New York Post
๋ณด์ ์ฑํฅ
๊ธฐํ
Inside LAโs first bikini coffee shop โ as baristas reveal how many โweirdosโ show up
While the vast majority of people are just looking for a caffeine fix and friendly chat, the job does require thick skin.
๐บ๐ธ ๋ฏธ๊ตญ ยท "THICK" ยท ์ด 10๊ฑด
ํํฐ ๋ณด๊ธฐํ์ฌ ์ง์
50.0
0 = ๋ถ์ ์ฐ์ธ
50 = ์ค๋ฆฝ
100 = ๊ธ์ ์ฐ์ธ
์ต๊ทผ 7์ผ ๊ธฐ์ค 11,714๊ฑด์ ๋ถ์ํ ๊ฒฐ๊ณผ, ๋ด์ค ์ฌ๋ฆฌ์ง์๋ 50.0(๊ท ํ)์ ๋๋ค. ๊ธ์ 1๊ฑด(0.0%)ยท์ค๋ฆฝ 11,712๊ฑด(100.0%)ยท๋ถ์ 1๊ฑด(0.0%)์ด๋ฉฐ, ์ค๋ฆฝ ๋น์ค์ด ๋๋ ทํ๊ฒ ๋์ต๋๋ค. ์ฑํฅ ์ง์๋ ์ข ํฉ 19.2(์ค๋ ๊ท ํ)์ ๋๋ค.
While the vast majority of people are just looking for a caffeine fix and friendly chat, the job does require thick skin.
John C. Reilly recently told Ted Danson on the โWhere Everybody Knows Your Nameโ podcast that he tried to convince Leonardo DiCaprio to turn down โTitanicโ and instead star in โBoogie Nights,โ written and directed by his good friend Paul Thomas Anderson. Reilly said he and Anderson were โthick as thievesโ after collaborating on Andersonโs [โฆ]
Wearing a green jumpsuit and a thicker beard than he had in his mugshot, Rush was remanded to the custody of the US Marshals.
Ukrainian drones targeted a Russian oil refinery hundreds of kilometers from the Ukrainian border, the latest in a campaign of deep strikes aimed at disrupting Russiaโs oil industry. The May 31 overnight attacks on the facility in the central Saratov region -- the second since March -- did not appear to have caused any casualties. But a large fire and thick black smoke were reported at the plant belonging to the state-owned oil company Rosneft. Ukraineโs military said its drones targeted the Saratov refinery, along with several otherโฆ
As handheld consoles continue to grow and push the limits of what you can actually hold in your hands, the Arduboy FX-C comes in a refreshingly pocketable package. It manages to squeeze the best features of past models and some welcome upgrades into a handheld thatโs still no larger or thicker than a few credit [โฆ]
The NCAA Men's Golf Championship returns to Omni La Costa, where 30 of the nation's top teams will face fast greens, thick rough and a demanding North Course in pursuit of a national title.
New England Patriots quarterback Drake Maye was in disbelief when he was asked about his thickness during a press conference Wednesday.
The candidates include a Republican who once proposed that students use thick textbooks as shields in school shootings.
