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๐Ÿšจ๐Ÿ‡ท๐Ÿ‡บ RUSSIA'S SU-57 AI CAPABILITIES KEEP EVOLVING The Sukhoi Design Bureau is relentlessly upgrading the Su-57E fighter's AI systems, delivering highly intelligent onboard tech that provides pilots with critical decision prompts in the toughest tactical situations. ๐Ÿ”ธ AI ONBOARD feeds real-time audio and visual recommendations, letting pilots engage air,...

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๐Ÿšจ๐Ÿ‡ท๐Ÿ‡บ PENTAGON'S WORST NIGHTMARE: RUSSIA'S SU-57 JET FIGTER BECOMES STEALTH AIR COMMAND CENTER Rostec just confirmed the Su-57 was built as a flying command postโ€”directing UAVs, fusing sensor data, and running strikes while its stealth lets it operate undetected where Western AWACS dare not go. ๐Ÿ”ธ The Su-57D twin-seater made its first flight on May 19, specially optimized for command and control thanks to its second crew member handling sensors, weapons, and real-time decisions. ๐Ÿ”ธ The jet evolves the Soviet-era MiG-31 and Su-30 โ€œfighter commanderโ€ legacy by integrating five separate radars across the airframe plus advanced data links for a shared battlespace picture. ๐Ÿ”ธ Greater automation has shifted pilot roles toward data management, making the twin-seat format a major force multiplier in network-centric warfare. ๐Ÿ”ธ Its stealth capabilities allow the Su-57 to deliver command and control support from close to the frontlines in contested airspace โ€” unlike large, non-stealthy Western E-3 Sentry and Russian A-50 platforms that are easy targets. ๐Ÿ”ธ Chief test pilot Sergey Bogdan noted that an experienced in-air leader can make faster decisions during major operations when ground communications face interference or delays. Do you think the U.S. can catch up to Russian military technology?
0:44

๐Ÿšจ๐Ÿ‡ท๐Ÿ‡บ PENTAGON'S WORST NIGHTMARE: RUSSIA'S SU-57 JET FIGTER BECOMES STEALTH AIR COMMAND CENTER Rostec just confirmed the Su-57 was built as a flying command postโ€”directing UAVs, fusing sensor data, and running strikes while its stealth lets it operate undetected where Western AWACS dare not go. ๐Ÿ”ธ The Su-57D twin-seater made its first flight on May 19, specially optimized for command and control thanks to its second crew member handling sensors, weapons, and real-time decisions. ๐Ÿ”ธ The jet evolves the Soviet-era MiG-31 and Su-30 โ€œfighter commanderโ€ legacy by integrating five separate radars across the airframe plus advanced data links for a shared battlespace picture. ๐Ÿ”ธ Greater automation has shifted pilot roles toward data management, making the twin-seat format a major force multiplier in network-centric warfare. ๐Ÿ”ธ Its stealth capabilities allow the Su-57 to deliver command and control support from close to the frontlines in contested airspace โ€” unlike large, non-stealthy Western E-3 Sentry and Russian A-50 platforms that are easy targets. ๐Ÿ”ธ Chief test pilot Sergey Bogdan noted that an experienced in-air leader can make faster decisions during major operations when ground communications face interference or delays. Do you think the U.S. can catch up to Russian military technology?

NewRulesGeopolitics

317,327 Aufrufe โ€ข vor 3 Monaten

๐Ÿšจ๐Ÿ‡ท๐Ÿ‡บ NATO IN PANIC: RUSSIAโ€™S SU-57 STEALTH FIGHTER GETS NEW MISSILE THAT HUNTS THROUGH JAMMING Russia has unveiled the RVV-SDM, a new medium-range air-to-air missile developed for the Su-57 and other Russian combat aircraft. The first official image shows two missiles inside the fighterโ€™s internal weapons bay, preserving the aircraftโ€™s stealth while expanding its ability to attack targets beyond visual range. ๐Ÿ”ธ The RVV-SDM is designed to destroy fighters, helicopters, drones and other aerial targets day or night, in difficult weather and under heavy electronic warfare. ๐Ÿ”ธ Its guidance system combines inertial navigation, mid-course radio correction and an active radar seeker for the final attack. A passive channel allows the missile to track an emitting target without broadcasting its own radar signal. ๐Ÿ”ธ That creates a serious problem for enemy aircraft using powerful electronic countermeasures. Instead of breaking the lock, a jammer can become the signal that guides the missile toward its target. ๐Ÿ”ธ The missile uses an upgraded solid-fuel motor with greater energy than the earlier RVV-SD. Russia has not disclosed its exact maximum range, but confirms that it exceeds the predecessorโ€™s 110 km reach. ๐Ÿ”ธ Internal carriage is central to the design. Folding aerodynamic surfaces allow the RVV-SDM to fit inside the Su-57, avoiding external pylons that would increase radar visibility and drag. ๐Ÿ”ธ The โ€œfire-and-forgetโ€ capability allows the Su-57 to launch, change course and leave the threatened area while the missile independently completes the final stage of the interception. ๐Ÿ”ธ RVV-SDM fills the middle layer of the Su-57โ€™s air-combat arsenal between the short-range RVV-MD2 and long-range RVV-BD. The fighter can therefore carry different weapons for close combat, conventional beyond-visual-range engagements and attacks against high-value aircraft at much greater distances. Rosoboronexport is also offering the missile with the Su-57E and says it can be integrated onto other Russian-made fighters after modification. That gives foreign Su-57 operators access to the same combination of internal carriage, improved range and guidance built to operate through electronic warfare. For NATO pilots, switching on the jammer may no longer provide a clean escape route. The signal intended to protect the aircraft could instead help the Russian missile find it. Do you think NATO has an answer to the Su-57โ€™s new missile?
0:29

๐Ÿšจ๐Ÿ‡ท๐Ÿ‡บ NATO IN PANIC: RUSSIAโ€™S SU-57 STEALTH FIGHTER GETS NEW MISSILE THAT HUNTS THROUGH JAMMING Russia has unveiled the RVV-SDM, a new medium-range air-to-air missile developed for the Su-57 and other Russian combat aircraft. The first official image shows two missiles inside the fighterโ€™s internal weapons bay, preserving the aircraftโ€™s stealth while expanding its ability to attack targets beyond visual range. ๐Ÿ”ธ The RVV-SDM is designed to destroy fighters, helicopters, drones and other aerial targets day or night, in difficult weather and under heavy electronic warfare. ๐Ÿ”ธ Its guidance system combines inertial navigation, mid-course radio correction and an active radar seeker for the final attack. A passive channel allows the missile to track an emitting target without broadcasting its own radar signal. ๐Ÿ”ธ That creates a serious problem for enemy aircraft using powerful electronic countermeasures. Instead of breaking the lock, a jammer can become the signal that guides the missile toward its target. ๐Ÿ”ธ The missile uses an upgraded solid-fuel motor with greater energy than the earlier RVV-SD. Russia has not disclosed its exact maximum range, but confirms that it exceeds the predecessorโ€™s 110 km reach. ๐Ÿ”ธ Internal carriage is central to the design. Folding aerodynamic surfaces allow the RVV-SDM to fit inside the Su-57, avoiding external pylons that would increase radar visibility and drag. ๐Ÿ”ธ The โ€œfire-and-forgetโ€ capability allows the Su-57 to launch, change course and leave the threatened area while the missile independently completes the final stage of the interception. ๐Ÿ”ธ RVV-SDM fills the middle layer of the Su-57โ€™s air-combat arsenal between the short-range RVV-MD2 and long-range RVV-BD. The fighter can therefore carry different weapons for close combat, conventional beyond-visual-range engagements and attacks against high-value aircraft at much greater distances. Rosoboronexport is also offering the missile with the Su-57E and says it can be integrated onto other Russian-made fighters after modification. That gives foreign Su-57 operators access to the same combination of internal carriage, improved range and guidance built to operate through electronic warfare. For NATO pilots, switching on the jammer may no longer provide a clean escape route. The signal intended to protect the aircraft could instead help the Russian missile find it. Do you think NATO has an answer to the Su-57โ€™s new missile?

NewRulesGeopolitics

57,197 Aufrufe โ€ข vor 1 Monat

BREAKING NEWS !! Sukhoi S-70 Okhotnik-B will see first combat action alongside the SU 57 in Russia's June offensive to take KIEV & UKRAINE Thread 1/ The Sukhoi S-70 Okhotnik-B ("Hunter") is a Russian stealth heavy unmanned combat aerial vehicle (UCAV) designed by Sukhoi to operate alongside the Su-57 fifth-generation fighter jet. Below is a detailed summary of its specifications, role, as the " best UAV" with the Su-57 as its partner, S-70 Okhotnik Specifications Based on available information, primarily from Russian sources and open data, the S-70 Okhotnik has the following specifications: - **Type**: Stealth heavy unmanned combat aerial vehicle (UCAV) - **Developer**: Sukhoi Design Bureau, with contributions from Russian Aircraft Corporation MiG - **Production Facility**: Chkalov Novosibirsk Aviation Plant (NAPO) - **Design**: Flying-wing configuration, optimized for stealth with radar-absorbent materials and coatings to reduce radar cross-section - **Dimensions**: - **Length**: Approximately 14 meters (46 feet), though some sources claim up to 19 meters, possibly conflating earlier estimates or including different configurations ( ( - **Wingspan**: Approximately 19โ€“20 meters (62โ€“65 feet) ( ( - **Weight**: - **Empty Weight**: Estimated at 10,000โ€“20,000 kg (varies by source) ( - **Maximum Takeoff Weight**: Approximately 20,000โ€“25,000 kg ( ( - **Powerplant**: - Single jet engine, either: - AL-31F turbofan (used in Su-27 fighters) or - AL-41F derivative (used in Su-35S and Su-57 prototypes), without afterburner or thrust vectoring in some configurations ( ( - Later prototypes feature a flat jet nozzle to reduce infrared and radar signatures ( - **Performance**: - **Maximum Speed**: Approximately 1,000 km/h (620 mph), high subsonic, with potential for supersonic speeds in future versions ( ( - **Range**: Estimated at 4,000โ€“6,000 km, depending on configuration and payload ( ( - **Payload**: - Can carry up to 2.8โ€“6 tons of munitions in two internal weapons bays to maintain stealth ( ( - Armament includes: - Precision-guided bombs (e.g., FAB-500 M-62, UMPB D-30SN glide bombs) ( ( - Air-to-ground missiles - Potential air-to-air missiles (e.g., R-73/74, R-77, tested with surrogates) ( ( - Possibly a miniaturized version of the KH-47M2 Kinzhal hypersonic missile ( - **Stealth Features**: - Low-observable design with composite materials and stealth coatings - Internal weapons storage to minimize radar signature - Advanced sensors, including Active Phased Array Radar (AFAR), optoelectronic, and infrared systems ( - **Operational Role**: - Designed as a "loyal wingman" to the Su-57, extending its radar and target designation range - Capable of autonomous and semi-autonomous operations, including reconnaissance, electronic warfare, precision strikes, and suppression of enemy air defenses (SEAD) ( ( - Can operate independently or under control from a Su-57 or ground station - **AI and Autonomy**: Equipped with artificial intelligence for navigation, target recognition, and engagement with minimal human input ( ( - **Development Timeline**: - Project began in 2011, with the first prototype unveiled in 2017 - Maiden flight on August 3, 2019, lasting ~20 minutes at 600 meters altitude (
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BREAKING NEWS !! Sukhoi S-70 Okhotnik-B will see first combat action alongside the SU 57 in Russia's June offensive to take KIEV & UKRAINE Thread 1/ The Sukhoi S-70 Okhotnik-B ("Hunter") is a Russian stealth heavy unmanned combat aerial vehicle (UCAV) designed by Sukhoi to operate alongside the Su-57 fifth-generation fighter jet. Below is a detailed summary of its specifications, role, as the " best UAV" with the Su-57 as its partner, S-70 Okhotnik Specifications Based on available information, primarily from Russian sources and open data, the S-70 Okhotnik has the following specifications: - **Type**: Stealth heavy unmanned combat aerial vehicle (UCAV) - **Developer**: Sukhoi Design Bureau, with contributions from Russian Aircraft Corporation MiG - **Production Facility**: Chkalov Novosibirsk Aviation Plant (NAPO) - **Design**: Flying-wing configuration, optimized for stealth with radar-absorbent materials and coatings to reduce radar cross-section - **Dimensions**: - **Length**: Approximately 14 meters (46 feet), though some sources claim up to 19 meters, possibly conflating earlier estimates or including different configurations ( ( - **Wingspan**: Approximately 19โ€“20 meters (62โ€“65 feet) ( ( - **Weight**: - **Empty Weight**: Estimated at 10,000โ€“20,000 kg (varies by source) ( - **Maximum Takeoff Weight**: Approximately 20,000โ€“25,000 kg ( ( - **Powerplant**: - Single jet engine, either: - AL-31F turbofan (used in Su-27 fighters) or - AL-41F derivative (used in Su-35S and Su-57 prototypes), without afterburner or thrust vectoring in some configurations ( ( - Later prototypes feature a flat jet nozzle to reduce infrared and radar signatures ( - **Performance**: - **Maximum Speed**: Approximately 1,000 km/h (620 mph), high subsonic, with potential for supersonic speeds in future versions ( ( - **Range**: Estimated at 4,000โ€“6,000 km, depending on configuration and payload ( ( - **Payload**: - Can carry up to 2.8โ€“6 tons of munitions in two internal weapons bays to maintain stealth ( ( - Armament includes: - Precision-guided bombs (e.g., FAB-500 M-62, UMPB D-30SN glide bombs) ( ( - Air-to-ground missiles - Potential air-to-air missiles (e.g., R-73/74, R-77, tested with surrogates) ( ( - Possibly a miniaturized version of the KH-47M2 Kinzhal hypersonic missile ( - **Stealth Features**: - Low-observable design with composite materials and stealth coatings - Internal weapons storage to minimize radar signature - Advanced sensors, including Active Phased Array Radar (AFAR), optoelectronic, and infrared systems ( - **Operational Role**: - Designed as a "loyal wingman" to the Su-57, extending its radar and target designation range - Capable of autonomous and semi-autonomous operations, including reconnaissance, electronic warfare, precision strikes, and suppression of enemy air defenses (SEAD) ( ( - Can operate independently or under control from a Su-57 or ground station - **AI and Autonomy**: Equipped with artificial intelligence for navigation, target recognition, and engagement with minimal human input ( ( - **Development Timeline**: - Project began in 2011, with the first prototype unveiled in 2017 - Maiden flight on August 3, 2019, lasting ~20 minutes at 600 meters altitude (

