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Microsoft’s Xbox division will eliminate around 3,200 roles during FY27, as 1,600 jobs were cut immediately.
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Axis Max Life has analysed over 6 lakh customer calls using voice AI software to identify high-intent customers and reshape its sales strategy.
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The centre will support innovation across brands, including Kwality Wall's, Cornetto, Magnum, Carte D'Or, Feast, and Twister
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GalaxEye says the maiden OptoSAR mission validated key systems and plans to launch two new OptoSAR satellites within the next 24 months.
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The project is expected to generate around 500 direct employment opportunities.
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Despite the strong earnings guidance, Samsung’s stock dropped as much as 7.9% in early trading.
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The DRHP filing follows reports that Eternal-backed Cult.fit was targeting an IPO fundraise of ₹3,500 crore to ₹4,000 crore at an estimated valuation of around $2 billion.
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Neo will roll out Tasket for AI-led task delegation, Friday for AI assistants and agents, Studio for knowledge management, and Drive for human-agent file collaboration.
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South Korean memory chipmaker SK Hynix, rival to Samsung and U.S.-basedMicron, is planning to sell nearly 17.8 million shares in a U.S. IPO, the companysaid on Monday. Should its shares sell well (and there’s indication that they will), the company could raise around $28 billion, based on SK Hynix’s closing share price last Friday in Seoul,Bloomberg reports. SK Hynix will be offering American depositary receipts (ADRs), a type of certificate that lets U.S. investors buy a foreign stock without trading directly on an overseas exchange. Each ADR will represent a tenth of a common share. It is expected to price those securities on Thursday and begin trading on Friday. Like Micron, SK Hynix is riding an AI-fueled boom credited to AI in both sales and stock price. Its first-quarter revenues were up nearly 200% over the same quarter last year, it said, and its stock is up about 260% so far this year. This is because systems that run AI are very memory intensive. As hyperscalers like Amazon, Microsoft, Google, and Oracle race to build out so-called AI factories, and as new AI data centers multiply nationwide, demand has outpaced supply, creating a shortage of memory chips — including high-bandwidth memory (HBM), DRAM, and NAND (the different types of chips that store and move data inside AI systems). The situation has been called “RAMageddon.” Apple executives said the shortage is forcing it to raiseprices on Mac computers and iPads. South Korean tech companies, led by SK Hynix and Samsung, have vowedto spend over $550 billionon building out new manufacturing capacity to keep up. That’s actually a risky venture. By the time those facilities are built, memory needs for AI may change, leaving them with more supply than the market wants and, potentially, crashing prices. But for now, Wall Street is looking for another Nvidia, andmemory chipmakers are among the closest optionsthat they have. Micron, the closest U.S. comparison, has shot up nearly 700% over the past year to a more than $1 trillion valuation, fueled by record AI-driven memory demand and revenue.
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Last week, researchers at cloud security firm Sysdig said they’d documented the first known case of “agentic ransomware.” It was an extortion operation, dubbed JadePuffer, in which an AI agent — not a human — handled the technical execution of a real-world cyberattack from start to finish. The agent broke into a vulnerable server, stole credentials, moved through the target’s network, encrypted files, and even wrote its own ransom note, adapting to obstacles along the way like a human hacker would. Coverage of the funding described it as run “without any human oversight,” with “no human at the keyboard.” That’s not quite thefullpicture. In aninterviewon Monday with CyberScoop, Sysdig’s Michael Clark, the company’s senior director of threat research, clarified that a human was still very much involved — just not in the technical execution. “A human still set up and pointed the operation and provisioned the infrastructure behind it, the command-and-control server, the staging server used for the stolen data and chose a victim,” Clark said. The credentials used to break into the victim’s database, he added, weren’t harvested by the AI agent itself; someone obtained them separately, through a prior compromise, and handed them to the operation. None of this contradicts Sysdig’s original claim, and the technical details of the attack remain notable on their own — wild, even. The agent got in through