The Federal Trade Commission has sent Instacart a civil investigative demand, seeking information about its AI-powered pricing tool, according to Reuters. This comes after a recently published pricing experiment study showed that the online grocery delivery app gave different users different prices for the same items from the same store location at the exact same time. Some of the testers saw prices up to 23 percent higher than what the other testers saw, though the average difference for the same list of items was around 7 percent. Those higher prices could cost customers over $1,000 more in expenses for the year.
“The Federal Trade Commission has a longstanding policy of not commenting on any potential or ongoing investigations,” the FTC told Reuters in a statement. “But, like so many Americans, we are disturbed by what we have read in the press about Instacart’s alleged pricing practices.”
When the study came out, Instacart told Engadget that the pricing variances were caused by some of its retail partners doing “limited, short-term and randomized tests” to better understand consumers. Those randomized pricing tests were enabled by Instacart’s AI pricing tool called Eversight developed by a company it purchased in 2022. Instacart told CNBC that “much of what’s been reported has mischaracterized how pricing works” on its platform. The spokesperson repeated that retailers conduct pricing tests on its app and said that “prices on Instacart do not change in real time,” aren’t based on supply or demand and that it never uses “personal, demographic, or user-level behavioral data to set item prices.”
This article originally appeared on Engadget at https://www.engadget.com/apps/the-ftc-is-reportedly-investigating-instacart-over-its-ai-pricing-tool-130000472.html?src=rss
Did you know Zwift has 200+ cycling routes that earn you a special achievement badge and XP bonus the first time they’re completed? And runners have 35+ routes that award badges bonuses as well!
Here’s everything you need to know about Zwift route achievements, including the little gotchas that trip up new Zwifters and a full list of all the routes that award an achievement badge.
Every Badge has a Bonus
When you earn a route achievement badge, you also receive a one-time XP bonus for completing that route. This bonus is equal to the XP you already earned for the route (20XP per km), so you effectively double your XPearning rate when finishing routes for the first time, helping you level up faster.
Note: It’s not exactly accurate that you double your XP earning rate when running a new route. This is how Zwift set up the badges for running routes in the early days, but in recent years, they’ve set up running badges to award XP at the same rate as riding badges, which means newer route badges earn you about half the bonus that older ones did.
What Counts (and What Doesn’t)
Route badges can be earned in three ways:
Select the route from the home screen, then ride the full route. The game will make the correct turns for you. You can do this as a “free ride” or a workout.
Join an event (group workout, social ride, race, Meetup, etc) that uses the route, and ride the full route. (If the event ends before the route is finished, you must continue riding in order to finish the route and earn the badge.)
Join a RoboPacer or friend using the route, then ride the full route. (This can be tricky, since you’ll probably start midway through the route. You must cross the route’s start and finish line to complete it.)
What will not earn the badge is you freely navigating the route when the game thinks you’re on a different route.
Many Zwifters have hopped into the game, completed a particular route, then tried to keep riding and complete another route in the same session. This only works if you teleport to a rider or RoboPacer on the new route. But if you try to navigate the route manually, Zwift won’t know which route you want to complete, and it won’t award the badge.
Your route selection is only saved when you select a route from the home screen menu, begin a new event, or teleport to another rider or RoboPacer.
Planning and Tracking Your Progress
Most Zwifters use the Companion app or the in-game badge indicator to track which routes have unearned badges (read how to do that here). These approaches aren’t without their drawbacks, though:
The in-game list requires you to fire up the game before browsing, it only shows the day’s scheduled worlds (unless you use the fake workout world hack), and it doesn’t include event-only routes.
The Companion app’s list could be super convenient in theory, but in practice the list isn’t accurate, with many routes shown as not awarding badges when in fact they do.
Some Zwifters use tools like Zwifthub to track which routes they’ve completed, but that requires manually marking the route as complete. (Which, to be fair, is rather satisfying.)
I’ve included a complete, up-to-date list of all routes with achievement badges below. You can copy and paste this into any spreadsheet if you’d like a list you can print, edit, etc.
Routes Without Badges
Some routes do not have achievement badges:
Certain Event-Only Routes: Some routes in Zwift are not available for free rides, and thus can only be ridden in official events. While Zwift has been enabling achievement badges for event-only routes in recent years, many older event-only routes don’t have them. Examples include fondo routes like the Gran Fondo, as well as many reverse routes and after-party routes.