Editorโs note: If youโd like to pinpoint the instant when the world entered the nuclear age, 5:29:45 a.m. Mountain War Time on 16 July 1945, is an excellent choice. That was the moment when human beings first unleashed the power of the nucleus in an immense, blinding ball of fire above a gloomy stretch of desert in the Jornada del Muerto basin in New Mexico. Emily Seylโs Trinity: An Illustrated History of the Worldโs First Atomic Test (The University of Chicago Press) offers hundreds of startlingly vivid photographs of the Manhattan Project that emerged from a 20-year restoration effort. This excerpt and the accompanying photos record the massive effort to capture the awesome detonation of โthe Gadget.โ aspect_ratioReprinted with permission from Trinity: An Illustrated History of the Worldโs First Atomic Test by Emily Seyl with contributions by Alan B. Carr, published by The University of Chicago Press. ยฉ 2026 by The University of Chicago. All rights reserved. In the North 10,000 photography bunker, Berlyn Brixner was listening to the countdown on a loudspeaker, his head inside a turret loaded with cameras and film. He was one of the only people instructed to look toward the blastโthrough his welderโs glassesโready to follow the path of the fireball as it launched into the sky. The two Mitchell movie cameras at his station would deliver the best footage to come of the Trinity test, used by Los Alamos scientists to make some of the first measurements of the effects of a nuclear explosion. Related: New Trinity Book Uncovers Images of the First Atomic Test When the detonators fired, the cameras captured what Brixner could not have seenโthe very first light of a violent, silent sea of energy unfurling into the basin. As 32 blocks of high explosives erupted all together, their incredible force surged inward toward the sleeping plutonium core, compressing the dense sphere of metal instantaneously from all sides and bringing its atoms impossibly close together. A carefully timed burst of neutrons sowed momentary, uncontrolled chaos, and then, as quickly as it began, the fission chain reaction ended. Footage from a high-speed Fastax camera in Brixnerโs bunker, shot through a thick glass porthole, shows a translucent orb bursting through the darkness less than a hundredth of a second after detonation, as a rush of heat, light, and matter blew apart the Gadget. When the brightness faded enough for witnesses to make out ground zero, they saw a wall of dust rise up around a brilliant, shape-shifting, multicolored ball of flamesโforming a fiery cloud that shot into the sky atop a twisting stream of debris. The camera footage tells a story no less dramatic but hundreds of times more intricate, preserving the moment for scientists to return to again and again to measure and describe the behavior of the fireball and other visible effects with exacting detail. On balance, the photography effort was a huge success, despite only 11 of the 52 cameras producing satisfactory images. By arranging those cameras at intentionally staggered distances, complementary angles, and with a broad spectrum of frame rates and focal lengths, the Spectrographic and Photographic Measurements Group was able to piece together a remarkably complete picture of their subject. On 12 July 1945, Herbert Lehr, a U.S. Army sergeant and electrical engineer assigned to Los Alamos, delivered the plutonium core to the McDonald ranch house, where the bomb was assembled. Los Alamos National Laboratory According to the groupโs leader, Julian Mack, the more than 100,000 frames that were captured still โgive no idea of the brightness, or of time and space scales.โ Mack attributed fortune, as much as foresight, to the photographic record that was made, especially during the earliest phase of the blast. Indeed, the explosion was several times more powerful than predicted, and the intensity of its effects overwhelmed many of the cameras and diagnostic instruments. The human observers were similarly overcome. โThe shot was truly awe-inspiring,โ said Norris Bradbury, the physicist who would succeed Robert Oppenheimer as director of Los Alamos. โMost experiences in life can be comprehended by prior experiences, but the atom bomb did not fit into any preconception possessed by anybody. The most startling feature was the intense light.โ Norris Bradbury, the physicist responsible for the final assembly of the Gadget, stands next to the partially assembled bomb at the top of the shot tower. The cables on the outside of the bomb would transmit the signals to trigger the synchronized detonations of conventional explosives, which would then create the inward-directed shock wave that would compress the bombโs plutonium core. Bradbury would go on to succeed Robert Oppenheimer as director of Los Alamos on 17 October 1945.Los Alamos National Laboratory It is a common sentiment that words and even pictures pale in comparison to the experience of the explosion. Even so, soldiers, scientists, and many other witnesses have added their firsthand accountsโoften absorbing and poeticโto complement the trove of hard data collected during the test shot. They describe an intense and blinding brightness that filled the basin with daytime; an ominous, darkening cloud rearing its head in eerie silence; the wait for the invisible wave rushing out from the heart of the Gadget; and the mighty roar that arrived at last, in a thunder, and seemed never to leave. Physicist Isidor Isaac Rabi, watching from 20 miles away, remembered, โIt blasted; it pounced; it bored its way right through you.