#BLACKPINK# WORLD TOUR [BORN PINK] NORTH AMERICA DALLAS∙HOUSTON∙ATLANTA ADDITIONAL SHOWS ANNOUNCEMENT ▶More info: #블랙핑크# #WORLDTOUR# #BORNPINK# #NORTH_AMERICA# #DALLAS# #HOUSTON# #ATLANTA# #ADDITIONAL_SHOWS# #ANNOUNCEMENT# #YG#

Due to overwhelming demand, four additional shows - Friday, January 15, Saturday, January 16, Friday, January 22, Saturday, January 23 – have been added to Eagles – Live in Concert at Sphere, the longest-running residency at the revolutionary venue with 72 shows in total. Presale signup starts now at The presale begins Wednesday, August 12 at 10am PT. Vibee Hotel & Experience Packages are available Thursday, August 6 at 10am PT at The general on-sale for the new shows will begin on Friday, August 14 at 10am PT at

BLACKPINK WORLD TOUR [BORN PINK] ASIA  KAOHSIUNG∙SINGAPORE∙MACAU ADDITIONAL SHOWS ANNOUNCEMENT #BLACKPINK# #블랙핑크# #BORNPINK# #BLACKPINK_WORLDTOUR# #BLACKPINK_BORNPINK# #ASIA# #KAOHSIUNG# #SINGAPORE# #MACAU ##ADDITIONAL_SHOWS# #ANNOUNCEMENT# #YG#

Additional footage shows firefighters battling the blaze at Marine Corps Air Station Miramar in San Diego, California, following the crash of a U.S. Marine Corps F-35B Lightning II earlier this morning.

New quest just landed on @layer3 👀 Teneo Beacon lets you earn by turning your devices into infrastructure for AI agents. This quest shows you how to start stacking fragments: 📡 Share unused bandwidth 🖥️ Add more devices = multiply earnings ⚡ Claim every 8 hours for up to 3x boosts 🤝 Refer friends for additional rewards 🧩 Stack Points + Fragments while you sleep Low effort. Passive rewards. Still early. Complete the quest before the leaderboard gets crowded👇

Regarding claims by Shri Lakshya Singh (App No. 260412053016) about his NEET (UG) 2026 OMR answer sheet: NTA has verified the record. The genuine OMR of the candidate is with NTA. It was also emailed to him at the registered e-mail address during the OMR Response Key challenge window. The image being circulated shows response markings that are not present on the genuine sheet. Additional bubbles have been shaded in by digital means to create a forged OMR sheet. Of 180 questions, the candidate attempted 54 (34 correct, 20 incorrect) and left 126 unattempted. The score of 116 marks is verified and stands as declared. Creating or circulating a forged OMR answer sheet is an offence under the Public Examinations (Prevention of Unfair Means) Act, 2024. More details on the Public Notice dated 20 July 2026:

WHOA: Palantir’s CEO just dropped a MAJOR hint about the Iran conflict on live TV — and it sounds like great news for the United States. Even CNBC’s Andrew Ross Sorkin reacted with an instant “Wow!” the moment he heard Alex Karp say it. KARP: “America has to rep his own interests.” “Do I think Iran’s been degraded? Yes.” “Are there things that I don’t think are in the public space that would be comforting to people if they are?Yes.” “And I’ll leave it at that...” SORKIN: “Wow!” “Pretty great!” overton_news 🔗 Captain has spoken about Palantir's role many of times... Karp is alluding to classified or underreported US & allied successes against Iran that go beyond what's publicly confirmed. In the broader context of the 2026 Iran conflict, which began with major US-Israeli strikes in late February that killed Supreme Leader Ali Khamenei & hit key military sites... Karp (whose company Palantir supplies AI/data tools used in targeting and intelligence) suggests Iran’s capabilities have been degraded more thoroughly than open-source reporting shows. The “comforting” things likely refer to things like additional assassinations of IRGC commanders, deeper damage to Iran’s nuclear infrastructure, missile/drone production, or command networks that haven’t been fully disclosed or successful covert operations that weaken Iran’s ability to retaliate or reconstitute forces during the current fragile ceasefire & negotiations. As a defense contractor CEO speaking carefully on live TV, Karp is signaling confidence in US/Israeli operational advantages without revealing specifics, while emphasizing America should pursue its own interests. This aligns with Palantir’s known role in providing “lethal capacity” tech in the region.💥 Kelly👊 @CaptKylePatriot

