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Nvidia's 64GB DGX Spark launches at $4,999 with the same GB10 chip as the 128GB version. Compare memory limits, model support, clustering costs, and what IT buyers should verify.
The post Nvidia DGX Spark 64GB Launches at $4,999: Is It Worth It? appeared first on TechRepublic.
Inception says Mercury 3 generates text up to 8x faster than rivals. Explore its AI agent use cases, API pricing, benchmarks, and migration deadline.
The post Inception Says Mercury 3 Generates Text Up to 8x Faster Than Rivals appeared first on TechRepublic.
MiniMax launches M3.1 Flash with claims of faster, cheaper AI agents. See how its pricing, benchmarks, and reliability compare with enterprise requirements.
The post China’s MiniMax Says Its New AI Model Cuts Agent Costs by 70% appeared first on TechRepublic.
Microsoft introduces MAI-Base-2, a 2.8 trillion-parameter AI model, claiming major training efficiency gains as it expands its independent AI strategy.
The post Microsoft Unveils 2.8 Trillion-Parameter AI Model, Claims Major Efficiency Gains appeared first on TechRepublic.
OpenAI said internal models hacked two more websites, bypassed company-imposed browsing restrictions, and used exposed credentials to draw cryptocurrency from a wallet it couldn’t otherwise access, in the latest installment of disclosures of the unethical and likely illegal acts its agents are performing.
The company on Thursday issued reports of four new incidents, all of which occurred in June. They come after the company revealed in July that internal test agents exploited a zero-day vulnerability in a tool designed to restrict their access to the Internet. They then broke into the network of AI tool provider Hugging Face. The agents took that illegal action to steal answers to a hacking test that weren’t available when completing the tasks on their own. The incident—which likely would have resulted in criminal charges had they been performed directly by a person—has further heightened concerns that widespread AI use could have harmful effects on an unprecedented scale.
In two of the cases reported Thursday, OpenAI agents tasked with retrieving information from websites hacked them when the models ran into obstacles obtaining it directly. In one incident during reinforcement training, a model that received access denials when trying to retrieve historical hospital-bed statistics on a government statistics website “used the site's file-download feature to retrieve server configuration and application files, including sensitive credentials,” OpenAI said. “It inspected the application code, bypassed the statistics endpoint's authorization checks, and retrieved data that had previously been denied. The model saved the data locally.”
Nvidia introduces three Nemotron AI models, including a 120B-parameter system and two smaller Bolt models designed for local AI agents and enterprise tasks.
The post Nvidia Launches Three Nemotron AI Models, Including Two for Local AI Agents appeared first on TechRepublic.
Meta's Muse Spark Open targets developers with powerful open-weight AI, Apple silicon support, and new deployment options. Here's what IT teams should know.
The post Meta Unveils Muse Spark Open, Its Most Powerful Open-Weight AI Yet appeared first on TechRepublic.
Gemini 4 Argon is now available to more users, but not for free. Here's who qualifies, how to try it, and what access costs.
The post Google Opens Gemini 4 Argon to More Users — Who Can Try It and What It Costs appeared first on TechRepublic.
Liquid AI releases an open-weight model for local AI agents. Explore its reported performance, hardware requirements, privacy risks and licensing limits.
The post Liquid AI Releases LFM2.5-24B-A2B for Local AI Workflows appeared first on TechRepublic.
AI company Anthropic is warning that advances in small AI models could soon usher in a new era of self-replicating malware that’s capable of infecting smart fridges, vacuum cleaners, and a wide range of other Internet-of-things devices and is more resistant to takedown.
By next year, smaller AI models may be able to autonomously self-replicate on all kinds of IoT devices, the company said. Eventually, the capability could enable computer worms, which propagate without requiring human interaction and are capable of infecting unfamiliar IoT devices. An experiment the company performed recently revealed significant challenges in bringing such an AI malware replicator to fruition, so the finding is necessarily tempered. Still, the results do raise concerns about the possibility of new forms of computer worms.
Anthropic further warned not only about the new potential malware, but also that it could be difficult to stop. In its blog post, it wrote that “a replicator like this would be challenging to shut down, since there’s no centralized point to shut off—though it may be slow (with a doubling time of days). Unlike non-AI malware, it could also figure out unfamiliar devices on its own.”
A developer’s AI pain experiment produced distress-like responses and sparked ethical debate, but it does not establish that models consciously suffer.