Ride Badge, But No Run Badge: while runners can run on all the “mixed-use” routes in Zwift, most of those routes don’t award an achievement badge for runners.
Run-Only Routes: Some routes are only available to runners, and thus have no achievement badge for cyclists. These routes always have a running achievement badge, though! Examples include Jon’s Route and Hudson Roll.
A Complete List of Route Badges on Zwift
Here is a complete list of Zwift routes that award an achievement badge (and bonus XP) upon initial completion. Since Zwift awards different badges and different XP amounts for riders vs runners, I’ve created two separate lists: one for routes that award badges when ridden, the other for routes that award badges when run.
The global artificial intelligence landscape just witnessed a significant tectonic shift. Beijing-based Moonshot AI has made waves with the reported capabilities of its advanced models, including a “thinking agent” designed to perform complex, multi-step reasoning. This development, often associated with their powerful Kimi chatbot, is not just another iterative update; it represents a fundamental change in how AI operates, moving from simple question-answering to autonomous problem-solving. The emergence of such powerful models from China signals a new phase in the AI race, one where reasoning and agency are the new frontiers.
The “Thinking Agent” Revolution
At the heart of this advancement is the concept of a “thinking agent.” Unlike traditional large language models (LLMs) that predict the next word in a sequence, a thinking agent is designed to formulate a plan, execute it using various tools, observe the results and refine its plan accordingly. Reports suggest that Moonshot’s advanced systems are capable of executing an astounding 200–300 sequential tool calls to achieve a single goal. This is a massive leap from current standards, where models typically struggle with more than a handful of sequential actions.
This capability transforms the user experience. Instead of asking, “What is the stock price of Company X?”, a user could task the agent with: “Analyze the last five years of financial reports for Company X, compare its performance with its top three competitors, taking into account recent market trends, and generate a comprehensive investment report.” The AI would then autonomously break this down into hundreds of smaller steps: searching for competitors, retrieving financial documents, performing calculations, reading market news and synthesizing the information into a final document. This shift from a passive information retrieval system to an active, problem-solving partner is the promise of the “thinking agent.”
Above: A visualization of a thinking agent’s complex decision-making process, involving hundreds of sequential steps and tool calls.
Closing the Gap: China’s AI Ambitions
The significance of Moonshot’s achievement cannot be overstated. For years, the prevailing narrative was that the U.S. held an insurmountable lead in generative AI. However, the rapid rise of Chinese labs like Moonshot, which was founded in 2023, challenges this view. By developing models with such deep reasoning capabilities, Moonshot is demonstrating that Chinese AI is not just about scale but also about sophisticated architectural innovation.
The release of models with “thinking” capabilities, similar to OpenAI’s recent o1 model (codenamed “Strawberry”), shows that Chinese labs are keeping pace with, and in some aspects potentially surpassing, their Western counterparts in the race for artificial general intelligence (AGI). This is a clear indicator of a vibrant and fiercely competitive AI ecosystem in China that is quickly closing the technological gap. For more on the global AI race, see this analysis of the U.S./China AI competition.
Stacking Up Against U.S. Titans
The competition is no longer just about who has the largest model or the most GPUs. It’s about who can build the smartest, most autonomous agent. Moonshot’s focus on long-horizon reasoning and massive sequential tool use places it in direct competition with the most advanced projects from OpenAI, Google DeepMind and Anthropic.
While U.S. companies have excelled at building foundational models, Moonshot’s approach highlights a different strategic focus: building systems that can reason and act in the real world over extended periods. This capability is crucial for automating complex workflows in industries like finance, law and software development. The fact that a relatively new startup is pushing the boundaries in this specific area is a testament to the agility and ambition of China’s AI sector.
Real-World Applications: What Can it Do?
The ability to execute hundreds of sequential tool calls opens up a world of possibilities. In software engineering, an agent could be given a high-level feature request and autonomously write code spanning multiple files, debug its own errors and write test cases. In scientific research, it could formulate hypotheses, search through vast databases of papers, design experiments and analyze results.