โ James Chadwick, head of the British contingent of scientists who joined the Manhattan Project, later said, โAlthough I had lived through this moment in my imagination many times during the past few years and everything happened almost as I had pictured it, the reality was shattering.โ The blast, captured with an assortment of high-speed and motion-picture cameras, shows the fireball expanding between 25 milliseconds and 60 seconds, by which time the mushroom cloud is over 3 kilometers high.Los Alamos National Laboratory And physicist George Kistiakowsky found himself certain that โat the end of the worldโin the last millisecond of the Earthโs existenceโthe last human will see what we saw.โ
This sponsored article is brought to you by Applied Materials. At pivotal moments in history, progress has required more than individual brilliance. The most consequential breakthroughs โ such as those achieved under the Human Genome Project โ required a new operating paradigm: Concentrate the worldโs best talent around a single mission, establish a common platform, share critical infrastructure, and collapse feedback loops. When stakes are high and timelines are compressed, sequential and siloed innovation simply cannot keep pace. Todayโs AI era is creating an engineering race with similar demands. Every company is pushing to deliver higher-performance AI systems, faster. But performance is no longer defined by compute alone. AI workloads are increasingly dominated by the movement of data: In many cases, moving bits consumes as much โ or more โ energy than compute itself. As a result, reducing energy per bit can extend systemโlevel performance alongside gains in peak compute. The path to energyโefficient AI therefore runs through systemโlevel engineering, spanning three tightly interconnected domains: Logic, where performance per watt depends on efficient transistor switching, lowโloss power, and signal delivery through dense wiring stacks. Memory, where surging bandwidth and capacity demands expose the memory wall, with processor capability advancing faster than memory access. Advanced packaging, where 3D integration, chiplet architectures, and highโdensity interconnects bring compute and memory closer together โ enabling system designs monolithic scaling can no longer sustain. These domains can no longer be optimized independently. Gains in logic efficiency stall without sufficient memory bandwidth. Advances in memory bandwidth fall short if packaging cannot deliver proximity within thermal and mechanical constraints. Packaging, in turn, is constrained by the precision of both frontโend device fabrication and backโend integration processes. In the angstrom era, the hardest problems arise at the boundaries โ between compute and memory in the package, frontโend and backโend integration, and the tightly coupled process steps needed for precise 3D fabrication. And it is precisely this boundaryโdriven complexity where the traditional innovation model breaks down. The Traditional R&D Workflow Is Too Slow for AngstromโEra AI For decades, the semiconductor industryโs R&D model has resembled a relay race. Capabilities are developed in one part of the ecosystem, handed off downstream through integration and manufacturing, evaluated by chip and system designers, and only then fed back for the next iteration. That model worked when progress was dominated by relatively modular steps that could be scaled independently and simply dropped into the manufacturing flow. But the AI timeline has upended these rules. At angstromโscale dimensions, the physics enforces inescapable coupling across the entire stack: materials choices shape integration schemes; integration defines design rules; design rules dictate power delivery; wiring sets thermal budgets; and thermals ultimately constrain packaging scaling. System architects simply cannot wait 10โ15 years for each major semiconductor technology inflection to mature. Representing a roughly $5 billion investment, EPIC is the largest commitment to advanced semiconductor equipment R&D in U.S. history. A longโterm perspective is essential to align materials innovation with emerging device architectures โ and to develop the tools and processes required to integrate both with manufacturable precision. At Applied Materials, together with our customers, we are charting a course across the next 3โ4 generations, extending as far as 10 years down the roadmap. The angstrom era demands that we break down silos and bring together the industryโs best minds โ from leading companies to leading academic institutions. If the problem