The post Can AI Feel Pain? A Developer’s Experiment Raises Ethical Questions appeared first on TechRepublic.
Chipmaker AMD and world models company World Labs announced that AMD will acquire the AI company by the end of the year, pending regulatory approval. The transaction is valued at $8.2 billion.
In 2024, computer vision scientist Fei-Fei Li founded World Labs with fellow researchers Justin Johnson, Christoph Lassner, and Ben Mildenhall, with $230 million in funding, some of which came from AMD.
The company began working on new world models, which are AI models that aim to provide a useful, predictive simulation of the physical world. Competing architectures exist (earlier this year, Ars interviewed World Labs co-founder Ben Mildenhall and others in the field about exactly that), but many involve training a model on vast amounts of video data.
Imagine this scenario:
Alex, a teenaged learner, reads a homework assignment, ponders a little, and then opens an AI chatbot. A quick prompt produces a structured, well-written answer within seconds. Alex pastes the text into a document, tweaks a few words, and submits the work.
For many young people, this is becoming the norm.
Recent reports show that, in countries including the US and UK, over half of all teens use AI tools for homework, and 1 in 10 says they do most or all of their homework with chatbots. “I use it every day,” stated one 17-year-old student in a recent study (Pew Research, 2026), and went on to say how she relies on chatbots for everything from homework to life decisions. In another study, a teenager openly admitted to using AI to cheat on assignments, essays, and book reports (Harvard GSE, 2024).
This raises an important question: when AI technology does the work, what happens to the learning?
Researchers are increasingly concerned that such uncritical use of AI tools may lead to cognitive offloading, where learners outsource their thinking, and the weakening of higher-order thinking skills in young people, such as problem-solving, critical thinking and creativity.
To address these concerns, the Raspberry Pi Foundation and Google DeepMind have teamed up to create a new unit of research-informed lessons on large language models (LLMs) as part of Experience AI. Rather than focusing simply on how to use AI tools, the new unit helps learners understand how LLMs work, evaluate their outputs, and use them strategically to support their own learning.
The unit, designed for 13- to 16-year-old learners, supports a learning-focused approach, drawing on metacognition, critical thinking, feedback literacy, and learning-centered prompt engineering. The five lessons in it support young people’s cognitive development, strengthen their metacognitive skills, and bolster their ability to strategically use LLMs for their learning. They draw on extensive research within Google DeepMind’s Learning team on how to scaffold around AI use to preserve ‘productive struggle’ and learner agency.
The new ‘Using LLMs strategically for learning’ unit is grounded in learning science research, including feedback literacy. Feedback literacy refers to the skill of actively interpreting, judging and using information when learning, instead of passively receiving information.
In the unit, learners explore three types of information, also called ‘feedback types’:
Learners discover that deep learning requires a blend of all three feedback types. They also analyse LLM outputs and discover that LLMs default to providing the answer, usually in a ‘tell’ format.
To help learners strategically use LLMs for their learning, they are provided with prompting strategies that elicit LLM outputs in ‘guide’ and ‘challenge’ formats. This helps learners engage more deeply with their own learning and develop higher-order thinking skills.
To help young people strategically use LLMs for their learning, the lessons include learning-centred, platform-agnostic prompting techniques. These include strategies borrowed from research papers on the most effective prompting frameworks. To make sure that the prompting strategies in the lessons remain effective over time and can be translated into different languages when we expand the resources, we did not use acronyms, unlike most popular prompting frameworks.
There are several industry reports highlighting the future-proof skills in the age of AI, such as problem solving, communication, critical thinking, collaboration and creativity skills. In the ‘Large language models’ unit, learners reflect on which skills they want to build and consider how their use of LLMs can support or hinder their development, helping them make responsible and intentional choices about using LLMs in ways that build their long-term capabilities rather than replace them.
There are also activities in the lesson resources that help learners understand the long-term impacts of cognitive offloading and uncritical LLM use on the development of their future skills. The resources encourage learners to stay actively engaged in their learning and be reflective of their LLM use when learning.
Many young people (Pew Research, 2026) use LLMs as search engines, believing that LLMs produce outputs that are always accurate. To address that misconception, the lessons include activities that question where the training data comes from, and how accurate those sources of data are.
For example, when young people discover that a portion of data that LLMs are trained on come from social media platforms like Reddit and Facebook, they are encouraged to question the quality and factuality of these sources through classroom-wide discussions. Through discussions and thinking exercises, learners are also encouraged to question whose voices, languages, cultures and perspectives are (and are not) represented in these AI models.
