trajectory is clear: as autonomy matures, the system can move more people with tighter headways and less variability than human-driven operations. The Numbers: “Spiky Demand” Is Where This System Wants To Win Vegas isn’t a steady-demand commuter city. It’s a burst-demand city: conventions, games, concerts, and tourist surges. Steve emphasized that this is exactly where the Loop model shines, because you can scale vehicles dynamically without rebuilding an entire transit line. During CES 2026, the Loop moved 90,000+ passengers, peaking at 6,600+ riders per hour, including 22,000+ trips to/from Resorts World, Encore, and Westgate. That’s on top of 3.5M+ total passengers since 2021. Steve Davis: “We’ve hit over 3 million passengers since 2021, and during CES 2026 alone, we shuttled more than 90,000 people, peaking at 6,600 passengers per hour without a hitch.” And beyond the numbers, there’s a secondary effect people don’t always talk about: for many riders, this is their first time in a Tesla, and it’s an unusually positive first impression. The Airport Connection: A Phased Plan With A Very Clear Endgame Connecting the system to Harry Reid International Airport is the crown jewel, and they’re doing it in phases to deliver value quickly while they work through the harder parts. Phase 1 (Live Now) Limited airport rides are already operating via a mix of tunnels and surface streets from existing stations, including Resorts World, Encore, Westgate, and LVCC. They’re doing roughly 50 test rides per day, and Steve noted 100 of ~130 vehicles are already “airport-ready” with transponders. Phase 2 (Next Couple Months) This is where things get meaningfully faster: a 2.2-mile dual tunnel from Westgate to 4744 Paradise Road, eliminating about two miles of surface traffic and stoplights. New stations are planned at Virgin Hotels, The Boring Company’s apartment complex, the former Gordon Biersch site, and Firefly. Fleet expands to 160 vehicles. Steve Davis: “Phase 2 kicks in soon: a 2.2-mile tunnel to Paradise Road, cutting out those surface miles and stoplights.” Phase 3 Extend to 5032 Palo Verde Road near Terminal 1, further removing surface bottlenecks around Tropicana and University Center. Fleet scales to 250–300 vehicles. Phase 4 (The “Holy Grail”) A direct underground station at the terminals, true curb-to-gate simplicity, fully underground. Steve Davis: “Phase 4 is the holy grail: a direct underground station right at the airport terminals.” The Big Build: 68 Miles, 104 Stations, Privately Funded The long-term vision is expansive: 68 miles of tunnels and 104 stations spanning the Strip, downtown, the stadium, and the airport. Core Strip construction begins this fall, with a 2027 target for that major phase, and further expansion into 2028–2029. Steve emphasized something important here: the funding model. These builds are privately funded, and the cost structure is the entire point: build rapidly and avoid “subway economics.” Steve Davis: “68 miles, 104 stations… all privately funded at about $10M per mile, versus billions for subways.” The Real Workhorses: Prufrock Boring Machines Up Close If the Loop is the user experience, Prufrock is the engine underneath it. Seeing Prufrock at an active dig site is hard to describe unless you’ve stood next to one. It’s enormous, loud, and relentlessly practical. The key advantage is that it changes the setup cost: it can launch from the surface without massive open pits, and it’s designed to move fast, with a long-term target of one mile per week. The machine isn’t just digging; it’s built around an integrated approach to lining, pumping, and maintaining the tunnel environment while staying cost-effective. Challenges They’re Solving In Real Time: Groundwater And Permitting One of the most interesting “myth-busting” moments was hearing Steve talk about tunnel conditions. Despite the desert setting, the tunnels are roughly 30 feet below grade, and in many areas, they’re fully submerged in groundwater, sand, clay, caliche, and water management, all part of the daily reality. Steve Davis: “Tunnels are 30 feet down, fully submerged in groundwater, desert myth busted.” They manage leaks through periodic sealing (foam, maintenance cycles) and now operate with stronger compliance processes for water treatment and disposal. The bigger long-term bottleneck, though, isn’t engineering; it’s approvals. Steve noted they need hundreds of permits (600+), and many can take months. Their push is toward a more streamlined, operator-style approval model, closer to how SpaceX is regulated: certify capability and safety, then execute without rearguing every step. Steve Davis: “Permitting’s the bottleneck… we’re advocating for a SpaceX-style operator license.” Fleet Scaling And The “Robovan” Strategy Right now, the fleet is about 130 Teslas, including Model Ys and Cybertrucks, tuned for tight turns and repeated high-frequency operations. The larger goal is to scale up to 1,200 vehicles as the network grows. And that’s where Robovan (high-occupancy, event-optimized vehicles) becomes strategically important. Steve’s framing was refreshingly clear: cars are more efficient for small groups. Robovans win when you can predict surges, like a Raiders game or a Sphere show, and load high-occupancy vehicles in advance. Steve Davis: “Robovans shine when everyone’s going to the same spot… that’s when you put the high occupancy vehicle in.” What’s Next: Suburbs, Regional Links, And Bigger Swing Ideas After the core network is built, they’re looking at suburban expansions (Henderson, Summerlin) via shorter demo segments first, proving utility for pedestrian and vehicle connectivity. And then Steve hinted at the kind of long-range thinking that gets people excited (and skeptical): longer-distance routes, potentially even Hyperloop concepts like Reno connections, if permitting and economics align. Steve Davis: “Suburbs like Henderson and Summerlin next… long-term? Hyperloop to Reno… private funding makes it doable if permitting catches up.” Final Take: Vegas Is Becoming A Live Testbed For A New Kind Of Transit This tour made one thing very clear: The Boring Company isn’t trying to win the “traditional public transit debate.” They’re trying to change the rules of what’s feasible, building faster, cheaper, and with an experience that people actually want to use. Watching FSD glide through the tunnels, seeing Prufrock tearing through the ground, and hearing the phased plan for the airport and Strip expansion straight from Steve… It’s hard not to feel like Vegas is a real-world preview of what mobility can look like when infrastructure is built like technology. Huge thanks to Steve Davis and The Boring Company team for the access and the time. And keep an eye out, I’m posting the full 47-minute video with this recap so you can see the ride, the sites, and the details for yourself. What do you think, would you ride the Loop instead of sitting in Strip traffic?

Tesla Owners Silicon Valley

447,027 views • 7 months ago

$ASTI Ascent Solar Technologies Space and Drone Solar Panels The "Going to Zero" or Mispriced Space/Drone Solar Play Intro and comparison to $RKLB and $RDW panels Let’s get the ugly stuff out of the way first. $ASTI is a distressed penny stock with a ~$5M-$10M market cap. • They burn millions in cash. • 2024 Revenue: ~$40k. 2025 Revenue (YTD): ~$60k. • They generate less revenue than a single Tesla Model Y. • They have diluted shareholders relentlessly. $ASTI just raised $2M in December with the potential of $3.5M more via warrants while being a ~$5M mcap "company". Yikes. To most, this is "uninvestable trash." Stay away. Full stop. So why did I buy ~5% of the float? IF the technology works and IF they execute then I believe this is a massive market pricing dislocation about to inflect. They have been grinding for years and may finally be hitting an inflection point. $RKLB Rocketlab is the king of space solar and they are my second largest position overall, but here is why $ASTI might be a very high risk but asymmetric bet in Space & Defense right now. 1. The Tech Pivot: Flexible CIGS vs. The World Ascent started in 2005 but pivoted 2 years ago from consumer to pure-play Space & Defense. They have sunk ~$250M and 20 years of R&D into proprietary CIGS (Copper-Indium-Gallium-Selenide) thin-film technology while building out fully domestic and vertically integrated manufacturing capabilities. The Physics: • Thickness: 0.03 mm (Thinner than paper). • Flexibility: Wraps around drones/satellites; rolls up like a poster. • Durability: "Self-Healing" capabilities against space radiation. Can take a bullet or micrometeoroid and keep working. Can handle shocks/vibration. Does not shatter. The Metric that Matters: Specific Power (W/kg) (aka energy to weight ratio) In space, mass means cost and difficult decision decisions. • Rocket Lab ($RKLB) / Spectrolab: ~150 W/kg (System level). • Ascent Solar ($ASTI): ~1,960 W/kg (Module level). $ASTI is roughly 10x lighter for the same power output potential (mass-wise). This frees up design limitations and cost. 2. The Competition: $RKLB & $RDW Rocket Lab (SolAero) & Redwire (iROSA): • Tech: Rigid Crystal Cells (Multi-junction) embedded in a fabric mesh. • Pros: Extreme Efficiency (~30%+). Perfect for limited surface area. • Cons: Heavy, Brittle, Expensive ($3k-$10k per Watt). Manufacturing multi-junction cells (SolAero) involves slowly growing crystals in a vacuum chamber. With radiation the panels degrade and loose efficiency over time which will limit the satellite lifespan. • Use Case: James Webb Telescope, Flagship missions. Ascent Solar (ASTI): • Tech: Flexible Thin-Film on Plastic. • Pros: Ultra-light, Durable, Cheap ($500-$1k per Watt). Manufacturing CIGS is roughly similar to printing newspapers (roll-to-roll). The panels are radiation degradation resistant and will outlive the satellite • Cons: Lower Efficiency (~17.5%). Requires 2x surface area. • Use Case: Mega-Constellations (Starlink/Amazon Leo), Small/Low cost satellites, Drones, Deformable surfaces. The lower efficiency is not an ASTI failing. It is the inherent physics trade-off of not using glass/rigid silicone. The downside however is increased atmospheric drag with very larger/massive panel sheets. Because ASTI modules are ~50% less efficient than rigid panels, they require ~2x the physical surface area to generate the same amount of power. In GEO (High Orbit): Drag doesn't matter. Weight savings are king. A massive solar array allows for more sensors and longer project lifespan. ASTI is highly competitive here. In LEO (Low Orbit): Atmospheric drag is real. A massive solar array acts like a large parachute, causing the satellite to de-orbit faster unless it burns more fuel to stay up. At LEO, smaller satellites are a better fit for ASTI. 3. Durability & Radiation "Self-Healing" Radiation Hardness This is ASTI's "Ace in the Hole" for physics. The Problem: In space, high-energy protons (radiation) smash into solar cells, creating atomic "defects" that trap electrons. Over time, this kills the panel's power output (degradation). The CIGS Advantage: CIGS (Copper-Indium-Gallium-Selenide) material has a unique property where heat (annealing) allows the atomic structure to relax and "heal" these defects. Self-Healing: Because CIGS heals at relatively low temperatures (often achieved just by the sun heating the panel), it suffers significantly less degradation than traditional Silicon or even some GaAs panels over long missions in high-radiation belts (like MEO or GEO). Lifespan: While a rigid GaAs panel might lose 15-20% of its power over 15 years (enough to kill a satellite), CIGS panels heal and can maintain a flatter power curve, potentially outlasting the satellite itself in high-radiation orbits. 4. Brittleness & Flexibility ASTI (CIGS on Polyimide): Flexible. You can roll it like a poster. It can take a bullet or micrometeoroid and the hole will just be a dead spot; the rest of the panel keeps working. It does not shatter. Redwire (ROSA) & Rocket Lab (SolAero): Brittle Cells on a Flex Blanket. $RDW's ROSA (Roll-Out Solar Array) typically uses rigid multi-junction cells (made by SolAero/Rocket Lab or Spectrolab) mounted on a flexible mesh fabric. The Risk: If you bend the cells too far, they crack. They rely on the mesh backing for flexibility, but the active generating material is still a brittle crystal wafer. Much heavier, more expensive, and less durable than $ASTI's option 5. The Inflection Point (Why Now?) After years of silent struggle, late 2025 has seen an explosion of activity. Recent Agreements (Nov/Dec 2025): NovaSpark: Hydrogen-powered military drones. $ASTI panels generate power in the field → NovaSpark creates hydrogen fuel. CisLunar Industries: Integrating ASTI solar with power conversion hardware for deep space longevity. Defiant Space: A strategic alliance to act as the "door opener" for classified DoD/NATO programs. More headlines: Ascent Solar Technologies Provides Leading Space Company with Thin-Film PV modules for Spacecraft Power Generation Testing in Cislunar Space December 03, 2025 08:00 ET Ascent Solar Technologies Delivers Thin-Film PV for Saltwater Environment Durability and Space-Based Power Beaming Testing October 14, 2025 08:00 ET Ascent Solar Enters Teaming Agreement with Emtel Energy USA to Advance Thin-Film PV Energy Storage Capabilities September 16, 2025 08:00 ET Ascent Solar Technologies Signs MOU with Star Catcher Industries to Improve Power Capabilities for Thin-Film Solar Technology in Space August 28, 2025 08:00 ET Ascent Solar Technologies Establishes Rapid Thin-Film PV Delivery Process to Provide Customized Space Solar Products Ahead of Schedule on Mission Enabling Timelines August 07, 2025 08:00 ET The Pipeline (From Aug Corporate Presentation) 18 new NDA's signed in 2025. They are field testing with 3 major players: • Company A: Mega-constellation (+2,500 satellites). • Company B: Space Defense (Explicitly mentioned "Golden Dome"). • Company C: Satellite Manufacturer (30-200 unit scale). Management: New board members include a former founding member of SpaceX and a retired Air Force General and Deputy Assistant Secretary for Contracting (acquisitions expert). The company started in 2005 based out of Colorado, but two years ago pivoted to Space & Defense and away from consumer applications. Made in USA: Defense contracts heavily favor domestic supply chains. ASTI manufactures in Colorado. This is a huge moat against cheap Chinese solar. In their Q3 report they note that their market has seen sudden recent acceleration. The space solar industry is currently only capable of 8 to 12 MW per year of production meanwhile the demand is growing to over 100 MW per year. 6. The Risk (The Sword of Damocles) ⚠️ This is critical. $ASTI just raised ~$2M in December. Attached to that raise are ~2 Million Warrants with a strike price of $1.70. These are exercisable immediately. If the stock rips to $3.00, warrant holders exercise at $1.70 and dump on the market for a risk-free 76% profit. This creates a massive "sell wall" and potential 40% dilution of the float. Summary: This is a binary bet. • Bear Case: They run out of cash in 6 months, dilution spirals, stock goes to $0. • Bull Case: They land one of the "Company A/B/C" contracts. Revenue jumps from $60k to projected $20M+ in 2026. The stock reprices from a "bankrupt penny stock" to a "critical defense/space supplier." I have gradually accumulated ~5% of the float. I am ready for it to go to zero. But if the space economy demands "Cheap, Light, and Durable," $ASTI is the only public pure-play. Disclaimer: This is a very high-risk microcap. Do your own due diligence. Not financial advice.

