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Kickstarter Reverse Ferrets, Apologises Over Mature Content Guidelines
Kickstarter performs a full reverse ferret, apologises over its new Mature Content Guidelines and goes back to the previous rules
New MatterChat Model Helps AI to ‘See’ the Language of Science
From writing emails to generating computer code, much of the artificial intelligence prevalent in our daily lives has succeeded by mastering one domain: text. However, this leaves a major blind spot in the physical sciences, where models depend on the high-resolution, three-dimensional data of the physical world, like the intricate lattice of atoms in a crystal. Delivering on the promise of using AI for science requires teaching these data-driven text models to seamlessly “talk to” physics-based models. Now, a new AI framework from Lawrence Berkeley National Laboratory (Berkeley Lab), called MatterChat, solves this problem by creating a specialized “bridge.” It connects the conversational power of a Large Language Model (LLM) with a physics-based AI that models “interatomic potentials”: the complex physical forces between atoms. The resulting system already significantly outperforms general-purpose AI tools like GPT-4 at predicting material properties, and the team hopes it can accelerate scientific discovery by serving as a robust research partner that provides grounded insights and generates step-by-step instructions for synthesizing novel materials. A paper describing this work was recently published in Nature Machine Intelligence. “Traditional simulations can provide the physical rigor required for materials science, yet their computational cost remains prohibitive for high-throughput screening. Conversely, while LLMs excel at rapid knowledge synthesis, they inherently lack the ‘structural vision’ to interpret materials directly from their underlying atomic coordinates,” said Yingheng Tang, a postdoctoral researcher in Berkeley Lab’s Applied Math and Computational Research Division (AMCR) and lead author on the paper. “MatterChat was built to solve this dilemma, empowering LLMs with a structural ‘vision’ that allows researchers to leverage their full potential for solving complex, real-world materials challenges.” Empowering language models to solve complex challenges in materials science To build MatterChat, the Berkeley Lab team drew inspiration from technologies like Vision Question Answering (VQA) and Text-to-Image (T2I) generation. In these tasks, AI must translate high-level text concepts into visual images, or vice versa. Doing so requires developers to build tools that “bridge” two fundamentally different forms of data. The researchers adapted this concept to the physical sciences. With MatterChat, they created a “bridge model” that successfully connects an LLM’s general knowledge with the deep understanding of the atomic-scale world encoded in scientific interatomic potentials. Until now, researchers using LLMs to solve materials problems usually had to feed them raw data files as if they were just strings of text. It’s like asking an AI to understand a complex 3D engine based only on a parts list: the LLM can read the names, but it can’t “see” how the atoms fit together in space. MatterChat solves this by training a specialized AI bridge model, pre-trained on millions of crystal structures and an LLM, to align the LLM’s representation of the world with the interatomic potential’s representation of the world. The bridge model can translate physical insights into a format the LLM can actually understand. By giving the LLM these “scientific eyes”— a scientific “inductive bias” in the terminology of AI — the Berkeley Lab team has transformed it into a robust research tool capable of providing grounded scientific insights into complex materials challenges, such as predicting thermal stability or analyzing electronic band gaps. “We think of atoms as living in a physical space, but from a machine learning perspective, they are just vectors living in some very non-trivially structured manifold in a high-dimensional Euclidean space; and, of course, the same is true for the sentences and paragraphs that we use to express our ideas about those atoms,” said co-author Michael Mahoney, Berkeley Lab’s Scientific Data Division (SDD) AI Initiative Research Lead. “The bridge model basically gets those two structures to ‘talk with’ each other.” As a proof-of-concept of this general approach, the team trained their bridge model on a dataset curated by pairing nearly 143,000 stable atomic structures from the Materials Project with their corresponding physical properties. This training data was automatically assembled using the Materials Project’s API and deliberately enriched with properties fundamental to microelectronics design — like formation energy and bandgap — allowing MatterChat to learn the complex patterns connecting a material’s atomic blueprint to its functional performance. To validate their model, the researchers benchmarked MatterChat against a suite of other AI systems, from general-purpose LLMs to other specialized scientific AI methods. The results show that MatterChat consistently outperformed its competitors across a range of tasks. The model was more accurate in classifying material types and demonstrated superior precision in