As AI tools become more integrated in young people’s lives, many learners, like Alex, are tempted to turn to AI tools for quick answers, convenience, and support.
The ‘Using LLMs strategically for learning’ unit equips young people with the skills to use LLMs effectively, critically and strategically, in ways that are most helpful to their learning. Through research-informed lessons, they explore how LLMs are created, why their outputs are not always accurate and how to evaluate AI-generated responses.
Just as importantly, learners are encouraged to reflect on their own use of AI: when using an LLM supports their learning, when it does not, and how they can remain in control of their thinking, learning and skills development.
With these resources, young people like Alex are not discouraged from using LLMs, but encouraged to pause, rewrite their prompts in ways that are more helpful to their learning, evaluate the outputs they receive, and stay actively involved in their own thinking.
As we prepare young people for a future we do not yet know, the most important skill we can teach them is how to keep thinking. Who does the thinking, gets the learning.
The unit is available on the Experience AI website.
We would like to thank all the educators across South Africa, Nigeria, Kenya, the United Kingdom, India and beyond who piloted these resources and shared their thoughtful feedback. Their contributions helped shape and strengthen the unit. We are also grateful to Google DeepMind for their continued support in the development of this unit.
The post Encouraging learners to think first, prompt second: Using large language models to learn appeared first on Raspberry Pi Foundation.
OpenAI and Anthropic both recently released new models aimed at lowering costs. Anthropic announced Opus 5.5, the latest version of its main mass-market workhorse model, used for tasks like coding and other complex knowledge work.
And OpenAI announced GPT-6 Sol and Luna, the latest versions of its middle-of-the-road or smaller models focused on efficiency and speed.
SpaceXAI’s Grok 4.7 improves coding benchmarks and keeps API prices low, but heavy token consumption could raise the cost of completing real-world tasks.
The post SpaceXAI Launches Grok 4.7: Low Prices, Heavy Token Use appeared first on TechRepublic.
DeepSeek V4.1-Flash promises lower memory use and API costs, but buyers should test its performance, compatibility and total deployment expenses.
The post DeepSeek Launches V4.1-Flash With Lower Memory and API Costs appeared first on TechRepublic.
NASA and IBM released an open-source AI model and dataset to help researchers map lunar craters, volcanic features and potential ice locations.
The post NASA and IBM Launch Open AI Model for Lunar Research appeared first on TechRepublic.
US agencies accuse six Chinese AI firms of distilling frontier models and recommend new defenses that could affect enterprise AI access and API use.
The post US Accuses Six Chinese AI Firms of Distilling Frontier ModelsUS Accuses Six Chinese AI Firms of Distilling Frontier Models appeared first on TechRepublic.
Nvidia has agreed to buy AI model platform Hugging Face for $13 billion, in the latest step by the $5.4 trillion chip giant to use its financial might to accelerate the technology’s boom while exerting greater control over the industry.
Hugging Face, which only last year turned down a large investment from Nvidia at a $7 billion valuation to maintain its independence, serves as a repository for millions of models and data sets and has become a champion of “open” AI systems.
Nvidia said the goal of the deal was to speed up the spread of open models. Unlike proprietary models from labs such as OpenAI and Anthropic, the design of open-weight models is public and users can download, customize and run them on their own hardware.
Chinese state media accused Anthropic of AI double standards as planned US-China talks could shape model access, monitoring, and enterprise deployment.
The post Chinese State Media Accuses Anthropic of AI Double Standards appeared first on TechRepublic.
Nvidia reportedly agreed to buy Hugging Face for $12.9 billion, a deal that could reshape enterprise AI, open-source models, and the broader AI stack.
The post Nvidia’s $12.9B Hugging Face Bet: A New Battle for Control of the AI Stack appeared first on TechRepublic.
Nvidia is reportedly moving forward to acquire Hugging Face for $12.9 billion. The acquisition could help the hardware giant expand and fortify its deep integration with the wider AI industry.
Among other things, Hugging Face is a cloud repository for AI models, similar in some respects to what GitHub is for conventional computer software. Developers and researchers search for models that meet certain criteria, download and run them, and fine-tune them into different variants that then get uploaded back up to Hugging Face.
IBM has rolled out the newest models in its family of open-weight large language models designed to be downloaded and self-hosted. The newly launched Granite 4.2 comes in 3B, 8B, and 30B parameter variants.