$ASTI Ascent Solar Technologies Space and Drone Solar Panels The "Going to Zero" or Mispriced Space/Drone Solar Play Intro and comparison to $RKLB and $RDW panels Let’s get the ugly stuff out of the way first. $ASTI is a distressed penny stock with a ~$5M-$10M market cap. • They burn millions in cash. • 2024 Revenue: ~$40k. 2025 Revenue (YTD): ~$60k. • They generate less revenue than a single Tesla Model Y. • They have diluted shareholders relentlessly. $ASTI just raised $2M in December with the potential of $3.5M more via warrants while being a ~$5M mcap "company". Yikes. To most, this is "uninvestable trash." Stay away. Full stop. So why did I buy ~5% of the float? IF the technology works and IF they execute then I believe this is a massive market pricing dislocation about to inflect. They have been grinding for years and may finally be hitting an inflection point. $RKLB Rocketlab is the king of space solar and they are my second largest position overall, but here is why $ASTI might be a very high risk but asymmetric bet in Space & Defense right now. 1. The Tech Pivot: Flexible CIGS vs. The World Ascent started in 2005 but pivoted 2 years ago from consumer to pure-play Space & Defense. They have sunk ~$250M and 20 years of R&D into proprietary CIGS (Copper-Indium-Gallium-Selenide) thin-film technology while building out fully domestic and vertically integrated manufacturing capabilities. The Physics: • Thickness: 0.03 mm (Thinner than paper). • Flexibility: Wraps around drones/satellites; rolls up like a poster. • Durability: "Self-Healing" capabilities against space radiation. Can take a bullet or micrometeoroid and keep working. Can handle shocks/vibration. Does not shatter. The Metric that Matters: Specific Power (W/kg) (aka energy to weight ratio) In space, mass means cost and difficult decision decisions. • Rocket Lab ($RKLB) / Spectrolab: ~150 W/kg (System level). • Ascent Solar ($ASTI): ~1,960 W/kg (Module level). $ASTI is roughly 10x lighter for the same power output potential (mass-wise). This frees up design limitations and cost. 2. The Competition: $RKLB & $RDW Rocket Lab (SolAero) & Redwire (iROSA): • Tech: Rigid Crystal Cells (Multi-junction) embedded in a fabric mesh. • Pros: Extreme Efficiency (~30%+). Perfect for limited surface area. • Cons: Heavy, Brittle, Expensive ($3k-$10k per Watt). Manufacturing multi-junction cells (SolAero) involves slowly growing crystals in a vacuum chamber. With radiation the panels degrade and loose efficiency over time which will limit the satellite lifespan. • Use Case: James Webb Telescope, Flagship missions. Ascent Solar (ASTI): • Tech: Flexible Thin-Film on Plastic. • Pros: Ultra-light, Durable, Cheap ($500-$1k per Watt). Manufacturing CIGS is roughly similar to printing newspapers (roll-to-roll). The panels are radiation degradation resistant and will outlive the satellite • Cons: Lower Efficiency (~17.5%). Requires 2x surface area. • Use Case: Mega-Constellations (Starlink/Amazon Leo), Small/Low cost satellites, Drones, Deformable surfaces. The lower efficiency is not an ASTI failing. It is the inherent physics trade-off of not using glass/rigid silicone. The downside however is increased atmospheric drag with very larger/massive panel sheets. Because ASTI modules are ~50% less efficient than rigid panels, they require ~2x the physical surface area to generate the same amount of power. In GEO (High Orbit): Drag doesn't matter. Weight savings are king. A massive solar array allows for more sensors and longer project lifespan. ASTI is highly competitive here. In LEO (Low Orbit): Atmospheric drag is real. A massive solar array acts like a large parachute, causing the satellite to de-orbit faster unless it burns more fuel to stay up. At LEO, smaller satellites are a better fit for ASTI. 3. Durability & Radiation "Self-Healing" Radiation Hardness This is ASTI's "Ace in the Hole" for physics. The Problem: In space, high-energy protons (radiation) smash into solar cells, creating atomic "defects" that trap electrons. Over time, this kills the panel's power output (degradation). The CIGS Advantage: CIGS (Copper-Indium-Gallium-Selenide) material has a unique property where heat (annealing) allows the atomic structure to relax and "heal" these defects. Self-Healing: Because CIGS heals at relatively low temperatures (often achieved just by the sun heating the panel), it suffers significantly less degradation than traditional Silicon or even some GaAs panels over long missions in high-radiation belts (like MEO or GEO). Lifespan: While a rigid GaAs panel might lose 15-20% of its power over 15 years (enough to kill a satellite), CIGS panels heal and can maintain a flatter power curve, potentially outlasting the satellite itself in high-radiation orbits. 4. Brittleness & Flexibility ASTI (CIGS on Polyimide): Flexible. You can roll it like a poster. It can take a bullet or micrometeoroid and the hole will just be a dead spot; the rest of the panel keeps working. It does not shatter. Redwire (ROSA) & Rocket Lab (SolAero): Brittle Cells on a Flex Blanket. $RDW's ROSA (Roll-Out Solar Array) typically uses rigid multi-junction cells (made by SolAero/Rocket Lab or Spectrolab) mounted on a flexible mesh fabric. The Risk: If you bend the cells too far, they crack. They rely on the mesh backing for flexibility, but the active generating material is still a brittle crystal wafer. Much heavier, more expensive, and less durable than $ASTI's option 5. The Inflection Point (Why Now?) After years of silent struggle, late 2025 has seen an explosion of activity. Recent Agreements (Nov/Dec 2025): NovaSpark: Hydrogen-powered military drones. $ASTI panels generate power in the field → NovaSpark creates hydrogen fuel. CisLunar Industries: Integrating ASTI solar with power conversion hardware for deep space longevity. Defiant Space: A strategic alliance to act as the "door opener" for classified DoD/NATO programs. More headlines: Ascent Solar Technologies Provides Leading Space Company with Thin-Film PV modules for Spacecraft Power Generation Testing in Cislunar Space December 03, 2025 08:00 ET Ascent Solar Technologies Delivers Thin-Film PV for Saltwater Environment Durability and Space-Based Power Beaming Testing October 14, 2025 08:00 ET Ascent Solar Enters Teaming Agreement with Emtel Energy USA to Advance Thin-Film PV Energy Storage Capabilities September 16, 2025 08:00 ET Ascent Solar Technologies Signs MOU with Star Catcher Industries to Improve Power Capabilities for Thin-Film Solar Technology in Space August 28, 2025 08:00 ET Ascent Solar Technologies Establishes Rapid Thin-Film PV Delivery Process to Provide Customized Space Solar Products Ahead of Schedule on Mission Enabling Timelines August 07, 2025 08:00 ET The Pipeline (From Aug Corporate Presentation) 18 new NDA's signed in 2025. They are field testing with 3 major players: • Company A: Mega-constellation (+2,500 satellites). • Company B: Space Defense (Explicitly mentioned "Golden Dome"). • Company C: Satellite Manufacturer (30-200 unit scale). Management: New board members include a former founding member of SpaceX and a retired Air Force General and Deputy Assistant Secretary for Contracting (acquisitions expert). The company started in 2005 based out of Colorado, but two years ago pivoted to Space & Defense and away from consumer applications. Made in USA: Defense contracts heavily favor domestic supply chains. ASTI manufactures in Colorado. This is a huge moat against cheap Chinese solar. In their Q3 report they note that their market has seen sudden recent acceleration. The space solar industry is currently only capable of 8 to 12 MW per year of production meanwhile the demand is growing to over 100 MW per year. 6. The Risk (The Sword of Damocles) ⚠️ This is critical. $ASTI just raised ~$2M in December. Attached to that raise are ~2 Million Warrants with a strike price of $1.70. These are exercisable immediately. If the stock rips to $3.00, warrant holders exercise at $1.70 and dump on the market for a risk-free 76% profit. This creates a massive "sell wall" and potential 40% dilution of the float. Summary: This is a binary bet. • Bear Case: They run out of cash in 6 months, dilution spirals, stock goes to $0. • Bull Case: They land one of the "Company A/B/C" contracts. Revenue jumps from $60k to projected $20M+ in 2026. The stock reprices from a "bankrupt penny stock" to a "critical defense/space supplier." I have gradually accumulated ~5% of the float. I am ready for it to go to zero. But if the space economy demands "Cheap, Light, and Durable," $ASTI is the only public pure-play. Disclaimer: This is a very high-risk microcap. Do your own due diligence. Not financial advice.

YeahDave

208,571 views • 8 months ago

In the 1920s, a Stanford psychologist tracked genius children for 50 years. Malcolm Gladwell breaks down what he discovered: Rich families → successful. Poor families → failures. Not average. Failures. Genius-level IQs that produced nothing. He spent 60 minutes at Microsoft explaining why we're wrong about success: The psychologist was named Terman. He gave IQ tests to 250,000 California schoolchildren. He identified the top 0.1%. Kids with IQs of 140 and above. His hypothesis: these children would become the leaders of academia, industry, and politics. He tracked them. And tracked them. For decades. The results split into three groups. The top 15% achieved real prominence. The middle group had average, moderately successful professional lives. And the bottom group? By any measure, failures. The difference wasn't personality. Wasn't habits. Wasn't work ethic. It was simple: the successful geniuses came from wealthy households. The failures came from poor families. Poverty is such a powerful constraint that it can reduce a one-in-a-billion brain to a lifetime of worse than mediocrity. There's a concept called "capitalization rate." It asks a simple question: what percentage of people who are capable of doing something actually end up doing that thing? In inner city Memphis, only 1 in 6 kids with athletic scholarships actually go to college. If our capitalization rate for sports in the inner city is 16%, imagine how low it must be for everything else. Here's something stranger. Gladwell read the birth dates of the 2007 Czech Junior Hockey Team: January 3rd. January 3rd. January 12th. February 8th. February 10th. February 17th. February 20th. February 24th. March 5th. March 10th. March 26th... 11 of the 20 players were born in January, February, or March. This isn't unique to the Czechs. Every elite hockey team in the world shows the same pattern. Every elite soccer team too. Why? The eligibility cutoff for youth leagues is January 1st. When you're 10 years old, a kid born in January has 10 months of maturity on a kid born in October. That's 3 or 4 inches of height. The difference between clumsy and coordinated. So we look at a group of 10 year olds, pick the "best" ones, give them special coaching, extra practice, more games. We think we're identifying talent. We're just identifying the oldest. Then we give the oldest more opportunities, and 10 years later they really are the best. Self-fulfilling prophecy. The capitalization rate for hockey talent born in the second half of the year? Close to zero. We're leaving half of all potential hockey players on the table because of an arbitrary date on a calendar. Kids born in the youngest cohort of their school class are 11% less likely to go to college. 11% of human potential squandered because we organize elementary school without reference to biological maturity. Now here's the part about math. Asian kids dramatically outperform Western kids in mathematics. The gap is enormous and consistent across decades of testing. Some people say it's genetic. It's not. It's attitudinal. When Asian kids face a math problem, they believe effort will solve it. When Western kids face a math problem, they believe the answer depends on innate ability they either have or don't. Here's the proof. The international math tests include a 120-question survey. It asks about study habits, parental support, attitudes. It's so long most kids don't finish it. A researcher named Erling Boe decided to rank countries by what percentage of survey questions their kids completed. Then he compared it to the ranking of countries by math performance. The correlation was 0.98. In the history of social science, there has never been a correlation that high. If you want to know how good a country is at math, you don't need to ask any math questions. Just make kids sit down and focus on a task for an extended period of time. If they can do it, they're good at math. Why do Asian cultures have this attitude? Gladwell's theory: rice farming. His European ancestors in medieval England worked about 1,000 hours a year. Dawn to noon, five days a week. Winters off. Lots of holidays. A peasant in South China or Japan in the same period worked 3,000 hours a year. Rice farming isn't just harder than wheat farming. It's a completely different relationship with work. There's a Chinese proverb: "A man who works dawn to dusk 360 days a year will not go hungry." His English ancestors would have said: "A man who works 175 days a year, dawn to 11, may or may not be hungry." If your culture does that for a thousand years, it becomes part of your makeup. When your kids sit down to face a calculus problem, that legacy of persistence translates perfectly. Now consider distance running. In Kenya, there are roughly a million schoolboys between 10 and 17 running 10 to 12 miles a day. In the United States, that number is probably 5,000. Our capitalization rate for distance running is less than 1%. Kenya's is probably 95%. The difference isn't genetic. The difference is what the culture values and where it spends its attention. Here's the most fascinating finding. 30% of American entrepreneurs have been diagnosed with a profound learning disability. Richard Branson is dyslexic. Charles Schwab is dyslexic. John Chambers can barely read his own email. This isn't coincidence. Their entrepreneurialism is a direct function of their disability. How do you succeed if you can't read or write from early childhood? You learn to delegate. You become a great oral communicator. You become a problem solver because your entire life is one big problem. You learn to lead. 80% of dyslexic entrepreneurs were captain of a high school sports team. Versus 30% of non-dyslexic entrepreneurs. By the time they enter the real world, they've spent their whole life practicing the four skills at the core of entrepreneurial success: delegation, oral communication, problem solving, and leadership. Ask them what role dyslexia played in their success and they don't say it was an obstacle. They say it's the reason they succeeded. A disadvantage that became an advantage. Here's what Gladwell wants you to understand: When we see differences in success, our default explanation is differences in ability. We forget how much poverty, stupidity, and attitude constrain what people can become. We refuse to admit that our own arbitrary rules are leaving talent on the table. We cling to naive beliefs that our meritocracies are fair. The capitalization argument is liberating. It says you don't look at a struggling group and conclude they're incapable. It says problems that look genetic or innate are often just failures of exploitation. It says we can make a profound difference in how well people turn out. If we choose to pay attention. This 60 minute Microsoft talk will teach you more about success than every self-help book you've ever read combined. Bookmark this & give it an hour today, no matter what.