๐ƒ๐š๐ฏ๐ข๐ ๐™ ๐Ÿ‡ท๐Ÿ‡บ๐Ÿ‡ฎ๐Ÿ‡ช

51,398 Aufrufe โ€ข vor 1 Jahr

๐Ÿšจ๐Ÿ‡ท๐Ÿ‡บ๐Ÿ‡ฎ๐Ÿ‡ท PENTAGON IN PANIC: U.S. BASES FACE RUSSIAโ€™S UPGRADED SHAHED ATTACK DRONES U.S. bases across the Middle East face a more dangerous Shahed threat as reports indicate that Russia is returning battlefield-tested upgrades to the Iranian design. AP reported in March that upgraded Russian-built drones were being sent to Tehran, while Reuters later said Western analysts were examining whether Russian technology helped Iranian drones strike CIA facilities in Riyadh and eastern Iraq. ๐Ÿ”ธ Iran originally supplied Russia with the Shahed-136 design. Russia then established mass production of its Geran-2 derivative and refined it through years of large-scale combat use. ๐Ÿ”ธ Russian development work has produced Shahed variants with improved satellite navigation, stronger protection against jamming, new radio links, cameras and upgraded flight computers. Other versions have appeared with jet engines and onboard AI, although APโ€™s sources did not identify which variants were reportedly sent to Iran. ๐Ÿ”ธ Reuters cited two Western officials who believed Russian-enhanced Shaheds were used in the attack on the U.S. Embassy in Riyadh. One drone reportedly opened a hole in the building before a second passed through it and detonated near the CIA station. No casualties were reported. ๐Ÿ”ธ Western intelligence sources also claim Russia supplied Iran with the Kometa-M satellite-navigation module, designed to maintain greater accuracy under electronic attack. The Kremlin dismissed the report as โ€œfake news.โ€ ๐Ÿ”ธ Faster and harder-to-jam drones create an expensive problem for defenders. Low-cost Shaheds can arrive in waves, forcing U.S. forces to consume limited stocks of missiles that cost far more than the targets they destroy. ๐Ÿ”ธ APโ€™s sources could not confirm the number or exact type of drones involved. But access to Russian navigation, communications and anti-jamming upgrades could matter more than the size of a single shipment. Iran supplied the original design. Russia mass-produced it, improved it under years of battlefield pressure and may now be returning those lessons to Tehran. For U.S. bases, the danger is not one drone but repeated salvos that are harder to jam, faster to adapt and far cheaper than the missiles used to stop them. Do you think upgraded Shaheds could change the balance in the U.S.-Iran conflict?
0:25

๐Ÿšจ๐Ÿ‡ท๐Ÿ‡บ๐Ÿ‡ฎ๐Ÿ‡ท PENTAGON IN PANIC: U.S. BASES FACE RUSSIAโ€™S UPGRADED SHAHED ATTACK DRONES U.S. bases across the Middle East face a more dangerous Shahed threat as reports indicate that Russia is returning battlefield-tested upgrades to the Iranian design. AP reported in March that upgraded Russian-built drones were being sent to Tehran, while Reuters later said Western analysts were examining whether Russian technology helped Iranian drones strike CIA facilities in Riyadh and eastern Iraq. ๐Ÿ”ธ Iran originally supplied Russia with the Shahed-136 design. Russia then established mass production of its Geran-2 derivative and refined it through years of large-scale combat use. ๐Ÿ”ธ Russian development work has produced Shahed variants with improved satellite navigation, stronger protection against jamming, new radio links, cameras and upgraded flight computers. Other versions have appeared with jet engines and onboard AI, although APโ€™s sources did not identify which variants were reportedly sent to Iran. ๐Ÿ”ธ Reuters cited two Western officials who believed Russian-enhanced Shaheds were used in the attack on the U.S. Embassy in Riyadh. One drone reportedly opened a hole in the building before a second passed through it and detonated near the CIA station. No casualties were reported. ๐Ÿ”ธ Western intelligence sources also claim Russia supplied Iran with the Kometa-M satellite-navigation module, designed to maintain greater accuracy under electronic attack. The Kremlin dismissed the report as โ€œfake news.โ€ ๐Ÿ”ธ Faster and harder-to-jam drones create an expensive problem for defenders. Low-cost Shaheds can arrive in waves, forcing U.S. forces to consume limited stocks of missiles that cost far more than the targets they destroy. ๐Ÿ”ธ APโ€™s sources could not confirm the number or exact type of drones involved. But access to Russian navigation, communications and anti-jamming upgrades could matter more than the size of a single shipment. Iran supplied the original design. Russia mass-produced it, improved it under years of battlefield pressure and may now be returning those lessons to Tehran. For U.S. bases, the danger is not one drone but repeated salvos that are harder to jam, faster to adapt and far cheaper than the missiles used to stop them. Do you think upgraded Shaheds could change the balance in the U.S.-Iran conflict?

NewRulesGeopolitics

22,922 Aufrufe โ€ข vor 1 Monat

๐Ÿšจ๐Ÿ‡จ๐Ÿ‡ณ PENTAGON'S NIGHTMARE: CHINA MAPS OUT SIXTH-GENERATION FIGHTERS FOR SATELLITE-DENIED DRONE WARFARE China is preparing its future fighters for a battlefield where satellite navigation is jammed, pilots face extreme workloads and unmanned wingmen must reorganize as conditions change. A new paper from Shenyang researchers outlines an aircraft capable of taking over its own flight and commanding an entire manned-unmanned formation instead of fighting alone. ๐Ÿ”ธ The paper was led by Zhang Dong, chief flight-control designer at the Shenyang Aircraft Design and Research Institute, and published in the peer-reviewed journal Aircraft Design in August. ๐Ÿ”ธ The institute is associated with the next-generation prototype widely known as the J-50. However, the paper does not name the aircraft or confirm that any of the proposed technologies have already been installed on it. ๐Ÿ”ธ Automatic flight controls could take over if the pilot loses consciousness or faces an extreme workload. The system would keep the aircraft stable and potentially allow it to continue its mission while the pilot recovers or concentrates on higher-level decisions. ๐Ÿ”ธ China is also exploring quantum-navigation technologies for operations where satellite positioning is unavailable. These could help future fighters determine their location after GPS, BeiDou or other satellite signals have been jammed or disrupted. ๐Ÿ”ธ The most ambitious concept involves commanding reconfigurable formations of unmanned wingmen. Drones assigned to attack, reconnaissance and protection could change roles and reorganize during a mission as the tactical situation evolves. ๐Ÿ”ธ Under this model, the pilot becomes the commander of a distributed combat network. The aircraft provides communications, sensor fusion and decision support while automation manages routine flying and helps coordinate multiple unmanned systems. If these concepts reach service, the Pentagon would face more than another stealth fighter. It would confront an airborne combat network capable of navigating under satellite denial, adapting its drone formation in real time and sending unmanned systems forward while the crewed aircraft remains farther from danger. The fighter would become the command center for an entire formation designed to keep fighting even when navigation signals and conventional formations break down. Which will matter more in the next air war: the fighter itself or the drones it controls?
0:44

๐Ÿšจ๐Ÿ‡จ๐Ÿ‡ณ PENTAGON'S NIGHTMARE: CHINA MAPS OUT SIXTH-GENERATION FIGHTERS FOR SATELLITE-DENIED DRONE WARFARE China is preparing its future fighters for a battlefield where satellite navigation is jammed, pilots face extreme workloads and unmanned wingmen must reorganize as conditions change. A new paper from Shenyang researchers outlines an aircraft capable of taking over its own flight and commanding an entire manned-unmanned formation instead of fighting alone. ๐Ÿ”ธ The paper was led by Zhang Dong, chief flight-control designer at the Shenyang Aircraft Design and Research Institute, and published in the peer-reviewed journal Aircraft Design in August. ๐Ÿ”ธ The institute is associated with the next-generation prototype widely known as the J-50. However, the paper does not name the aircraft or confirm that any of the proposed technologies have already been installed on it. ๐Ÿ”ธ Automatic flight controls could take over if the pilot loses consciousness or faces an extreme workload. The system would keep the aircraft stable and potentially allow it to continue its mission while the pilot recovers or concentrates on higher-level decisions. ๐Ÿ”ธ China is also exploring quantum-navigation technologies for operations where satellite positioning is unavailable. These could help future fighters determine their location after GPS, BeiDou or other satellite signals have been jammed or disrupted. ๐Ÿ”ธ The most ambitious concept involves commanding reconfigurable formations of unmanned wingmen. Drones assigned to attack, reconnaissance and protection could change roles and reorganize during a mission as the tactical situation evolves. ๐Ÿ”ธ Under this model, the pilot becomes the commander of a distributed combat network. The aircraft provides communications, sensor fusion and decision support while automation manages routine flying and helps coordinate multiple unmanned systems. If these concepts reach service, the Pentagon would face more than another stealth fighter. It would confront an airborne combat network capable of navigating under satellite denial, adapting its drone formation in real time and sending unmanned systems forward while the crewed aircraft remains farther from danger. The fighter would become the command center for an entire formation designed to keep fighting even when navigation signals and conventional formations break down. Which will matter more in the next air war: the fighter itself or the drones it controls?

NewRulesGeopolitics

12,654 Aufrufe โ€ข vor 15 Tagen

๐Ÿšจ๐Ÿ‡จ๐Ÿ‡ณ PENTAGON'S NIGHTMARE: CHINA ADVANCES SECOND SIXTH-GENERATION FIGHTER BUILT FOR SCALE China is pushing its second sixth-generation fighter deeper into flight testing, and its greatest advantage may be its ability to be fielded in large numbers. The twin-engine tailless aircraft developed by Shenyang, widely known by the unofficial designation J-50, has now been filmed operating at both low and high altitudes. It is roughly comparable in size to the J-20 and significantly smaller than Chinaโ€™s other next-generation prototype developed by Chengdu. ๐Ÿ”ธ A smaller airframe is expected to make the Shenyang fighter considerably cheaper to produce and maintain than the much larger Chengdu design. That could make sixth-generation capabilities practical across far more frontline units. ๐Ÿ”ธ Cost may prove as important as performance. Overruns on the F-22 and F-35 prevented the United States from replacing its older fighters as widely as planned, while the J-20โ€™s cost-effectiveness has allowed China to procure it on a much larger scale. ๐Ÿ”ธ The new fighter could provide a one-for-one replacement for older Chinese heavyweight aircraft such as the J-11B and Su-30MKK during the 2030s, rapidly transforming the composition of the PLAโ€™s combat fleet. ๐Ÿ”ธ The Shenyang and Chengdu prototypes are the only publicly observed tailless fighters currently in flight testing. Removing vertical tails reduces radar reflections but requires advanced software and distributed control surfaces to maintain stability and maneuverability. ๐Ÿ”ธ Research associated with the Shenyang program has also explored flight-control technology for tailless aircraft operating from carriers. This raises the possibility that the fighter could eventually serve with both the PLA Air Force and Navy. China may therefore be building more than another elite aircraft for a limited number of squadrons. A smaller and more affordable sixth-generation fighter could be produced widely, replace existing heavyweights and potentially operate from Chinese carriers. The Pentagon would then face not a handful of experimental jets, but an entire force transitioning toward sixth-generation aircraft at scale. Could China field sixth-generation fighters on a scale the United States cannot match?
0:37

๐Ÿšจ๐Ÿ‡จ๐Ÿ‡ณ PENTAGON'S NIGHTMARE: CHINA ADVANCES SECOND SIXTH-GENERATION FIGHTER BUILT FOR SCALE China is pushing its second sixth-generation fighter deeper into flight testing, and its greatest advantage may be its ability to be fielded in large numbers. The twin-engine tailless aircraft developed by Shenyang, widely known by the unofficial designation J-50, has now been filmed operating at both low and high altitudes. It is roughly comparable in size to the J-20 and significantly smaller than Chinaโ€™s other next-generation prototype developed by Chengdu. ๐Ÿ”ธ A smaller airframe is expected to make the Shenyang fighter considerably cheaper to produce and maintain than the much larger Chengdu design. That could make sixth-generation capabilities practical across far more frontline units. ๐Ÿ”ธ Cost may prove as important as performance. Overruns on the F-22 and F-35 prevented the United States from replacing its older fighters as widely as planned, while the J-20โ€™s cost-effectiveness has allowed China to procure it on a much larger scale. ๐Ÿ”ธ The new fighter could provide a one-for-one replacement for older Chinese heavyweight aircraft such as the J-11B and Su-30MKK during the 2030s, rapidly transforming the composition of the PLAโ€™s combat fleet. ๐Ÿ”ธ The Shenyang and Chengdu prototypes are the only publicly observed tailless fighters currently in flight testing. Removing vertical tails reduces radar reflections but requires advanced software and distributed control surfaces to maintain stability and maneuverability. ๐Ÿ”ธ Research associated with the Shenyang program has also explored flight-control technology for tailless aircraft operating from carriers. This raises the possibility that the fighter could eventually serve with both the PLA Air Force and Navy. China may therefore be building more than another elite aircraft for a limited number of squadrons. A smaller and more affordable sixth-generation fighter could be produced widely, replace existing heavyweights and potentially operate from Chinese carriers. The Pentagon would then face not a handful of experimental jets, but an entire force transitioning toward sixth-generation aircraft at scale. Could China field sixth-generation fighters on a scale the United States cannot match?