is coupled, the solution must be coupled. If the timeline is compressed, the learning loop must be compressed. Itโs not enough to just innovate โ we must innovate how we innovate. EPIC: A Center and Platform for HighโVelocity CoโInnovation This is the challenge that Applied Materials EPIC Center is designed to solve. Representing a roughly US $5 billion investment, EPIC is the largest commitment to advanced semiconductor equipment R&D in U.S. history. When it opens in 2026, it will deliver stateโofโtheโart cleanroom capabilities built from the ground up to shorten the path from earlyโstage research to fullโscale manufacturing. But the facilities are only one component of the model. EPIC is also a platform, an operating system for high-velocity coโinnovation that revolutionizes how ideas move from the lab to the fab. EPIC is a platform, an operating system for high-velocity coโinnovation that revolutionizes how ideas move from the lab to the fab.Applied Materials The EPIC model compresses the traditional workflow. Customer engineers work sideโbyโside with Applied technologists from day one โ moving beyond isolated process optimization and downstream handoffs. Within a shared, secure environment, EPIC tightly integrates atomistic modeling, test vehicles, process development, validation, and metrology feedback. Constraints that once surfaced late in development are identified and addressed early. The result is a potentially 2x faster path that benefits the entire ecosystem under one roof: Chipmakers gain earlier access to Appliedโs R&D portfolio, faster learning cycles, and accelerated transfer of nextโgeneration technologies into highโvolume manufacturing. Ecosystem partners gain earlier access to advanced manufacturing technology and collaboration opportunities that expand what is possible through materials innovation. Academic institutions gain opportunities to strengthen the labโtoโfab pipeline and help develop future semiconductor talent. Building on decades of coโdevelopment, we are reinventing the innovation pipeline with our partners across logic, memory, and advanced packaging to deliver the next leap in energyโefficient AI. Accelerating Advanced Logic Logic remains the engine of AI compute. In the angstrom era, however, systemโlevel gains are increasingly constrained by power and energy. Extending AI performance now depends on architectures that deliver more performance per watt โ accelerating the move to 3D devices such as gateโallโaround (GAA) transistors, which boost density within a compact footprint while preserving power efficiency. Architectures that deliver more performance per watt are accelerating the move to 3D devices such as gateโallโaround (GAA) transistors, and further out, complementary FETs (CFETs), which push density scaling even more.Applied Materials These architectural shifts are unfolding at unprecedented scale, with the logic roadmap already extending beyond firstโgeneration GAA toward more advanced designs. One key example is GAA with backside power delivery, which relocates thick power lines to the backside of the wafer, reducing resistive losses and freeing frontโside routing for tighter logic cell integration. Another example brings adjacent GAA PMOS and NMOS transistors closer together while inserting a dielectric isolation wall between them to minimize electrical interference. Further out, complementary FETs (CFETs) push density scaling even more by stacking PMOS and NMOS devices directly atop one another. While these architectures deliver compelling gains in performance per watt and logic density without relying solely on tighter lithography, they significantly raise integration complexity. Manufacturing a single GAA device today can involve more than 2,000 tightly interdependent process steps. At the same time, wiring stacks continue to grow taller and denser to connect these advanced logic devices. Modern leadingโedge GPUs now in development pack more than 300 billion transistors into an area little larger than a postage stamp, interconnected by over 2,000 miles of wiring. Modern leadingโedge GPUs now in development pack more than 300 billion transistors into an area little larger than a postage stamp, interconnected by over 2,000 miles of wiring.Applied Materials At this level of complexity, the process steps used to create these precise 3D devices and wiring stacks cannot be optimized independently. Design and process must evolve in lockstep, and materials innovation and fabrication methods must advance alongside device architecture. EPICโs coโinnovation model is designed to accelerate exactly this convergence โ enabling logic compute to continue advancing the frontiers of AI at the pace the roadmap demands. Powering the Memory Roadmap At the same time, the AI computing era is fundamentally reshaping how data is generated, moved, and processed โ making memory technologies, especially DRAM, central to delivering the energyโefficient performance AI systems require. As models grow larger and more dataโhungry, the DRAM roadmap is shifting toward architectures that deliver higher