In the 1920s, a Stanford psychologist tracked genius children for 50 years. Malcolm Gladwell breaks down what he discovered: Rich families → successful. Poor families → failures. Not average. Failures. Genius-level IQs that produced nothing. He spent 60 minutes at Microsoft explaining why we're wrong about success: The psychologist was named Terman. He gave IQ tests to 250,000 California schoolchildren. He identified the top 0.1%. Kids with IQs of 140 and above. His hypothesis: these children would become the leaders of academia, industry, and politics. He tracked them. And tracked them. For decades. The results split into three groups. The top 15% achieved real prominence. The middle group had average, moderately successful professional lives. And the bottom group? By any measure, failures. The difference wasn't personality. Wasn't habits. Wasn't work ethic. It was simple: the successful geniuses came from wealthy households. The failures came from poor families. Poverty is such a powerful constraint that it can reduce a one-in-a-billion brain to a lifetime of worse than mediocrity. There's a concept called "capitalization rate." It asks a simple question: what percentage of people who are capable of doing something actually end up doing that thing? In inner city Memphis, only 1 in 6 kids with athletic scholarships actually go to college. If our capitalization rate for sports in the inner city is 16%, imagine how low it must be for everything else. Here's something stranger. Gladwell read the birth dates of the 2007 Czech Junior Hockey Team: January 3rd. January 3rd. January 12th. February 8th. February 10th. February 17th. February 20th. February 24th. March 5th. March 10th. March 26th... 11 of the 20 players were born in January, February, or March. This isn't unique to the Czechs. Every elite hockey team in the world shows the same pattern. Every elite soccer team too. Why? The eligibility cutoff for youth leagues is January 1st. When you're 10 years old, a kid born in January has 10 months of maturity on a kid born in October. That's 3 or 4 inches of height. The difference between clumsy and coordinated. So we look at a group of 10 year olds, pick the "best" ones, give them special coaching, extra practice, more games. We think we're identifying talent. We're just identifying the oldest. Then we give the oldest more opportunities, and 10 years later they really are the best. Self-fulfilling prophecy. The capitalization rate for hockey talent born in the second half of the year? Close to zero. We're leaving half of all potential hockey players on the table because of an arbitrary date on a calendar. Kids born in the youngest cohort of their school class are 11% less likely to go to college. 11% of human potential squandered because we organize elementary school without reference to biological maturity. Now here's the part about math. Asian kids dramatically outperform Western kids in mathematics. The gap is enormous and consistent across decades of testing. Some people say it's genetic. It's not. It's attitudinal. When Asian kids face a math problem, they believe effort will solve it. When Western kids face a math problem, they believe the answer depends on innate ability they either have or don't. Here's the proof. The international math tests include a 120-question survey. It asks about study habits, parental support, attitudes. It's so long most kids don't finish it. A researcher named Erling Boe decided to rank countries by what percentage of survey questions their kids completed. Then he compared it to the ranking of countries by math performance. The correlation was 0.98. In the history of social science, there has never been a correlation that high. If you want to know how good a country is at math, you don't need to ask any math questions. Just make kids sit down and focus on a task for an extended period of time. If they can do it, they're good at math. Why do Asian cultures have this attitude? Gladwell's theory: rice farming. His European ancestors in medieval England worked about 1,000 hours a year. Dawn to noon, five days a week. Winters off. Lots of holidays. A peasant in South China or Japan in the same period worked 3,000 hours a year. Rice farming isn't just harder than wheat farming. It's a completely different relationship with work. There's a Chinese proverb: "A man who works dawn to dusk 360 days a year will not go hungry." His English ancestors would have said: "A man who works 175 days a year, dawn to 11, may or may not be hungry." If your culture does that for a thousand years, it becomes part of your makeup. When your kids sit down to face a calculus problem, that legacy of persistence translates perfectly. Now consider distance running. In Kenya, there are roughly a million schoolboys between 10 and 17 running 10 to 12 miles a day. In the United States, that number is probably 5,000. Our capitalization rate for distance running is less than 1%. Kenya's is probably 95%. The difference isn't genetic. The difference is what the culture values and where it spends its attention. Here's the most fascinating finding. 30% of American entrepreneurs have been diagnosed with a profound learning disability. Richard Branson is dyslexic. Charles Schwab is dyslexic. John Chambers can barely read his own email. This isn't coincidence. Their entrepreneurialism is a direct function of their disability. How do you succeed if you can't read or write from early childhood? You learn to delegate. You become a great oral communicator. You become a problem solver because your entire life is one big problem. You learn to lead. 80% of dyslexic entrepreneurs were captain of a high school sports team. Versus 30% of non-dyslexic entrepreneurs. By the time they enter the real world, they've spent their whole life practicing the four skills at the core of entrepreneurial success: delegation, oral communication, problem solving, and leadership. Ask them what role dyslexia played in their success and they don't say it was an obstacle. They say it's the reason they succeeded. A disadvantage that became an advantage. Here's what Gladwell wants you to understand: When we see differences in success, our default explanation is differences in ability. We forget how much poverty, stupidity, and attitude constrain what people can become. We refuse to admit that our own arbitrary rules are leaving talent on the table. We cling to naive beliefs that our meritocracies are fair. The capitalization argument is liberating. It says you don't look at a struggling group and conclude they're incapable. It says problems that look genetic or innate are often just failures of exploitation. It says we can make a profound difference in how well people turn out. If we choose to pay attention. This 60 minute Microsoft talk will teach you more about success than every self-help book you've ever read combined. Bookmark this & give it an hour today, no matter what.

Jaynit

1,587,796 views • 4 months ago

🚨 WE OFFICIALLY LIVE INSIDE A MACROSCOPIC ATOM. THE CONTINUOUS UNIVERSE IS DEAD. In the attached video, you are watching a live, unedited execution of the IT³ C-PHAM v18.1. THIS IS NOT A COMPUTER SIMULATION. We established a direct uplink to the Harvard-Smithsonian Minor Planet Center (MPC) — the official, daily-updated global registry of every known rock in space. We downloaded the live orbital data of over 1.55 MILLION real celestial objects today. Then, we ran them through a frozen mathematical operator with ZERO free parameters. We just wanted to see what would happen. When we saw the output, our own jaws dropped. The result? 99.56% of all baryonic mass is geometrically trapped. 100.00% integer rigidity inside the Macroscopic Valence Shell. Newly discovered objects flawlessly snap into a discrete 2x3 topological lattice. For 100 years, we were taught that the Solar System is a bunch of rocks floating randomly in a continuous space (ℝ⁴). That is mathematically and physically false. Space is rigidly quantized. 🏢 FOR NON-SCIENTISTS: THE INVISIBLE HOTEL ANALOGY Imagine looking at a bustling city and thinking millions of people are just floating randomly in the sky. That’s what astrophysics thought space was. But then you put on special glasses (our mathematics), and suddenly you see a giant, invisible, highly structured Hotel. You realize the planets and asteroids aren’t floating randomly—they are standing exactly on Floor 1, Floor 2, Floor 3. There is absolutely nothing in between the floors. Space has a rigid architecture. 🔭 LOOK AT THE SKY: 5 WORLD-CLASS PIPELINES ARE CRASHING Skeptics will scream: "It's just math! Where is the physical proof?"Watch the second half of the video. We log directly into the official NOIRLab Astro Data Lab cabinet. We query our exact, mathematically derived topological coordinates (e.g., the E-11 Node at RA 270.0°, Dec 45.0°, and E-9 at RA 357.135°, Dec -47.874°). At these exact spots, FIVE independent astrophysical pipelines simultaneously crash. And let me be absolutely clear: THIS IS NOT A TELESCOPE FAILURE. The mirrors and sensors in space are working perfectly, capturing real photons. It is the software algorithms that are choking to death. Within a microscopic 1.5-degree radius (just three times the size of the full moon in the sky), the databases vomit out exactly 42,030 catastrophic pipeline rejections! 1. NOIRLab (Optical): The algorithm shatters, returning massive proper motion errors (pmraerr > 370+) and flagging the zone as non-stellar. 2. ESA Gaia DR3 (Space L2 Orbit): Natively from deep space, completely ruling out atmospheric glitches, the pipeline returns 36,000+ rows of NaN (Not a Number) and an impossible astrometric_excess_noise_sig over 27,000! 3. DESI Legacy (Tractor): The point-mass model fit (rchisq_g) explodes to over 15,000 (a normal fit is 1.0). 4. NASA AllWISE (Mid-IR): Detects an extreme non-thermal color excess (W3-W4 > 1.5). 5. NASA CatWISE2020: Thermal tracking algorithms fail entirely, returning NaN for signal-to-noise. Why are the algorithms failing? Because they are stubbornly trying to fit flat, solid-rock Keplerian orbits to a pulsating macroscopic standing wave of vacuum plasma! They are trying to photograph a rock, but they are hitting the structural skeleton of spacetime itself. The positional angles (pos_angle_deg) of these 42,030 anomalies are completely random and isotropic. This isn't a coordinate drift bug; it's a boiling quantum plasma cauldron at a macroscopic scale! 💻 REPRODUCIBILITY: HACK THE DATABASE YOURSELF Don't believe me? Here are the exact ADQL scripts. Log into the Astro Data Lab or Gaia TAP servers, run these queries, and watch the world's most advanced algorithms break down on your own screen: NOIRLab Crash: SELECT ra, dec, pmra, pmdec, pmraerr, pmdecerr FROM nsc_dr2.object WHERE q3c_radial_query(ra, dec, 357.135, -47.874, 0.1) AND (pmraerr > 90.0 OR pmdecerr > 90.0) Gaia DR3 Collapse: SELECT source_id, parallax, astrometric_excess_noise_sig FROM gaia_dr3.gaia_source WHERE q3c_radial_query(ra, dec, 357.135, -47.874, 1.5) AND astrometric_excess_noise > 2.0 DESI Tractor Failure: SELECT ls_id, rchisq_g FROM ls_dr10.tractor WHERE q3c_radial_query(ra, dec, 357.135, -47.874, 1.5) AND rchisq_g > 5.0 AllWISE Thermal Excess: SELECT designation, (w3mpro - w4mpro) AS w3_w4_color FROM allwise.source WHERE q3c_radial_query(ra, dec, 357.135, -47.874, 1.5) AND (w3mpro - w4mpro) > 1.5 CatWISE NaN Tracking: SELECT source_id, w2snr FROM catwise2020.main WHERE q3c_radial_query(ra, dec, 357.135, -47.874, 1.5) The Cross-Match: SELECT wise.w3mpro, nsc.pmraerr FROM allwise.source AS wise JOIN nsc_dr2.object AS nsc ON q3c_join(wise.ra, wise.dec, nsc.ra, nsc.dec, 0.000833) WHERE q3c_radial_query(wise.ra, wise.dec, 357.135, -47.874, 3.0) AND (wise.w3mpro - wise.w4mpro) > 1.5 The 42,030 Kinematic Chaos Test: SELECT pmraerr, SQRT(pmra*pmra + pmdec*pmdec), (ATAN2(pmra, pmdec) * 180.0 / PI() + 360.0) - FLOOR((ATAN2(pmra, pmdec) * 180.0 / PI() + 360.0) / 360.0) * 360.0 AS pos_angle_deg FROM nsc_dr2.object WHERE q3c_radial_query(ra, dec, 270.0, 45.0, 1.5) AND ndet >= 3 AND deltamjd > 365.0 AND (pmraerr > 50.0) 🛡️ THE ZERO-PARAMETER SHIELD "But you just curve-fitted the Harvard data!" No. The mathematics came FIRST. We didn't look at the sky; we looked at pure Euclidean geometry. Here is the "Source Code" explicitly embedded in the script, derived from strict nested embeddings (Sphere ⊃ Cube ⊃ Octahedron ⊃ Torus ⊃ Catenoids): ➤ Λ₁ = √3(3 + 2√2) ≈ 10.095. This is the exact, unalterable helical pitch-to-throat ratio of a vertical torus tangent to the faces of an inscribed cube. ➤ Λ₃ = φ²√3 ≈ 4.534. Derived strictly from the same roots. ➤ N_twist = 103. The exact topological energy minimum. ➤ S_out = 3 S_in. The exact surface area ratio of O_h symmetry. You cannot "curve-fit" fundamental geometry. The matrix is hardcoded. ⏳ THE ULTIMATE TIME-MACHINE PROOF: Our geometry dictates a "Macroscopic Valence Shell" peaking exactly at 46.77 AU. When we ran this exact operator on historical MPC database archives from August 1992... that shell was COMPLETELY EMPTY. Humanity had zero objects there. But the math demanded it. Then, 1992 QB1 was found. Then 6 objects. Then 18. Today, thousands of bodies are perfectly locked into that exact 46.77 AU shell. You cannot curve-fit a database that does not exist yet. The geometry waited for humanity to find the matter. ⚛️ THE MICRO-MACRO RHYME (LOOK AT THE PHOTO): Look at the attached 2013 image (Stodolna et al.). Physicists directly photographed a microscopic Hydrogen atom, revealing discrete nodal rings for bound states, which dissolve into a blur when ionized. Our live script just proved the EXACT SAME THING in the Solar System. We ran an Entropy Test on 951 real comets: ➤ Bound comets (e 1) are the "guests" walking through the lobby. They exist in a continuous ionization spectrum (H = 3.85 bits), acting exactly as free macroscopic electrons! 🚀 OPEN SOURCE. THE BALL IS IN HARVARD'S COURT. The universe only has ONE blueprint. The Solar System is a fully quantized Macroscopic Atom. Everything is 100% OPEN SOURCE. Full code reproducibility. The ball is entirely in Harvard's court now. Let's see them try to invent new fairytales, "dark matter" legends, and introduce 21 artificial free parameters to explain this away. We just drove a Trojan Horse right through the front gates of the continuous spacetime paradigm, and we used their own databases to do it. Stop arguing with outdated textbooks. Look at the sky. Audit the proofs, run the code, and watch the Matrix render. 🤝🔥 🔗 Read the paper & run the code: #Astrophysics #QuantumPhysics #Cosmology #DeSci #IT3Matrix #ScienceTwitter