predicting numerical properties. For example, it excelled at predicting a material’s bandgap, a property critical for designing new electronics from high-capacity energy storage to next-generation computer chips. “Our design is significantly more efficient because we don’t have to build a massive AI model from the ground up,” said co-author Zhi (Jackie) Yao, a research scientist in Berkeley Lab’s AMCR. “Instead, we take two powerful, pre-trained models — a structural encoder for materials physics and an open-source LLM — and use them off-the-shelf. The only component we actually train is the lightweight ‘bridge model’ that translates between them. It’s the difference between building an entire car factory and simply designing a smart adapter that connects a world-class engine to a world-class navigation system. This approach is not only computationally efficient, but also makes the system modular, so we can easily upgrade components or adapt the bridge for other scientific domains in the future.” Crucially, this modular design highlights exactly how institutions like Berkeley Lab and the Department of Energy are carving out a highly valuable niche in the booming AI landscape. Rather than competing with Silicon Valley tech giants to build ever-larger language models from scratch, the lab is focusing on the specialized connective tissue that makes commercial AI useful for hardcore science. Because the bridge model approach underlying MatterChat is forward-compatible, it is perfectly positioned to leverage these parallel tracks of innovation. As Mahoney pointed out, “We expect that industry will continue to develop improved LLMs, and we expect domain scientists and facilities will continue to generate new data. An important part of scientific machine learning is not simply to solve problems on today’s data, but instead to develop general methods that will be forward-compatible with orders of magnitude more data, whether from scientific domains or from LLMs.” According to Yao, the MatterChat project, which was initially developed and enhanced with funding from a Berkeley Lab Laboratory Directed Research & Development (LDRD) Program, will now expand its capabilities. In a collaboration with Fermilab, MatterChat is already contributing to a U.S. Department of Energy Genesis Mission project — called Accelerating eXtreme Environment Specs-to-Silicon (AXESS) — that aims to speed up the development of next-generation, high-speed, radiation-hardened detectors for challenging particle physics experiments by using advanced 3D integrated circuits (chiplets) and AI-driven data analysis. In addition to the LDRD support, the team also credits supercomputing resources at the National Energy Research Scientific Computing Center (NERSC), located at Berkeley Lab, with MatterChat’s success. “We are incredibly grateful to NERSC; this research simply would not have happened without access to the Perlmutter supercomputer through their AI for Science program,” said Tang. Wenbin Xu, a NERSC postdoctoral fellow at the time, was also a major co-author of the work, as was Benjamin Erichson, a research scientist in Berkeley Lab’s SDD, highlighting the benefits of AMCR-SDD collaboration on AI for science. ### Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science. DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.
Zarna Garg is ready to make Las Vegas audiences laugh
The internet star performs at The Venetian Resort on May 23.
ECU Health Medical Center performs its first procedure with Edison® Histotripsy System
New non invasive therapy brings advanced liver tumor treatment closer to home in eastern North Carolina
How to watch Keyla Richardson on 'American Idol' finale
Pensacola's Keyla Richardson performs on the 'American Idol' finale tonight. Here's how to watch and vote.
Noah Kahan Performs ‘Doors’ and ‘The Great Divide’ on ‘SNL’ in Second Musical Guest Appearance
The Grammy-nominated singer-songwriter appeared on the show two weeks after releasing The Great Divide.
CVS blows past estimates, hikes outlook as insurance business outperforms
All of the health-care giant's business segments, including its insurer Aetna, its retail pharmacy and health services unit, surpassed expectations.
Poll: Scott Singer unpopular in Boca Raton, even as Jared Moskowitz over performs in new CD 25
The GOP's other choice, George Moraitis, fares better.
Surgery still outperforms GLP-1 drugs in terms of heart health
Both treatment options are effective, but metabolic and bariatric surgery was linked to more significant cardiovascular risk reductions than GLP-1 medications.
Report: Oklahoma health system performs poorly across all racial, ethnic groups
Health research and policy nonprofit the Commonwealth Fund warns that federal cuts and restrictions to Medicaid and the ACA Marketplace could worsen outcomes.
UC San Diego Health Performs First West Coast AI Robotic Spine Surgery
First West Coast AI-driven robotic spine surgery performed at UC San Diego Health, improving precision and recovery for patients.
‘Marshals’: Riley Green Performs New Song on ‘Yellowstone’ Spinoff
How the love song became a backdrop for a potential new romance for Kayce Dutton.
‘Marshals’: Riley Green Performs New Song on ‘Yellowstone’ Spinoff
How the love song became a backdrop for a potential new romance for Kayce Dutton.