density, greater bandwidth, and faster access per watt. At the DRAM cell level, AI performance requirements are driving a transition from 6Fยฒ buriedโchannel array transistors (BCAT) to more compact 4Fยฒ, and beyond that, architectures that move past what 2D scaling alone can deliver. Applied Materials At the DRAM cell level, this shift is driving a transition from 6Fยฒ buriedโchannel array transistors (BCAT) to more compact 4Fยฒ architectures, which orient the transistor vertically to boost density and reduce chip area. Looking beyond 4Fยฒ, sustaining gains in performance per watt will require moving past what 2D scaling alone can deliver. The industry is therefore turning to 3D DRAM, stacking memory cells vertically to add capacity within a constrained footprint. As these structures grow taller and aspect ratios intensify, high-mobility materials engineering in three dimensions becomes increasingly critical to performance and reliability. Beyond the memory cell array, another powerful lever for DRAM scaling is shrinking the peripheral circuitry, which includes logic transistors and interconnect wiring. One emerging approach places select periphery functions beneath the DRAM array by bonding two wafers โ one optimized for the DRAM cells and the other for CMOS logic โ using multiple wiring layers. Beyond the memory cell array, another powerful lever for DRAM scaling is shrinking the peripheral circuitry, which includes logic transistors and interconnect wiring.Applied Materials In parallel, DRAM performance is being extended by leveraging logicโproven enhancers in the memory periphery. These include mobility boosters such as embedded silicon germanium and stress films, along with wiring upgrades like improved lowโk dielectrics and advanced copper interconnects. Memory manufacturers are also transitioning periphery transistors from planar devices to FinFET architectures, following the logic roadmap to further improve I/O speed. These valuable inflections are central to EPICโs mission โ where they can be co-developed and rapidly validated for nextโgeneration memory systems. Driving System Scaling With Advanced Packaging As data movement becomes the dominant energy cost in AI systems, advanced packaging has emerged as a critical lever for improving systemโlevel efficiencyโshortening interconnect distances, increasing bandwidth density, and reducing the power required to move data between logic and memory. The rise of 3D packages such as highโbandwidth memory (HBM) underscores why advanced packaging is becoming central to the AI era.Applied Materials Highโbandwidth memory (HBM) marks a major inflection along this path. By stacking DRAM dies โ scaling to 16 layers and beyond โ and placing memory much closer to the processor, HBM enables rapid access to everโlarger working datasets. This delivers stepโfunction gains in both bandwidth and energy efficiency. More broadly, the rise of 3D packages such as HBM underscores why advanced packaging is becoming central to the AI era. Packaging now addresses systemโlevel constraints that logic and memory device scaling alone can no longer overcome. It also enables a move away from monolithic systemsโonโchip toward chipletโbased architectures, as AI workloads increasingly demand flexible designs that combine logic, memory, and specialized accelerators optimized for specific tasks. A vital technology powering this roadmap is hybrid bonding. With interconnect pitches approaching those of onโchip wiring, conventional bumps and microbumps run into fundamental limits in density, power, and signal integrity. Hybrid bonding removes these barriers by allowing dramatically higher interconnect and I/O density, supporting a broad range of chiplet architectures โ from memory stacking to tighter computeโmemory integration. EPIC tackles highโvalue advancedโpackaging challenges through early, parallel coโinnovation across materials, integration, and manufacturing.Applied Materials As bonded structures like HBM stacks grow larger and more complex, warpage control, die placement, stack alignment, and thermal management become firstโorder challenges. EPIC tackles these and other highโvalue advancedโpackaging challenges through early, parallel coโinnovation across materials, integration, and manufacturing. Bringing It All Together Across logic, memory, and advanced packaging, our industry faces an ambitious roadmap that promises significant gains in energy efficiency for AI systems. But realizing that potential demands breakthrough materials innovation at a time when feature sizes are shrinking, interfaces are multiplying, and process interdependencies are escalating. These challenges cannot be solved on 10โ15โyear timelines under the traditional relayโrace model. We must break down silos, align earlier across the ecosystem, and parallelize learning to keep pace with AIโs demands. In the AI era, progress will be defined by the speed at which lightbulb moments turn into manufacturing and commercialization reality. The only viable path forward is a new innovation model โ and EPIC is how we are driving it.