🚨 WE OFFICIALLY LIVE INSIDE A MACROSCOPIC ATOM. THE CONTINUOUS UNIVERSE IS DEAD. In the attached video, you are watching a live, unedited execution of the IT³ C-PHAM v18.1. THIS IS NOT A COMPUTER SIMULATION. We established a direct uplink to the Harvard-Smithsonian Minor Planet Center (MPC) — the official, daily-updated global registry of every known rock in space. We downloaded the live orbital data of over 1.55 MILLION real celestial objects today. Then, we ran them through a frozen mathematical operator with ZERO free parameters. We just wanted to see what would happen. When we saw the output, our own jaws dropped. The result? 99.56% of all baryonic mass is geometrically trapped. 100.00% integer rigidity inside the Macroscopic Valence Shell. Newly discovered objects flawlessly snap into a discrete 2x3 topological lattice. For 100 years, we were taught that the Solar System is a bunch of rocks floating randomly in a continuous space (ℝ⁴). That is mathematically and physically false. Space is rigidly quantized. 🏢 FOR NON-SCIENTISTS: THE INVISIBLE HOTEL ANALOGY Imagine looking at a bustling city and thinking millions of people are just floating randomly in the sky. That’s what astrophysics thought space was. But then you put on special glasses (our mathematics), and suddenly you see a giant, invisible, highly structured Hotel. You realize the planets and asteroids aren’t floating randomly—they are standing exactly on Floor 1, Floor 2, Floor 3. There is absolutely nothing in between the floors. Space has a rigid architecture. 🔭 LOOK AT THE SKY: 5 WORLD-CLASS PIPELINES ARE CRASHING Skeptics will scream: "It's just math! Where is the physical proof?"Watch the second half of the video. We log directly into the official NOIRLab Astro Data Lab cabinet. We query our exact, mathematically derived topological coordinates (e.g., the E-11 Node at RA 270.0°, Dec 45.0°, and E-9 at RA 357.135°, Dec -47.874°). At these exact spots, FIVE independent astrophysical pipelines simultaneously crash. And let me be absolutely clear: THIS IS NOT A TELESCOPE FAILURE. The mirrors and sensors in space are working perfectly, capturing real photons. It is the software algorithms that are choking to death. Within a microscopic 1.5-degree radius (just three times the size of the full moon in the sky), the databases vomit out exactly 42,030 catastrophic pipeline rejections! 1. NOIRLab (Optical): The algorithm shatters, returning massive proper motion errors (pmraerr > 370+) and flagging the zone as non-stellar. 2. ESA Gaia DR3 (Space L2 Orbit): Natively from deep space, completely ruling out atmospheric glitches, the pipeline returns 36,000+ rows of NaN (Not a Number) and an impossible astrometric_excess_noise_sig over 27,000! 3. DESI Legacy (Tractor): The point-mass model fit (rchisq_g) explodes to over 15,000 (a normal fit is 1.0). 4. NASA AllWISE (Mid-IR): Detects an extreme non-thermal color excess (W3-W4 > 1.5). 5. NASA CatWISE2020: Thermal tracking algorithms fail entirely, returning NaN for signal-to-noise. Why are the algorithms failing? Because they are stubbornly trying to fit flat, solid-rock Keplerian orbits to a pulsating macroscopic standing wave of vacuum plasma! They are trying to photograph a rock, but they are hitting the structural skeleton of spacetime itself. The positional angles (pos_angle_deg) of these 42,030 anomalies are completely random and isotropic. This isn't a coordinate drift bug; it's a boiling quantum plasma cauldron at a macroscopic scale! 💻 REPRODUCIBILITY: HACK THE DATABASE YOURSELF Don't believe me? Here are the exact ADQL scripts. Log into the Astro Data Lab or Gaia TAP servers, run these queries, and watch the world's most advanced algorithms break down on your own screen: NOIRLab Crash: SELECT ra, dec, pmra, pmdec, pmraerr, pmdecerr FROM nsc_dr2.object WHERE q3c_radial_query(ra, dec, 357.135, -47.874, 0.1) AND (pmraerr > 90.0 OR pmdecerr > 90.0) Gaia DR3 Collapse: SELECT source_id, parallax, astrometric_excess_noise_sig FROM gaia_dr3.gaia_source WHERE q3c_radial_query(ra, dec, 357.135, -47.874, 1.5) AND astrometric_excess_noise > 2.0 DESI Tractor Failure: SELECT ls_id, rchisq_g FROM ls_dr10.tractor WHERE q3c_radial_query(ra, dec, 357.135, -47.874, 1.5) AND rchisq_g > 5.0 AllWISE Thermal Excess: SELECT designation, (w3mpro - w4mpro) AS w3_w4_color FROM allwise.source WHERE q3c_radial_query(ra, dec, 357.135, -47.874, 1.5) AND (w3mpro - w4mpro) > 1.5 CatWISE NaN Tracking: SELECT source_id, w2snr FROM catwise2020.main WHERE q3c_radial_query(ra, dec, 357.135, -47.874, 1.5) The Cross-Match: SELECT wise.w3mpro, nsc.pmraerr FROM allwise.source AS wise JOIN nsc_dr2.object AS nsc ON q3c_join(wise.ra, wise.dec, nsc.ra, nsc.dec, 0.000833) WHERE q3c_radial_query(wise.ra, wise.dec, 357.135, -47.874, 3.0) AND (wise.w3mpro - wise.w4mpro) > 1.5 The 42,030 Kinematic Chaos Test: SELECT pmraerr, SQRT(pmrapmra + pmdecpmdec), (ATAN2(pmra, pmdec) * 180.0 / PI() + 360.0) - FLOOR((ATAN2(pmra, pmdec) * 180.0 / PI() + 360.0) / 360.0) * 360.0 AS pos_angle_deg FROM nsc_dr2.object WHERE q3c_radial_query(ra, dec, 270.0, 45.0, 1.5) AND ndet >= 3 AND deltamjd > 365.0 AND (pmraerr > 50.0) 🛡️ THE ZERO-PARAMETER SHIELD "But you just curve-fitted the Harvard data!" No. The mathematics came FIRST. We didn't look at the sky; we looked at pure Euclidean geometry. Here is the "Source Code" explicitly embedded in the script, derived from strict nested embeddings (Sphere ⊃ Cube ⊃ Octahedron ⊃ Torus ⊃ Catenoids): ➤ Λ₁ = √3(3 + 2√2) ≈ 10.095. This is the exact, unalterable helical pitch-to-throat ratio of a vertical torus tangent to the faces of an inscribed cube. ➤ Λ₃ = φ²√3 ≈ 4.534. Derived strictly from the same roots. ➤ N_twist = 103. The exact topological energy minimum. ➤ S_out = 3 S_in. The exact surface area ratio of O_h symmetry. You cannot "curve-fit" fundamental geometry. The matrix is hardcoded. ⏳ THE ULTIMATE TIME-MACHINE PROOF: Our geometry dictates a "Macroscopic Valence Shell" peaking exactly at 46.77 AU. When we ran this exact operator on historical MPC database archives from August 1992... that shell was COMPLETELY EMPTY. Humanity had zero objects there. But the math demanded it. Then, 1992 QB1 was found. Then 6 objects. Then 18. Today, thousands of bodies are perfectly locked into that exact 46.77 AU shell. You cannot curve-fit a database that does not exist yet. The geometry waited for humanity to find the matter. ⚛️ THE MICRO-MACRO RHYME (LOOK AT THE PHOTO): Look at the attached 2013 image (Stodolna et al.). Physicists directly photographed a microscopic Hydrogen atom, revealing discrete nodal rings for bound states, which dissolve into a blur when ionized. Our live script just proved the EXACT SAME THING in the Solar System. We ran an Entropy Test on 951 real comets: ➤ Bound comets (e 1) are the "guests" walking through the lobby. They exist in a continuous ionization spectrum (H = 3.85 bits), acting exactly as free macroscopic electrons! 🚀 OPEN SOURCE. THE BALL IS IN HARVARD'S COURT. The universe only has ONE blueprint. The Solar System is a fully quantized Macroscopic Atom. Everything is 100% OPEN SOURCE. Full code reproducibility. The ball is entirely in Harvard's court now. Let's see them try to invent new fairytales, "dark matter" legends, and introduce 21 artificial free parameters to explain this away. We just drove a Trojan Horse right through the front gates of the continuous spacetime paradigm, and we used their own databases to do it. Stop arguing with outdated textbooks. Look at the sky. Audit the proofs, run the code, and watch the Matrix render. 🤝🔥 🔗 Read the paper & run the code: #Astrophysics #QuantumPhysics #Cosmology #DeSci #IT3Matrix #ScienceTwitter