For everyday users, this could mean a personal assistant that can plan a complex multi-city vacation, booking flights and hotels based on a set of preferences and budget constraints, all without human intervention. The move is from a conversational partner to a capable executive assistant.
The Broader Chinese AI Landscape
Moonshot AI is not an outlier but part of a larger, dynamic ecosystem. Other Chinese tech giants and startups are also making significant strides. Alibaba’s Qwen series of models, for instance, has consistently ranked at the top of open-source leaderboards, demonstrating world-class performance in coding and mathematics. DeepSeek and Zhipu AI are other key players pushing the frontiers of open-source and commercial AI models.
This collective effort, driven by intense domestic competition and strong government support, has created a formidable AI industry that is now a primary rival to the U.S. The landscape is no longer dominated by a single country but is a bipolar competition that is accelerating the pace of innovation globally.
Above: A conceptual illustration of the balanced AI competition between major tech companies in the U.S. and China.
Wrapping Up
The emergence of Moonshot AI’s advanced “thinking” models is a watershed moment. It signals that the era of simple chatbots is ending, replaced by a new generation of AI agents capable of complex, multi-step reasoning and autonomous action. This development not only showcases the rapid advancements of China’s AI ecosystem but also intensifies the global competition for AI supremacy. As Chinese and U.S. labs push each other to new heights, we can expect to see an even faster rate of innovation, leading to AI systems with capabilities we are only just beginning to imagine. The future of AI is not being written in one country, but in a dynamic, high-stakes rivalry that is reshaping the technological world.
The Disney+ video streaming service is finally available on Quest. While better late than never, it’s actually missing out on a few key features for now.
The News
The Horizon Store finally has it own dedicated Disney+ app, which supports Quest 2, Quest 3 and Quest 3S. The app lets you stream the full catalogue of 2D videos on Quest and also download and replay them when offline.
While most content is served up in 1080p, the app also includes select titles in Dolby Vision 4K HDR on Quest 3/S (streaming only), as well as a limited selection of content from Hulu and ESPN.
There a few caveats though. For now, the app is US-only; Disney says it will rollout international availability in early 2026. What’s more, it’s lacking the ability to stream 3D film, like the Apple Vision Pro app does, which came to the headset in 2024 as a launch day feature.
Disney notes that “all content is only available to stream in 2D on Quest devices at this time,” which does seem to leave some room for hope in the future though.
Notably, on Vision Pro, Disney+ subscribers can stream a variety of recent films, including Avatar: The Way of Water, Avengers Endgame, Doctor Strange in the Multiverse of Madness, Frozen II, Star Wars Episode VII, and Encanto.
My Take
There doesn’t seem to be any real downside to Disney eventually allowing its 3D films on Quest, which could mean it likely comes down to a few scenarios: Apple may have timed exclusive rights to 3D movies from Disney, or maybe the films have been specifically mastered in a Vision Pro-specific format that can’t be streamed directly to Quest. There’s no saying for now.
Either way, Disney has definitely hit the nail on timing by bringing its (mostly) full app to Quest; new Quest users will likely be looking for all of the same apps they might find on a smart TV, which Quest sadly doesn’t really have yet though, giving Disney some more visibility over competing platforms.
For now, Quest users looking to stream from Netflix, Hulu, Max, Paramount+, or Apple TV need to sign in and stream in Quest’s built-in Internet browser. Native XR video streaming apps include Amazon Prime Video, Peacock, PlutoTV, Starz, and YouTube.
Earlier this year Sony sued Tencent for copyright infringement over its Light of Motiram game, calling it a “slavish clone” of Horizon Zero Dawn. Then, earlier this month, Tencent agreed to stop promoting and publicly testing the game. Now, the two companies have reached a “confidential settlement” and the case has been dismissed, according to court documents seen by The Verge. Light of Motiram has also disappeared from Steam and Epic’s game stores.
“SIE and Tencent are pleased to have reached a confidential resolution and will have no further public comment on this matter,” Tencent’s spokesperson told The Verge.
When Sony first filed its lawsuit in July 2025, it said that Tencent’s game appeared to copy aspects of not just Horizon Zero Dawn, but other franchise games including Horizon Forbidden West and Lego Horizon Adventures. That included the post-apocalyptic setting with humans and machines coexisting, the visual appearance of characters and even the marketing materials — something Engadget certainly noticed when Tencent first announced the game.