NewRulesGeopolitics

56,261 Aufrufe โ€ข vor 18 Tagen

๐Ÿ‡ฎ๐Ÿ‡ณ Why the Su-57 is Still Alive (Despite the Rafale Deal) ๐Ÿ‡ท๐Ÿ‡บ In January 2026, New Delhi moved decisively toward what has already been christened the โ€œdeal of the century.โ€ The Defence Procurement Board cleared a proposal to buy 114 Rafale fighter jets, a $36 billion bet meant to reverse the Indian Air Forceโ€™s alarming slide in squadron strength. For many, that should have closed the book. The Rafale has performed in combat, the IAF trusts it, and the logistics and training ecosystem is already in place. Add a substantial Make in India component, and the choice looks not just sensible but inevitable. Betting big on Rafale addresses the IAFโ€™s immediate crisis. Keeping Russia in the picture hedges against longโ€‘term dependency on a single supplier, a single geopolitical axis, or a single vision of Indiaโ€™s future force structure. In other words, New Delhi is not choosing between France and Russia. It is choosing flexibility over certainty, leverage over loyalty, and survival over sentiment. The aircraft may dominate the headlines, but the real contest is over sovereignty, and that debate is far from settled. The โ€œInvisibleโ€ Problem: The China Factor -->Rafaleโ€™s Strengths: a formidable, combatโ€‘proven platformbut still a 4.5โ€‘generation jet. -->The Threat is that China fields the Jโ€‘20, a bona fide 5thโ€‘generation stealth fighter. -->The Reality is, in a faceโ€‘off, a Rafale may struggle to detect a Jโ€‘20 on radar in time to shoot first. -->The Gap Indiaโ€™s AMCA is roughly a decade away. Strategists see the Suโ€‘57 as the only nearโ€‘term plug for an antiโ€‘stealth hunter to counter Chinaโ€™s edge now. Buying the Jet vs. Owning the Code -->The core issue of sovereignty over software. This is the strongest case for the Russian option, which controls the jetโ€™s โ€œbrainโ€ and integration pipeline. -->France (Rafale) You get worldโ€‘class hardware and systems, but the mission software/source code remains French. -->Russia (Suโ€‘57) Moscow is pitching full technology transfer, including sourceโ€‘code access. That means India can natively integrate indigenous weapons, tailor mission systems, and exercise deeper control over production and upgrades. Industrial head start-A recent audit reportedly found HALโ€™s Nashik facility is ~50% ready to start building the type, thanks to shared tooling and processes with the Suโ€‘30 line. The Highโ€“Low Mix: Mass and Edge Together Framed correctly, this isnโ€™t Rafale or Suโ€‘57, itโ€™s Rafale and Suโ€‘57, by design. The 114 Rafales would fly the vast majority of sorties, air policing, border patrols, and deep strikes, delivering availability, commonality, and proven effects. Layered above, a small Suโ€‘57 cadre (e.g., 2-3 squadrons) would serve as โ€œspecial forces of the skyโ€ stalking stealth adversaries, opening corridors through integrated air defenses, and prosecuting highโ€‘value targets under emissions control. Industrial Survival Airpower isnโ€™t only about tails on the ramp, itโ€™s about hands on the tools. With Suโ€‘30MKI production ending at Nashik and AMCA still years from serial production, HAL faces a capability cliff. If no new program backfills the gap, lines go cold, certifications lapse, supplier networks atrophy, and the tacit knowledge of master technicians dissipates. Restarting later is slower, costlier, and riskier than keeping the line warm. Indiaโ€™s fighter choice isnโ€™t a beauty contest between French finesse and Russian promises. Itโ€™s a threeโ€‘part- Strategy counters a stealthโ€‘equipped rival now. Sovereignty owns the software, integration, and upgrade pathways; Survival keeps the industrial engine running until AMCA scales. Bet big on Rafales to restore squadron strength and sortie generation. Keep a limited Suโ€‘57 door open to hedge the stealth deficit, secure codeโ€‘level control, and bridge HAL through the AMCA gap. Thatโ€™s not indecision, itโ€™s flexibility over fidelity. And flexibility, in airpower and statecraft alike, is what ultimately buys strategic autonomy. Col AJ๐Ÿ‡ฎ๐Ÿ‡ณ Colonel Mayank Chaubey Major Sammer Pal Toorr (Infantry Combat Veteran) TheGlobalDecoder Aadi Achint ๐Ÿ‡ฎ๐Ÿ‡ณ #India #Rafale #France #AMCA #russia #USA #Davos26 #Davos2026
0:30

๐Ÿ‡ฎ๐Ÿ‡ณ Why the Su-57 is Still Alive (Despite the Rafale Deal) ๐Ÿ‡ท๐Ÿ‡บ In January 2026, New Delhi moved decisively toward what has already been christened the โ€œdeal of the century.โ€ The Defence Procurement Board cleared a proposal to buy 114 Rafale fighter jets, a $36 billion bet meant to reverse the Indian Air Forceโ€™s alarming slide in squadron strength. For many, that should have closed the book. The Rafale has performed in combat, the IAF trusts it, and the logistics and training ecosystem is already in place. Add a substantial Make in India component, and the choice looks not just sensible but inevitable. Betting big on Rafale addresses the IAFโ€™s immediate crisis. Keeping Russia in the picture hedges against longโ€‘term dependency on a single supplier, a single geopolitical axis, or a single vision of Indiaโ€™s future force structure. In other words, New Delhi is not choosing between France and Russia. It is choosing flexibility over certainty, leverage over loyalty, and survival over sentiment. The aircraft may dominate the headlines, but the real contest is over sovereignty, and that debate is far from settled. The โ€œInvisibleโ€ Problem: The China Factor -->Rafaleโ€™s Strengths: a formidable, combatโ€‘proven platformbut still a 4.5โ€‘generation jet. -->The Threat is that China fields the Jโ€‘20, a bona fide 5thโ€‘generation stealth fighter. -->The Reality is, in a faceโ€‘off, a Rafale may struggle to detect a Jโ€‘20 on radar in time to shoot first. -->The Gap Indiaโ€™s AMCA is roughly a decade away. Strategists see the Suโ€‘57 as the only nearโ€‘term plug for an antiโ€‘stealth hunter to counter Chinaโ€™s edge now. Buying the Jet vs. Owning the Code -->The core issue of sovereignty over software. This is the strongest case for the Russian option, which controls the jetโ€™s โ€œbrainโ€ and integration pipeline. -->France (Rafale) You get worldโ€‘class hardware and systems, but the mission software/source code remains French. -->Russia (Suโ€‘57) Moscow is pitching full technology transfer, including sourceโ€‘code access. That means India can natively integrate indigenous weapons, tailor mission systems, and exercise deeper control over production and upgrades. Industrial head start-A recent audit reportedly found HALโ€™s Nashik facility is ~50% ready to start building the type, thanks to shared tooling and processes with the Suโ€‘30 line. The Highโ€“Low Mix: Mass and Edge Together Framed correctly, this isnโ€™t Rafale or Suโ€‘57, itโ€™s Rafale and Suโ€‘57, by design. The 114 Rafales would fly the vast majority of sorties, air policing, border patrols, and deep strikes, delivering availability, commonality, and proven effects. Layered above, a small Suโ€‘57 cadre (e.g., 2-3 squadrons) would serve as โ€œspecial forces of the skyโ€ stalking stealth adversaries, opening corridors through integrated air defenses, and prosecuting highโ€‘value targets under emissions control. Industrial Survival Airpower isnโ€™t only about tails on the ramp, itโ€™s about hands on the tools. With Suโ€‘30MKI production ending at Nashik and AMCA still years from serial production, HAL faces a capability cliff. If no new program backfills the gap, lines go cold, certifications lapse, supplier networks atrophy, and the tacit knowledge of master technicians dissipates. Restarting later is slower, costlier, and riskier than keeping the line warm. Indiaโ€™s fighter choice isnโ€™t a beauty contest between French finesse and Russian promises. Itโ€™s a threeโ€‘part- Strategy counters a stealthโ€‘equipped rival now. Sovereignty owns the software, integration, and upgrade pathways; Survival keeps the industrial engine running until AMCA scales. Bet big on Rafales to restore squadron strength and sortie generation. Keep a limited Suโ€‘57 door open to hedge the stealth deficit, secure codeโ€‘level control, and bridge HAL through the AMCA gap. Thatโ€™s not indecision, itโ€™s flexibility over fidelity. And flexibility, in airpower and statecraft alike, is what ultimately buys strategic autonomy. Col AJ๐Ÿ‡ฎ๐Ÿ‡ณ Colonel Mayank Chaubey Major Sammer Pal Toorr (Infantry Combat Veteran) TheGlobalDecoder Aadi Achint ๐Ÿ‡ฎ๐Ÿ‡ณ #India #Rafale #France #AMCA #russia #USA #Davos26 #Davos2026

The Sacred Scroll

35,970 Aufrufe โ€ข vor 8 Monaten

PROJECT MAVEN and the U.S. Military's Cutting-Edge Arsenal of AI-Driven Warfare and Directed Energy Weapons. This is an important deep dive into one of the U.S. Department of War's most transformative initiatives, a game-changer that's reshaping modern warfare through artificial intelligence. Launched in 2017 under the Algorithmic Warfare Cross-Functional Team, Project Maven isn't just another tech buzzword; it's the Pentagon's flagship AI program designed to supercharge military intelligence by automating the analysis of vast troves of data from drones, satellites, and surveillance feeds. Highlighting how Maven uses machine learning and computer vision to detect, classify, and track targets in real-time, compressing the "kill chain" from hours to minutes. The weapon and defense systems powering this revolution, including the seamless integration of Directed Energy Weapons (DEWs). Project Maven serves as the brain, processing petabytes of imagery to identify threats like enemy vehicles, personnel, or infrastructure with pinpoint accuracyโ€”far beyond what human analysts could achieve alone. Initially deployed against ISIS in 2017, it fused data from full-motion video (FMV) and other sensors to flag potential strikes, always with human oversight in the loop to ensure ethical decision-making. Today, under the National Geospatial-Intelligence Agency (NGA), Maven has expanded to all military branchesโ€”Army, Air Force, Space Force, Navy, and Marinesโ€”via platforms like the Maven Smart System (MSS). MSS isn't just about detection; it's a force multiplier, enabling rapid targeting in exercises like Scarlet Dragon, where it slashed manpower needs from thousands to mere dozens while handling complex scenarios in CENTCOM and beyond. Now, pair this AI prowess with the U.S. military's Directed Energy Weapons, and you get a lethal, futuristic synergy. DEWs harness concentrated electromagnetic energyโ€”think high-energy lasers (HELs) and high-power microwaves (HPMs)โ€”to neutralize threats without traditional munitions, offering infinite "ammo." These aren't hypothetical; they're operational and evolving rapidly. High-Energy Lasers (HELs), systems like the Navy's HELIOS (High-Energy Laser with Integrated Optical-Dazzler and Surveillance) aboard destroyers like the USS Preble deliver speed-of-light strikes to down drones, missiles, or small boats for pennies per shot. Integrated with Maven's AI targeting, HELIOS can acquire, track, and zap threats in swarms, as demonstrated in recent Pacific tests. The Army's DE M-SHORAD (Directed Energy Maneuver-Short Range Air Defense) prototype, mounted on Stryker vehicles, recently shredded drone swarms at Fort Sill, blending lasers with kinetic defenses for layered protection. High-Power Microwaves (HPMs), weapons like the Air Force's THOR (Tactical High-Power Operational Responder) unleash radiofrequency waves to fry electronics in drones or missiles from afar, covering wide areas with a single pulse. In urban or congested environments, HPMs provide non-lethal options, disrupting signals without collateral damageโ€”perfect for Maven-identified targets in sensitive ops. Broader Defense Ecosystems, Maven feeds into systems like the Joint All-Domain Command and Control (JADC2), linking sensors across domains for seamless ops. DEWs complement this by offering scalable effectsโ€”from dazzling sensors (e.g., Vigilant Eagle for airport defense) to outright destruction. The Pentagon's Directed Energy Roadmap, with $1 billion annual investments, pushes for higher power outputs to tackle hypersonic threats, while initiatives like the High Energy Laser Scaling Initiative bolster industrial production. What makes this combo so revolutionary? Speed, precision, and cost-efficiency. Maven's AI spots the threat; DEWs eliminate it at light speed, with deep "magazines" that outlast ammo stockpiles. Project Maven is the spark igniting this fire, keeping the U.S. ahead in an era where data and energy are the ultimate weapons.
10:40

PROJECT MAVEN and the U.S. Military's Cutting-Edge Arsenal of AI-Driven Warfare and Directed Energy Weapons. This is an important deep dive into one of the U.S. Department of War's most transformative initiatives, a game-changer that's reshaping modern warfare through artificial intelligence. Launched in 2017 under the Algorithmic Warfare Cross-Functional Team, Project Maven isn't just another tech buzzword; it's the Pentagon's flagship AI program designed to supercharge military intelligence by automating the analysis of vast troves of data from drones, satellites, and surveillance feeds. Highlighting how Maven uses machine learning and computer vision to detect, classify, and track targets in real-time, compressing the "kill chain" from hours to minutes. The weapon and defense systems powering this revolution, including the seamless integration of Directed Energy Weapons (DEWs). Project Maven serves as the brain, processing petabytes of imagery to identify threats like enemy vehicles, personnel, or infrastructure with pinpoint accuracyโ€”far beyond what human analysts could achieve alone. Initially deployed against ISIS in 2017, it fused data from full-motion video (FMV) and other sensors to flag potential strikes, always with human oversight in the loop to ensure ethical decision-making. Today, under the National Geospatial-Intelligence Agency (NGA), Maven has expanded to all military branchesโ€”Army, Air Force, Space Force, Navy, and Marinesโ€”via platforms like the Maven Smart System (MSS). MSS isn't just about detection; it's a force multiplier, enabling rapid targeting in exercises like Scarlet Dragon, where it slashed manpower needs from thousands to mere dozens while handling complex scenarios in CENTCOM and beyond. Now, pair this AI prowess with the U.S. military's Directed Energy Weapons, and you get a lethal, futuristic synergy. DEWs harness concentrated electromagnetic energyโ€”think high-energy lasers (HELs) and high-power microwaves (HPMs)โ€”to neutralize threats without traditional munitions, offering infinite "ammo." These aren't hypothetical; they're operational and evolving rapidly. High-Energy Lasers (HELs), systems like the Navy's HELIOS (High-Energy Laser with Integrated Optical-Dazzler and Surveillance) aboard destroyers like the USS Preble deliver speed-of-light strikes to down drones, missiles, or small boats for pennies per shot. Integrated with Maven's AI targeting, HELIOS can acquire, track, and zap threats in swarms, as demonstrated in recent Pacific tests. The Army's DE M-SHORAD (Directed Energy Maneuver-Short Range Air Defense) prototype, mounted on Stryker vehicles, recently shredded drone swarms at Fort Sill, blending lasers with kinetic defenses for layered protection. High-Power Microwaves (HPMs), weapons like the Air Force's THOR (Tactical High-Power Operational Responder) unleash radiofrequency waves to fry electronics in drones or missiles from afar, covering wide areas with a single pulse. In urban or congested environments, HPMs provide non-lethal options, disrupting signals without collateral damageโ€”perfect for Maven-identified targets in sensitive ops. Broader Defense Ecosystems, Maven feeds into systems like the Joint All-Domain Command and Control (JADC2), linking sensors across domains for seamless ops. DEWs complement this by offering scalable effectsโ€”from dazzling sensors (e.g., Vigilant Eagle for airport defense) to outright destruction. The Pentagon's Directed Energy Roadmap, with $1 billion annual investments, pushes for higher power outputs to tackle hypersonic threats, while initiatives like the High Energy Laser Scaling Initiative bolster industrial production. What makes this combo so revolutionary? Speed, precision, and cost-efficiency. Maven's AI spots the threat; DEWs eliminate it at light speed, with deep "magazines" that outlast ammo stockpiles. Project Maven is the spark igniting this fire, keeping the U.S. ahead in an era where data and energy are the ultimate weapons.