Dr. Logvinovich

959,822 views • 20 days ago

I think the Singularity could be BORING We were promised flying cars and warp drives. We got same-day delivery and better autocomplete. And somehow, impossibly, we’re bored by it. This is the Boring Singularity. The idea that the most transformative period in human history will feel, to the people living through it, like a long and uneventful Tuesday. I want to explain why this happens. It comes down to three layers. The first is neurological. The second is architectural. The third is physical. Together they create a perfect storm of invisible progress that our minds are designed to ignore. Layer One: The Neurological Filter Here is a thought experiment. Imagine a caveman breaks his arm. For weeks he is miserable. He cannot hunt, cannot gather, cannot contribute. Then the bone heals. Within days of regaining function, he has completely forgotten the misery. The memory of suffering serves no purpose once the threat has passed. Evolution deleted it so he could return to baseline and focus on survival. We do this with everything. We did it with antibiotics. We did it with smartphones. We will do it with longevity. Psychologists call this hedonic adaptation. The human brain is an adaptation machine that returns us to a baseline level of experience regardless of how much our circumstances improve. And here is the critical finding. It only takes about three months for the “new normal” to cement itself. Any change that plays out over months or years, no matter how revolutionary, simply becomes background noise. Think about what this means for the Singularity. If anti-gravity cars were introduced tomorrow, they would be miraculous for a month, a status symbol for a year, and a frustrating utility that needs maintenance by year three. The internet is a collective telepathic hive mind that moves petabits at lightspeed. It is genuinely god-like power. We experience it as checking emails. The Singularity might already be here. We just cannot feel it because our brains are not designed to feel sustained amazement. They are designed to adapt and move on. Layer Two: The Hidden Infrastructure We expected Blade Runner. Neon towers and chrome robots serving drinks at the bar. What we are actually getting is something I call “Reverse Trantor.” In classic science fiction, advanced civilizations build upward and inward. They create city-planets like Trantor in Foundation or Coruscant in Star Wars, layer upon layer of visible technology. Our trajectory is the opposite. We are pushing the infrastructure outward and downward, into spaces humans never see. Consider where the robots actually are. They are not walking down the street. They are in mines and fulfillment centers and vertical farms. The real automation revolution is happening in dark warehouses where no human needs to flip a light switch. You order a package and it arrives faster than it used to. That is the entire perceptible output of a massive transformation in logistics. That’s not to say that you’ll never see humanoid robots milling around, only that the vast majority of them will be away from the public. The same principle applies to computation itself. In hindsight, it will look like the entire purpose of inventing computers was to run AI, and everything else was just the bootloader. We are heading toward a world where 99% of all CPU and GPU cycles are dedicated to machine-to-machine processes, and less than one percent is for human-facing tasks. The vast majority of the intelligence infrastructure will be completely invisible to us, humming along as background noise. And the really heavy stuff will be in space. Earth has a finite ability to dissipate heat. To run truly massive AI systems, we will likely move the servers to orbital platforms or Lagrange points where they can vent entropy into the void. The megastructures will exist. They will just be invisible points of light, indistinguishable from stars and space dust. This is the architectural reality of the Boring Singularity. The magic gets hidden in the walls and launched into orbit. What remains on Earth is green and quiet and looks suspiciously like a return to the pastoral. That’s not a bad thing, and it’s not to say that we return to a “steady state” equilibrium forever. Layer Three: The Hard Limits The final layer is the most sobering. We are hitting the physical ceiling of discovery itself. The Golden Age of science fiction emerged during a specific historical anomaly. Between 1905 and 1970, in a single human lifetime, we went from the Wright Brothers to the Moon, from Newtonian physics to quantum mechanics and the structure of DNA. That created an expectation of constant improvement. Fundamental discoveries happen every decade. The exponential curve goes up forever. Star Trek promised we would keep finding new energy sources and new physics for centuries. The data suggests otherwise. Research on scientific progress shows that we must double research effort every thirteen years just to maintain the same rate of economic growth. Studies of citation patterns reveal that the disruptiveness of new scientific papers dropped by ninety percent between 1945 and 2010. We are publishing more but saying less. The low-hanging fruit is gone. The cost curve tells the story most clearly. In the 1930s, you could discover a new particle with a tabletop experiment in a university lab for a few thousand dollars. It only took a few days to duplicate the splitting of the atom. To confirm the Higgs Boson, however, we needed the Large Hadron Collider, which cost nearly five billion dollars and took decades to build. The next generation of particle physics might require a hundred billion dollars, or trillions. And the math suggests that to probe the truly fundamental structure of reality at the Planck scale, you would need an accelerator the size of a galaxy. We cannot build that. So physics becomes theoretical not because we lack curiosity, but because we can no longer afford to test our hypotheses. Meanwhile, our imagination has outpaced physical reality. We grew up on fiction that treats the laws of physics as suggestions that can be bypassed with clever engineering. And that felt true (at the time) because we kept finding cool exploits, like fiber optics and nuclear fission. But the speed of light appears to be absolute. Thermodynamics is non-negotiable. We can imagine teleportation and warp drives, but there is no known physics that could enable them. This is the Sigmoid Curve in action. Progress is not an exponential line to infinity. It is an S-curve. We have likely passed the steepest part of fundamental discovery, and we are entering the plateau. The Inverted Star Wars So where does this leave us? I think the best model is actually Star Wars, just inverted. In Star Wars, they have had faster-than-light travel and droids for thousands of years. The technology has faded completely into the background. A hyperdrive failure is treated like a flat tire. It is annoying, not existential. Because the tech tree is fully unlocked, all the drama shifts to politics and governance and ideology. The Empire versus the Republic. Trade routes and treaties and coups. We are heading somewhere similar, with one crucial inversion. Our droids will be smarter, but our ships will be slower. We are likely trapped in this solar system by the speed of light. There is no Outer Rim to escape to if you dislike the politics. But our AI systems will be genuinely superintelligent, an invisible omniscient layer managing supply chains and governance and the allocation of resources. This intensifies the politics because there is no exit valve. We are stuck here with each other and with very powerful tools. The optimistic reading is that this represents maturity. For the last century, technology has moved faster than culture, causing constant anxiety. Future shock, always. If technology moves to a plateau, culture finally has time to catch up. Human decisions, not technological accidents, become what determines history. We stop waiting for a gadget to save us. We realize that if we want a better world, we have to build it with the tools we already have, because no new fundamental laws of reality are coming to rescue us. The Verdict The Boring Singularity is not a prediction of stagnation. Things will still change. We will probably see radical longevity and hyper-efficient energy and algorithmic governance that makes traffic and logistics invisible. It will be, by any historical standard, a utopia of convenience. But it will not feel like the future we were promised. The changes will be incremental enough that our brains adapt before we can appreciate them. The infrastructure will be hidden in warehouses and orbiting platforms we never see. And the truly magical discoveries, the new forces of nature and new physics, may simply be too expensive and complex to pursue. The Singularity is not ending with a bang or a whimper. It is ending with a shrug. And because we are humans, we will probably find something to complain about anyway.

I think the Singularity could be BORING We were promised flying cars and warp drives. We got same-day delivery and better autocomplete. And somehow, impossibly, we’re bored by it. This is the Boring Singularity. The idea that the most transformative period in human history will feel, to the people living through it, like a long and uneventful Tuesday. I want to explain why this happens. It comes down to three layers. The first is neurological. The second is architectural. The third is physical. Together they create a perfect storm of invisible progress that our minds are designed to ignore. Layer One: The Neurological Filter Here is a thought experiment. Imagine a caveman breaks his arm. For weeks he is miserable. He cannot hunt, cannot gather, cannot contribute. Then the bone heals. Within days of regaining function, he has completely forgotten the misery. The memory of suffering serves no purpose once the threat has passed. Evolution deleted it so he could return to baseline and focus on survival. We do this with everything. We did it with antibiotics. We did it with smartphones. We will do it with longevity. Psychologists call this hedonic adaptation. The human brain is an adaptation machine that returns us to a baseline level of experience regardless of how much our circumstances improve. And here is the critical finding. It only takes about three months for the “new normal” to cement itself. Any change that plays out over months or years, no matter how revolutionary, simply becomes background noise. Think about what this means for the Singularity. If anti-gravity cars were introduced tomorrow, they would be miraculous for a month, a status symbol for a year, and a frustrating utility that needs maintenance by year three. The internet is a collective telepathic hive mind that moves petabits at lightspeed. It is genuinely god-like power. We experience it as checking emails. The Singularity might already be here. We just cannot feel it because our brains are not designed to feel sustained amazement. They are designed to adapt and move on. Layer Two: The Hidden Infrastructure We expected Blade Runner. Neon towers and chrome robots serving drinks at the bar. What we are actually getting is something I call “Reverse Trantor.” In classic science fiction, advanced civilizations build upward and inward. They create city-planets like Trantor in Foundation or Coruscant in Star Wars, layer upon layer of visible technology. Our trajectory is the opposite. We are pushing the infrastructure outward and downward, into spaces humans never see. Consider where the robots actually are. They are not walking down the street. They are in mines and fulfillment centers and vertical farms. The real automation revolution is happening in dark warehouses where no human needs to flip a light switch. You order a package and it arrives faster than it used to. That is the entire perceptible output of a massive transformation in logistics. That’s not to say that you’ll never see humanoid robots milling around, only that the vast majority of them will be away from the public. The same principle applies to computation itself. In hindsight, it will look like the entire purpose of inventing computers was to run AI, and everything else was just the bootloader. We are heading toward a world where 99% of all CPU and GPU cycles are dedicated to machine-to-machine processes, and less than one percent is for human-facing tasks. The vast majority of the intelligence infrastructure will be completely invisible to us, humming along as background noise. And the really heavy stuff will be in space. Earth has a finite ability to dissipate heat. To run truly massive AI systems, we will likely move the servers to orbital platforms or Lagrange points where they can vent entropy into the void. The megastructures will exist. They will just be invisible points of light, indistinguishable from stars and space dust. This is the architectural reality of the Boring Singularity. The magic gets hidden in the walls and launched into orbit. What remains on Earth is green and quiet and looks suspiciously like a return to the pastoral. That’s not a bad thing, and it’s not to say that we return to a “steady state” equilibrium forever. Layer Three: The Hard Limits The final layer is the most sobering. We are hitting the physical ceiling of discovery itself. The Golden Age of science fiction emerged during a specific historical anomaly. Between 1905 and 1970, in a single human lifetime, we went from the Wright Brothers to the Moon, from Newtonian physics to quantum mechanics and the structure of DNA. That created an expectation of constant improvement. Fundamental discoveries happen every decade. The exponential curve goes up forever. Star Trek promised we would keep finding new energy sources and new physics for centuries. The data suggests otherwise. Research on scientific progress shows that we must double research effort every thirteen years just to maintain the same rate of economic growth. Studies of citation patterns reveal that the disruptiveness of new scientific papers dropped by ninety percent between 1945 and 2010. We are publishing more but saying less. The low-hanging fruit is gone. The cost curve tells the story most clearly. In the 1930s, you could discover a new particle with a tabletop experiment in a university lab for a few thousand dollars. It only took a few days to duplicate the splitting of the atom. To confirm the Higgs Boson, however, we needed the Large Hadron Collider, which cost nearly five billion dollars and took decades to build. The next generation of particle physics might require a hundred billion dollars, or trillions. And the math suggests that to probe the truly fundamental structure of reality at the Planck scale, you would need an accelerator the size of a galaxy. We cannot build that. So physics becomes theoretical not because we lack curiosity, but because we can no longer afford to test our hypotheses. Meanwhile, our imagination has outpaced physical reality. We grew up on fiction that treats the laws of physics as suggestions that can be bypassed with clever engineering. And that felt true (at the time) because we kept finding cool exploits, like fiber optics and nuclear fission. But the speed of light appears to be absolute. Thermodynamics is non-negotiable. We can imagine teleportation and warp drives, but there is no known physics that could enable them. This is the Sigmoid Curve in action. Progress is not an exponential line to infinity. It is an S-curve. We have likely passed the steepest part of fundamental discovery, and we are entering the plateau. The Inverted Star Wars So where does this leave us? I think the best model is actually Star Wars, just inverted. In Star Wars, they have had faster-than-light travel and droids for thousands of years. The technology has faded completely into the background. A hyperdrive failure is treated like a flat tire. It is annoying, not existential. Because the tech tree is fully unlocked, all the drama shifts to politics and governance and ideology. The Empire versus the Republic. Trade routes and treaties and coups. We are heading somewhere similar, with one crucial inversion. Our droids will be smarter, but our ships will be slower. We are likely trapped in this solar system by the speed of light. There is no Outer Rim to escape to if you dislike the politics. But our AI systems will be genuinely superintelligent, an invisible omniscient layer managing supply chains and governance and the allocation of resources. This intensifies the politics because there is no exit valve. We are stuck here with each other and with very powerful tools. The optimistic reading is that this represents maturity. For the last century, technology has moved faster than culture, causing constant anxiety. Future shock, always. If technology moves to a plateau, culture finally has time to catch up. Human decisions, not technological accidents, become what determines history. We stop waiting for a gadget to save us. We realize that if we want a better world, we have to build it with the tools we already have, because no new fundamental laws of reality are coming to rescue us. The Verdict The Boring Singularity is not a prediction of stagnation. Things will still change. We will probably see radical longevity and hyper-efficient energy and algorithmic governance that makes traffic and logistics invisible. It will be, by any historical standard, a utopia of convenience. But it will not feel like the future we were promised. The changes will be incremental enough that our brains adapt before we can appreciate them. The infrastructure will be hidden in warehouses and orbiting platforms we never see. And the truly magical discoveries, the new forces of nature and new physics, may simply be too expensive and complex to pursue. The Singularity is not ending with a bang or a whimper. It is ending with a shrug. And because we are humans, we will probably find something to complain about anyway.

