Apple Patent Reveals Home Robot With Built-In Display

Apple is moving quickly into the smart home market, and Bloomberg’s Mark Gurman says the company has big plans that go well beyond smart speakers. In a new report, Gurman outlines Apple’s goals for AI-powered robots, advanced security systems, and home companions with personality that might change how we use technology at home.  

The Star of the Show: Apple’s 2027 Tabletop Robot 

Apple’s main smart home project is a tabletop robot planned for release in 2027. Jennifer Pattison Tuohy from The Verge explained on Tech News Weekly that this device goes beyond typical smart displays. She described it as resembling an iPad mounted on a movable arm that can swivel and reposition itself, allowing it to track and follow users’ movements in a room, much like a human head.  

This device functions as more than just a screen. The Bloomberg report notes it can turn to face whoever is speaking and is designed to make eye contact with people who are not looking at it, enhancing user engagement. It acts as a virtual companion, managing FaceTime calls and video chats, with the display automatically tracking users as they move around the room.  

One of the most interesting features is that the device will have its own personality. German reports say it could interrupt conversations between friends about dinner plans and suggest nearby restaurants or relevant recipes. This active step differs from the typical command-and-response style of today’s voice assistants.  

When Personality Blends Practicability 

The idea of a device with real personality led to a lively discussion on Tech News Weekly. Mikah Sargent pointed out a situation many people might experience: “When I walk up to this thing, it’s, you know, moving its head back and forth. And then if you were to walk into a room, the thing would just go into this mode of only doing what it’s told to do.”  

Personalization might be the key to getting people to use these devices. As Patterson said, there’s a fine line between an endearing personality and annoyance. The fine line is really determined by functionality. The main question is whether users will find the interruptions helpful or annoying. Some people might like getting restaurant suggestions while planning dinner, but others would see it as too much.  

A Complete Smart Home Ecosystem 

Apple’s plans go beyond the tabletop robot. The Bloomberg report describes a smart home security system with battery-powered cameras. These could last several months to a year on a single charge. Patterson to Hoyt was skeptical, saying there are no battery-powered cameras that can currently do that. The cameras would reportedly feature facial recognition to distinguish between household members and potential intruders. The system could also automate password functions, using cameras and infrared sensors to detect occupancy and activity in the home.  

A new smart display is also expected next spring, using what Apple calls its LLM series. This signals a major shift in how Apple handles voice assistants. As Patterson Tuohy explained, Apple, like Amazon, had to do something short of stripping away the old Siri and building a whole new Siri.  

The Multi-User Challenge 

A major technical advance mentioned in the report is real multi-user support. The new operating system, reportedly called Charismatic, would change depending on who was looking at the device. This could make Apple’s smart home products some of the first to truly support multiple users, solving a problem many smart home devices have had for years.  

Market Implications And User Adoption 

The success of Apple’s smart home will likely depend on finding the right balance between usefulness and temperament. As Pattison Tuohy said, I think there isn’t the demand for it yet for AI companions. But she also noted that loneliness is real and that some people already use voice assistants for company. The robot suggests Apple is taking a measured approach, likely ensuring the technology and user experience meet its standards before launch. This extended development timeline also accounts for competitive response and market education.  

Apple seems to be focusing on personality and a smooth, intuitive design to stand out, rather than just competing on features or price. It’s still unclear whether people will like devices that interrupt conversations, but Apple’s history suggests it often spotlights user needs before the market does.  

The Bloomberg report suggests a time when Apple devices do more than just respond to commands. They take an active role in daily life at home. Whether people find this useful or inclusive can shape the future of smart home design for everyone.

Source: Apple’s Rumored Smart Home AI-Powered Expansion 

Google Expands Personal AI Features Across Gmail and Docs

Google has introduced new personal intelligence AI features powered by its Gemini models. These updates integrate user information from Gmail, Docs, Photos, and Search to deliver more tailored assistance. Announced in early 2026, the enhancements include an AI overhaul in Gmail, a new ping feature, and improved connectivity with Docs and Sheets for all users.  

Here are the main updates to Google’s personal AI features.  