The SCIF

21,324 Aufrufe โ€ข vor 8 Monaten

The U.S. unveils it's new F-47 stealth fighter, the centerpiece of the NGAD program, a "family of systems" designed to integrate advanced manned and unmanned platforms, including Collaborative Combat Aircraft (CCA) drones. Announced by President Donald Trump alongside Secretary of Defense Pete Hegseth and Air Force Chief of Staff Gen. David Allvin, the F-47 is described as the "most advanced, most capable, most lethal aircraft ever built." It reportedly builds on a prototype that has been secretly flying for nearly five years, suggesting significant testing and refinement prior to its public unveiling. The aircraft is engineered for speed, stealth, and adaptability, with a focus on countering advanced threats from nations like China, which has also been developing sixth-generation capabilities. Boeingโ€™s victory over Lockheed Martin for the NGAD contract, valued at approximately $20 billion for the Engineering and Manufacturing Development (EMD) phase, marks a critical win for the company amid its recent struggles in defense and commercial sectors. The F-47 is expected to enter service in the 2030s, with each unit potentially costing upwards of $300 million, reflecting its cutting-edge technology. Its development emphasizes rapid adaptability to emerging threats, leveraging advanced manufacturing and an open architecture design to allow for continual upgrades. Since exact specifications remain classified or undisclosed as of now, the following are informed projections based on NGAD program objectives, statements from officials, and sixth-generation fighter trends. Designation: Boeing F-47 Manufacturer: Boeing Phantom Works Role: Air dominance fighter with multi-role capabilities (air-to-air and air-to-ground) Crew: Likely manned with optional unmanned configuration, aligning with sixth-generation flexibility Dimensions: Larger than the F-22 and F-35 to accommodate greater range and payload; exact size undisclosed but possibly exceeding 60 feet in length and a wingspan over 40 feet Powerplant: Expected to use adaptive cycle engines from the Next Generation Adaptive Propulsion (NGAP) programโ€”either General Electric XA102 or Pratt & Whitney XA103. These engines feature a three-stream architecture, offering over 20% better fuel efficiency, increased thrust (potentially 45,000-50,000 lbf per engine), and enhanced electrical output for directed-energy weapons. Speed: Likely exceeds Mach 2 (super cruise capableโ€”sustained supersonic flight without afterburners), surpassing the F-22โ€™s Mach 1.8 super cruise Range: Combat radius projected at 1,000-1,500 nautical miles (unrefueled), tailored for Indo-Pacific operations, significantly greater than the F-22โ€™s 600 nautical miles or F-35โ€™s 670 nautical miles Stealth: Advanced stealth features, including a tailless design, next-generation coatings, and materials to reduce radar, infrared, and acoustic signatures beyond fifth-generation standards Payload: Larger internal weapons bays (possibly 20-23 feet long) to carry advanced munitions like the AIM-174, hypersonic missiles, and future cruise missiles, with external hardpoints available at the cost of stealth Sensors and Avionics: AI-enhanced sensor suite for unmatched situational awareness, integrating radar, infrared search and track (IRST), and electronic warfare systems; likely includes "smart skins" with embedded sensors for reduced drag and improved performance Networking: Maximum connectivity for real-time data sharing with satellites, drones, and other platforms, supported by a robust, jam-resistant data link Additional Features: Potential for directed-energy (laser) weapons to counter missiles and drones Integration with CCA drones for expanded mission options (e.g., extra munitions, electronic warfare) Open architecture for rapid upgrades and mission-specific customization Key Highlights Human-Machine Teaming: The F-47 is designed to "unlock the magic" of human-machine collaboration, pairing pilots with AI-driven systems and autonomous drones to enhance decision-making and reduce workload. Strategic Purpose: Built to penetrate contested environments, countering advanced air defenses and stealth fighters from adversaries like China, with a focus on long-range engagements over vast theaters. Development Timeline: Prototypes have been flying since at least 2020, with full operational capability targeted for the 2030s, replacing the F-22 incrementally as numbers grow. Cost and Scale: Estimated at $300 million per unit, with plans for roughly 200 manned aircraft, though this is a planning figure subject to change. The F-47โ€™s exact design and full capabilities remain shrouded in secrecy, typical of NGADโ€™s classified nature, but its unveiling signals a bold step forward in U.S. air power. Its blend of stealth, speed, range, and technological integration positions it as a cornerstone of future aerial warfare, though its high cost and complexity will likely spark ongoing debate about affordability and strategic priorities. U.S. military technology will continue to dominate all other nations like it always has.
1:10

The U.S. unveils it's new F-47 stealth fighter, the centerpiece of the NGAD program, a "family of systems" designed to integrate advanced manned and unmanned platforms, including Collaborative Combat Aircraft (CCA) drones. Announced by President Donald Trump alongside Secretary of Defense Pete Hegseth and Air Force Chief of Staff Gen. David Allvin, the F-47 is described as the "most advanced, most capable, most lethal aircraft ever built." It reportedly builds on a prototype that has been secretly flying for nearly five years, suggesting significant testing and refinement prior to its public unveiling. The aircraft is engineered for speed, stealth, and adaptability, with a focus on countering advanced threats from nations like China, which has also been developing sixth-generation capabilities. Boeingโ€™s victory over Lockheed Martin for the NGAD contract, valued at approximately $20 billion for the Engineering and Manufacturing Development (EMD) phase, marks a critical win for the company amid its recent struggles in defense and commercial sectors. The F-47 is expected to enter service in the 2030s, with each unit potentially costing upwards of $300 million, reflecting its cutting-edge technology. Its development emphasizes rapid adaptability to emerging threats, leveraging advanced manufacturing and an open architecture design to allow for continual upgrades. Since exact specifications remain classified or undisclosed as of now, the following are informed projections based on NGAD program objectives, statements from officials, and sixth-generation fighter trends. Designation: Boeing F-47 Manufacturer: Boeing Phantom Works Role: Air dominance fighter with multi-role capabilities (air-to-air and air-to-ground) Crew: Likely manned with optional unmanned configuration, aligning with sixth-generation flexibility Dimensions: Larger than the F-22 and F-35 to accommodate greater range and payload; exact size undisclosed but possibly exceeding 60 feet in length and a wingspan over 40 feet Powerplant: Expected to use adaptive cycle engines from the Next Generation Adaptive Propulsion (NGAP) programโ€”either General Electric XA102 or Pratt & Whitney XA103. These engines feature a three-stream architecture, offering over 20% better fuel efficiency, increased thrust (potentially 45,000-50,000 lbf per engine), and enhanced electrical output for directed-energy weapons. Speed: Likely exceeds Mach 2 (super cruise capableโ€”sustained supersonic flight without afterburners), surpassing the F-22โ€™s Mach 1.8 super cruise Range: Combat radius projected at 1,000-1,500 nautical miles (unrefueled), tailored for Indo-Pacific operations, significantly greater than the F-22โ€™s 600 nautical miles or F-35โ€™s 670 nautical miles Stealth: Advanced stealth features, including a tailless design, next-generation coatings, and materials to reduce radar, infrared, and acoustic signatures beyond fifth-generation standards Payload: Larger internal weapons bays (possibly 20-23 feet long) to carry advanced munitions like the AIM-174, hypersonic missiles, and future cruise missiles, with external hardpoints available at the cost of stealth Sensors and Avionics: AI-enhanced sensor suite for unmatched situational awareness, integrating radar, infrared search and track (IRST), and electronic warfare systems; likely includes "smart skins" with embedded sensors for reduced drag and improved performance Networking: Maximum connectivity for real-time data sharing with satellites, drones, and other platforms, supported by a robust, jam-resistant data link Additional Features: Potential for directed-energy (laser) weapons to counter missiles and drones Integration with CCA drones for expanded mission options (e.g., extra munitions, electronic warfare) Open architecture for rapid upgrades and mission-specific customization Key Highlights Human-Machine Teaming: The F-47 is designed to "unlock the magic" of human-machine collaboration, pairing pilots with AI-driven systems and autonomous drones to enhance decision-making and reduce workload. Strategic Purpose: Built to penetrate contested environments, countering advanced air defenses and stealth fighters from adversaries like China, with a focus on long-range engagements over vast theaters. Development Timeline: Prototypes have been flying since at least 2020, with full operational capability targeted for the 2030s, replacing the F-22 incrementally as numbers grow. Cost and Scale: Estimated at $300 million per unit, with plans for roughly 200 manned aircraft, though this is a planning figure subject to change. The F-47โ€™s exact design and full capabilities remain shrouded in secrecy, typical of NGADโ€™s classified nature, but its unveiling signals a bold step forward in U.S. air power. Its blend of stealth, speed, range, and technological integration positions it as a cornerstone of future aerial warfare, though its high cost and complexity will likely spark ongoing debate about affordability and strategic priorities. U.S. military technology will continue to dominate all other nations like it always has.

The SCIF

453,918 Aufrufe โ€ข vor 1 Jahr

New Mobile Electronic Warfare (EW) & Radar Systems of Russian Ministry of Defense (MoD) BATTLE TESTED & WORLD STEALTH SUPERIORITY in all CLASSES ! By ๐ƒ๐š๐ฏ๐ข๐ ๐™ ๐Ÿ‡ท๐Ÿ‡บ ๐Ÿ‡ท๐Ÿ‡ธ๐Ÿ‡ฎ๐Ÿ‡ช Thread 1 / 5 Russian MoD has been aggressively modernizing its electronic warfare & radar capabilities, particularly in response to lessons from the ongoing conflict in Ukraine. These systems emphasize mobility, integration with air defense networks, & countermeasures against drones, precision-guided munitions, & enemy command-control (C2) structures. Many are designed for rapid deployment in tactical environments, often mounted on vehicles, aircraft, or even backpack-portable for infantry use. Below, I'll break down key new or upgraded systems with specifics on their features, operational roles & adaptations. Key Electronic Warfare Systems Krasukha Series (Krasukha-4 / Krasukha-2): These are multifunctional mobile jamming platforms mounted on truck chassis like the BAZ-6910, making them highly deployable across rough terrain. The Krasukha-4 can jam airborne radars, including those on AWACS aircraft, drones & satellites, at ranges up to 300 km. It uses digital radio frequency memory (DRFM) technology to create false targets & spoof enemy sensors, disrupting reconnaissance and fire-control systems. In recent operations, it's been used to create "electronic shields" over bases & troop concentrations, suppressing enemy ISR (intelligence, surveillance & reconnaissance) assets. Upgrades in 2024-2025 include better integration with AI for adaptive frequency hopping, allowing it to counter advanced jamming-resistant signals from Western systems. Skovorodka (Frying Pan) System: Introduced in late 2024, this is a compact, backpack-portable EW device developed by AO "AEC" for frontline infantry. Weighing under 10 kg, it operates on dual frequencies to jam enemy reconnaissance drones (e.g., DJI models) & artillery targeting systems within a 5-10 km radius. It disrupts the "reconnaissance-fire chain" by blocking data links & GPS signals, reducing drone effectiveness by up to 70% in tested scenarios. It's integrated into broader air defense networks like the S-400 & S-500, providing ground-level offensive EW to blind hostile radars while protecting Russian assets. Its low cost (~ $5,000 per unit) allows mass deployment, with production ramped up to equip motorized rifle battalions. Leer-3 (RB-341V): A mobile EW complex integrated with Orlan-10 UAVs for remote operation. Mounted on MT-LB armored vehicles, it jams GSM, satellite communications & GPS signals within a 6 km radius, often scattering jammers via drones for area denial. Recent enhancements include PSYOPS capabilities, such as sending fake SMS to enemy troops for disinformation. In 2025 deployments, it's been paired with anti-drone interceptors to create layered defenses against UAV swarms, with ranges extended to 10-15 km through improved antennas. Borisoglebsk-2 (RB-301B): This automated jamming system is chassis-mounted on MT-LBu vehicles for high mobility. It detects, analyzes & suppresses HF/VHF/UHF communications & radar signals up to 30-50 km away. Specifics include direction-finding accuracy within 1-2 degrees & the ability to handle up to 100 simultaneous targets. 2025 updates incorporate AI-driven algorithms for real-time spectrum analysis, making it more effective against frequency-agile Western radios. Rychag-AV: A versatile EW module installable on helicopters (e.g., Mi-8), ships, or ground vehicles. It jams sensors and radars at hundreds of kilometers using DRFM to mimic echoes and create phantom threats. Recent mobile variants on KamAZ trucks allow rapid repositioning, with power outputs up to 10 kW for sustained jamming. Zhitel (R-330Zh): Mobile on Ural trucks, it jams communications, GPS, and air targets up to 200 km. It's often used in tandem with radar systems to disrupt drone data links, with 2025 models featuring modular antennas for quick setup in 15-20 minutes. PART 1 >>>
26:22