a known bug inLangflow, a popular open-source tool for building LLM apps, then moved on to a production MySQL server and exploited another known flaw to gain admin access. It encrypted over 1,300 configuration records and not only left behind a ransom note that it wrote itself but it left a Bitcoin address where the ransom could be sent. Sysdig hasn’t disclosed who was targeted. The techniques were fairly ordinary apparently, what stood out was the speed and transparency involved. The agent fixed a failed login in 31 seconds, narrating its own reasoning in natural-language code comments the whole way. One detail that initially seemed to muddy the picture has since been clarified. Clark had told CyberScoop that Sysdig found “multiple models were used in the attack,” citing harvested keys for OpenAI, Anthropic, DeepSeek, and Gemini — language that left open the question of whether several models actively powered different stages of the intrusion. Asked to clarify, Clark told TechCrunch that those keys were simply part of what the agent stole, not evidence of what was driving it. “The agent swept the Langflow host for anything valuable — provider API keys, cloud credentials, cryptocurrency wallets, and database configs — and those provider keys were part of the loot,” he said via email. “They are indicative of what the attacker considered worth taking, but they do not tell us which model was making the decisions.” On the model actually running JadePuffer, Clark said Sysdig “was not able to identify the specific model driving the agent” and has no visibility into its system prompt or configuration. Microsoft researcher Geoff McDonald’s theory,offered on LinkedInseveral days ago, is worth revisiting in that light. McDonald suspected an open-weight model with safety training stripped out, rather than a frontier model, was behind the attack, based on his own red-teaming experience showing frontier labs’ safety layers hold up well. Sysdig’s own account doesn’t confirm or rule that out. McDonald’s post also warned that ransomware campaigns are now bounded primarily by attacker budget rather than human effort, raising the possibility of “thousands or tens of thousands of simultaneous campaigns.” That concern is a little harder to square with what Clark described Monday. (If a human still has to choose each victim, provision infrastructure, and obtain database credentials for every operation, that’s a bit of a bottleneck, at least.) Either way, Clark told CyberScoop, while Sysdig hasn’t seen the same operation hit other victims yet, given how cheap it is to run an agent, he expects that to change.
With the latest iOS 27 developer beta, Apple is giving testers an early look at one of the upcoming improvements toits AI-powered Siri: the ability to adjust how quickly and expressively the AI assistant speaks. In iOS 27 beta 3, out today, Apple has enabled the voice controls for “Pace” and “Expressivity” that were previously labeled as “Coming soon” in the first developer beta releases. The update is part of Apple’s broader effort to make Siri feel more natural and personal, as it rebuilds the assistant around generative AI. Like ChatGPT and others offering voice AI assistants, letting users customize how the AI sounds is an important aspect in helping connect people with the new technology. However, ChatGPT’s voice-customization options allow users to go even further, as the ability to adjust the AI’s warmth and enthusiasmwasrolledout in December 2025, alongside options to configure the base style and tone. The latter lets users adjust OpenAI’s assistant to be more friendly, professional, candid, or quirky, among other styles. This is reflected not only in how ChatGPT speaks, but also in how it presents information to the user. You can customize Siri’s voice in iOS 27 beta 3pic.twitter.com/qFV8L1e7di First introduced atApple’s Worldwide Developers Conference(WWDC 26) in June, Siri’s voice controls let users personalize their Siri experience beyond just choosing a male- or female-sounding assistant. Now beta testers will be able to switch between a range of voices with different accents, and then use sliders to change how slowly or quickly Siri speaks and how much human-like emotion its voice conveys. As you make the adjustments, Siri will practice saying some common things, like “You have one new message,” so you can get a sense of how the different voices sound. The AI version of Siri is deeply integrated across the updated version of iOS, where it will allow iPhone owners to start conversations by speaking, swiping down from the Dynamic Island at the top of the screen and typing, tapping on the phone’s side button, or even by using the brand-new stand-alone Siri app. Other, more minor updates are also rolling out with iOS 27 beta 3, including anupdatedReminders app icon. (We should note some people on X are also reporting losing access to the new Siri after updating, or seeing their phone again begin indexing their data — typically, the first step in optimizing Siri AI for search.)