Gmail, AI, Enhancements 

  • AI Overviews Search: users can search their inbox by asking questions in plain language, such as “Who was the plumber that gave me a quote for the bathroom renovation last year?” Google will find and summarize the relevant emails.  
  • AI inbox: This optional inbox view organizes your emails into concise, personalized summaries. It spotlights urgent tasks and filters out trivial messages.  
  • Help me write and suggest replies: these tools allow the AI to draft or suggest personalized email responses. They are now available to all users for free.  
  • The premium tool checks grammar, tone, and organization much like other editing services.  

Google Docs And Workspace Enhancements 

  • Document Drafting: Gemini can create documents from your files and emails, like turning meeting notes into a newsletter in a doc.  
  • Contextual editing: tools such as match writing style and match doc format help adjust mood and layout to match another file.  
  • Sheets and Drive. In Sheets, the Fill with Gemini feature automatically generates tables with structured or summarized data from the web. In Drive, the AI extracts key information from multiple documents.  

Personalization And Privacy 

  • Opt-in Personal Intelligence. Users can connect Gmail, Photos, and YouTube to their AI. This provides personalized suggestions, such as a custom travel plan based on hotel bookings and vacation details.  
  • Privacy protections: Google says that your personal data will not be used to train the main Gemini models.  
  • Availability: these features will launch for English-speaking users in the US who are paid Google AI Pro and Ultra subscribers in this region, with access before a wider rollout.  

Today, 3 billion people use Gmail to stay connected and get their work done. AI has played a key role in this, from smart replies to advanced spam-blocking technology.  

Email has changed a lot since Gmail started in 2004. With more emails than ever, keeping your inbox organized is just as important as the messages themselves. To help you manage your inbox more easily, we are updating Gmail with Gemini, your personal inbox assistant that seamlessly introduces new features.  

Ask your Inbox anything: AI Overviews 

Your inbox is full of important information, but locating it can require searching through numerous emails. Even after finding the right message, reading through everything for answers can be time-consuming. That’s why we’re bringing you AI overviews.  

AI overviews work in Gmail just like they do in Google Search, turning information into answers without extra effort. When you open an email conversation with many replies, Gmail summarizes the whole conversation into key points.  

When you ask a question in your inbox, Gemini provides a simple AI overview with the answer. Instead of searching for keywords or digging through old emails, you can ask in plain language, such as “Who was the plumber that gave me a code for the bathroom renovation last year?” Google finds the answer and gives you the details you need.  

Starting today, everyone can use AI overview conversation summaries in Gmail for free. The ability to ask your inbox questions with AI overviews is only available to Google AI Pro and Ultra subscribers.  

Get Things Done Faster: Help Me Write 

Starting today, everyone can use Help Me Write to improve emails or start new ones, and the new suggested replies update Smart Replies by using your conversations’ context to offer quick, relevant responses that match how you write.  

For example, if you are planning a family gathering and your aunt asks if she should bring cake instead of pie, you can quickly compose a reply in your own style, which you can edit before sending. Proofreading also checks grammar, tone, and style so your emails are polished.   

Help me write, and suggested replies are available to everyone for free. The proofread feature, which checks grammar, tone, and style, is available only to Google AI Pro and Ultra subscribers.  

Next month, we’ll update Help Be Right to offer better personalization by using information from your other Google Apps.  

See What Matters Most: AI Inbox 

Your inbox gets a lot of updates. Some are important while others are just clutter. The new AI inbox helps you focus on what matters by filtering out the noise.  

Trusted testers will get access to the AI inbox first, and it will become available to more users in the coming months. Availability details for all users will be announced as the rollout progresses.  

Subscribe To The New Gmail Today 

Gemini 3 makes many of these improvements possible. These new features are rolling out today in the US for Gmail users and Google AI Pro and Ultra subscribers. We’re starting with English and will add more languages and regions soon.

Source: Gmail is entering the Gemini era 

OpenAI Rolls Out GPT-5.4 Mini With Agent Mode for GitHub Copilot 

GitHub Copilot now supports OpenAI’s latest coding model, GPT-5.4, designed for complex development tasks and advanced problem-solving.  

GPT 5.4 launched shortly after Anthropic released Cloud Opus 4.6. Both are available to Co-Pilot subscribers on the Pro, Pro+, Business, and Enterprise plans.  

A major difference: context size GPT 5.4 supports up to 400 tokens, while Cloud OPS 4.6 supports up to 192k. This allows Cloud OPS 4.6 to handle larger codebases more efficiently.  