New Mobile Electronic Warfare (EW) & Radar Systems of Russian Ministry of Defense (MoD) BATTLE TESTED & WORLD STEALTH SUPERIORITY in all CLASSES ! By ๐ƒ๐š๐ฏ๐ข๐ ๐™ ๐Ÿ‡ท๐Ÿ‡บ ๐Ÿ‡ท๐Ÿ‡ธ๐Ÿ‡ฎ๐Ÿ‡ช Thread 1 / 5 Russian MoD has been aggressively modernizing its electronic warfare & radar capabilities, particularly in response to lessons from the ongoing conflict in Ukraine. These systems emphasize mobility, integration with air defense networks, & countermeasures against drones, precision-guided munitions, & enemy command-control (C2) structures. Many are designed for rapid deployment in tactical environments, often mounted on vehicles, aircraft, or even backpack-portable for infantry use. Below, I'll break down key new or upgraded systems with specifics on their features, operational roles & adaptations. Key Electronic Warfare Systems Krasukha Series (Krasukha-4 / Krasukha-2): These are multifunctional mobile jamming platforms mounted on truck chassis like the BAZ-6910, making them highly deployable across rough terrain. The Krasukha-4 can jam airborne radars, including those on AWACS aircraft, drones & satellites, at ranges up to 300 km. It uses digital radio frequency memory (DRFM) technology to create false targets & spoof enemy sensors, disrupting reconnaissance and fire-control systems. In recent operations, it's been used to create "electronic shields" over bases & troop concentrations, suppressing enemy ISR (intelligence, surveillance & reconnaissance) assets. Upgrades in 2024-2025 include better integration with AI for adaptive frequency hopping, allowing it to counter advanced jamming-resistant signals from Western systems. Skovorodka (Frying Pan) System: Introduced in late 2024, this is a compact, backpack-portable EW device developed by AO "AEC" for frontline infantry. Weighing under 10 kg, it operates on dual frequencies to jam enemy reconnaissance drones (e.g., DJI models) & artillery targeting systems within a 5-10 km radius. It disrupts the "reconnaissance-fire chain" by blocking data links & GPS signals, reducing drone effectiveness by up to 70% in tested scenarios. It's integrated into broader air defense networks like the S-400 & S-500, providing ground-level offensive EW to blind hostile radars while protecting Russian assets. Its low cost (~ $5,000 per unit) allows mass deployment, with production ramped up to equip motorized rifle battalions. Leer-3 (RB-341V): A mobile EW complex integrated with Orlan-10 UAVs for remote operation. Mounted on MT-LB armored vehicles, it jams GSM, satellite communications & GPS signals within a 6 km radius, often scattering jammers via drones for area denial. Recent enhancements include PSYOPS capabilities, such as sending fake SMS to enemy troops for disinformation. In 2025 deployments, it's been paired with anti-drone interceptors to create layered defenses against UAV swarms, with ranges extended to 10-15 km through improved antennas. Borisoglebsk-2 (RB-301B): This automated jamming system is chassis-mounted on MT-LBu vehicles for high mobility. It detects, analyzes & suppresses HF/VHF/UHF communications & radar signals up to 30-50 km away. Specifics include direction-finding accuracy within 1-2 degrees & the ability to handle up to 100 simultaneous targets. 2025 updates incorporate AI-driven algorithms for real-time spectrum analysis, making it more effective against frequency-agile Western radios. Rychag-AV: A versatile EW module installable on helicopters (e.g., Mi-8), ships, or ground vehicles. It jams sensors and radars at hundreds of kilometers using DRFM to mimic echoes and create phantom threats. Recent mobile variants on KamAZ trucks allow rapid repositioning, with power outputs up to 10 kW for sustained jamming. Zhitel (R-330Zh): Mobile on Ural trucks, it jams communications, GPS, and air targets up to 200 km. It's often used in tandem with radar systems to disrupt drone data links, with 2025 models featuring modular antennas for quick setup in 15-20 minutes. PART 1 >>>

๐ƒ๐š๐ฏ๐ข๐ ๐™ ๐Ÿ‡ท๐Ÿ‡บ ๐Ÿ‡ท๐Ÿ‡ธ๐Ÿ‡ฎ๐Ÿ‡ช

18,801 Aufrufe โ€ข vor 9 Monaten

$AMD Massive Rotation from $NVDA $INTC๐Ÿงต Not Financial Advice! DYOR! 5-10 minutes before the bell today, last trading day of May 2026, massive rotation out of $INTC and $NVDA into $AMD. I wrote this thread this morning on what $TSM said on Energy Efficiency is now TOP Priotity and why AMD is the biggest winner. Of course I did not have influence on this rebalancing, I was just pointing out why Dr. Su saw this coming years ago. (Check the picture to understand more). I been talking about Agentic AI for like 3-4 years now. OpenClaw broke the CPU:GPU Ratio 1:4 narrative to 1:1 to 5:1 in late Jan and Feb 2026. I will link various threads where you can understand the full picture from supply chain, to TSMC expansion, and different Wafer Ratio for EPYC Venice and MI455X. Energy efficiency is a structural, long-term driver behind institutional rotation from $NVDA and $INTC into $AMD (with spillover strength in $AVGO for complementary networking/custom silicon). This isn't just short-term rebalancing, it's a massive bet on the shift from AI training (performance-at-any-cost) to inference, deployment, and embodied/agentic systems (where total cost of ownership, power draw, and scalability dominate). Precisely What I been writing about $AMD for years now, probably at least more than 5,000 threads.This is the FOMO from Institutions to own $AMD. Do know that AMD is the least owned Semi Stock among vs Peers. AI infrastructure is moving beyond massive training clusters to widespread inference for Agentic AI (running models 24/7) and embodied AI (robots, autonomous agents, edge devices). These workloads prioritize: ~Tokens-per-watt and performance-per-watt ~Lower total power consumption for data centers facing grid constraints ~Better economics at scale (cost-per-token, TCO) ~Thermal and power efficiency for on-device/robotics use Hyperscalers are now thinking more about Margin, Profitability, and $/M Tokens At $516/share. AMD Fwd PEG Ratio is still 35/100+= 0.35 AKA very cheap IMO for the growth and potential. A. Why institutions rotated out of $NVDA? Because Agentic AI is going to dominated by CPUs for years to come, moving violently to 5-10-20:1 CPU:GPU Ratio as enterprises are demanding more than 10-20 agents to run tasks. Now, that does not mean training is going away, Inference is just going to grow much faster. B. Why instiutitons rotated out of $INTC? Because AMD x86 unit share is only at 30-31% but Revenue share is already at 46.2% according to Mercury Research. And Dr. Su wants 50-60% market share, and that would mean 60-70%+ Revenue share where the CPUs TAM Is now already at $200B in 2026 and projected to be $500B by 2030. C. Why $AMD? Because AMD secured meaningful 2nm Capacity, Advanced Packaging and Memory through 2027-2028. And TSMC is expanding 2 primary 2nm Fabs toward 60-65k WPM each, and speeding up 5 2nm Fabs in Taiwan. With total up to 12 2nm Fabs through 2027/2028. 2nm Capacity is expected to be 140k+ WPM toward end of 2026, and 220-240k WPM by end of 2027. Apple has secured 35-45k WPM. And AMD does not have to worry about allocation competition until late 2027 from $AVGO for $META and $GOOGL(This may change) D. Agentic AI will evolve to 24/7 Autonomous Agent, and that will become the foundational layer for Robotic or Physical AI. Agentic AI (autonomous systems that plan, reason, use tools, self-correct, pursue long-horizon goals, and adapt) provides the high-level cognitive architecture. It turns raw perception and low-level control into useful, general-purpose behavior in the physical world. Physical AI (or Embodied AI) refers to AI that senses, understands, and acts directly in the real world through robots, actuators, and sensors. Agentic capabilities are what make this scalable and useful beyond narrow, scripted tasks. Reactive/programmed machines โ†’ To proactive, goal-oriented autonomous agents. How does this work? Autonomous Agent layer is the brain ~Vision-Language-Action models or robotics foundation models. ~Agentic loops: Planning, chain-of-thought reasoning, reflection, tool use (simulators, APIs), multi-step task decomposition. ~Persistent 24/7 operation with Memory, world modeling, continuous learning. Institutions may not like $AMD from 2022-2025, but they cannot stop this evolution and it is inevitable. Part of my main thesis for AMD to get to $5 Trillion Market Cap Long Term. Conclusion: Institutions are rotating capital toward AMD not merely for tactical rebalancing, but because Dr. Lisa Su and her team anticipated this exact inflection years in advance and have been methodically engineering AMDโ€™s platform to dominate it. Dr. Su has long championed the convergence of Agentic AI as the high-level cognitive foundation for Physical AI and robotics. As far back as her 2023/2024 CES keynote and earlier strategic commentary, she described Physical AI (including humanoid robotics and edge autonomy) as โ€œthe next big thingโ€; a natural extension of agentic workflows moving from digital reasoning to real-world action. She emphasized that enabling persistent, 24/7 autonomous agents requires a full-stack approach: high-performance CPUs for orchestration and motion control, dedicated accelerators for real-time vision and multimodal inference, and open software ecosystems for rapid development. This vision aligns precisely with the structural drivers weโ€™ve discussed. As AI shifts from training to massive-scale inference and embodiment, energy efficiency, total cost of ownership, and heterogeneous compute become first-order advantages. AMDโ€™s Instinct MI350/MI355 series, Ryzen AI Embedded processors, and EPYC platforms deliver superior performance-per-watt and balanced CPU + GPU + NPU integration ideal for power-constrained robots that must run sophisticated agentic reasoning loops without excessive thermal or battery drain. Dr. Su has repeatedly highlighted the rising importance of CPUs in agentic systems (moving toward 1:1 or even CPU-heavy ratios with GPUs), positioning AMDโ€™s strengths in orchestration, memory handling, and efficiency as critical for the next phase of growth. AMD is engineered for the deployment realities of embodied agents: scalable, efficient, and deployable at the edge and in physical systems. The institutional flows out of NVDA and INTC into AMD reflect recognition of this prepared leadership. Dr. Su didnโ€™t just see the future of Agentic AI powering robotics, she has spent years building the silicon, software, and partnerships to make it practical and economically viable. This rotation signals confidence that the companies best positioned for the physical, always-on intelligence layer will capture the highest-volume opportunities in the coming decade. Not Financial Advice! DYOR!
2:25

$AMD Massive Rotation from $NVDA $INTC๐Ÿงต Not Financial Advice! DYOR! 5-10 minutes before the bell today, last trading day of May 2026, massive rotation out of $INTC and $NVDA into $AMD. I wrote this thread this morning on what $TSM said on Energy Efficiency is now TOP Priotity and why AMD is the biggest winner. Of course I did not have influence on this rebalancing, I was just pointing out why Dr. Su saw this coming years ago. (Check the picture to understand more). I been talking about Agentic AI for like 3-4 years now. OpenClaw broke the CPU:GPU Ratio 1:4 narrative to 1:1 to 5:1 in late Jan and Feb 2026. I will link various threads where you can understand the full picture from supply chain, to TSMC expansion, and different Wafer Ratio for EPYC Venice and MI455X. Energy efficiency is a structural, long-term driver behind institutional rotation from $NVDA and $INTC into $AMD (with spillover strength in $AVGO for complementary networking/custom silicon). This isn't just short-term rebalancing, it's a massive bet on the shift from AI training (performance-at-any-cost) to inference, deployment, and embodied/agentic systems (where total cost of ownership, power draw, and scalability dominate). Precisely What I been writing about $AMD for years now, probably at least more than 5,000 threads.This is the FOMO from Institutions to own $AMD. Do know that AMD is the least owned Semi Stock among vs Peers. AI infrastructure is moving beyond massive training clusters to widespread inference for Agentic AI (running models 24/7) and embodied AI (robots, autonomous agents, edge devices). These workloads prioritize: ~Tokens-per-watt and performance-per-watt ~Lower total power consumption for data centers facing grid constraints ~Better economics at scale (cost-per-token, TCO) ~Thermal and power efficiency for on-device/robotics use Hyperscalers are now thinking more about Margin, Profitability, and $/M Tokens At $516/share. AMD Fwd PEG Ratio is still 35/100+= 0.35 AKA very cheap IMO for the growth and potential. A. Why institutions rotated out of $NVDA? Because Agentic AI is going to dominated by CPUs for years to come, moving violently to 5-10-20:1 CPU:GPU Ratio as enterprises are demanding more than 10-20 agents to run tasks. Now, that does not mean training is going away, Inference is just going to grow much faster. B. Why instiutitons rotated out of $INTC? Because AMD x86 unit share is only at 30-31% but Revenue share is already at 46.2% according to Mercury Research. And Dr. Su wants 50-60% market share, and that would mean 60-70%+ Revenue share where the CPUs TAM Is now already at $200B in 2026 and projected to be $500B by 2030. C. Why $AMD? Because AMD secured meaningful 2nm Capacity, Advanced Packaging and Memory through 2027-2028. And TSMC is expanding 2 primary 2nm Fabs toward 60-65k WPM each, and speeding up 5 2nm Fabs in Taiwan. With total up to 12 2nm Fabs through 2027/2028. 2nm Capacity is expected to be 140k+ WPM toward end of 2026, and 220-240k WPM by end of 2027. Apple has secured 35-45k WPM. And AMD does not have to worry about allocation competition until late 2027 from $AVGO for $META and $GOOGL(This may change) D. Agentic AI will evolve to 24/7 Autonomous Agent, and that will become the foundational layer for Robotic or Physical AI. Agentic AI (autonomous systems that plan, reason, use tools, self-correct, pursue long-horizon goals, and adapt) provides the high-level cognitive architecture. It turns raw perception and low-level control into useful, general-purpose behavior in the physical world. Physical AI (or Embodied AI) refers to AI that senses, understands, and acts directly in the real world through robots, actuators, and sensors. Agentic capabilities are what make this scalable and useful beyond narrow, scripted tasks. Reactive/programmed machines โ†’ To proactive, goal-oriented autonomous agents. How does this work? Autonomous Agent layer is the brain ~Vision-Language-Action models or robotics foundation models. ~Agentic loops: Planning, chain-of-thought reasoning, reflection, tool use (simulators, APIs), multi-step task decomposition. ~Persistent 24/7 operation with Memory, world modeling, continuous learning. Institutions may not like $AMD from 2022-2025, but they cannot stop this evolution and it is inevitable. Part of my main thesis for AMD to get to $5 Trillion Market Cap Long Term. Conclusion: Institutions are rotating capital toward AMD not merely for tactical rebalancing, but because Dr. Lisa Su and her team anticipated this exact inflection years in advance and have been methodically engineering AMDโ€™s platform to dominate it. Dr. Su has long championed the convergence of Agentic AI as the high-level cognitive foundation for Physical AI and robotics. As far back as her 2023/2024 CES keynote and earlier strategic commentary, she described Physical AI (including humanoid robotics and edge autonomy) as โ€œthe next big thingโ€; a natural extension of agentic workflows moving from digital reasoning to real-world action. She emphasized that enabling persistent, 24/7 autonomous agents requires a full-stack approach: high-performance CPUs for orchestration and motion control, dedicated accelerators for real-time vision and multimodal inference, and open software ecosystems for rapid development. This vision aligns precisely with the structural drivers weโ€™ve discussed. As AI shifts from training to massive-scale inference and embodiment, energy efficiency, total cost of ownership, and heterogeneous compute become first-order advantages. AMDโ€™s Instinct MI350/MI355 series, Ryzen AI Embedded processors, and EPYC platforms deliver superior performance-per-watt and balanced CPU + GPU + NPU integration ideal for power-constrained robots that must run sophisticated agentic reasoning loops without excessive thermal or battery drain. Dr. Su has repeatedly highlighted the rising importance of CPUs in agentic systems (moving toward 1:1 or even CPU-heavy ratios with GPUs), positioning AMDโ€™s strengths in orchestration, memory handling, and efficiency as critical for the next phase of growth. AMD is engineered for the deployment realities of embodied agents: scalable, efficient, and deployable at the edge and in physical systems. The institutional flows out of NVDA and INTC into AMD reflect recognition of this prepared leadership. Dr. Su didnโ€™t just see the future of Agentic AI powering robotics, she has spent years building the silicon, software, and partnerships to make it practical and economically viable. This rotation signals confidence that the companies best positioned for the physical, always-on intelligence layer will capture the highest-volume opportunities in the coming decade. Not Financial Advice! DYOR!