This article originally appeared on Engadget at https://www.engadget.com/gaming/sony-settles-with-tencent-over-slavish-horizon-clone-120042886.html?src=rss
On Dec. 21, 2015, SpaceX launched the Orbcomm-2 mission on an upgraded version of its Falcon 9 rocket. That night, just days before Christmas, the company successfully landed the first stage for the first time. The story behind this remarkable achievement is nowhere more fully told than in the book Reentry, authored by Ars Technica Senior Space Editor Eric Berger and published in 2024. To mark the tenth anniversary, Ars is reprinting a slightly condensed chapter from the book that tells the inside story of this landing. The chapter begins in June 2015 with a tragedy, the disintegration of a Falcon 9 rocket carrying the CRS-7 cargo supply mission for NASA. It was the first time a Falcon 9 had been lost in flight.
Seconds after the Dragon-bearing Falcon 9 rocket broke apart over the Atlantic Ocean, David Giger shouted into his headset, “Dragon is alive!”
In the decade since he joined the company straight out of graduate school, Giger had taken on management of the entire Dragon program, reporting directly to Elon Musk. He watched the CRS-7 launch from mission control in Hawthorne not with a particular role, but rather providing a leadership presence. Giger could sense the Dragon mission team, mostly younger engineers, freeze up as video showed debris from the rocket showering back to Earth. A lot of the people involved in the hairy early flights of Dragon, including the C2 mission in 2012, had moved on to other positions at SpaceX or departed.
Ninja Warrior feels like a fun concept to make the jump into VR, until you consider the practicalities of adapting this experience for the medium. There’s something appealing about translating large-scale obstacle courses into a fitness game of sorts, but these courses featured in the original TV show involve Wipeout-esque feats of acrobatics, strength, and nerve to conquer. Never mind the space necessary to run, jump and dive through them.
Maybe, if you could traverse without using your feet but retaining that sense of athleticism, it could work.
Ninja Warrior VR takes heavy inspiration from titles like Fall Guys in its switch from reality TV to the virtual world, replacing its super-strength competitors with stylized ninja-like characters. The jump also shifts the aesthetics of the experience, and allows the experience to betray your initial expectations in one key aspect, the one thing transforming this into one of the most promising fitness-fueled party titles of 2025. Rather than moving with your feet, you move entirely using your hands.
It seems absurd at first, and certainly it takes some getting used to. While at first you would assume you might run on the spot or use analog sticks to move around each course, everything is controlled using motion controls and your hands. To move forward, you reach your arm forward and physically pull it back toward your chest to pull your character toward wherever you placed it. You can use either hand or both in quick succession to move at speed, but wherever you place that hand, it will remain firmly planted in place until you place down your other hand.
This is the main way you traverse just about every obstacle in the game. Want to jump? Push off from where you are with your hands. You can do this with one or two hands, but you will need to rapidly increase your speed at this task if you want to, say, jump across thin platforms with moving bollards spinning in sequence that will punish you for lurking too long. All against a surprisingly strict time limit that’s longer than the real show, but not exactly friendly either.
The only use of buttons on the controller is the grip button for grabbing onto obstacles like hanging bars, such as the famous obstacle from the series that requires players to hang and jump between two sliding metal bars. This requires you to use all your skills to traverse and is quite challenging to overcome, and we’re still only in round 1!
While you could critique the game under these parameters for being too hard – it’s also similar locomotion to Gorilla Tag or Orion Drift, which isn’t what you’d expect – this arguably works well for Ninja Warrior VR. The reality TV show is a Herculean effort few can successfully beat, and making it too simple risks making it trivial for players when the full game releases.
With the commentators voicing in your ear, there’s still a lot of fun to be had by trying your luck, never mind how amusing it looks from an outsider’s perspective. Provided you’re careful, that is – during my experience, I accidentally punched the demo assistant more than once in the intensity of trying to traverse just this first obstacle course.