Believe it or not... There’s a cancer treatment where they don’t cut you open. They don’t poison you. They don’t burn you. And it’s FDA approved. They aim sound at the tumor. Actual ultrasound waves that literally shred cancer cells without touching the healthy tissue next to it. Wild. It’s called histotripsy. And the craziest part—patients walk out the same day, asking if anything even happened. No incision. No pain. No downtime. Just a robotic arm focusing sound waves so precisely they create microscopic bubbles inside the tumor. How? The ultrasound pulses create tiny bubble clouds that rapidly expand and collapse, mechanically breaking apart the targeted tissue while sparing nearby healthy structures. The body then gradually clears away the destroyed tissue over time. Histotripsy is currently FDA-authorized for treating certain liver tumors, and researchers are studying its use for additional cancers. It isn’t suitable for every patient, so treatment depends on the type, size, and location of the tumor. Healthy tissue just millimeters away is left untouched. Pretty nuts. Recent data shows around 90% tumor control at 12 months, with treatment zones shrinking over time. Most people feel little or nothing afterward. Let that sink in. The FDA approved this technology in 2023 after more than 20 years of research at the University of Michigan. Researchers now believe this technology may not only destroy tumors, but also help activate the immune system. Imagine treating cancer without destroying the body in the process. That’s the future many researchers are working toward. Follow: Quantum Medicine ✅️

Can test-time scaling work for diffusion language models? In our #ICML2026# paper "UnMaskFork," we show that having multiple masked diffusion language models collaborate on a single answer improves performance on coding and math tasks. Blog: Test-time scaling is an actively researched technique that boosts LLM performance by using inference-time compute, for example, by having a model think longer or repeatedly refine its answers. This allows us to enhance performance simply by increasing computation during inference without relying on additional training, giving us the flexibility to balance compute costs and performance based on the specific use case. Unlike standard LLMs that generate text left-to-right, masked diffusion language models (MDLMs) generate text by gradually filling in a fully masked sequence. MDLMs can generate multiple parts of a sequence in parallel, offering potential speed-ups, and they can generate flexibly while seeing the entire sequence at once. This makes them an actively studied new paradigm in language modeling. We found that the standard LLM approach of "raising the temperature to increase randomness and generate diverse answers" does not work well for MDLMs like Dream-Coder. Instead of relying on this randomness, our proposed method, UnMaskFork (UMF), creates diversity through "model switching." Multiple MDLMs share the task of unmasking a single answer, and we use Monte Carlo Tree Search to search for a promising sequence in which different models handle different stages. Each model picks up where the others left off, filling in the parts it is most confident about. This collaborative approach allows us to explore diverse answers while maintaining generation quality, consistently outperforming existing test-time scaling methods on coding benchmarks and scaling effectively on math as well. Test-time scaling is also crucial for advancing MDLMs, and our work shows that UMF can sidestep the difficulties specific to them. UMF requires no additional training or changes to the models; it works simply by combining pre-trained models at inference time. This allows us to leverage the diversity of diffusion language models trained on different data and with different methods to improve performance. We believe the value of UMF will only grow as more diverse MDLMs emerge. This work is part of our broader research into "collective intelligence of AI," alongside methods like AB-MCTS and Sakana Fugu that have multiple LLMs collaborate. We'll continue pursuing research that turns model diversity into a source of strength. For details of the algorithm and illustrative examples showing how this collaboration works, please see our blog and paper. Paper: 🐟