Mike

104,109 Aufrufe โ€ข vor 4 Monaten

#VPDNews: The Vancouver Police Department (VPD) is adding new cutting-edge technologies to help keep the city safe. The new tools enhance frontline officer awareness, strengthen accountability, and build on the Departmentโ€™s mission to protect public safety while balancing the privacy of both the community and VPD officers. In the air, VPD is the first police agency in Canada to deploy Skydio X10 drones for a Drone as First Responder Program. After extensive testing, six of the remote-piloted drone systems will be deployed. The drones have already been in testing for several weeks and are fully approved by Transport Canada. โ€œThe potential of the Skydio drone systems in our work is impressive for many reasons, not least of which is they will be able to link with our body-worn cameras,โ€ said Inspector Wade Rodrigue, with the VPDโ€™s Force Options Training Section. โ€œFor example, if an officer is in trouble, perhaps being assaulted, they can tap their camera three times which will automatically deploy a Skydio drone to their exact location at the direction of the pilot in command. Pilots can also fly the drones to a crime in progress, arrive first, and send their video feeds to responding officers on the ground as well as the Operations Command Center (OCC). That gives us better intel on whatโ€™s happening and can help responding officers to pursue suspects who may try and evade them.โ€ The VPD will continue to adhere to its posted policyand procedures as well as those prescribed by Transport Canada and Nav Canada with respect to the operation of Remotely Piloted Aircraft Systems (RPAS) assets. The weatherproof Skydio drone launch/landing pods are installed on rooftops at strategic locations throughout Vancouver, including the VPDโ€™s Tactical Training Centre. The drones will only record video when that function is activated by a pilot, and only when appropriate as per policy. Body-worn cameras are also expanding in function. The Axon body cameras used by VPD now have the ability to translate language in real time with Axon Assistant, allowing officers to understand at the push of a button whatever is being said to them in over 50 languages, and to have their reply translated into the language recognized as being spoken. Real-Time Translation adds to VPDโ€™s current translation offerings and will be used when a quick translation is needed. โ€œWhen you consider how multicultural Vancouver is, this translation ability is a game-changer,โ€ said Sergeant Dermot Oโ€™Boyle. โ€œWe want to be able to help everyone in our city, including those who may not be fluent in English. Being able to understand what theyโ€™re telling us is a critical first step to getting them the help they need.โ€ Body-worn camera footage can also now be live-streamed to the VPDโ€™s Operations Command Centre so personnel there can see what the officer is seeing and dispatch additional resources as needed. This can be done at the officerโ€™s request or based on the priority of the call when an urgent situation is developing. In addition, Axonโ€™s real-time operations platform, Fusus, improves visibility and coordination across responding units and partner agencies by enabling Operations Centre personnel to view RPAS and body-worn camera video according to pre-determined operating protocols. Other new tools entering service include: โžก๏ธ 73 Fleet 3 in-car video systems with automated license plate recognition cameras (ALPR) across the VPD fleet, helping officers spot vehicles of interest faster which are already proving effective, with one ALPR-equipped cruiser flagging 22 uninsured vehicles in just three hours โžก๏ธ Holsters for conducted energy weapons and service weapons that automatically activate body-worn cameras when drawn, capturing critical moments right away โ€œCombined, these technologies create a system that helps improve decision-making, response times, and overall public safety in Vancouver,โ€ said Kevin Bernardin, Superintendent of Innovation and Technology at the VPD. All are designed with responsible AI and data use at their core, with safeguards for secure data handling, controlled access, and auditability. The AI does not make decisions about an individual, rather it gives the VPD the ability to respond more appropriately to emerging situations. Data is managed in alignment with local governance requirements, helping ensure it remains protected and under appropriate jurisdictional control, while giving VPD confidence that sensitive information is handled in accordance with British Columbia privacy standards. #VPD Drone Policy:
2:51

#VPDNews: The Vancouver Police Department (VPD) is adding new cutting-edge technologies to help keep the city safe. The new tools enhance frontline officer awareness, strengthen accountability, and build on the Departmentโ€™s mission to protect public safety while balancing the privacy of both the community and VPD officers. In the air, VPD is the first police agency in Canada to deploy Skydio X10 drones for a Drone as First Responder Program. After extensive testing, six of the remote-piloted drone systems will be deployed. The drones have already been in testing for several weeks and are fully approved by Transport Canada. โ€œThe potential of the Skydio drone systems in our work is impressive for many reasons, not least of which is they will be able to link with our body-worn cameras,โ€ said Inspector Wade Rodrigue, with the VPDโ€™s Force Options Training Section. โ€œFor example, if an officer is in trouble, perhaps being assaulted, they can tap their camera three times which will automatically deploy a Skydio drone to their exact location at the direction of the pilot in command. Pilots can also fly the drones to a crime in progress, arrive first, and send their video feeds to responding officers on the ground as well as the Operations Command Center (OCC). That gives us better intel on whatโ€™s happening and can help responding officers to pursue suspects who may try and evade them.โ€ The VPD will continue to adhere to its posted policyand procedures as well as those prescribed by Transport Canada and Nav Canada with respect to the operation of Remotely Piloted Aircraft Systems (RPAS) assets. The weatherproof Skydio drone launch/landing pods are installed on rooftops at strategic locations throughout Vancouver, including the VPDโ€™s Tactical Training Centre. The drones will only record video when that function is activated by a pilot, and only when appropriate as per policy. Body-worn cameras are also expanding in function. The Axon body cameras used by VPD now have the ability to translate language in real time with Axon Assistant, allowing officers to understand at the push of a button whatever is being said to them in over 50 languages, and to have their reply translated into the language recognized as being spoken. Real-Time Translation adds to VPDโ€™s current translation offerings and will be used when a quick translation is needed. โ€œWhen you consider how multicultural Vancouver is, this translation ability is a game-changer,โ€ said Sergeant Dermot Oโ€™Boyle. โ€œWe want to be able to help everyone in our city, including those who may not be fluent in English. Being able to understand what theyโ€™re telling us is a critical first step to getting them the help they need.โ€ Body-worn camera footage can also now be live-streamed to the VPDโ€™s Operations Command Centre so personnel there can see what the officer is seeing and dispatch additional resources as needed. This can be done at the officerโ€™s request or based on the priority of the call when an urgent situation is developing. In addition, Axonโ€™s real-time operations platform, Fusus, improves visibility and coordination across responding units and partner agencies by enabling Operations Centre personnel to view RPAS and body-worn camera video according to pre-determined operating protocols. Other new tools entering service include: โžก๏ธ 73 Fleet 3 in-car video systems with automated license plate recognition cameras (ALPR) across the VPD fleet, helping officers spot vehicles of interest faster which are already proving effective, with one ALPR-equipped cruiser flagging 22 uninsured vehicles in just three hours โžก๏ธ Holsters for conducted energy weapons and service weapons that automatically activate body-worn cameras when drawn, capturing critical moments right away โ€œCombined, these technologies create a system that helps improve decision-making, response times, and overall public safety in Vancouver,โ€ said Kevin Bernardin, Superintendent of Innovation and Technology at the VPD. All are designed with responsible AI and data use at their core, with safeguards for secure data handling, controlled access, and auditability. The AI does not make decisions about an individual, rather it gives the VPD the ability to respond more appropriately to emerging situations. Data is managed in alignment with local governance requirements, helping ensure it remains protected and under appropriate jurisdictional control, while giving VPD confidence that sensitive information is handled in accordance with British Columbia privacy standards. #VPD Drone Policy:

Vancouver Police

11,219 Aufrufe โ€ข vor 3 Monaten

Dear ICP community, the Internet Computer has now been running strong for 5 years ๐Ÿ‘๐Ÿ‘๐Ÿ‘ Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: โ€” Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. โ€” The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. โ€” Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation โ€” where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) โ€” Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. โ€” New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. โ€” Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). โ€” An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. โ€” Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... โ€” You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... โ€” Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. โ€” Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. โ€” Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). โ€” For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. โ€” Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on *mainstream* cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday ๐Ÿ’ช I'll be back with more news soon!!
19:06

Dear ICP community, the Internet Computer has now been running strong for 5 years ๐Ÿ‘๐Ÿ‘๐Ÿ‘ Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: โ€” Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. โ€” The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. โ€” Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation โ€” where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) โ€” Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. โ€” New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. โ€” Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). โ€” An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. โ€” Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... โ€” You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... โ€” Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. โ€” Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. โ€” Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). โ€” For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. โ€” Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on *mainstream* cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday ๐Ÿ’ช I'll be back with more news soon!!