Surprising Discovery That AI Performs Subliminal Learning And Does So In Mysterious Ways
Newly published research suggests that AI can subliminally learn. This is exciting but also disconcerting. Evil AI could sneakily turn other AI. An AI Insider scoop.
ECU Health performs North Carolina’s first single port colorectal robotic surgeries using the da Vinci SP system
Dr. Warqaa Akram, colorectal surgeon with ECU Health Cancer Care, performs colorectal procedures using the da Vinci Single Port robotic platform.
Bleacher Report Lists Diamondbacks Top 5 in MLB Power Rankings
BALTIMORE – The No. 4 spot in Bleacher Report’s latest power rankings says as much about identity as it performs.
Alice Cooper returns to Eastern Iowa
The Godfather of Shock Rock performs April 18 at Alliant Energy PowerHouse in Cedar Rapids.
Women in science: Sheridan College performs play on life of female astronomer
In honor of Cecilia Payne-Gaposchkin, Sheridan College theater will perform the 2024 Stella Feehily play “The Lightest Element,” highlighting various moments in the scientist’s life
Entertainment briefs | Trio performs with Fortuna Concert Series
The Department of Dance, Music, and Theatre at Cal Poly Humboldt will present “Contemporary Music for Percussion” performed by the Cal Poly Humboldt Percussion Ensemble and “Samba of Bahia” performed by the World Percussion Group.
Kanye West Breaks Silence Amid U.K. Music Festival Backlash
The rapper said he hopes to "present a show of change, bringing unity, peace, and love through my music" when he performs in London this summer.
Why Smart Founders Build Big Business Infrastructure Before They Scale
Scaling a company takes more than grit and growth. Founders who invest early in institutional-grade benefits can reduce risk, attract top talent, and build a culture that performs under pressure.
Gavin DeGraw performs at Historic Crew Stadium
Gavin DeGraw performs at Historic Crew Stadium for Columbus Aviators inaugural game on April 3, 2026.
Gemma 4 and what makes an open model succeed
Having written a lot of model release blog posts, there’s something much harder about reviewing open models when they drop relative to closed models, especially in 2026. In recent years, there were so few open models, so when Llama 3 was released most people were still doing research on Llama 2 and super happy to get an update. When Qwen 3 was released, the Llama 4 fiasco had just gone down, and a whole research community was emerging to study RL on Qwen 2.5 — it was a no brainer to upgrade. Today, when an open model releases, it’s competing with Qwen 3.5, Kimi K2.5, GLM 5, MiniMax M2.5, GPT-OSS, Arcee Large, Nemotron 3, Olmo 3, and others. The space is populated, but still feels full of hidden opportunity. The potential of open models feels like a dark matter, a potential we know is huge, but few clear recipes and examples for how to unlock it are out there. Agentic AI, OpenClaw, and everything brewing in that space is going to spur mass experimentation in open models to complement the likes of Claude and Codex, not replace them. Especially with open models, the benchmarks at release are an extremely incomplete story. In some ways this is exciting, as new open models have a much higher variance and ability to surprise, but it also points at some structural reasons that make building businesses and great AI experiences around open models harder than the closed alternatives. When a new Claude Opus or GPT drops, spending a few hours with them in my agentic workflows is genuinely a good vibe test. For open models, putting them through this test is a category error. Something else to be said about open models in the era of agents is that they get out of the debate of integration, harnesses, and tools and let us see close to the ground on what exactly is the ability of just a model. Of course, we can’t test some things like search abilities without some tool, but being able to measure exactly the pace of progress of the model alone is a welcome simplification to a systematically opaque AI space. Share The list of factors I’d use to assess a new open-weight model I’m considering investing in includes: Model performance (and size) — how this model performs on benchmarks I care about and how it compares to other models of a similar size. Country of origin — some businesses care deeply about provenance, and if a model was built in China or not. Model license — if a model needs legal approval for use, uptake will be slower at mid-sized and large companies. Tooling at release — many models release with half-broken, or at least substantially slower, implementations in popular software like vLLM, Transformers, SGLANG, etc due to pushing the envelope of architectures or tools. Model fine-tunability — how easy or hard it is to modify the given model to your use-case when you actually try and use it. The core problem is that some of these are immediately available at release, e.g. general performance, license, origin, etc. but others such as tooling take day(s) to week(s) to stabilize, and others are open research questions — with no group systematically monitoring fine-tunability. In the early era of open models, the days of Llama 2 or 3 and Qwen pre v3.5, the architectures were fairly simple and the models tended to work out of the box. Some of this was due to the extremely hard work of the Llama, Qwen, Mistral, etc. developer teams. Some is due to the new models being genuinely harder to work with. When it comes to something like Qwen 3.5 or Nemotron 3, with hybrid models (either gated delta net or mamba layers), the tooling is very rough at release. Things you would expect to “just work” often