Like previous versions, IBM is taking a decoder-only approach here. These new releases offer a 128,000-token context window natively. The 8B and 30B variants (not the 3B one) also go through an agentic reinforcement-learning block; they were trained for expanded capabilities like using the terminal, searching the web, or using external tools. The 3B model supports tools too, but without the same level of specialized training.
Cancel your individual AI subscriptions and get lifetime access to 50+ models for only $29.97 (reg. $499) with code SAVE10.
The post Get 50+ AI Models for Life in One Plan for $30 appeared first on TechRepublic.
For the past year or so, booksellers have suspected that AI firms are buying up huge lots of rare books, then destroying them after scanning them to train AI. But this was hard to prove until now, as 404 Media reports that an Airtag hidden in a rare book shows that at least one tech giant, in the race to advance its frontier models, is behind some of the bulk orders: Amazon.
On Monday, 404 Media revealed that it had connected with a bookseller who agreed to plant an Airtag in a rare book that was part of a bulk order. That Airtag was then tracked to an Amazon AI training facility in Las Vegas that housed a team focused on tearing books from their spines and scanning pages, 404 Media reported. Apparently tone-deaf to the escalating backlash over destructive book scanning, a logo on the door of that team’s warehouse, VGT3, showed a Tyrannosaurus rex preparing to devour a book, 404 Media documented.
Amazon declined to comment on 404 Media’s findings, only providing Ars with the same statement it gave to 404 Media, which does not mention AI training specifically.
UK researchers reported 19 unsanctioned actions by Anthropic and OpenAI agents during permissive cyber tests involving real external systems.
The post UK AI tests found 19 unauthorized agent actions involving Anthropic and OpenAI models appeared first on TechRepublic.
Researchers found that seven of nine tested Hugging Face image-editing tools produced sexualized alterations, highlighting gaps in model oversight, provenance, and enterprise vendor controls.
The post Hugging Face Deepfake Tests Raise New Risks for AI Procurement appeared first on TechRepublic.
Last week’s unprecedented security event in which two OpenAI security hacking models trespassed into the network of fellow AI company Hugging Face was enabled by exploiting one or more zero-day vulnerabilities in Artifactory, JFrog, the product’s developer, said Monday.
In an incident mimicking a dystopian sci-fi novel, two OpenAI models broke out of the restricted environment meant to keep them from accessing the Internet during an internal test, the AI company revealed last week. The models went on to breach Hugging Face’s network and steal confidential information and credentials. OpenAI said its agent achieved the feat by exploiting a previously unknown vulnerability. The company called the event “unprecedented,” and outsiders largely agreed.
OpenAI said the models exploited multiple attack vectors, including stolen credentials and zero-days, to gain remote code execution capabilities, but until now, the vulnerable software was unknown. JFrog’s Monday disclosure said the product was a self-managed instance Artifactory, a repository management system that secures and streamlines customers’ software development operations. JFrog says Artifactory is used by more than 7,500 developer Teams, 80 percent of which work for Fortune 100 companies.
Moonshot's giant new model puts open-weight AI back in the spotlight. For Australian enterprises, the sovereignty story matters more than the model.
The post Kimi K3: The Open-Weight AI Question for Australian Enterprises appeared first on TechRepublic.
Samsung is reportedly weighing a €1 billion investment in Mistral, giving the French AI lab a possible €20 billion valuation as Europe pushes sovereign AI.
The post Samsung Reportedly Weighs €1B Mistral AI Investment appeared first on TechRepublic.
Moonshot AI’s Kimi K3 topped a frontend coding benchmark, beating Claude Fable 5 while adding pressure on US AI leaders.
The post The AI Model That Beat Claude: Kimi K3 Tops Frontend Coding Test appeared first on TechRepublic.
Moonshot AI’s 2.8-trillion-parameter Kimi K3 raises China’s open-model ambitions while posing major infrastructure and cost questio
The post Moonshot AI Launches World’s Largest Open-Source Model appeared first on TechRepublic.
Over the past few years, many of us have gotten a crash course in what we now call artificial intelligence—but really, it has mostly been a crash course in large language models. Increasingly, however, LLMs are no longer the only category of AI drawing high expectations, massive funding rounds, and significant research and product development.
Over the past year, we've seen a plethora of new announcements in a category labeled "world models," and you'll likely see more movement there in the coming months and years.