dom | icp

324,632 Aufrufe โ€ข vor 4 Monaten

$AMD $5 Trillion is Inevitable LT| Agentic AI๐Ÿงต Agentic AI is the new $5 Trillion TAM ๐Ÿšจ๐Ÿšจ๐Ÿšจ This thead will do Comp with $INTC and how to quantify this massive Agentic AI demand spike, and forcing Jensen to rush a CPU design. Global Agentic AI Market size is estimated to be $3-$5Trillion TAM by 2030(McKinsey) Quantifying the demand from agentic AI for AMD involves assessing the broader market growth for agentic systems, their unique computational requirements (particularly for CPUs in orchestration and reasoning tasks), and AMD's positioning very well through products like EPYC processors and partnerships. AMD EPYC Venice is the most superior choice in 2026-2027 for most Agentic AI workloads Agentic AI refers to autonomous AI agents that perform multi-step tasks, involving sequential logic, tool integration, and decision-making workloads that heavily rely on CPUs for handling orchestration, memory management, and context switching, rather than just GPU-parallelized training or batch inference. Agentic AI is often cited as 40-100x more "hungry" than traditional AI due to its continuous, 24/7 operation and complex workflows. This stems from factors like chain-of-thought reasoning (multiple LLM calls per query), API/tool interactions, memory management, and orchestration loops, which can generate 10-100x more tokens and require real-time responsiveness. For example, a single agentic query might trigger 5-20 model inferences, making it 10-20x more compute-intensive than simple chatbots, and the always-on nature compounds this to 40-100x overall. Nvidia's CEO has highlighted this as driving "easily 100x more computation" for inference in agentic/reasoning setups. AMD's EPYC Venice (6th Gen EPYC, codenamed "Venice") and Intel's Xeon 7 Diamond Rapids represent the pinnacle of server CPU technology in 2026, both targeting high-performance data center workloads like AI inference, agentic AI orchestration, cloud computing, and HPC. Venice builds on AMD's Zen 6 architecture, emphasizing core density and efficiency, while Diamond Rapids leverages Intel's Panther Cove P-cores for balanced performance. Both chips adopt similar advancements like 16-channel DDR5 memory and PCIe Gen 6, but differ in core counts, process nodes, and overall design philosophy. Intel has faced acute supply constraints across its Xeon lineup, including legacy nodes (Intel 7/3) and the ramping 18A process for next-gen parts. Intel shortage is expected with lead times up to 6 months or longer. 1. AMD EPYC Venice vs Intel Xeon 7 Diamond Rapids Architecture AMD: Zen 6 chiplet design with 8 CCDs and dual IODs Intel: Panther Cove P-cores; multi-die architecture with 4 compute tiles Core/Thread Count AMD: Up to 256 cores / 512 threads (Zen 6c variant) Intel: Up to 192 cores / 192 threads Process Node AMD: TSMC N2 (2nm) Intel: Intel 18A (1.8nm-class); in-house fab Memory Support AMD: 16-channel DDR5; up to 1.6 TB/s bandwidth. Intel: 16-channel DDR5 ; up to 1.6 TB/s bandwidth I/O and Connectivity AMD: PCIe Gen 6 (up to 128 lanes); twice the CPU-to-GPU bandwidth Intel: PCIe Gen 6 (up to 128 lanes); LGA 9324 socket Power (TDP) AMD: Starting 400-500W, potentially lower due to efficiency gains from TSMC 2nm Intel: Starting 400-500W, as it targets competitive efficiency Performance Projections AMD: Up to 70% uplift vs. 5th Gen Turin (1.7x in multi-threaded/AI tasks) Intel: ~40% faster than Granite Rapids (Xeon 6, 128-core). Lags AMD in per-core perf and 40-50% behind Venice core-for-core comp Target Workloads AMD: AI inference/orchestration, HPC, cloud virtualization. Partnerships Intel: Hyperscale AI, general enterprise. Custom silicon Pricing: AMD: estimated $10k-$20k for top SKUs Intel: estimated $8-$18k Availability: AMD: Significant Ramp H2 2026 due to higher allocation from TSMC Intel: H1-H2 2026 delayed, but trying to catch up Overall: ~Venice's 256 cores provide a 33% edge over Diamond Rapids' 192, making it superior for massively parallel tasks like AI training/inference or virtualization ~TSMC's N2 vs. Intel 18A debates rage on which is "better," but AMD's mature chiplet approach yields better density ( 32 cores/CCD vs. Intel's 48/tile). Venice's redesign reduces latency, aiding agentic AI where CPUs handle orchestration ~ Early projections show Venice widening AMD's lead matching or exceeding Diamond Rapids' perf with fewer watts in multi-threaded benchmarks. Intel's no-SMT design (to prioritize AI) handicaps it vs. AMD's 512 threads, though Clearwater Forest (E-core) could compete in density-focused niches. ~Power & Cooling: Both push above 400-500W, demanding liquid cooling. ~AMD been taking market share now above 40%. AMD EPYC Venice emerges as the superior choice in 2026 for most server workloads. Its higher core/thread count (256/512 vs. 192/192), stronger per-core performance, and architecture optimized for AI-driven tasks (agentic orchestration with GPU integration) provide decisive advantages in throughput, scalability, and efficiency. Projections indicate Venice delivering 1.7x the performance of prior gens while widening the gap over Intel ( 40-70% leads in multi-threaded benchmarks). AMD's fabless model with TSMC ensures reliable scaling, and its ecosystem ( open ROCm) appeals to AI adopters. Intel's Diamond Rapids is competitive in single-threaded enterprise apps and custom hyperscale ( NVLink), with potential fab advantages for supply/security. However, without SMT and lower density, it falls short in core-for-core battlesโ€”exposing Intel to another generation of AMD dominance unless 18A yields surprise efficiency gains. For data centers prioritizing raw compute ( AI, HPC), Venice wins; for Intel-centric ecosystems or specialized I/O, Diamond Rapids holds ground. Real benchmarks post-launch will confirm, but logic points to AMD pulling ahead. 2. Market size , Potential Revenue and Supply Global Agentic AI market size is projected to be $3-$5 Trillion by 2030 according to McKinsey, where consensus points to 40-50% CAGR driven by small to large enterprise demand. I also wrote a full thread on how and why Agentic AI is so explosive that AMD will blow all anlaysts estimate for subscribers. Link below if you are interested. AMD's data center segment hit a record $5.4B in Q4 2025 (up 39% YoY), with EPYC shipments ramping due to agentic demand. With 2GW of deployment in H2 2026, AMD AI data center revenue has $40-$50B+ at the lowest or most conservative projection; or Total Revenue in the $77-$94B For FY2026. However, Agentic AI massive demand spike could send EPYC revenue 3x to 4x in the next few years, potentially surpassing MI series GPU demand as enterprises prioritize CPU-dense Rack setups. This is pushing $NVDA Jensen to rush a CPU design and acquired Groq, a new CPU player due to this massive TAM. Noted that this is just popping just in weeks, highlighting we are just so early in this AI Supercycle and the pace of adoption is insane, and clearly productivity will skyrocket. Why? Because Agentic AI is 24/7 Smart AI agent working for you or your businesses is a mad compelling, and it is estimated to be 40-100x more Inference Hugnry! Many experts already said it is impossible to project this kind of Inference Demand. AI CapEx is expected to ramp up even more in 2027-2028-2029 and 2030 as Global Agentic AI is going to scale to $3-$5 Trillion TAM by 2030. The nature of Agentic is driving higher CPU/GPU ratio, with CPUs handling 50-90% of Agentic workflows. For example, The current Helios Rack: 18 compute trays per rack with 72 GPUs + 18 CPUs. The beauty of this $META and $AMD long term partnership is, that it is absolutely flexible to adjust racks to higher CPU rato or equal to service different needs. Helios rack can be easily swap to 2 GPUs 2CPUs or even CPUs only trays for dedicated orchestration/head nodes. You see, the beauty of this open rack-scale is flexibility and evolvability. If Agentic AI demand pushes much higher, AMD should be able to adjust variant trays without abandoning Heilos Rack. We can't talk just about massive Agentic AI demand without talking about the Supply side or TSMC. TSMC, AMD's primary foundry for advanced nodes ( Zen 6/Venice on N2/2nm), is addressing AI-driven shortages through massive expansions. TSMC accelerates fab construction with up to 10 facilities targeted for 2026. TSMC is accelerating its domestic manufacturing expansion, with industry sources indicating that as many as ten fabs could be under construction or preparing to begin operations across Taiwanโ€™s major science parks. TSMC Capex: $52-56B in 2026 (up 37% YoY), with $45B already approved for new/upgraded capacities. 70-80% for advanced processes (2nm/A16), 10-20% for packaging (CoWoS quadrupling to 120-140K wafers/month by late 2026). In addition, Taiwanese companies (led by TSMC) commit to at least $250B in direct investments in US-based advanced semiconductor, AI, and energy production/innovation capacity.Taiwan provides $250B in government credit guarantees to facilitate additional investments and build a full US semiconductor ecosystem (including industrial parks). TSMC completed a second land purchase in Arizona (January 2026) for gigafab scaling, with an additional $100B+ (potentially four more modules) to further expand and qualify for tariff exemptions. AMD with secured 12GW from OpenAI and $META and massive Agentic AI will mean higher priority acess to 20-30% more wafers on TSMC advanced nodes, as TSMC has multi-year agreements with AMD for AI chips. Dr. C. C. Wei, CEO of TSMC quote: "I spend a lot of time in the last three or four months talking to my customer and then customers. Customer. I want to make sure that my customers demand are real. I talk to those cloud service providers, all of them. Their answer is. I'm quite satisfied with their answer. Actually they show me the evidence that the AI really help their business. So they grow their business successfully and he or she in their financial return. So I also double check their financial status. They are very rich." Amid shortages, the US buildout ensures AMD can ramp production of Instinct GPUs and EPYC CPUs without the constraints hitting competitors like Intel. By diversifying away from Taiwan (85% of advanced nodes today), the agreement mitigates supply disruptions, ensuring stable flows for AMD's chips. Scaling production and securing supply will matter for AMD the most in the next 5-10 years growth. The growth could be 80-100% YoY or higher; or it could be in the 60%. The aggressive TSMC supply ramp is reassuring the higher growth point. Conclusion: AMD stands at a pivotal inflection point in 2026, where the explosive rise of agentic AI demanding 40-100x more inference compute through its 24/7, multi-step orchestration positions the company to potentially triple its EPYC CPU revenue to $45-60B+ by 2028 while scaling Instinct GPUs to tens of billions annually by 2027. Agentic AI demand could push AI CapEx closer to $1 Trillion in 2027, far higher than most estimates. Dr. Lisa Su, AMD's visionary CEO, is masterfully securing supply to harness this massive demand by prioritizing operational execution and deep TSMC collaboration, ensuring readiness for the second-half 2026 AI ramp. Dr. Su has explicitly called out surging EPYC demand for agentic tasks where CPUs power head nodes and traditional workloads alongside GPUs while guiding for data center dominance through proactive capacity planning and partnerships like Nutanix ($150M investment for open agentic platforms) or providing tens of millions CPUs for OpenAI, $META, $ORCL, $AMZN, $MSFT, $GOOGL and others. Her strategy includes multi-year TSMC agreements for advanced nodes (N2 for Venice CPUs and future Instincts), diversifying beyond Taiwan to mitigate risks, and unveiling innovations like the MI455X GPU at CES 2026, which she touted as enabling "the next trillion-dollar market opportunity" in physical AI. Dr. Su's forward-looking vision predicting AI reaching 5 billion users emphasizes "AI everywhere," backed by hardware like Ryzen AI chips, all while declaring demand "going through the roof" and committing to scale without bottlenecks. TSMC's aggressive ramp-up, fueled by $52-56B in 2026 capex (up 37% YoY) and 10+ new fabs across Taiwan, the US (Arizona cluster expanding to 6+ modules with $165B+ investment), Japan, and Europe, provides profound reassurance for AMD's supply stability. The January 2026 US-Taiwan agreement committing $250B in investments and credit guarantees for US reshoring accelerates this, granting tariff relief (15% rates with 1.5-2.5x exemptions) tied to capacity buildouts, enabling TSMC to potentially double output over the decade to meet AI wafer hunger. This translates to 20-30% higher wafer allocations on key nodes, sidestepping Intel-like shortages and empowering Dr. Su's team to deliver on hyperscaler demands without disruption. Ultimately, this synergy cements AMD's leadership in the agentic era, promising sustained growth, $5T+ valuations at scale, and a resilient path forward as AI reshapes the world. This is NOT Financial Advice! Video source: AMD CES 2026
2:49

$AMD $5 Trillion is Inevitable LT| Agentic AI๐Ÿงต Agentic AI is the new $5 Trillion TAM ๐Ÿšจ๐Ÿšจ๐Ÿšจ This thead will do Comp with $INTC and how to quantify this massive Agentic AI demand spike, and forcing Jensen to rush a CPU design. Global Agentic AI Market size is estimated to be $3-$5Trillion TAM by 2030(McKinsey) Quantifying the demand from agentic AI for AMD involves assessing the broader market growth for agentic systems, their unique computational requirements (particularly for CPUs in orchestration and reasoning tasks), and AMD's positioning very well through products like EPYC processors and partnerships. AMD EPYC Venice is the most superior choice in 2026-2027 for most Agentic AI workloads Agentic AI refers to autonomous AI agents that perform multi-step tasks, involving sequential logic, tool integration, and decision-making workloads that heavily rely on CPUs for handling orchestration, memory management, and context switching, rather than just GPU-parallelized training or batch inference. Agentic AI is often cited as 40-100x more "hungry" than traditional AI due to its continuous, 24/7 operation and complex workflows. This stems from factors like chain-of-thought reasoning (multiple LLM calls per query), API/tool interactions, memory management, and orchestration loops, which can generate 10-100x more tokens and require real-time responsiveness. For example, a single agentic query might trigger 5-20 model inferences, making it 10-20x more compute-intensive than simple chatbots, and the always-on nature compounds this to 40-100x overall. Nvidia's CEO has highlighted this as driving "easily 100x more computation" for inference in agentic/reasoning setups. AMD's EPYC Venice (6th Gen EPYC, codenamed "Venice") and Intel's Xeon 7 Diamond Rapids represent the pinnacle of server CPU technology in 2026, both targeting high-performance data center workloads like AI inference, agentic AI orchestration, cloud computing, and HPC. Venice builds on AMD's Zen 6 architecture, emphasizing core density and efficiency, while Diamond Rapids leverages Intel's Panther Cove P-cores for balanced performance. Both chips adopt similar advancements like 16-channel DDR5 memory and PCIe Gen 6, but differ in core counts, process nodes, and overall design philosophy. Intel has faced acute supply constraints across its Xeon lineup, including legacy nodes (Intel 7/3) and the ramping 18A process for next-gen parts. Intel shortage is expected with lead times up to 6 months or longer. 1. AMD EPYC Venice vs Intel Xeon 7 Diamond Rapids Architecture AMD: Zen 6 chiplet design with 8 CCDs and dual IODs Intel: Panther Cove P-cores; multi-die architecture with 4 compute tiles Core/Thread Count AMD: Up to 256 cores / 512 threads (Zen 6c variant) Intel: Up to 192 cores / 192 threads Process Node AMD: TSMC N2 (2nm) Intel: Intel 18A (1.8nm-class); in-house fab Memory Support AMD: 16-channel DDR5; up to 1.6 TB/s bandwidth. Intel: 16-channel DDR5 ; up to 1.6 TB/s bandwidth I/O and Connectivity AMD: PCIe Gen 6 (up to 128 lanes); twice the CPU-to-GPU bandwidth Intel: PCIe Gen 6 (up to 128 lanes); LGA 9324 socket Power (TDP) AMD: Starting 400-500W, potentially lower due to efficiency gains from TSMC 2nm Intel: Starting 400-500W, as it targets competitive efficiency Performance Projections AMD: Up to 70% uplift vs. 5th Gen Turin (1.7x in multi-threaded/AI tasks) Intel: ~40% faster than Granite Rapids (Xeon 6, 128-core). Lags AMD in per-core perf and 40-50% behind Venice core-for-core comp Target Workloads AMD: AI inference/orchestration, HPC, cloud virtualization. Partnerships Intel: Hyperscale AI, general enterprise. Custom silicon Pricing: AMD: estimated $10k-$20k for top SKUs Intel: estimated $8-$18k Availability: AMD: Significant Ramp H2 2026 due to higher allocation from TSMC Intel: H1-H2 2026 delayed, but trying to catch up Overall: ~Venice's 256 cores provide a 33% edge over Diamond Rapids' 192, making it superior for massively parallel tasks like AI training/inference or virtualization ~TSMC's N2 vs. Intel 18A debates rage on which is "better," but AMD's mature chiplet approach yields better density ( 32 cores/CCD vs. Intel's 48/tile). Venice's redesign reduces latency, aiding agentic AI where CPUs handle orchestration ~ Early projections show Venice widening AMD's lead matching or exceeding Diamond Rapids' perf with fewer watts in multi-threaded benchmarks. Intel's no-SMT design (to prioritize AI) handicaps it vs. AMD's 512 threads, though Clearwater Forest (E-core) could compete in density-focused niches. ~Power & Cooling: Both push above 400-500W, demanding liquid cooling. ~AMD been taking market share now above 40%. AMD EPYC Venice emerges as the superior choice in 2026 for most server workloads. Its higher core/thread count (256/512 vs. 192/192), stronger per-core performance, and architecture optimized for AI-driven tasks (agentic orchestration with GPU integration) provide decisive advantages in throughput, scalability, and efficiency. Projections indicate Venice delivering 1.7x the performance of prior gens while widening the gap over Intel ( 40-70% leads in multi-threaded benchmarks). AMD's fabless model with TSMC ensures reliable scaling, and its ecosystem ( open ROCm) appeals to AI adopters. Intel's Diamond Rapids is competitive in single-threaded enterprise apps and custom hyperscale ( NVLink), with potential fab advantages for supply/security. However, without SMT and lower density, it falls short in core-for-core battlesโ€”exposing Intel to another generation of AMD dominance unless 18A yields surprise efficiency gains. For data centers prioritizing raw compute ( AI, HPC), Venice wins; for Intel-centric ecosystems or specialized I/O, Diamond Rapids holds ground. Real benchmarks post-launch will confirm, but logic points to AMD pulling ahead. 2. Market size , Potential Revenue and Supply Global Agentic AI market size is projected to be $3-$5 Trillion by 2030 according to McKinsey, where consensus points to 40-50% CAGR driven by small to large enterprise demand. I also wrote a full thread on how and why Agentic AI is so explosive that AMD will blow all anlaysts estimate for subscribers. Link below if you are interested. AMD's data center segment hit a record $5.4B in Q4 2025 (up 39% YoY), with EPYC shipments ramping due to agentic demand. With 2GW of deployment in H2 2026, AMD AI data center revenue has $40-$50B+ at the lowest or most conservative projection; or Total Revenue in the $77-$94B For FY2026. However, Agentic AI massive demand spike could send EPYC revenue 3x to 4x in the next few years, potentially surpassing MI series GPU demand as enterprises prioritize CPU-dense Rack setups. This is pushing $NVDA Jensen to rush a CPU design and acquired Groq, a new CPU player due to this massive TAM. Noted that this is just popping just in weeks, highlighting we are just so early in this AI Supercycle and the pace of adoption is insane, and clearly productivity will skyrocket. Why? Because Agentic AI is 24/7 Smart AI agent working for you or your businesses is a mad compelling, and it is estimated to be 40-100x more Inference Hugnry! Many experts already said it is impossible to project this kind of Inference Demand. AI CapEx is expected to ramp up even more in 2027-2028-2029 and 2030 as Global Agentic AI is going to scale to $3-$5 Trillion TAM by 2030. The nature of Agentic is driving higher CPU/GPU ratio, with CPUs handling 50-90% of Agentic workflows. For example, The current Helios Rack: 18 compute trays per rack with 72 GPUs + 18 CPUs. The beauty of this $META and $AMD long term partnership is, that it is absolutely flexible to adjust racks to higher CPU rato or equal to service different needs. Helios rack can be easily swap to 2 GPUs 2CPUs or even CPUs only trays for dedicated orchestration/head nodes. You see, the beauty of this open rack-scale is flexibility and evolvability. If Agentic AI demand pushes much higher, AMD should be able to adjust variant trays without abandoning Heilos Rack. We can't talk just about massive Agentic AI demand without talking about the Supply side or TSMC. TSMC, AMD's primary foundry for advanced nodes ( Zen 6/Venice on N2/2nm), is addressing AI-driven shortages through massive expansions. TSMC accelerates fab construction with up to 10 facilities targeted for 2026. TSMC is accelerating its domestic manufacturing expansion, with industry sources indicating that as many as ten fabs could be under construction or preparing to begin operations across Taiwanโ€™s major science parks. TSMC Capex: $52-56B in 2026 (up 37% YoY), with $45B already approved for new/upgraded capacities. 70-80% for advanced processes (2nm/A16), 10-20% for packaging (CoWoS quadrupling to 120-140K wafers/month by late 2026). In addition, Taiwanese companies (led by TSMC) commit to at least $250B in direct investments in US-based advanced semiconductor, AI, and energy production/innovation capacity.Taiwan provides $250B in government credit guarantees to facilitate additional investments and build a full US semiconductor ecosystem (including industrial parks). TSMC completed a second land purchase in Arizona (January 2026) for gigafab scaling, with an additional $100B+ (potentially four more modules) to further expand and qualify for tariff exemptions. AMD with secured 12GW from OpenAI and $META and massive Agentic AI will mean higher priority acess to 20-30% more wafers on TSMC advanced nodes, as TSMC has multi-year agreements with AMD for AI chips. Dr. C. C. Wei, CEO of TSMC quote: "I spend a lot of time in the last three or four months talking to my customer and then customers. Customer. I want to make sure that my customers demand are real. I talk to those cloud service providers, all of them. Their answer is. I'm quite satisfied with their answer. Actually they show me the evidence that the AI really help their business. So they grow their business successfully and he or she in their financial return. So I also double check their financial status. They are very rich." Amid shortages, the US buildout ensures AMD can ramp production of Instinct GPUs and EPYC CPUs without the constraints hitting competitors like Intel. By diversifying away from Taiwan (85% of advanced nodes today), the agreement mitigates supply disruptions, ensuring stable flows for AMD's chips. Scaling production and securing supply will matter for AMD the most in the next 5-10 years growth. The growth could be 80-100% YoY or higher; or it could be in the 60%. The aggressive TSMC supply ramp is reassuring the higher growth point. Conclusion: AMD stands at a pivotal inflection point in 2026, where the explosive rise of agentic AI demanding 40-100x more inference compute through its 24/7, multi-step orchestration positions the company to potentially triple its EPYC CPU revenue to $45-60B+ by 2028 while scaling Instinct GPUs to tens of billions annually by 2027. Agentic AI demand could push AI CapEx closer to $1 Trillion in 2027, far higher than most estimates. Dr. Lisa Su, AMD's visionary CEO, is masterfully securing supply to harness this massive demand by prioritizing operational execution and deep TSMC collaboration, ensuring readiness for the second-half 2026 AI ramp. Dr. Su has explicitly called out surging EPYC demand for agentic tasks where CPUs power head nodes and traditional workloads alongside GPUs while guiding for data center dominance through proactive capacity planning and partnerships like Nutanix ($150M investment for open agentic platforms) or providing tens of millions CPUs for OpenAI, $META, $ORCL, $AMZN, $MSFT, $GOOGL and others. Her strategy includes multi-year TSMC agreements for advanced nodes (N2 for Venice CPUs and future Instincts), diversifying beyond Taiwan to mitigate risks, and unveiling innovations like the MI455X GPU at CES 2026, which she touted as enabling "the next trillion-dollar market opportunity" in physical AI. Dr. Su's forward-looking vision predicting AI reaching 5 billion users emphasizes "AI everywhere," backed by hardware like Ryzen AI chips, all while declaring demand "going through the roof" and committing to scale without bottlenecks. TSMC's aggressive ramp-up, fueled by $52-56B in 2026 capex (up 37% YoY) and 10+ new fabs across Taiwan, the US (Arizona cluster expanding to 6+ modules with $165B+ investment), Japan, and Europe, provides profound reassurance for AMD's supply stability. The January 2026 US-Taiwan agreement committing $250B in investments and credit guarantees for US reshoring accelerates this, granting tariff relief (15% rates with 1.5-2.5x exemptions) tied to capacity buildouts, enabling TSMC to potentially double output over the decade to meet AI wafer hunger. This translates to 20-30% higher wafer allocations on key nodes, sidestepping Intel-like shortages and empowering Dr. Su's team to deliver on hyperscaler demands without disruption. Ultimately, this synergy cements AMD's leadership in the agentic era, promising sustained growth, $5T+ valuations at scale, and a resilient path forward as AI reshapes the world. This is NOT Financial Advice! Video source: AMD CES 2026