David Shapiro (L/0)

14,232 views • 7 months ago

In a newly released technical update, SpaceX's leadership team, which includes communications manager Dan Huot, Director of Satellite Engineering Ian Dahl, and CEO Elon Musk, detailed a highly ambitious infrastructure roadmap to design, manufacture, and operate specialized artificial intelligence computing satellites at scale. Positioned as a major strategic pillar to dramatically elevate civilizational energy and processing capacity on the Kardashev scale, this strategy moves past traditional communications architectures into massive orbital server arrays. Here is the complete breakdown of the core technologies and timelines driving this space-based intelligence revolution: 🛰️ AI1 satellite power and compute capacity Ian Dahl and Elon Musk introduced the baseline performance targets for the first-generation AI1 satellite, explaining how its custom hardware is engineered to operate like an orbital data center server rack. Ian Dahl noted that their direct operational experience with xAI guided them to target a 150-kilowatt peak power capacity. To manage active machine learning workloads continuously, Elon Musk explained that the satellite is optimized to maintain a sustained average compute power envelope of 120 kilowatts, which directly mirrors the real-world performance of a terrestrial NVIDIA server rack. The official presentation slides outline several key operational metrics for this payload configuration: ⚡ The custom architecture delivers a 150 kW peak compute payload. 🔋 The system maintains a 120 kW sustained average compute payload under active workloads. ⚖️ The hardware achieves a highly optimized power-to-weight density of 70 kW per ton. 🔄 The layout features a completely interchangeable compute provider design. "We thought that the right place to start is around the 150 kilowatt peak power level. But as we look at the workloads with our experience with xAI, we see that we can support about 120 kilowatts of average compute. The 150 kilowatt peak power level roughly matches what, say, an NVIDIA GV300 rack would do. A more reasonable operating envelope would be around 120 kilowatts average power, but it can peak up to 150. So it is basically thinking about it as a rack of compute in space." --- 📐 AI1 satellite dimensions and thermal efficiency specs Elon Musk detailed the physical layout of the AI1 satellite, highlighting the massive dimensions required to accommodate its immense power and cooling hardware. He shared specific design criteria, explaining that the engineering relies on a custom 150 kW solar array paired with a high-capacity deployable liquid radiator thermal management system. The technical specifications of this vehicle layout include: 📏 The structural frame features a massive 70-meter wingspan. ↕️ The vehicle spans a total deployed height of 20 meters. ☀️ The onboard solar array delivers an efficiency of 250 W/m² using technology manufactured in Bastrop, Texas. 🌡️ The thermal system utilizes a 110 m² deployable liquid radiator to cleanly dump waste heat. 🔄 The cooling architecture incorporates redundant pumping loops for mission safety. 🛡️ The exterior contains integrated micrometeoroid shielding to protect the fluid lines. 🧭 The double-sided radiators achieve a dissipation rate of 1400 watts per square meter while remaining oriented knife-edge to the sun. "The assumptions here are 250 watts per square meter for the solar array and about 1400 watts per square meter for the radiators. The radiators are double-sided, radiating on both sides, and they're oriented knife-edge to the sun. They have about a 70-meter wingspan, so these are fairly large." --- 🧩 Simplified design architecture built on Starlink V3 tech Elon Musk explained that despite the satellite's imposing size, its internal architecture is fundamentally much simpler than a standard Starlink satellite. Because it lacks heavy phased array and parabolic communications antennas, the entire vehicle layout is completely streamlined around a few essential structural modules: 🎛️ The hardware framework is arranged around a centralized compute module. ☀️ Large deployable solar arrays extend outward to capture orbital energy. 🌡️ A deployable liquid-radiator thermal management system controls active operational temperatures. 🔄 The engineering team heavily leverages the component evolution and manufacturing experience gained from developing the Starlink V3 vehicle platform. "The AI satellite is actually much simpler than a Starlink satellite. A Starlink satellite has gigantic phased array antennas, parabolic antennas, and a lot of laser links, making it much more complicated. An AI satellite is essentially a lot of solar cells, a radiator, and you still need some laser links, but you don't have all of the super complex antennas that you have on a Starlink satellite. A lot of this is technology we've already made for the Starlink V3 satellites." --- 🔌 Interchangeable compute reference designs and high connectivity Elon Musk outlined a modular hardware approach for the satellite's payload, allowing it to house a variety of industry-standard processing units depending on client requirements. This interchangeable compute rack is supported by a high-bandwidth connectivity loop that links separate orbital units together or transmits data directly back to Earth. The core network parameters include: 🧠 Reference designs are fully established to seamlessly accommodate NVIDIA Reuben chips. 💾 The system architecture is built to support alternative setups using NVIDIA GB300 chips. 💻 Custom hardware layouts are explicitly designed to integrate Google TPUs. 🌐 The onboard communications setup delivers roughly 1 terabit of laser link connectivity. ⏱️ The network closes the communication loop directly with the main Starlink constellation at an ultra-low latency of only 3 milliseconds. "Our current reference design is for NVIDIA Reuben chips, or it could be either GB300 or Reuben chips. We'll also have a reference design for TPUs. Essentially, you can put up any existing chips into orbit. There would also be probably something on the order of a terabit of laser link connectivity from the satellite. Then you can connect these racks of compute to each other by the laser links or directly to the Starlink constellations. Light travels 300 kilometers per millisecond, so that's about three milliseconds away." --- 🏭 The "gigasat" AI satellite and solar production hub in Bastrop, Texas Dan Huot highlighted that the primary production hub for this entire hardware ecosystem is anchored at their sprawling complex in Bastrop, Texas, officially designated as the Gigasat factory. Elon Musk verified that construction is already actively underway on the solar manufacturing facility to feed the project's supply line, with plans moving forward to construct the adjacent AI satellite assembly lines. The physical footprint and timeline of this manufacturing hub are defined by the following benchmarks: 🗺️ The company has over 1,000 acres of land currently owned or under contract for the site. 🏢 The manufacturing complex boasts a massive structural building potential exceeding 11 million square feet. ⚙️ The facility will vertically integrate production to manufacture solar ingots, wafers, solar cells, and completed AI satellites. 📅 Both the solar and AI satellite production lines are targeted to be operational at a viable volume by the end of next year. "We're going to be building a lot of satellites and we're going to be building them here in Bastrop. We already have the solar manufacturing facility under construction, and then we will be building out the AI sat production building soon. We expect to have the AI sat production, the solar production, and all of that operating at some reasonable volume by the end of next year." --- 🏢 The 100-million-square-foot "terafab" chip factory Elon Musk revealed a massive, long-term scaling strategy to build an immense chip manufacturing facility dubbed the "terafab" to completely bypass global semiconductor volume constraints. This manufacturing infrastructure is designed to transition the company into next-generation industrial scaling by producing highly specialized computing components at an unprecedented volume. The scale of this infrastructure project is defined by several extraordinary engineering and production benchmarks: 🏭 The colossal factory is projected to span approximately 100 million square feet, making it ten times larger than the current Tesla Gigafactory Texas. ⚡ The facility is structurally engineered to achieve a massive manufacturing output of 1 terawatt per year once fully operational. 📦 This unprecedented physical footprint provides the capacity required to manufacture 1 billion full-reticle equivalent chips annually. 🔌 Each individual chip manufactured by the facility is designed to run at a power capacity of 1 kilowatt. 🇺🇸 The total scaled output of the facility represents an energy footprint that is exactly double the current annual electricity consumption of the entire United States. "In order to get to the next order of magnitude, you need a gigantic chip factory. To give you a sense of scale here, we expect that the terafab is going to be around 100 million square feet, which is 10 times the size of the Tesla Gigafactory Texas. From a logic die standpoint, that's like having a billion chips per year with a kilowatt per reticle, scaling to a terawatt per year. That is twice the current electricity consumption of the United States." --- 📶 Next-generation high-volume Starlink terminals Dan Huot and Elon Musk introduced their next-generation Starlink user terminals, which have been redesigned specifically to achieve massive manufacturing throughput. Elon Musk pointed out that these newer models will be produced in vastly higher volumes than current hardware designs to fulfill their long-term global deployment targets: 📈 The upgraded user hardware is manufactured at a much higher volume capacity than existing units. 🌍 The company's ultimate target is to successfully deploy a few hundred million of these next-generation terminals worldwide. "In fact, these are the new Starlink terminals, which we made in much higher volume than the current terminals. Ultimately, we think there's probably going to be a few hundred million Starlink terminals out there." --- 📈 Aspirational timeline for orbital AI compute scaling Elon Musk laid out an ambitious, multi-year execution timeline detailing how the company plans to progressively scale space-based processing power. The roadmap targets an initial run-rate by the end of next year and sets an aggressive pace to increase total operational capacity sequentially through a structured, multi-phase timeline: 1️⃣ The initial target aims to hit an annualized run-rate of 1 gigawatt of space AI compute by the end of next year. 2️⃣ The capacity scales to an annualized rate of 10 gigawatts within the next two and a half years. 3️⃣ The operational envelope expands to reach 100 gigawatts in three and a half years. 4️⃣ The long-term deployment plan scales directly to a full terawatt capacity per year using the output of the terafab. "The goal is to get to roughly an annualized rate of a gigawatt per year by the end of next year in terms of space AI compute. Then aspirationally, we want to scale that by an order of magnitude per year. In two and a half years, hitting an annualized rate of 10 gigawatts a year in space, and in three and a half years, maybe a hundred gigawatts, going beyond that with the terafab to scale to a terawatt per year." --- 🌕 Ultimate scaling via lunar production and mass drivers Elon Musk explained that scaling three orders of magnitude past a single terawatt forces a transition completely off-planet to avoid the logistical penalty of Earth's deep gravity well. The vision relies on establishing manufacturing infrastructure directly on the moon to leverage localized resource loops and zero-atmosphere physics: 🌙 The company plans to establish localized raw production lines on the moon to fabricate solar panels, photovoltaics, and radiators from lunar materials. ⚡ Manufacturing components locally avoids the massive fuel and mass penalties of transporting heavy structural materials from Earth. 🧲 Because the moon has no atmosphere and only one-sixth of Earth's gravity, the facility will utilize an electromagnetic mass driver to launch completed satellites. 🚀 Operating essentially as a linear electric motor rail gun, this mechanism will shoot fully assembled AI satellites straight into deep space without relying on chemical rockets. "The only way that we can really see that you can achieve that is on the moon with a mass driver, essentially where you do local production of photovoltaics, solar panels, and radiators on the moon. Because the moon has no atmosphere and only one-sixth Earth's gravity, you can accelerate the AI satellites into deep space without a rocket. You can basically shoot them into space using an electromagnetic gun, like a rail gun type—it's basically a linear electric motor."