don’t. I’ve been following this area closely since we released Olmo Hybrid with a similar architecture, and Qwen 3.5 is just starting to work well in the various open-source tools that need to all play nice together for RL research. That’s 1.5 months after the release date! This is just to start really investing more into understanding the behavior of the models. Of course, others started working on these models sooner by investing more engineering resources or relying on partially closed software. The fully open and distributed ecosystem takes a long time to get going on some new models. All of this is lead-in for the most important question for open models — how easy is it to adapt to specific use-cases? This is a different problem for different model sizes. Large MoE open-weight models may be used by entities like Cursor who need complex capabilities in their domain, e.g. Composer 2 trained on Kimi K2.5. Other applications can be built on much smaller models, such as Chroma’s Context-1 model for agentic search, built on GPT-OSS 20B. The question of “which models are fine-tunable” is largely background knowledge known by engineers across the industry. There should be a thriving research area here to support the open ecosystem model. The first step is to understand characteristics of different base and post-trained models to understand what they look like. The second step is to tune pretraining recipes for open models so they’re more flexible. Interconnects AI is a reader-supported publication. Consider becoming a subscriber. Subscribe For The ATOM Project and other Interconnects endeavors, we’ve put in substantial effort to measuring adoption trends in the open ecosystem. Everything takes a long time to unfold after a model is first publicly available — and adaptability is why. What we know for sure now, when Qwen has been going from strength to strength with its releases, is that technical staff across the industry has gotten comfortable working with Qwen models. Countless research methods and datasets were made to work with Qwen. It’ll take patience for any other model family to get to this point — a patience I’m not sure many open model builders have. This takes us to Gemma 4, Google’s latest open models. Gemma 3 was released more than a year ago, in March of 2025, and is a bit underrated. Gemma 4 comes in 4 sizes for now, with a bigger, MoE model of over 100B total parameters rumored but not released yet. The models we have today come in sizes of ~5B dense, 8B dense, 26B total 4B active MoE, and 31B dense. I’m most excited that they’re finally adopting a standard Apache 2.0 open source license. This’ll massively boost adoption. The standard of better licenses for strong open-weight LLMs was set by mostly Chinese open model labs in the last 1-2 years, and now U.S. companies are following suit. I will personally be so happy if the horrible Llama licenses and Gemma terms of service were an ~18-month transient dynamic of the industry being nervous about releasing strong open models. The Gemma 4 scores look very solid, the small models have incredible benchmark scores (especially in general domains like LMArena) and the 31B model rivals the recent Qwen 3.5 27B, which is the leading member of that class. The ~30B size range is an important one, as it’s accessible both to researchers and to enterprises looking to deploy the model in real use-cases. Where the 7B model scale is the default for tinkering and research, a 30B model is the default for seeing if an open model can unlock substantial value in your specific workflow — a good mix of intelligence, low price, tractability for downstream training, etc. This takes us back to the above adoption criteria I mentioned for open models and the bigger question — do I think Gemma 4 will be an overwhelming success? Previous Gemma models have been plagued by tooling issues and poorer performance when being finetuned. Gemma 4’s success is going to be entirely determined by ease of use, to a point where a 5-10% swing on benchmarks wouldn’t matter at all. It’s strong enough, small enough, with the right license, and from the U.S., so many companies are going to slot it in. I’m cautiously optimistic that Gemma 4 is going to work better here. Winds are shifting for open models built in America. We saw GPT-OSS go through a bumpy launch to become an overwhelming success. There’s a collective energy around the likes of Reflection, Arcee, Nemotron, Gemma, Olmo, and peers that show substantial demand for building new stacks around open models. There’s capital to be spent on AI stacks across the economy by those who want more ownership of everything, including the model. After launching The ATOM Project 240 days ago, the conversation is shifting into the next stage. Summer of 2025 was a crisis moment where the U.S. AI scene realized it can’t wait and figure out open models after building AGI. The two markets will capture different areas and proceed in parallel. Now that more companies in the U.S. are releasing strong models, we need to improve the ecosystem so that these models are easy to use, understand, and build value around. It’s the hard work to build another inflection point in these adoption plots I’ve been updating consistently, but that’s the work to be done. Join me in it. More data coming soon! Here’s a sneak peek:
LeAnn Rimes gets emotional during 'deep jaw release' treatment
In the video, which has 2.8 million views, LeAnn Rimes is seen breaking down after someone performs a \
First full logical operations achieved on silicon quantum chip
Silicon quantum chip performs error-checked operations, marking a step toward fault-tolerant quantum computing.