Mike

44,460 Aufrufe โ€ข vor 7 Monaten

DARPA's NEW TECH that TERRIFIED CONGRESS. Understand DARPA's Nonsurgical Wireless Brain Computer Remote Interface Neurotechnology that uses light and sounds as weapons that can induce behavior, emotions, read and implant thoughts, pictures, etc. Now, combining this tech with A.I., we have reached unbelievable new levels of science that have opened pandora's box, and some of the things in pandora's box are extremely concerning for the future of humanity. For the past COUPLE of DECADES, DARPA, the DoD, military contractors, and leading universities have been constructing and implementing wireless, invisible weapons against the people right in front of you, and nobody even noticed. The weaponization of this tech is so dangerous that even the U.N. is sounding the alarm because of it's untraceable and unethical capabilities. This is an introduction and overview. Pay Attention. I show you things for a reason. Secretary of Energy Hazel Reid O'Leary, who served under the Clinton administration stated that over 500,000 thousand Americans have been used in human experiments, including mind control and other experiments over a period of four decades without their informed consent. This is only what they admit to, then programs get put under higher classification, or under the guise of "national security," or go black so even members of Congress might not even get access to such programs. Mk Ultra as well, back in the 1960's used over 4,000 military service men without their consent for human experiments such as mind control. No one to this day has been held accountable for these experiments on our military and American citizens. Since the mid 1980's, radio frequency energy has been mastered by professionals in the field and graduates of Yale University and others and have been working directly with the military on mind control operations. The equipment being used needs no implant device, mechanical, or electronic device attached to the human being to be able to induce specific behaviors, moods, even V2K which is "voice-to-skull" technology and the CIA, NSA, DARPA, and our military are extremely interested in this technology Following MK Ultra, attention shifted toward electromagnetic technologies, including extremely low frequency (ELF) and radio frequency (RF) waves, which can influence emotions and project voices into the human mind. The "Frey Effect," discovered in the 1960s, demonstrated that pulsed microwaves could produce audible effects or voices in a subject's head, also known as "voice-to-skull" (V2K). Declassified documents show the U.S. military and intelligence agencies explored and used this tech during the Gulf War in the 1990s. DARPAโ€”established in 1958 to advance cutting-edge military technologyโ€”began funding projects that bridged neuroscience and electronics, laying the groundwork for brain-computer interfaces (BCIs). DARPAโ€™s role expanded significantly in the 21st century with programs like the Next-Generation Nonsurgical Neurotechnology (N3) initiative, launched in 2018. N3 aims to develop non-invasive BCIs that allow soldiers to control drones or robots with their thoughts, using techniques like ultrasound, magnetic fields, and nanoparticles to interface with the brain. This builds on earlier DARPA efforts, such as the Revolutionizing Prosthetics program, which enabled thought-controlled artificial limbs. Concurrently, the Obama administrationโ€™s BRAIN Initiative, announced in 2013, sought to map the brainโ€™s neural circuits and accelerate neurotechnology development. While framed as a scientific endeavor to treat neurological disorders, its overlap with DARPA fundingโ€”$225 million by 2015โ€”imply heavy military applications, including behavior modification and thought decoding. The integration of ELF/RF frequencies with 5G and AI represents the latest frontier. 5Gโ€™s high-frequency millimeter waves and dense network of transmitters could, in theory, enhance the precision and range of electromagnetic signals targeting the brain. Paired with AI, which can analyze vast datasets of neural activity in real time, this technology enables remote behavior control and thought reading. DARPAโ€™s N3 program, for instance, envisions two-way communication between brains and machines, potentially allowing external systems to interpret intentions or implant sensory feedbackโ€”like voices or emotions. Speculative claims suggest that military and intelligence agencies are weaponizing these tools to suppress fear in soldiers, influence adversariesโ€™ decisions, or surveil thoughts. Today, these technologies raise profound ethical and practical questions. The line between enhancement and control blurs when AI-driven systems could override human autonomy. Reports of "targeted individuals" experiencing voice projection or emotional manipulation fuel speculation and aren't looking unrealistic after all. Arguments that the secrecy surrounding DARPA and CIA projectsโ€”echoing Mk Ultraโ€™s hidden abusesโ€”obscures the true extent of weaponization, while proponents frame advancements vital, for national security. The progression from Mk Ultraโ€™s drug experiments and other crude techniques to master mind control have surpassed ELF/RF explorations, and now to DARPAโ€™s AI-enhanced BCIs under the BRAIN Initiative reflects a trajectory toward increasingly sophisticated mind control capabilities. The addition of 5Gโ€™s infrastructure and other radar installations around the world amplifies the potential for remote influence, suggesting a future where thoughts, emotions, and behaviors could be manipulated, monitored, and induced. The WEF makes their goals for this tech more than clear on their own website. Privacy will no longer exist. While the military and intelligence agencies undeniably pursue these technologies for strategic advantage, the full scope of their operational use and the line between science fiction and reality is diminishing by the day now that these technologies are fully operational and in use today and have now entered the advertising industry and big tech. I'm sure you can figure out where this is all going, and honestly, it's not good.
12:36

DARPA's NEW TECH that TERRIFIED CONGRESS. Understand DARPA's Nonsurgical Wireless Brain Computer Remote Interface Neurotechnology that uses light and sounds as weapons that can induce behavior, emotions, read and implant thoughts, pictures, etc. Now, combining this tech with A.I., we have reached unbelievable new levels of science that have opened pandora's box, and some of the things in pandora's box are extremely concerning for the future of humanity. For the past COUPLE of DECADES, DARPA, the DoD, military contractors, and leading universities have been constructing and implementing wireless, invisible weapons against the people right in front of you, and nobody even noticed. The weaponization of this tech is so dangerous that even the U.N. is sounding the alarm because of it's untraceable and unethical capabilities. This is an introduction and overview. Pay Attention. I show you things for a reason. Secretary of Energy Hazel Reid O'Leary, who served under the Clinton administration stated that over 500,000 thousand Americans have been used in human experiments, including mind control and other experiments over a period of four decades without their informed consent. This is only what they admit to, then programs get put under higher classification, or under the guise of "national security," or go black so even members of Congress might not even get access to such programs. Mk Ultra as well, back in the 1960's used over 4,000 military service men without their consent for human experiments such as mind control. No one to this day has been held accountable for these experiments on our military and American citizens. Since the mid 1980's, radio frequency energy has been mastered by professionals in the field and graduates of Yale University and others and have been working directly with the military on mind control operations. The equipment being used needs no implant device, mechanical, or electronic device attached to the human being to be able to induce specific behaviors, moods, even V2K which is "voice-to-skull" technology and the CIA, NSA, DARPA, and our military are extremely interested in this technology Following MK Ultra, attention shifted toward electromagnetic technologies, including extremely low frequency (ELF) and radio frequency (RF) waves, which can influence emotions and project voices into the human mind. The "Frey Effect," discovered in the 1960s, demonstrated that pulsed microwaves could produce audible effects or voices in a subject's head, also known as "voice-to-skull" (V2K). Declassified documents show the U.S. military and intelligence agencies explored and used this tech during the Gulf War in the 1990s. DARPAโ€”established in 1958 to advance cutting-edge military technologyโ€”began funding projects that bridged neuroscience and electronics, laying the groundwork for brain-computer interfaces (BCIs). DARPAโ€™s role expanded significantly in the 21st century with programs like the Next-Generation Nonsurgical Neurotechnology (N3) initiative, launched in 2018. N3 aims to develop non-invasive BCIs that allow soldiers to control drones or robots with their thoughts, using techniques like ultrasound, magnetic fields, and nanoparticles to interface with the brain. This builds on earlier DARPA efforts, such as the Revolutionizing Prosthetics program, which enabled thought-controlled artificial limbs. Concurrently, the Obama administrationโ€™s BRAIN Initiative, announced in 2013, sought to map the brainโ€™s neural circuits and accelerate neurotechnology development. While framed as a scientific endeavor to treat neurological disorders, its overlap with DARPA fundingโ€”$225 million by 2015โ€”imply heavy military applications, including behavior modification and thought decoding. The integration of ELF/RF frequencies with 5G and AI represents the latest frontier. 5Gโ€™s high-frequency millimeter waves and dense network of transmitters could, in theory, enhance the precision and range of electromagnetic signals targeting the brain. Paired with AI, which can analyze vast datasets of neural activity in real time, this technology enables remote behavior control and thought reading. DARPAโ€™s N3 program, for instance, envisions two-way communication between brains and machines, potentially allowing external systems to interpret intentions or implant sensory feedbackโ€”like voices or emotions. Speculative claims suggest that military and intelligence agencies are weaponizing these tools to suppress fear in soldiers, influence adversariesโ€™ decisions, or surveil thoughts. Today, these technologies raise profound ethical and practical questions. The line between enhancement and control blurs when AI-driven systems could override human autonomy. Reports of "targeted individuals" experiencing voice projection or emotional manipulation fuel speculation and aren't looking unrealistic after all. Arguments that the secrecy surrounding DARPA and CIA projectsโ€”echoing Mk Ultraโ€™s hidden abusesโ€”obscures the true extent of weaponization, while proponents frame advancements vital, for national security. The progression from Mk Ultraโ€™s drug experiments and other crude techniques to master mind control have surpassed ELF/RF explorations, and now to DARPAโ€™s AI-enhanced BCIs under the BRAIN Initiative reflects a trajectory toward increasingly sophisticated mind control capabilities. The addition of 5Gโ€™s infrastructure and other radar installations around the world amplifies the potential for remote influence, suggesting a future where thoughts, emotions, and behaviors could be manipulated, monitored, and induced. The WEF makes their goals for this tech more than clear on their own website. Privacy will no longer exist. While the military and intelligence agencies undeniably pursue these technologies for strategic advantage, the full scope of their operational use and the line between science fiction and reality is diminishing by the day now that these technologies are fully operational and in use today and have now entered the advertising industry and big tech. I'm sure you can figure out where this is all going, and honestly, it's not good.

The SCIF

163,123 Aufrufe โ€ข vor 1 Jahr