In a newly released technical update, SpaceX's leadership team, which includes communications manager Dan Huot, Director of Satellite Engineering Ian Dahl, and CEO Elon Musk, detailed a highly ambitious infrastructure roadmap to design, manufacture, and operate specialized artificial intelligence computing satellites at scale. Positioned as a major strategic pillar to dramatically elevate civilizational energy and processing capacity on the Kardashev scale, this strategy moves past traditional communications architectures into massive orbital server arrays. Here is the complete breakdown of the core technologies and timelines driving this space-based intelligence revolution: 🛰️ AI1 satellite power and compute capacity Ian Dahl and Elon Musk introduced the baseline performance targets for the first-generation AI1 satellite, explaining how its custom hardware is engineered to operate like an orbital data center server rack. Ian Dahl noted that their direct operational experience with xAI guided them to target a 150-kilowatt peak power capacity. To manage active machine learning workloads continuously, Elon Musk explained that the satellite is optimized to maintain a sustained average compute power envelope of 120 kilowatts, which directly mirrors the real-world performance of a terrestrial NVIDIA server rack. The official presentation slides outline several key operational metrics for this payload configuration: ⚡ The custom architecture delivers a 150 kW peak compute payload. 🔋 The system maintains a 120 kW sustained average compute payload under active workloads. ⚖️ The hardware achieves a highly optimized power-to-weight density of 70 kW per ton. 🔄 The layout features a completely interchangeable compute provider design. "We thought that the right place to start is around the 150 kilowatt peak power level. But as we look at the workloads with our experience with xAI, we see that we can support about 120 kilowatts of average compute. The 150 kilowatt peak power level roughly matches what, say, an NVIDIA GV300 rack would do. A more reasonable operating envelope would be around 120 kilowatts average power, but it can peak up to 150. So it is basically thinking about it as a rack of compute in space." --- 📐 AI1 satellite dimensions and thermal efficiency specs Elon Musk detailed the physical layout of the AI1 satellite, highlighting the massive dimensions required to accommodate its immense power and cooling hardware. He shared specific design criteria, explaining that the engineering relies on a custom 150 kW solar array paired with a high-capacity deployable liquid radiator thermal management system. The technical specifications of this vehicle layout include: 📏 The structural frame features a massive 70-meter wingspan. ↕️ The vehicle spans a total deployed height of 20 meters. ☀️ The onboard solar array delivers an efficiency of 250 W/m² using technology manufactured in Bastrop, Texas. 🌡️ The thermal system utilizes a 110 m² deployable liquid radiator to cleanly dump waste heat. 🔄 The cooling architecture incorporates redundant pumping loops for mission safety. 🛡️ The exterior contains integrated micrometeoroid shielding to protect the fluid lines. 🧭 The double-sided radiators achieve a dissipation rate of 1400 watts per square meter while remaining oriented knife-edge to the sun. "The assumptions here are 250 watts per square meter for the solar array and about 1400 watts per square meter for the radiators. The radiators are double-sided, radiating on both sides, and they're oriented knife-edge to the sun. They have about a 70-meter wingspan, so these are fairly large." --- 🧩 Simplified design architecture built on Starlink V3 tech Elon Musk explained that despite the satellite's imposing size, its internal architecture is fundamentally much simpler than a standard Starlink satellite. Because it lacks heavy phased array and parabolic communications antennas, the entire vehicle layout is completely streamlined around a few essential structural modules: 🎛️ The hardware framework is arranged around a centralized compute module. ☀️ Large deployable solar arrays extend outward to capture orbital energy. 🌡️ A deployable liquid-radiator thermal management system controls active operational temperatures. 🔄 The engineering team heavily leverages the component evolution and manufacturing experience gained from developing the Starlink V3 vehicle platform. "The AI satellite is actually much simpler than a Starlink satellite. A Starlink satellite has gigantic phased array antennas, parabolic antennas, and a lot of laser links, making it much more complicated. An AI satellite is essentially a lot of solar cells, a radiator, and you still need some laser links, but you don't have all of the super complex antennas that you have on a Starlink satellite. A lot of this is technology we've already made for the Starlink V3 satellites." --- 🔌 Interchangeable compute reference designs and high connectivity Elon Musk outlined a modular hardware approach for the satellite's payload, allowing it to house a variety of industry-standard processing units depending on client requirements. This interchangeable compute rack is supported by a high-bandwidth connectivity loop that links separate orbital units together or transmits data directly back to Earth. The core network parameters include: 🧠 Reference designs are fully established to seamlessly accommodate NVIDIA Reuben chips. 💾 The system architecture is built to support alternative setups using NVIDIA GB300 chips. 💻 Custom hardware layouts are explicitly designed to integrate Google TPUs. 🌐 The onboard communications setup delivers roughly 1 terabit of laser link connectivity. ⏱️ The network closes the communication loop directly with the main Starlink constellation at an ultra-low latency of only 3 milliseconds. "Our current reference design is for NVIDIA Reuben chips, or it could be either GB300 or Reuben chips. We'll also have a reference design for TPUs. Essentially, you can put up any existing chips into orbit. There would also be probably something on the order of a terabit of laser link connectivity from the satellite. Then you can connect these racks of compute to each other by the laser links or directly to the Starlink constellations. Light travels 300 kilometers per millisecond, so that's about three milliseconds away." --- 🏭 The "gigasat" AI satellite and solar production hub in Bastrop, Texas Dan Huot highlighted that the primary production hub for this entire hardware ecosystem is anchored at their sprawling complex in Bastrop, Texas, officially designated as the Gigasat factory. Elon Musk verified that construction is already actively underway on the solar manufacturing facility to feed the project's supply line, with plans moving forward to construct the adjacent AI satellite assembly lines. The physical footprint and timeline of this manufacturing hub are defined by the following benchmarks: 🗺️ The company has over 1,000 acres of land currently owned or under contract for the site. 🏢 The manufacturing complex boasts a massive structural building potential exceeding 11 million square feet. ⚙️ The facility will vertically integrate production to manufacture solar ingots, wafers, solar cells, and completed AI satellites. 📅 Both the solar and AI satellite production lines are targeted to be operational at a viable volume by the end of next year. "We're going to be building a lot of satellites and we're going to be building them here in Bastrop. We already have the solar manufacturing facility under construction, and then we will be building out the AI sat production building soon. We expect to have the AI sat production, the solar production, and all of that operating at some reasonable volume by the end of next year." --- 🏢 The 100-million-square-foot "terafab" chip factory Elon Musk revealed a massive, long-term scaling strategy to build an immense chip manufacturing facility dubbed the "terafab" to completely bypass global semiconductor volume constraints. This manufacturing infrastructure is designed to transition the company into next-generation industrial scaling by producing highly specialized computing components at an unprecedented volume. The scale of this infrastructure project is defined by several extraordinary engineering and production benchmarks: 🏭 The colossal factory is projected to span approximately 100 million square feet, making it ten times larger than the current Tesla Gigafactory Texas. ⚡ The facility is structurally engineered to achieve a massive manufacturing output of 1 terawatt per year once fully operational. 📦 This unprecedented physical footprint provides the capacity required to manufacture 1 billion full-reticle equivalent chips annually. 🔌 Each individual chip manufactured by the facility is designed to run at a power capacity of 1 kilowatt. 🇺🇸 The total scaled output of the facility represents an energy footprint that is exactly double the current annual electricity consumption of the entire United States. "In order to get to the next order of magnitude, you need a gigantic chip factory. To give you a sense of scale here, we expect that the terafab is going to be around 100 million square feet, which is 10 times the size of the Tesla Gigafactory Texas. From a logic die standpoint, that's like having a billion chips per year with a kilowatt per reticle, scaling to a terawatt per year. That is twice the current electricity consumption of the United States." --- 📶 Next-generation high-volume Starlink terminals Dan Huot and Elon Musk introduced their next-generation Starlink user terminals, which have been redesigned specifically to achieve massive manufacturing throughput. Elon Musk pointed out that these newer models will be produced in vastly higher volumes than current hardware designs to fulfill their long-term global deployment targets: 📈 The upgraded user hardware is manufactured at a much higher volume capacity than existing units. 🌍 The company's ultimate target is to successfully deploy a few hundred million of these next-generation terminals worldwide. "In fact, these are the new Starlink terminals, which we made in much higher volume than the current terminals. Ultimately, we think there's probably going to be a few hundred million Starlink terminals out there." --- 📈 Aspirational timeline for orbital AI compute scaling Elon Musk laid out an ambitious, multi-year execution timeline detailing how the company plans to progressively scale space-based processing power. The roadmap targets an initial run-rate by the end of next year and sets an aggressive pace to increase total operational capacity sequentially through a structured, multi-phase timeline: 1️⃣ The initial target aims to hit an annualized run-rate of 1 gigawatt of space AI compute by the end of next year. 2️⃣ The capacity scales to an annualized rate of 10 gigawatts within the next two and a half years. 3️⃣ The operational envelope expands to reach 100 gigawatts in three and a half years. 4️⃣ The long-term deployment plan scales directly to a full terawatt capacity per year using the output of the terafab. "The goal is to get to roughly an annualized rate of a gigawatt per year by the end of next year in terms of space AI compute. Then aspirationally, we want to scale that by an order of magnitude per year. In two and a half years, hitting an annualized rate of 10 gigawatts a year in space, and in three and a half years, maybe a hundred gigawatts, going beyond that with the terafab to scale to a terawatt per year." --- 🌕 Ultimate scaling via lunar production and mass drivers Elon Musk explained that scaling three orders of magnitude past a single terawatt forces a transition completely off-planet to avoid the logistical penalty of Earth's deep gravity well. The vision relies on establishing manufacturing infrastructure directly on the moon to leverage localized resource loops and zero-atmosphere physics: 🌙 The company plans to establish localized raw production lines on the moon to fabricate solar panels, photovoltaics, and radiators from lunar materials. ⚡ Manufacturing components locally avoids the massive fuel and mass penalties of transporting heavy structural materials from Earth. 🧲 Because the moon has no atmosphere and only one-sixth of Earth's gravity, the facility will utilize an electromagnetic mass driver to launch completed satellites. 🚀 Operating essentially as a linear electric motor rail gun, this mechanism will shoot fully assembled AI satellites straight into deep space without relying on chemical rockets. "The only way that we can really see that you can achieve that is on the moon with a mass driver, essentially where you do local production of photovoltaics, solar panels, and radiators on the moon. Because the moon has no atmosphere and only one-sixth Earth's gravity, you can accelerate the AI satellites into deep space without a rocket. You can basically shoot them into space using an electromagnetic gun, like a rail gun type—it's basically a linear electric motor."

Ming

22,203 views • 3 months ago

War is a Battle of INTELLIGENCE! Listener Questions Philosopher Stefan Molyneux unpacks Iran's real average IQ near 84 via Richard Lynn's data, shattering online test myths, political censorship and ideological fury to reveal intelligence's raw grip on civilizations. Questions: "Would you say your love for the music of Freddy Mercury is the primary factor that has unconsciously influenced you to repeatedly make false claims on X that the average IQ of Iran is 104-106. Firstly Stef, I’m a man who is deeply fascinated by the topic of IQ. And having said that I know that the IQ of Iran is roughly 84. I of course am not an IQ scientist/researcher myself but I have several sources from the top IQ experts some of which you probably have heard of (or maybe even interviewed on your show!). Now data from the most prominent IQ scientist Richard Lynn confirms the average IQ of Iran to be 84 (Citation 'The Wealth of Nations (2002), pg.133'. Which conversely is not only 20 points lower than your outrageous 104 IQ claim but is also even lower than Iraq which is 87. Now of course in a country of 90 million people you might have a million people with an IQ of 104 and let’s say 90,000 people with an IQ of >129 (See Normal Distribution charts attached below). So you will have some very smart people in Iran, but these people are an extreme minority generated by the bell curve of genetics being thrown 90 million times!, so as a whole on average most of the 90 million Iranians are borderline intellectually disabled. Now Stef back to your claim. Your claim is OUTRAGEOUS! You claim the IQ of Iran is 104-106, which is complete nonsense. I mean Stef think about what you’re saying Stef. Your saying the IQ of Iran a dysfunctional country majority populated with brainwashed borderline mentally dysfunctional people is the same as a highly industrious, innovative and merit based futuristic tech megalopolis like Japan. I mean spend one day in Japan and spend one day in Iran and I imagine the experience would be like worlds apart, yet you Stef claim that the IQ of Iran is the same as Japan??? WHATTT???? Does randomly firing missiles every so often like a child having a tantrum prove anything but the government of Iran is full of low IQ dysfunctional people. Now I know the IQ of the government is not necessarily the same as the general population. BUT, if the IQ of the general population was really that much higher than the governing leaders, then those moronic leaders would have been deposed of by the people a long time ago. Yet they weren't, so we should infer the general population isn’t in fact that much more intelligent than the government’s. Stef, in the West you might encounter Iranians who are very smart, but it goes without saying this sample is a very biased sample. What is particularly strange about all this, is you Stef are a man with a very high knowledge about IQ’s effect on national wealth and civil decorum and functionality, yet despite Iran being a highly dysfunctional nation at practically any level you can look at, you Stef echo a claim that the IQ of Iran is the same as Japan and even higher than Western Europe! How ABSURD! Which brings me back to my initial question. (Continued...) "freedomain (Continuing on ->) So Stef, when otherwise rational people act out of character and push fake news based on data that is from a biased sample (or is possibly even data that has been manipulated and propagated as a psy-op by the Iranian government) when they otherwise wouldn't ordinarily do so, one naturally has to ask WHY??? In my opinion, I think I know the answer. Stef, the only thing that explains this BIZARRE behaviour of yours (tweeting unverified claims about IQ), is you Stef have a bias in favour of Iran which I think stems from your love of Freddy Mercury music. Would you agree Stef? "(*** Stef you did tweet out a Youtube video of a Freddy Mercury song at about the same time you made your Iran IQ tweets, so I think the evidence is clear Stef, Freddy Mercury was on your mind, when you made those fake news Iran 104 IQ tweets.) "Now, lastly Stef you might be asking, why putting out a FALSE tweet falsely claiming the IQ of Iran is 104 even matter's that much? To that I would say it matters a whole lot because IQ of course matters a whole lot, including in regards to military objectives and war. Like ask why would invading Iran be any different from Iraq? Did Trump invade Iran because he thought it would lead to a different outcome because Trump views the population of Iran to be quote 'high IQ people'? (as Trump even said so as much in the last 24 hours.) "BUT … Mr. Trump, what if Iran isn't populated by high IQ people, what if Mr. Trump you were misled by fake news that Mr. Molyneux negligently tweeted rather than read the actual source material. (Lynn, 'The Wealth of Nations' (2002) IQ of Iran = 84). Well, Mr. Trump if you believed the average IQ of the population of Iran was 104 then you Mr. Trump might have assumed that all you had to do was take out the authoritarian government and then the high IQ population will swiftly bring about peace and democracy. But Mr. Trump, what if the average IQ of Iran is not 104? What if the top IQ scientists such as Lynn were right and the average IQ of Iran is in fact 84? Well then Mr. Trump, then your dealing with a very low IQ population, who will be frankly impossible to deal with, they will inevitably vote for another dictator and you Mr. Trump will find yourself in another forever war. That is why national IQ matters and that is why putting out false unverified and unsubstantiated tweets about IQ is a great error of judgment. Lastly to conclude, I believe in the great predictive power of IQ like you Stef, which is why I think it would be incumbent upon you to put out a correction unless you have verified sources from other top IQ researchers that support your OUTRAGEOUS Iran 104 National IQ claim." "Hi Stef! when communicating philosophy or philosophical ideas to the average person, and they respond with confusion or indifference, how do we know whether we have failed to adequately relay the ideas or if it is just too complex a language for them to grasp? I know everyone is capable of understanding philosophy on some level even if they’re lower IQ, so could the disconnect come from the disparity of time spent in the subject?" "Your Sunday (3/15) show was incredible. As a Catholic, that opening monologue challenging today's Christians to answer with clarity what Jesus commands via the Good Samaritan parable, especially when it comes to the specific child abuse scenario you laid out, it left me with a deep sense of sorrow for what you endured, and my own sense of frustration with the leadership of the Church. "The reality that not one, no church leaders, Anglican or not, lay or religious influencers; no one has tried to dialogue with you to address these essential issues, when you have been pointing them out for decades is more than disappointing to hear. "(Not as disappointing as some of the immediate live caller responses yesterday... the second woman was out of line, and the gentleman who landed on the idea that if you had not been abused, we wouldn't have received your philosophy gifts was hard to stomach.) "Nonetheless, if one (myself) was going to try to reach out, share your 3/15 show and try to coordinate a dialogue just to deal with the Good Samaritan and cold abuse topic, is that something you would be comfortable with? "second question "a few days ago, someone asked about how you feel about the Shroud of Turin, maybe you missed it but there has been renewed interest in the Shroud the last few years when a new photo 'negative' that appeared in 2024 along with new studies that for many was very compelling ... from a reason and evidence standpoint... to the image being impossible to reproduce. "I could be wrong but I felt like your answer lumped it so quickly with other things ... I believe u mentioned people of faith like Aquinas have searched for 'proof' for centuries. And you found that people of faith who are seeking proof to be an interesting paradox. "are you able to revisit this topic and assess what authenticating (if we are to believe the new reporting) that specific historical artifact could mean for at least establishing how the image came to be on the cloth? "again appreciate all u do to improve the world via philosophy and advocating for objective morality." "America in WW2 has burned down some and bombed all the major cities in Japan, even using the atomic bomb to create a glimpse of hell never before seen on earth. Yet today, the alliance between US and Japan couldnt be friendlier. In the middle east, we’ve also bombed them to next week and back for 30 or so years. And they still, understandably, hate the US. What do you think causes this difference in point of view between the countries? Is it religion/culture? Could it be IQ? Forgive me if this is too politcal or naive a question. Thank you!" "My martial arts club have a Hazing ritual that I believe is immoral and useless. I trying to get it banned. "Whats your opinions on hazing? Is it for example always immoral?" 0:00:00 The IQ Controversy 0:12:15 Discussing Iranian IQ Studies 0:29:34 Philosophy and Resistance to Truth 0:34:59 Philosophers and Child Abuse 0:38:16 The Shroud of Turin and Faith 0:45:21 Moral Dilemmas and Divine Intervention 0:48:37 Bumper Stickers and Performative Virtue 0:51:10 Closing Thoughts and Support GET FREEDOMAIN MERCH! SUBSCRIBE TO ME ON X! Follow me on Youtube! GET MY NEW BOOK 'PEACEFUL PARENTING', THE INTERACTIVE PEACEFUL PARENTING AI, AND THE FULL AUDIOBOOK! Join the PREMIUM philosophy community on the web for free! Subscribers get 12 HOURS on the "Truth About the French Revolution," multiple interactive multi-lingual philosophy AIs trained on thousands of hours of my material - as well as AIs for Real-Time Relationships, Bitcoin, Peaceful Parenting, and Call-In Shows! You also receive private livestreams, HUNDREDS of exclusive premium shows, early release podcasts, the 22 Part History of Philosophers series and much more! See you soon!