Open UI’s new fast version, GPT‑5.4, is now available in GitHub Co‑Pilot. Early tests show it’s their best mini model so far. It responds quickly, explores codebases well, and works especially well with GREP‑style tools.  

Keep in mind this model starts with a 0.33x premium request multiplier, but pricing may change.  

Availability in GitHub Copilot 

GPT-5.4 Mini is available to Copilot Pro, Pro +, Business, and Enterprise users. You will be able to select the model in the Model Picker in Visual Studio Code, and later in all modes: chat, ask, edit, and agent.  

  • Visual Studio versions and later in all modes: agent, ask.  
  • JetBrains versions and later in all models: ask, edit, agent  
  • Xcode versions and later in all models: ask, agent  
  • Eclipse versions and later in all modes: ask, agent.  
  • Github.com  
  • GitHub, Mobile, iOS, and Android.  
  • GitHub, CLI.  

Although GPT-5.4 Mini is available in all the versions listed above, it works best with the latest versions. We recommend upgrading to get the best experience.  

Enabling access. 

For administrative and business plans, administrators must enable the GPT-5.4 Mini policy in Copilot settings. After that, users in the organization will see GPT-5.4 Mini in the model picker in Visual Studio.  

If you are using your own API key, go to Manage Models in the Picker, select GPT-5.4 Mini, and enter your API key when asked.  

Learn more: to see all the models you can use in GitHub Co-Pilot, check out our documentation, and get started.  

GitHub Co‑Pilot with GPT 5.4 is compatible with numerous development environments, including Visual Studio Code, Visual Pro, JetBrains IDEs, Xcode, Eclipse, GitHub.com, GitHub Mobile, and GitHub CLI.  

GitHub Copilot is Microsoft’s AI-powered coding assistant. It uses large language models (LLMs) within code editors to suggest code, explain snippets, and help with common programming tasks directly as you work.  

GitHub Copilot functions as an autocomplete tool for coding. It analyzes your code and suggests the next line or even entire functions. Some developers use it to build complete applications with minimal human input, a trend known as “wide coding,” rather than debating or explaining the code, and focus on presenting these developments, as AI controversy can be exhausting regardless of personal opinions.  

AI coding tools are evolving rapidly, and they’re becoming part of developers’ work. Whether we like it or not, some people use them all the time while others stick to traditional coding.  

GitHub is making a major leap by releasing OpenAI 5.4 to all 12 Copilot users, introducing powerful computer control and a 1 million–token context window.  

What Sets GPT 5.4 Apart 

This update is more than a small step forward. GPT 5.4 introduces autonomous computer control, enabling the model to simulate mouse and keyboard actions across different applications without human intervention. Now, Copilot can manage multi-step work rules that are used to require manual switching between tools.  

The 1 million-token context vendor lets developers include entire codebases in conversations. GitHub internal tests show that GPT 5.4 is achieving new levels of success on real-world development tasks, especially those with complex multi-step processes.  

OpenAI’s benchmarks show GPT-5.4 Mini has 33% fewer factual errors than GPT-5.2 and outperformed professionals in 83% of benchmarked tasks, up from 70.9% in the prior version.  

How To Access GPT 5.4 

Individual Pro and Pro+ users get immediate access via the model Pika. Enterprise and business administrators need to manually integrate GPT-5.4 in their corporate policy settings before team members can use it.  

GitHub recommends upgrading to the latest IDE versions to ensure full compatibility and access to prompt tuning capabilities. Using outdated versions may limit your experience: the model’s interface may appear, while newer features do not function as intended.  

Looking At The Bigger Picture. 

GPT-5.4 Mini launches with two versions: Thinking, optimized for step-by-step reasoning, and Pro Design, for enterprise production workloads. The Thinking version allows users to outline their reasoning at the start and provide input during a response, offering benefits for debugging complex logic chains.  

OpenAI will retire GPT 5.2 on June 5, 2026, giving teams about 3 months to update their procedures. Developers who rely on Copilot agents should start testing GPT 5.4 now to identify any prompt adjustments needed before it is rolled out. the sweet  

Have you begun integrating tools like GitHub and Copilot into your workflow, or do you still prefer traditional development methods? 