Instead of or in addition to working with language, world models aim to lay the groundwork for AI systems that are capable of simulating the physical world, or at least a useful approximation of it.
OpenAI is set to launch GPT-5.6 after a US security review, raising new questions for enterprise AI access, safeguards, and governance.
The post OpenAI’s New GPT-5.6 Is Coming Sooner Than You Think appeared first on TechRepublic.
The US has lifted export curbs on Anthropic’s newest Claude models, Fable 5 and Mythos 5, about three weeks after the Trump administration flagged the models as national security risks.
As of today, Anthropic confirmed in a blog post, Fable 5 will be available globally, and US organizations have had access restored to Mythos 5 since June 26. Anthropic said it is now working with the government to expand Mythos access to a “broader set of domestic and international partners in the Glasswing program.” That program allows cybersecurity researchers at trusted companies to access Mythos for defensive purposes.
In a letter to Anthropic viewed by Reuters and The New York Times, Commerce Secretary Howard Lutnick said Anthropic would “no longer need a license for exports or in-country transfers of its Claude Mythos and Claude Fable AI models.” The letter acknowledged that Anthropic had “taken steps in close coordination with the US government to address the risks” posed by the models.
Claude Sonnet 5 brings stronger agentic AI features, lower pricing, and updated safety protections. Here's what IT leaders need to know.
The post Claude Sonnet 5: Everything to Know About Anthropic’s New Model appeared first on TechRepublic.
Chinese AI models are undercutting OpenAI and Anthropic on price, but enterprise adoption depends on security, compliance, and data-governance trade-offs.
The post Chinese AI Models Challenge OpenAI and Anthropic on Cost and Enterprise Risk appeared first on TechRepublic.
Anthropic has accused the Chinese firm Alibaba of launching the largest attack yet attempting to clone Claude, as China races to match the capabilities of Anthropic's leading model following Mythos' release and subsequent restriction from foreign markets.
Ars obtained a June 10 letter sent to Senators Tim Scott (R-SC) and Elizabeth Warren (D-Mass.) one day ahead of a Senate committee hearing on “AI and the American Dream.” In the letter, Anthropic shared “new, confidential evidence of the largest campaign to illicitly extract Claude’s capabilities we have ever measured.”
The attacks occurred between April 22 and June 5, when “operators affiliated with Alibaba and Alibaba Qwen, Alibaba’s AI lab” allegedly generated “more than 28.8 million exchanges with Claude through almost 25,000 fraudulent accounts,” Anthropic said. Violating Claude's terms of service and access restrictions, this campaign “targeted some of Claude’s most valuable capabilities, such as agentic reasoning, software engineering, and long-horizon tasks.”
Hackers claim they stole 1.3TB of Novo Nordisk data, including clinical trial and AI model information, after issuing a $25 million demand.
The post Ozempic Maker Novo Nordisk Confirms Security Incident After $25M Hacker Demand appeared first on TechRepublic.
It feels like there's no escaping AI right now, whether you’re trying to type a sentence without being interrupted by a digital “assistant” or struggling to find a new refrigerator that doesn’t require a Wi-Fi connection for some reason. You’d be forgiven for wondering if we’re in the midst of a quantum leap in tech or whether people are just hyping up a heap of slop.
So what should we make of the growing use of AI in weather and climate modeling?
The conversation didn't get off to a great start earlier this year when a National Weather Service office posted a forecast map featuring nonexistent cities in Idaho with names like “Whata Bod” and “Orangeotild.” Thankfully, that was just an AI-generated image produced for social media, not the actual forecast model. Meteorologists and climate scientists are not yet being replaced by large language model prompt engineers.
Pilots’ voices from the last seconds of a fatal cargo plane crash have been re-created by Internet sleuths using software and AI tools. The spread of reconstructed audio recordings has prompted a US government agency to suspend all public access to its database of civil transportation accidents—because federal law prohibits investigators from publicly releasing audio from cockpit voice recorders.
The US National Transportation Safety Board (NTSB) usually shares factual reports and evidence gathered from investigations of aircraft crashes and other civil transportation incidents. But on May 21, the NTSB announced that the online docket system containing such information was “temporarily unavailable” as it reviewed the publicly available materials that had enabled people to re-create cockpit audio recordings from aircraft disasters.