Known for its cloud infrastructure that allows developers to deploy agents without managing servers,Vercelhas quietly become one of the most central companies in AI software. The company currently sees 6 million deployments a day, half of them triggered by coding agents, and more than 1 trillion tokens flow throughthe company’s AI gatewaydaily. After the company’s ShipNYC conference last week, we sat down with Vercel CEO Guillermo Rauch for his take on this moment in AI, and how platform companies like Vercel end up competing with major labs. Here’s a lightly edited transcript. It feels like there’s a different energy in the community this year, fewer pilot programs and more focus on how to make things work well in practice. I’m sure you’ve seen that a lot with clients, but I’m curious what that journey has looked like within Vercel. Last year was about prototyping. The sky’s the limit, unleash the agents, everyone can build, and so on. We did that, and we learned a lot because we had hundreds of agents organically developed and deployed within the company, and then you started getting into the realities of agents in production, and some of the challenges. The biggest lesson for me was the home-run use cases, the two killer apps of agents. One is the coding agent, of course. That’s driving a lot of the token utilization in the world, but when you produce so much software, you need somewhere to put it. The second killer app of agents is the internal agent that helps you run the company. The challenge there is, how do you securely access data? How do you audit what the agent is doing? How do you get a trail of all of the tool calls and access controls that the agent had to incur in order to get a job done? To solve that, we came up with this framework called Eve, where you can lay out an agents’ instructions and skills in natural language. And another tool is Vercel Sandbox, where you put the agent in a little cage. It can have the freedom still to express its intelligence, but then you can apply policy on what data it can access and what data can leave the sandbox. What sort of problems does that help you avoid? For [the] sandbox, the biggest advantage is data control. A real risk of AI that I always think about is, when you get a coding IDE like Devin or Cursor, if you’re in the wrong setting, they may train on your entire codebase. I remember talking to the president of Airbus about this. You have decades of wealth of very specific C++ code for aerospace engineering. Someone comes in and installs the wrong developer tool and boom, all the code goes out to the cloud for training. I’m curious to hear more about that second killer use case. We all know about coding agents, but what does an internal corporate agent look like in practice? So, there’s a sales rep sitting out there [in Vercel’s office]. She works on install base. Her job is to grow existing accounts. The bottleneck for people like her has not been her creativity, intelligence, ability to build relationships, it’s been data. “I don’t understand what accounts are growing faster. Give me the five accounts that have added the most seats in the last two weeks, so that I can prioritize my work.” She couldn’t ask that question in the past. She needed to wait until a Q1 project for a new sales dashboard completed. We were in that bottleneck for years at Vercel, and it was really frustrating because on the R&D side, we’re the fastest-moving company in the world. But on the sales engine, the Salesforce engineering [side], I was so incompetent. I had never opened Salesforce in my life when I started. Now I feel like I can actually have impact across the entire company, because Eve can be used for our customer-facing agents and can be used to improve productivity. Same technology, it’s just APIs. Agents are forcing companies to open up, and that will have dramatic long-term implications. So many of these SaaS giants build their entire kingdoms on trapping your data, and that’s incompatible with agents. How do you see client relationships with the big AI labs changing? Last year there were a lot of people picking one lab partner — saying they would build everything on OpenAI or Anthropic. Now they’re saying, I understand how this all works — model, harness, data platform, sandbox, gateway — every piece is plug and play. You can use OpenAI, you can use Anthropic, or you can use Gemini. We’re seeing a lot of growth of Gemini, even though it’s not on the news as much, because people are optimizing for production now. The reality is, when you’re optimizing for production, you start looking at a price/performance, and Gemini models have awesome price/performance characteristics. You also bring in open models, so DeepSeek and GLM-5.2 are taking off. The data doesn’t lie. There are places where you’re in direct competition with the labs too, right? Just the other week, OpenAI released a new set of tools that publish directly to the web without having to leave the OpenAI enclave. It’s a natural next step for them to host little websites. And it’s a great opening for us, because now people will think of ChatGPT as a tool for making websites. And then if they keep asking the model questions about web hosting, the model recommends us. But you’re right, as the models or platforms add more capabilities, they come in direct competition with the infrastructure platforms that already exist. I really think at this point we’re deciding on whether the model and the agent are going to be coupled. Do you get all your intelligence from one place? Or do you get a module or a library or a building block from one provider, and then you build on top of it. That’s more like software engineering has always been, and that’s really what we’re bringing to market. We’re going to be the AWS of this generation, so obviously we’re fighting for a world of open protocols.
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