Sources: GitHub Copilot Adds GPT-5.4 with Native Computer Control for Devs 

GitHub Copilot unlocks OpenAI’s GPT-5.4 in VS Code and other coding platforms — Adding even more vibe coding options

Samsung Debuts HBM 4E Memory for Nvidia AI Servers, Newsroom Confirms

Samsung Electronics unveiled its 7th-generation high-bandwidth HBM4E at NVIDIA GTX 2026 in San Jose on Monday and plans to ship samples by mid-2026.  

This marks Samsung’s first demonstration of its next-generation memory technology before mass production, strengthening its position as a supplier of advanced memory AI systems.  

According to Samsung, its HBM4E chips each support a read data rate of 16 Gbps per pin. This is 36% faster than the previous generation’s 11 Gbps and twice as fast as the current industry standard of 8 Gbps. The chips also offer 4 TB/s of bandwidth, the maximum rate at which the memory can transfer data.  

Samsung expects to deliver samples of the standard HBM4E products to customers in mid-2026. Custom versions of the chips, designed for specific processors and called application-specific integrated circuits (ASIC) solutions, are set to begin initial VFAP production on several projects in the second half of the year.  

Samsung also confirmed that its 6th-generation HBM4 memory, which is distinct from the neural-oriented HBM4E, will be used in Vera Rubin and Media’s AI computing platform. Mass production of HBM4 (not HBM4E) began in February.  

The HBM4 uses advanced DRAM and logic processes for memory control.  

The sixth-generation chips reach 11.7 Gbps and may be further optimized.  

Samsung demonstrated hybrid copper bonding, a new packaging technology that improves die-to-die connectivity and heat dissipation methods compared to current methods.  

Samsung’s booth featured a special media gallery to highlight its supplier partnership. In addition to HBM4, Samsung displayed several products for AI infrastructure, including SoCAMM2, non-power-server DRAM modules, and the PM1763 solid‑state drive.  

The company also introduced LPDDR5X and LPDDR6 mobile memory for AI workloads on personal devices. LPDDR5X reaches speeds up to 25 GB/s and reduces power use by up to 15%. LPDDR6 increases memory bandwidth to 30–35 Gbps.  

For a hands-on experience and to learn more about Samsung’s innovative AI solutions, we invite you to visit booth 1207 at GTC 2026.  

The highlight of Samsung’s display at NVIDIA GTC 2026 is the new 6th-generation HBM4, now in mass production and made for NVIDIA. Variable-bin platform. HBM4 is set to speed up future AI applications, offering steady data rates of 11.6 gigabits per second (Gbps) above the industry standard of 8 Gbps, and can be boosted to 13 Gbps.  

Samsung’s advanced 10nm-class DRAM process powers HBM4 and the next-generation HBM4E, delivering top-tier quality and performance. Both products will be on display at GTC 2026.  

Samsung will showcase hybrid copper bonding technology, which enables next-generation HPM to scale to more layers and lowers heat resistance.  

An Alliance Taking the AI Era to a New Level 

Samsung and NVIDIA’s close partnership will be featured in a special NVIDIA gallery at the booth. The gallery showcases a wide range of Samsung’s latest technologies, including HBM4, SoCAMM2, and the PM1763 SSD, in our design for in-air infrastructure.  

Samsung’s SoCAMM2, now in mass production, is the industry’s first low-power DRAM server memory model, delivering efficient, scalable AI system performance with high bandwidth and flexible integration for next-gen infrastructure.  

The PM1763 SSD leverages PCIe 6.0 for fast data transfers and capacity demonstrated on servers using NVIDIA’s programming model.  

Samsung’s PM1763 SSD is part of the new NVIDIA BlueField-4 STX Reference Architecture, a blueprint for building AI-optimized storage systems to deliver faster storage on the NVIDIA Vera Rubin platform. It will demonstrate how it improves energy efficiency and system performance for inference workloads tasks where AI systems generate outputs based on data.  

Memory Architecture To Scale, Intelligent Manufacturing 

At GTC 2026, Samsung will showcase its work with NVIDIA on developing an AI factory and an automated, AI-assisted chip manufacturing system. This includes plans to use NVIDIA-accelerated computing and specialized processing for AI tasks to expand Samsung’s AI factory and speed up digital-twin manufacturing, where digital copies of factories are used for simulation with NVIDIA libraries and tools for virtual collaboration. Together, they support one of the world’s most complete chip manufacturing systems covering memory, logic, foundry, and advanced packaging.  