To me, though, that’s proof of everything Ninja Warrior VR does well, rather than a negative. You need space to play, and time to get used to moving with your hands. But after overcoming this hurdle you don’t just feel immediately hooked on the thrill of the challenge, you’re resolute and determined to clear these hurdles however possible. It’s tiring, but it’s exciting to overcome a challenge, setting yourself up slowly in just the right spot for a new obstacle, and to overcome it. With a crowd of friends or onlookers, as I had here, the cheers as you overcome that obstacle are both fueling and grin-inducing. It’s a true joy, only possible by the rather bold direction the team has taken to bring this game to life.
Ninja Warrior VR is not a one-to-one physical recreation because that’s not feasible. While its cartoon appearance is unusual at first, it’s through this direction that MyDearest has maintained the spirit of the show and created something that could provide hours of entertainment in good company. That’s from just the first round of the four-round gauntlet that makes up a typical Ninja Warrior match, and the team has promised future updates to keep expanding the game with more levels over time. If the team can maintain that schedule and the degree of fun found in this demo for repeat matches and new stages, this could become a new party game of choice for having a good time with friends in VR.
Ninja Warrior VR is out today on the Meta Quest platform.
In the age of AI, we believe kids still need to learn to code. We’re also testing how AI tools can be used to help with teaching and learning programming. This blog dives into our new classroom unit ‘Programming with AI’, which we’ve developed for teachers to introduce students to learning to code using tools built on large language models (LLMs). We share an overview of the unit, and the early excitement of educators and learners with whom we’re exploring this new approach to teaching and learning programming.
Students completing activities from lesson 1
Bridging the gap in programming education
As India is accelerating its efforts to modernise digital and computing education, it’s the ideal place for us to test how best to introduce learners to AI-assisted approaches to programming and teach them responsible AI use.
We decided to design a ‘Programming with AI’ unit of eight lessons that support students aged 14–16 to progress from block-based programming to text-based Python programming, Python being one of the most widely employed programming languages in industry and academia. In the new unit, students are guided to use LLM-based tools safely, ethically, and responsibly in order to undertake data analysis projects in Python. Students learn how to use the tools, such as Gemini and ChatGPT, to deepen their understanding, experiment with ideas, and connect classroom concepts with real-world technological applications.
The ‘Programming with AI’ unit is currently being piloted across five residential schools under our partner, the Telangana Social Welfare Residential Educational Institutions Society. In the coming months, we will evaluate the impact of this AI-supported approach to learning programming on student engagement, motivation, and overall learning outcomes.
Key design decisions during unit development
During the design phase of the unit, we identified three main challenges we wanted to address:
The first was the transition from block-based to text-based coding. The students we were designing for are familiar with block-based programming like Scratch, but Python needs precisely typed code (syntax). This is a huge shift, and we wanted to offer a way to bridge the gap.
We also had to figure out how to teach prompting LLM tools to students who are learning English as their second language.
Finally, LLMs are non-deterministic. This means that LLM tools don’t always produce the same output, even when given the same prompt. They also do not generate correct code consistently.
To solve the first problem, we decided that the initial lessons in the unit should start by showing familiar Scratch blocks right next to the equivalent Python syntax. This direct comparison makes the transition from block-based to text-based programmer smoother.
Comparison of Python syntax with equivalent Scratch block
To teach prompting, we built a gradual path. The unit starts with a debugging code activity, where students get code with errors and a prompt to make an LLM tool fix the errors. Later, they learn to break down a prompt into their goal (what output they want) and the context (additional information for the LLM tool). Eventually, they use a structured framework to prompt the tool to generate complete, bug-free code.
Handling the unpredictable outputs of LLM tools we turned into a learning activity. In one lesson, students are given different chatbot-generated code segments and asked to analyse and reflect on the code quality. This crucial exercise helps them build the critical thinking and code evaluation skills they need for programming with AI tools.
The progression of Python and generative AI concepts covered in the unit
Over the eight lessons, there is a progression of Python concepts and generative AI concepts, as shown in the image above. They lead students to complete two challenging, real-world data analysis projects.
Preparing the educators
To ensure the successful pilot of the ‘Programming with AI’ unit, a comprehensive teacher training program was essential. We understood that teachers needed confidence and competence not only in the subject matter — programming — but also in guiding students to ethically and effectively use LLM tools.