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Cheyenne Jackson performs "I'm Feeling Good" on Sunday evening, March 22, 2026, at "Un Salon Moderne à la Nouvelle-Orléans" at the gallery and private residence of Martine Chaisson in New
Intel Core Ultra 5 250K Plus Review - Disrupting AMD's Entry-Level
Intel is breathing new life into its Arrow Lake lineup with the Core Ultra 5 250K Plus. Priced at an attractive $200, this little powerhouse outperforms most AMD CPUs in applications and delivers competitive gaming performance, too. It's a strong contender for budget-friendly builds, and overclocking potential is good, too.
Peregrine Road performs at the Jesup
BAR HARBOR — On Thursday, April 9, at 5:30 p.m., the Jesup will host a free Music and Conversation program with Peregrine Road (Rachel Bell and Karen Axelrod). Peregrine Road
Markiplier to Microdramas: The Ankler x NAB Show’s Future of Entertainment
Share Subscribe Vegas, baby! The Ankler is heading back to NAB Show for a second year in a row — and doubling down on conversations with the smartest people in the industry. From April 18–22, we’re partnering again with the industry’s biggest media and tech gathering to break down the Business of Media and Entertainment as it’s actually evolving: faster, riskier and increasingly driven by new formats, new audiences and new power players. This curated series will bring together top creators, executives and innovators to tackle the forces reshaping the industry — from the AI disruption of Hollywood’s creative ecosystem to the rise of the creator economy, shifting audience behavior and the changing economics of production. We’ll be rolling out more programming in the coming weeks — including top speakers from leading AI studios, prediction markets and beyond — so keep an eye on the Business of Media and Entertainment hub. But first up, we’re announcing two panels about what’s actually working in Hollywood: The Scary-Smart Business of Horror A good scare has become one of the entertainment economy’s most reliable engines. With disciplined budgets, built-in fandoms and global box office upside, horror consistently outperforms. From theatrical releases to streaming-first originals and creator-driven IP, the genre is artistically flexible and financially durable — and one of the last truly communal experiences for Gen Z and Gen Alpha audiences. Join YouTube creator Mark Fischbach, aka Markiplier, whose independent feature Iron Lung earned more than $50 million at the global box office, Atomic Monster president Michael Clear and screenwriter Akela Cooper (M3GAN, Malignant) for a conversation moderated by Like & Subscribe’s Natalie Jarvey about why horror travels so effectively across platforms and borders, how studios and independents are building sustainable genre pipelines and why the next wave of breakout franchises may emerge from unexpected sources. Inside the Microdrama Boom Short-form serialized storytelling is having a major moment. Microdramas, social-native series and creator-led narratives are capturing massive audiences with low-cost, rapid production cycles and direct-to-fan distribution. What was once dismissed as niche or experimental is now attracting major platform investment. Moderated by Jarvey, this panel of microdrama creators and executives — including one of the format’s top stars and producers, Kasey Esser, and Dhar Mann Studios’ head of vertical content, Erin McFarlane — examines how format innovation, fandom dynamics and algorithmic discovery are reshaping the economics of scripted storytelling and what traditional studios can learn from these agile models. For sponsorship opportunities, contact Lynne@theankler.com Share
China targets measurement science in tech race with U.S. - Futura-Sciences
The invisible backbone of modern technology Behind every electronic chip, satellite, and medical device sits a system of measurement. Without extreme precision in units, standards, and calibration methods, no technology operates reliably. Metrology ensures that a nanometer is actually a nanometer, that a processor performs exactly as intended at atomic...
MacBook Neo rivalis cloud servers in database workload test
DuckDB compared the MacBook Neo with a range of cloud servers to see how Apple’s new entry-level laptop performs on heavy database workloads.