Yong Ho Song, Executive Vice President at Samsung Electronics, will discuss the company’s strategic partnership and its AI and digital twin initiatives in transforming semiconductor manufacturing in his GTC 2026 talk.  

Effective Memory For Local Intelligence 

Samsung’s memory solutions also enable high-efficiency local AI on personal devices. At GTC 2026, Samsung will present customized and efficient options for personal AI supercomputers, including the PM9E3 and PM9E1. NAND types of flash memory for Nvidia DJX Spark  

Samsung will also showcase DRAM (Dynamic Random Access Memory) Solutions, including LPDDR5X and LPDDR6, low-power DDR (double data rate) memory designed for easy use in smartphones, tablets, and other devices. These offer faster data speeds and lower latency. LPDDR5X reaches up to 25 Gbps and reduces power use by up to 15%, enabling fast mobile experiences, high-resolution gaming, and AI features without draining the battery.  

Building on this, LPDDR6 increases bandwidth to a scalable 32–35 Gbps per pin and adds advanced power-management features, including productive voltage scaling and dynamic refresh control. These features deliver the performance required for upcoming edge AI workloads. 

Source: 

Samsung Unveils HBM4E, Showcasing Comprehensive AI Solutions, NVIDIA Partnership and Vision at NVIDIA GTC 2026

Samsung unveils HBM4E at Nvidia GTC, raises bar for AI memory

Apple Patent Suggests Future iPads Could Combine Multiple Chips for More Power

Apple is exploring technology that would enable future iPads to interconnect multiple silicon chips, significantly enhancing computational throughput, according to recent patents.  

Key Findings from Apple’s Patent Activity: 

  • First, a patent for an optical communication bar reveals a modular photonic interconnect system. Here, data travels between chips or electronic assemblies via light rather than electrical wiring, enabling high bandwidth and low latency.  
  • Another related innovation is the patent for modular multiple-display electronic devices. This concept allows two iPads or other mobile devices to connect via an accessory, functioning as a single, more powerful computer in which one device serves as a display while the other serves as a digital keyboard or mouse, sharing computing resources.  
  • Looking further ahead, analysts indicate that Apple plans to work with Intel on future M-series chip production. Starting around 2027, the collaboration would use Intel’s Foveros (Direct 3D) stacking method to vertically bond chip components, aiming to boost performance and power efficiency.  
  • In addition, a separate patent envisions joining two iPads with a hinge to form a notebook-style device, allowing both iPads to function together and greatly enhancing the platform’s Pro features.  

These patent activities collectively highlight Apple’s clear focus on modular silicon approaches to realize more powerful, interconnected mobile devices.  

Apple is looking at an accessory that could let two iPads connect and work together like a notebook computer, according to a new patent filing reported by AppleInsider.  

Apple filed a patent application titled “Modular Multiple Display Electronic Devices.” Today, with the US Patent and Trademark Office, the filing describes how two iPads or phones could connect with an accessory. In this setup, one device could serve as a display while the other acts as a dynamic keyboard.  

The patent describes an accessory that uses two small connectors and a hinge, letting mobile devices attach on either side. This connector allows the devices to transfer data and work together as a single system.  

Patent images show that the accessory can create a notebook-style setup. One device lies flat on a surface while the hinged connector at the back props up a second device. Users can set up the devices in both portrait and landscape positions.  

Using a second display as a keyboard: a dynamic keyboard would allow the device to serve as both an input surface and a customizable interface, adapting its functions to what the user is doing much like the MacBook Pro Touch Bar. However, compared to a traditional keyboard, this display would likely lack the depth and tactile feedback that a dedicated physical keyboard provides.  

The patent also suggests that attaching two devices along their longest edges can create a book-style arrangement similar to the Microsoft Surface Duo’s design.  

Apple has filed many patents for second-screen devices, including one called “System with Multiple Electronic Devices.” This patent describes how two or more devices can act as a single device when they are close together, using proximity sensors. While many Apple patents never become products, they often reveal what the company is working on.