STEM and ICT teachers participated in the training
The training was an intensive, three-day programme: two days were held in-person for deep collaboration and one day was virtual for reinforcing key information. We covered five critical areas:
Hands-on activities to ensure comfort with the Python programming language
Exploring the capabilities and limitations of LLM tools
Effective prompt writing, which teachers would in turn model for their students to use for code debugging and idea generation
Reviewing lesson flow and learning objectives to understand the structure of a lesson
Adapting specific pedagogies to integrate an LLM tool in teaching the Python programming language
This training ensured our educators were not just teaching a new unit, but felt like confident facilitators ready to lead the shift towards AI-supported learning.
Initial student reactions: Excitement, familiarity with LLM tools, and seamless transition
The pilot of the ‘Programming with AI’ unit began during the second week of December 2025 across five selected schools. It was immediately met with enthusiastic student engagement — they were highly excited to learn Python programming using LLM tools.
When teachers initiated discussions about AI, students demonstrated their familiarity with LLMs, chatbots, and related concepts. They were not only aware of generative AI but could readily name and describe the functions of LLM tools, telling us they were actively using these tools to create projects in various other subjects.
Students could easily connect with the activities in the lessons as they had foundational understanding of programming, which they developed while learning block-based coding in Scratch. This prior knowledge enabled their seamless transition to the new learning. Perhaps most impressive was the students’ rapid adoption of LLM tools to debug their code. This skill can ease what is traditionally a steep learning curve when students first learn text-based programming.
We are now eagerly anticipating their curiosity and progress in the upcoming lessons.
Looking ahead and collecting impact data
With the initial enthusiasm confirmed, the pilot program moves into its next critical phase. Teachers will continue to deliver the unit over the next few months in the five selected schools in Telangana.
The teachers and team at our ‘Programming with AI’ training.
During this extended observation period, our primary focus will be on rigorous data collection and impact assessment. We will closely monitor how students interact with the provided learning materials and how they use LLM tools throughout the unit. We will systematically assess whether the new teaching approach successfully helps students achieve the defined learning objectives for Python programming. Crucially, we will capture comprehensive data to understand the impact of this AI-integrated approach on engagement, skill acquisition, and problem-solving compared to traditional methods.
Based on the synthesis and analysis of all the gathered data, we will share our gathered insights early next year. The findings will inform our plans for more widely introducing the AI-integrated unit and for developing similar content for other settings and age groups.
U.S. video game hardware spending fell 27% year over year in November to $695 million, according to market analyst company Circana. “This is the lowest video game hardware spending total for a November month since the $455 million reached during the November 2005 tracking period,” Circana says. Furthermore, only 1.6 million units of hardware were sold in the U.S. in November, which is “the lowest total for a November month since 1995 (1.4 million).” The Verge reports: The rising costs of consoles probably didn’t help. The PlayStation 5 and Xbox Series of consoles both turned five in November, but customers looking to pick up one of the consoles brand new are having to grapple with higher prices following price hikes this year. Those hikes have led to an “all-time November high” for the average price paid for a new unit of video game hardware of $439, Circana says — a number that’s up 11 percent from 2024. (In November 2019, the average price was $235, according to Circana analyst Mat Piscatella.)
The Armbian Linux project introduced today a new utility called Armbian Imager for flashing the Armbian operating system to your single-board computer.
Longtime Slashdot reader schwit1 shares a report from Ars Technica: Every other week, it seems, a new Chinese launch company pops up with a rocket design and a plan to reach orbit within a few years. For a long time, the majority of these companies revealed designs that looked a lot like SpaceX’s Falcon 9 rocket. The first of these copy cats, the medium-lift Zhuque-3 rocket built by LandSpace, launched earlier this month. Its primary mission was nominal, but the Zhuque-3 rocket failed its landing attempt, which is understandable for a first flight. Doubtless there will be more Chinese Falcon 9-like rockets making their debut in the near future. However, over the last year, there has been a distinct change in announcements from China when it comes to new launch technology. Just as SpaceX is seeking to transition from its workhorse Falcon 9 rocket — which has now been flying for a decade and a half — to the fully reusable Starship design, so too are Chinese companies modifying their visions.