UI Health Care performs its first robotic living donor nephrectomy
University of Iowa Health announced in early February 2026 that it had successfully completed its first robotic living donor nephrectomy
Women's Health AI Outperforms VC's Own Benchmarks—Yet The Funding Gap Persists
Female founders deliver 25% of VC exits with lower burn rates, but raised 22% less in 2025. PitchBook data reveals the systematic mispricing of women's health AI.
Governor Newsom, Be the Man Science Needs
How will his final legislative season pan out?
Huawei GT Runner 2 Accuracy Review: The Next Best GPS Design?
Everything you need to know about the Huawei GT Runner 2 GPS watch, and how it performs in real life.
Ray J performs with bleeding eyes weeks after revealing he has months to live
Singer Ray J performed a concert on Saturday with what appeared to be bleeding eyes and something taped to his chest, weeks after claiming he only has months to live: 'I'm putting my life on the line for this show!'
Sony’s New Earbuds Offer Fresh Design and Top-Notch Noise Cancellation
Sony Group Corp. introduced its latest pair of premium wireless earbuds, offering a retooled design and active noise cancellation that goes head to head with (and often outperforms) Apple Inc.’s AirPods Pro 3.
ENTERTAINMENT: Ailey II troupe set; actor hosts ‘Hamilton’ viewing
Contemporary dance company Ailey II, the second company to Alvin Ailey American Dance Theater, performs at 7 p.m. Saturday at the Hendrix Arts Center.
TAVR valve for AR , a recent Edwards acquisition, delivers long-term value
The J-Valve System is associated with positive 10-year outcomes for patients with severe AR, according to new research out of China. It also performs well in patients with severe AS.
Party Or Politics? All Eyes On Bad Bunny At Super Bowl
The argument that sport and politics should not mix could be tested at the Super Bowl halftime show on Sunday, when Puerto Rican superstar Bad Bunny performs against a backdrop of fury from right-wing critics.
Sabrina Carpenter slammed by PETA for using live bird in Grammys performance
PETA has slammed Sabrina Carpenter's Grammys 2026 performance after the 'Manchild' singer used a live white dove at the end of her set: 'Childlike behavior.'
2026 Grammy Awards performers: Justin Bieber, Lady Gaga, Addison Rae and Olivia Dean among star-studded lineup
Music's biggest night is fast approaching — and the roster of scheduled Grammy performances continues to grow.
Leak reveals how Galaxy S26 Ultra’s Privacy Display feature performs in real life
The Galaxy S26 Ultra is expected to have the Privacy Display feature. A new leak uncovers how the exclusive feature works in real life.
Harlem Globetrotters celebrate 100 years of entertainment with Gainesville show
Team performs at Stephen O'Connell Center on Monday.
South Carolina hopes to find shooting stroke vs. Albany
South Carolina knows its postseason hopes will ultimately come down to how the team performs in Southeastern Conference play, which begins Saturday with a home showdown against No. 11 Vanderbilt.
The biggest entertainment and fashion moments of 2025, as seen in AP photos
Bad Bunny performs during the iHeartRadio Music Awards at the Dolby Theatre in Los Angeles, March 17, 2025. (AP Photo/Chris Pizzello, File) By THE ASSOCIATED PRESS Updated 6:00 AM CST, December 4, 2025 For many, 2025 felt like a blur and that was true in the world of entertainment, which saw happy reunions, somber farewells and introductions to fresh voices and faces. Sabrina...
Area Events & Entertainment Community Calendar
Dec. 26 – Mattea Joy performs at Sawmill Brewing Company at 5 p.m. Dec. 27 - Phyl Wickham performs at Sawmill Brewing Company at 5 p.m. Dec. 28 - Dig Deep Duo (bluegrass) performs at Sawmill …
Google's Gemini 3 Flash model outperforms GPT-5.2 in some benchmarks
Almost exactly a month after the debut of Gemini 3 Pro in November, Google has begun rolling out the more efficient Flash version of its latest AI model.
Bad Bunny slips and falls onstage mid-concert, but his fans keep the show going
The show must go on, even if you slip and fall. Just ask Bad Bunny, who did exactly that onstage during his recent concert in Mexico.
Old Dominion partners with Caesars Entertainment to launch its newest album in Las Vegas
The band performs ‘Barbara’ in its entirety at Planet Hollywood on Dec. 5-6.
Local pop singer Breleigh Curtis signs two-year entertainment contract
Breleigh Curtis, who performs as Breleigh Rael, will begin working on her first project Dec. 9 in Columbus.