Source:  Apple Patent Suggests Two iPads Could Be Connected Together for Notebook-Style Computing 

Apple Explores Liquid Glass UI for iOS 27, Bloomberg Mark Gurman Reports

To summarize 

  • Macworld says Apple will keep the liquid glass design from iOS 26, even though some people think it is unpopular. Bloomberg’s Mark Gurman says adoption rates are actually high.  
  • IOS 27 will be designed for the new iPhone 4’s bigger design. It will support split-screen apps and offer better video and gaming experiences.  
  • Apple plans to add a system-wide liquid glass slider in iOS 27. This will let users control the visual effect, but the design will still be a main feature.  

Last year, Apple updated all its operating systems macOS 26, watchOS 26, visionOS 26, and iOS 26  with a major design change. The new “liquid glass” look added glass-like translucency and animations to every device. While not everyone liked it, a new report says Apple will keep liquid glass for now.  

Building on this, in his latest Power On newsletter, Bloomberg reporter Mark Gurman counters claims of liquid glass’s unpopularity.  

The idea that iOS 26 and Liquid Glass represent a crisis for Apple or constitute an unforgivable offense against good design that customers around the world despise is greatly overblown. He writes that the vast majority of users appear happy with the update, and the adoption rate has steadily climbed.  

He also says that liquid glass wasn’t merely the idea of a few people who have left Apple. According to Gurman, the executive team fully supported the design, and several current design leaders helped develop it over time. So, a sudden change is unlikely.  

But in a post on X, Gurman says Apple is still working on ways to let users control how glassy “liquid glass” looks. In iOS 26.2, Apple added a setting to choose between clear and tinted. He says Apple is still trying to give users more detailed control. Apple had been working on a system-wide liquid glass slider for iOS 26 to adjust the intensity of the glass effect, but it couldn’t be implemented for engineering reasons. Apple is trying again now for iOS 27  TBD if it lands.  

It’s clear that the opening keynote at WWDC26 will not include a big apology or a new design language. Still, the event should have some news. Gurman thinks one major change is coming to iOS 27 this year.

Source:  Apple plans to retain Liquid Glass UI in iOS 27 with new customization controls, improved multitasking, and design tweaks despite mixed user feedback. 

Google Adds Personal Context Memory to Gemini AI, Google Blog Confirms

Google is adding a new feature called Personal Intelligence to its Gemini AI. This feature aims to make Gemini’s answers more useful by using personal information from certain Google apps. If users approve, Gemini can access data from Gmail, Google Photos, YouTube, and search history to help answer questions and plan. Google says the goal is to help you daily with tasks like finding information or planning activities while letting users control how their data is used.  

Personal Intelligence: What Is It? 

With Personal Intelligence, users can link specific Google Ads to Gemini. After linking, Gemini leverages details from emails, photos, or past searches to deliver answers. For example, instead of searching your inbox or photos manually, Gemini records the information for you when prompted. Google states that the feature remains off by default, and users can choose which apps to link, disconnect them at any time, or disable personalization for any check.  

Personal Intelligence: How It Works 

Google states Personal Intelligence performs two core functions. It interprets data from multiple sources. It retrieves specific details from emails or images to answer queries. By integrating text, images, and videos, it tailors responses to better match user needs.  

Google illustrated the feature in action: Gemini helped a user identify their car’s tire size by checking prior data, then suggested options from travel photos, including ratings and prices.  

Google says this feature is useful for daily tasks like planning trips, shopping, or recording details you might not remember right away. However, the company admits the system may struggle with subtle or changing personal situations, such as shifting interests or relationships.  

Privacy and Data Controls 

Google says privacy remains a key part of how Personal Intelligence works. The company says Gemini does not print directly to users’ email inboxes, Gmail, or photo libraries. Instead, it uses that data only to answer specific requests on this blog. In its blog, Google explained that training uses limited information, such as prompts and responses, and that personal details are removed or hidden first.  

Gemini shows or explains where its answers came from, so users can check the information. If something looks wrong, users can correct it or ask for more details. They can also turn off personalization for a chat or use a temporary chat without personal data. Google also says Gemini avoids making guesses about sensitive topics like health unless the user asks.  

Availability and Rollout 

Personal Intelligence will be available over the next week for Google AI Pro and AI Ultra subscribers in the US. It works on the web, Android, and iOS, and supports all Gemini models in the modern figure. Google says it will also be added to AI mode in Search soon. The company plans to expand to more countries and free users later, but there is no set timeline for these. The feature is not available for Workspace or educational accounts and will not work with those account types at this time.  