The trend began with the Chinese government. In November 2024 the government announced a significant shift in the design of its super-heavy lift rocket, the Long March 9. Instead of the previous design, a fully expendable rocket with three stages and solid rocket boosters strapped to the sides, the country’s state-owned rocket maker revealed a vehicle that mimicked SpaceX’s fully reusable Starship. Around the same time, a Chinese launch firm named Cosmoleap announced plans to develop a fully reusable “Leap” rocket within the next few years. An animated video that accompanied the funding announcement indicated that the company seeks to emulate the tower catch-with-chopsticks methodology that SpaceX has successfully employed.
But wait, there’s more. In June a company called Astronstone said it too was developing a stainless steel, methane-fueled rocket that would also use a chopstick-style system for first stage recovery. Astronstone didn’t even pretend to not copy SpaceX, saying it was “fully aligning its technical approach with Elon Musk’s SpaceX.” And then, on Friday, the state-aligned China.com reported that a company called “Beijing Leading Rocket Technology” took things a step further. It has named its vehicle “Starship-1,” adding that the new rocket will have enhancements from AI and is billed as a “fully reusable AI rocket.”
An anonymous reader quotes a report from the Guardian: The Massachusetts Institute of Technology (MIT) community is grieving after the “shocking” shooting death of the director of its plasma science and fusion center, according to officials. Nuno FG Loureiro, 47, had been shot multiple times at his home in the affluent Boston suburb of Brookline on Monday night when police said they received a call to investigate. Emergency responders brought Loureiro to a hospital, and the award-winning scientist was pronounced dead there Tuesday morning, the Norfolk county district attorney’s office said in a statement.
The Boston Globe reported speaking with a neighbor of Loureiro who heard gunshots, found the academic lying on his back in the foyer of their building and then called for help alongside the victim’s wife. The statement from the Norfolk district attorney’s office said an investigation into Loureiro’s slaying remained ongoing later Tuesday. But the agency did not immediately release any details about a possible suspect or motive in the killing, which gained widespread attention across academic circles, the US and in Loureiro’s native Portugal.
Portugal’s minster of foreign affairs announced Loureiro’s death in a public hearing Tuesday, as CNN reported. Separately, MIT president Sally Kornbluth issued a university-wide letter expressing “great sadness” over the death of Loureiro, whose survivors include his wife. “This shocking loss for our community comes in a period of disturbing violence in many other places,” said Kornbluth’s letter, released after a weekend marred by deadly mass shootings at Brown University in Rhode Island — about 50 miles away from MIT — as well as on Australia’s Bondi Beach. The letter concluded by providing a list of mental health resources, saying: “It’s entirely natural to feel the need for comfort and support.”
Kubernetes 1.35, dubbed ‘Treenetes,’ delivers in-place pod resource adjustments, a capability that benefits AI training workloads and edge computing deployments.
Intel’s Video Processing Library “libvpl” is out with a new version ahead of the holidays. The only major change with libvpl 2.16 is adding experimental APIs for AI-assisted video encode functionality…
LG is teaming up with Dolby for a new collection of speakers that the companies are unveiling ahead of CES 2026. The LG Sound Suite features a modular lineup the H7 soundbar, M7 and M5 wireless surround speakers and the W7 subwoofer. The speakers can be combined in more than two dozen different configurations, from a pair to a full 13.1.7 channel surround sound home theater setup.
The main pitch of the LG Sound Suite is its application of Dolby Atmos FlexConnect. This is the audio brand’s tech for optimizing sound from wherever wireless speakers are placed. It’s meant to deliver optimal sound even from unusual locations, even when you’re limited by outlet locations, furniture placement or other quirks of a room’s layout. The collaboration with LG brings FlexConnect to a soundbar for the first time. When the H7 soundbar is used as the lead device, the suite will bring Dolby Atmos FlexConnect audio to any television. In a future software update, LG will also bring support for Dolby Atmos FlexConnect directly to some of its premium TVs, including select 2025 models.
This article originally appeared on Engadget at https://www.engadget.com/audio/speakers/dolby-and-lg-introduce-a-modular-home-audio-system-for-ces-2026-010000126.html?src=rss
For over a year, in close partnership with MediaTek, Collabora has been enhancing Chromium on Genio and Kompanio platforms to fully unlock the hardware users depend on every day, delivering out-of-the-box hardware-accelerated video decoding (H.264, HEVC, VP8, VP9) and encoding (H.264, HEVC) for high-performance, video-centric Chromium applications.