War is a Battle of INTELLIGENCE! Listener Questions Philosopher Stefan Molyneux unpacks Iran's real average IQ near 84 via Richard Lynn's data, shattering online test myths, political censorship and ideological fury to reveal intelligence's raw grip on civilizations. Questions: "Would you say your love for the music of Freddy Mercury is the primary factor that has unconsciously influenced you to repeatedly make false claims on X that the average IQ of Iran is 104-106. Firstly Stef, I’m a man who is deeply fascinated by the topic of IQ. And having said that I know that the IQ of Iran is roughly 84. I of course am not an IQ scientist/researcher myself but I have several sources from the top IQ experts some of which you probably have heard of (or maybe even interviewed on your show!). Now data from the most prominent IQ scientist Richard Lynn confirms the average IQ of Iran to be 84 (Citation 'The Wealth of Nations (2002), pg.133'. Which conversely is not only 20 points lower than your outrageous 104 IQ claim but is also even lower than Iraq which is 87. Now of course in a country of 90 million people you might have a million people with an IQ of 104 and let’s say 90,000 people with an IQ of >129 (See Normal Distribution charts attached below). So you will have some very smart people in Iran, but these people are an extreme minority generated by the bell curve of genetics being thrown 90 million times!, so as a whole on average most of the 90 million Iranians are borderline intellectually disabled. Now Stef back to your claim. Your claim is OUTRAGEOUS! You claim the IQ of Iran is 104-106, which is complete nonsense. I mean Stef think about what you’re saying Stef. Your saying the IQ of Iran a dysfunctional country majority populated with brainwashed borderline mentally dysfunctional people is the same as a highly industrious, innovative and merit based futuristic tech megalopolis like Japan. I mean spend one day in Japan and spend one day in Iran and I imagine the experience would be like worlds apart, yet you Stef claim that the IQ of Iran is the same as Japan??? WHATTT???? Does randomly firing missiles every so often like a child having a tantrum prove anything but the government of Iran is full of low IQ dysfunctional people. Now I know the IQ of the government is not necessarily the same as the general population. BUT, if the IQ of the general population was really that much higher than the governing leaders, then those moronic leaders would have been deposed of by the people a long time ago. Yet they weren't, so we should infer the general population isn’t in fact that much more intelligent than the government’s. Stef, in the West you might encounter Iranians who are very smart, but it goes without saying this sample is a very biased sample. What is particularly strange about all this, is you Stef are a man with a very high knowledge about IQ’s effect on national wealth and civil decorum and functionality, yet despite Iran being a highly dysfunctional nation at practically any level you can look at, you Stef echo a claim that the IQ of Iran is the same as Japan and even higher than Western Europe! How ABSURD! Which brings me back to my initial question. (Continued...) "freedomain (Continuing on ->) So Stef, when otherwise rational people act out of character and push fake news based on data that is from a biased sample (or is possibly even data that has been manipulated and propagated as a psy-op by the Iranian government) when they otherwise wouldn't ordinarily do so, one naturally has to ask WHY??? In my opinion, I think I know the answer. Stef, the only thing that explains this BIZARRE behaviour of yours (tweeting unverified claims about IQ), is you Stef have a bias in favour of Iran which I think stems from your love of Freddy Mercury music. Would you agree Stef? "(*** Stef you did tweet out a Youtube video of a Freddy Mercury song at about the same time you made your Iran IQ tweets, so I think the evidence is clear Stef, Freddy Mercury was on your mind, when you made those fake news Iran 104 IQ tweets.) "Now, lastly Stef you might be asking, why putting out a FALSE tweet falsely claiming the IQ of Iran is 104 even matter's that much? To that I would say it matters a whole lot because IQ of course matters a whole lot, including in regards to military objectives and war. Like ask why would invading Iran be any different from Iraq? Did Trump invade Iran because he thought it would lead to a different outcome because Trump views the population of Iran to be quote 'high IQ people'? (as Trump even said so as much in the last 24 hours.) "BUT … Mr. Trump, what if Iran isn't populated by high IQ people, what if Mr. Trump you were misled by fake news that Mr. Molyneux negligently tweeted rather than read the actual source material. (Lynn, 'The Wealth of Nations' (2002) IQ of Iran = 84). Well, Mr. Trump if you believed the average IQ of the population of Iran was 104 then you Mr. Trump might have assumed that all you had to do was take out the authoritarian government and then the high IQ population will swiftly bring about peace and democracy. But Mr. Trump, what if the average IQ of Iran is not 104? What if the top IQ scientists such as Lynn were right and the average IQ of Iran is in fact 84? Well then Mr. Trump, then your dealing with a very low IQ population, who will be frankly impossible to deal with, they will inevitably vote for another dictator and you Mr. Trump will find yourself in another forever war. That is why national IQ matters and that is why putting out false unverified and unsubstantiated tweets about IQ is a great error of judgment. Lastly to conclude, I believe in the great predictive power of IQ like you Stef, which is why I think it would be incumbent upon you to put out a correction unless you have verified sources from other top IQ researchers that support your OUTRAGEOUS Iran 104 National IQ claim." "Hi Stef! when communicating philosophy or philosophical ideas to the average person, and they respond with confusion or indifference, how do we know whether we have failed to adequately relay the ideas or if it is just too complex a language for them to grasp? I know everyone is capable of understanding philosophy on some level even if they’re lower IQ, so could the disconnect come from the disparity of time spent in the subject?" "Your Sunday (3/15) show was incredible. As a Catholic, that opening monologue challenging today's Christians to answer with clarity what Jesus commands via the Good Samaritan parable, especially when it comes to the specific child abuse scenario you laid out, it left me with a deep sense of sorrow for what you endured, and my own sense of frustration with the leadership of the Church. "The reality that not one, no church leaders, Anglican or not, lay or religious influencers; no one has tried to dialogue with you to address these essential issues, when you have been pointing them out for decades is more than disappointing to hear. "(Not as disappointing as some of the immediate live caller responses yesterday... the second woman was out of line, and the gentleman who landed on the idea that if you had not been abused, we wouldn't have received your philosophy gifts was hard to stomach.) "Nonetheless, if one (myself) was going to try to reach out, share your 3/15 show and try to coordinate a dialogue just to deal with the Good Samaritan and cold abuse topic, is that something you would be comfortable with? "second question "a few days ago, someone asked about how you feel about the Shroud of Turin, maybe you missed it but there has been renewed interest in the Shroud the last few years when a new photo 'negative' that appeared in 2024 along with new studies that for many was very compelling ... from a reason and evidence standpoint... to the image being impossible to reproduce. "I could be wrong but I felt like your answer lumped it so quickly with other things ... I believe u mentioned people of faith like Aquinas have searched for 'proof' for centuries. And you found that people of faith who are seeking proof to be an interesting paradox. "are you able to revisit this topic and assess what authenticating (if we are to believe the new reporting) that specific historical artifact could mean for at least establishing how the image came to be on the cloth? "again appreciate all u do to improve the world via philosophy and advocating for objective morality." "America in WW2 has burned down some and bombed all the major cities in Japan, even using the atomic bomb to create a glimpse of hell never before seen on earth. Yet today, the alliance between US and Japan couldnt be friendlier. In the middle east, we’ve also bombed them to next week and back for 30 or so years. And they still, understandably, hate the US. What do you think causes this difference in point of view between the countries? Is it religion/culture? Could it be IQ? Forgive me if this is too politcal or naive a question. Thank you!" "My martial arts club have a Hazing ritual that I believe is immoral and useless. I trying to get it banned. "Whats your opinions on hazing? Is it for example always immoral?" 0:00:00 The IQ Controversy 0:12:15 Discussing Iranian IQ Studies 0:29:34 Philosophy and Resistance to Truth 0:34:59 Philosophers and Child Abuse 0:38:16 The Shroud of Turin and Faith 0:45:21 Moral Dilemmas and Divine Intervention 0:48:37 Bumper Stickers and Performative Virtue 0:51:10 Closing Thoughts and Support GET FREEDOMAIN MERCH! SUBSCRIBE TO ME ON X! Follow me on Youtube! GET MY NEW BOOK 'PEACEFUL PARENTING', THE INTERACTIVE PEACEFUL PARENTING AI, AND THE FULL AUDIOBOOK! Join the PREMIUM philosophy community on the web for free! Subscribers get 12 HOURS on the "Truth About the French Revolution," multiple interactive multi-lingual philosophy AIs trained on thousands of hours of my material - as well as AIs for Real-Time Relationships, Bitcoin, Peaceful Parenting, and Call-In Shows! You also receive private livestreams, HUNDREDS of exclusive premium shows, early release podcasts, the 22 Part History of Philosophers series and much more! See you soon!

Freedomain - with Stefan Molyneux, MA

11,040 views • 5 months ago