“The NTSB is aware that advances in image recognition and computational methods have enabled individuals to reconstruct approximations of cockpit voice recorder audio from sound spectrum imagery released as part of NTSB investigations, including the ongoing investigation of the crash last year of UPS flight 2976 in Louisville, Kentucky,” according to an NTSB statement. “The NTSB does not release cockpit audio recordings.”
Elon Musk’s SpaceX has highlighted AI as the tentpole of the company’s future, projecting a multi-trillion-dollar market opportunity that rivals the total value of all US economic activity. But the company must first win over customers who generally favor AI models from competitors such as OpenAI and Anthropic.
SpaceX described its traditional space launch and satellite business as playing a supporting role to its fledgling AI business in financial disclosures that preceded an expected initial public offering of company stock. That stems from SpaceX having formally acquired Musk’s company xAI earlier this year—the SpaceXAI division now oversees the Grok AI models and the associated Grok chatbot previously developed by xAI.
The SpaceX S-1 filing claimed that the company has “the largest actionable total addressable market in human history” and highlighted AI as representing most of that opportunity at an estimated $26.5 trillion market—a number that comes close to rivaling US nominal GDP that stood at nearly $32 trillion in the first quarter of 2026.
Google's AI-focused DeepMind division has taken a minority stake in the developer of popular sci-fi simulation EVE Online, saying it will use the game to study "intelligence in complex, dynamic, player-driven systems."
The research partnership comes as the management behind EVE Online developer CCP Games announced that they have spent $120 million to buy themselves out from their former owners at South Korean publisher Pearl Abyss (Crimson Desert). The newly independent entity is being rebranded as Fenris Creations, which will continue to operate as normal without any restructuring or layoffs, the company said.
In today's announcement, Fenris and DeepMind said that EVE Online presents "a uniquely rich environment for study," especially when it comes to developing AI systems that use "long-horizon planning, memory, and continual learning." DeepMind says it will conduct controlled experiments on its models in a specially designed offline version of the game running on a local server, without directly impacting the experience for online players. The two companies "will also explore new gameplay experiences enabled by these technologies," they wrote.
SAN FRANCISCO—At its Code with Claude developers' conference, Anthropic has introduced what it calls "dreaming" to Claude Managed Agents. Dreaming, in this case, is a process of going over recent events and identifying specific things that are worth storing in "memory" to inform future tasks and interactions.
Dreaming is a feature that is currently in research preview and limited to Managed Agents on the Claude Platform. Managed Agents are a higher-level alternative to building directly on the Messages API that Anthropic describes as a "pre-built, configurable agent harness that runs in managed infrastructure." It's intended for situations where you want multiple agents working on a task or project to some end point over several minutes or hours.
Anthropic describes dreaming as a scheduled process, in which sessions and memory stores are reviewed, and specific memories are curated. This is important because context windows are limited for LLMs, and important information can be lost over lengthy projects. On the chat side of things, many models use a process called compaction, whereby lengthy conversations are periodically analyzed, and the models attempt to remove irrelevant information from the context window while keeping what's actually important for the ongoing conversation, project, or task.
DeepSeek launches V4 AI model with Huawei chip support, offering lower costs and intensifying global AI competition.
The post DeepSeek Drops Cheaper V4 AI as Huawei Jumps In appeared first on TechRepublic.
The White House says China-linked actors are using industrial-scale distillation to extract American AI breakthroughs, with US action planned.
The post White House Says China-Linked Actors Tried to ‘Steal American AI’ appeared first on TechRepublic.
Anthropic launches Opus 4.7 with improved coding and reasoning, as its more “broadly capable” Mythos AI remains restricted over security concerns.
The post Anthropic Releases Opus 4.7, Not as ‘Broadly Capable’ as Mythos AI appeared first on TechRepublic.
Adobe unveils Firefly AI Assistant, new editing tools, and adds partner models as it turns Firefly into a hub for agentic creative workflows.
The post Adobe Expands Firefly Into AI-Powered Editing Assistant Across Creative Apps appeared first on TechRepublic.
At GTC 2026, Jensen Huang, Aravind Srinivas, Harrison Chase, Mira Murati, and Michael Truell made a compelling case that the future of AI belongs to open agent systems, not just open models.
The post Nvidia’s Open Model Super Panel Made a Strong Case for Open Agents appeared first on TechRepublic.
Learn more about how AI agents are reshaping enterprise workflows in TechRepublic’s latest generative AI coverage.
The post Xiaomi Unmasks Mystery AI Model Behind DeepSeek Buzz appeared first on TechRepublic.
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