  • Open the Gemini app.  
  • Tap on settings.  
  • Select Personal Intelligence, a tool that lets Gemini use information you allow from other Google services to answer questions.  
  • Choose which connector apps apps that link and share your data, like Gmail or Google Photos you want Gemini to access.

Source: Google introduces Personal Intelligence in Gemini: What is it, how it works 

Tesla Showcases Optimus Gen 3 Dexterity in New Engineering Update, Tesla Social Reveals

Optimus Gen 3 or V3 may look so real, you’ll need to poke it, says Musk. Wall Street is revising its models for possible major revenues. Meanwhile, roboticists warn that dexterity, safety, and cost remain big challenges.  

To set the context, this article breaks down the timeline, production plans, market outlook, and engineering challenges ahead of the upcoming reveal. We also look at what investors expect and the new career opportunities created by Tesla’s latest AI project. Let’s begin by reviewing the confirmed timeline milestones for Optimus.  

Confirmed Optimus Timeline Milestone 

Tesla’s October 22 statements give the most detailed schedule yet. Musk said, “We look forward to unveiling Optimus V3 probably in Q1,” hinting at a February showcase. Analysts expect a major public test of Tesla’s robotics plans in early spring 2026.  

First-generation production lines are already being installed at Freeman and Giga in Texas. According to the investor deck, internal pilots will deploy 7,000 robots across Tesla shops in 2025. Those points set the stage for later aspirations for one end production capacity  

The timeline now seems solid, but there are still risks to actually making it happen. To understand the scale of Tesla’s ambitions and operational steps ahead, let’s take a closer look at the company’s production plans.  

Tesla Scale Ambitions Stated 

Musk confirmed a goal of 1 million units in a few years. He called it key to global factory automation and management and set a long-term price target of $30,000 per robot.  

To hit the $30,000 price target, Tesla will cut costs and simplify supply chains. Its vehicle expertise will standardize parts and lower the cost of electronics. Executors believe costs could fall to $20,000 per robot, protecting margins.  

Suppliers warn that 10,000 parts per robot will make it hard to make the 1 million unique gold. Tesla believes in-house production will reduce delays and sets its unique mix of AI hardware and manufacturing skills.  

Big goals alone won’t solve the technical problems or main safety standards. Building reliable human-health robots depends on thorough management of parts and logistics with an eye on critical engineering challenges. Let’s identify the obstacles that make slow progress.  

Critical Manufacturing Hurdles Ahead 

Making a working robot is easier than scaling to millions. Rodney Brooks calls it a humanoid robot bubble and says dexterity remains unsolved. Touch sensors and joints are still in research, not mass production.  

Every Optimus contains roughly 10,000 K components, many of which are custom-designed for Tesla. Subsequently, a single late supply can halt an entire factory automation cell. Therefore, Musk conceded the ramp will be limited by the slowest part during Q&A.  

Battery performance, heat management, and the robots’ ability to withstand faults or need thorough testing before unit robots are ready for use. The next prototype should help identify the remaining problems to solve. These technical issues are important to consider as we turn to market demand and revenue forecasts.  

Global Market Forecast Variances 

Market researchers cannot agree on potential demand. Grandview Research banks humanoid robot sales at $4.04 billion by 2030, a conservative trajectory. In contrast, ABI Research models multi-value and revenue earlier, and RGB’s rapid adoption of factory automation.  

  • Q3 2025 Revenue column $28.095 Billion up 20% YOY  
  • Free Cash Flow: $3.99 Billion.  
  • Cash and Investments: $41 Billion.  
  • Vehicle Deliveries: 497,099 units  

Tesla’s goal of producing 1 million robots is well above either forecast. Management also promotes a long-term price target of $30,000 per robot, which could translate into $30 billion in annual hardware revenue. Because of this, some investors believe optimists could eventually surpass Tesla’s car business.  

Doubters argue that price, production volume, and regulations will slow down adoption. Still, even cautious estimates suggest that companies making human-like art robots would see strong double-digit growth. Next, to assess whether these ambitions are plausible, we examine how experts view the path forward.  

Independent Expert Skepticism Mounts 

Rodney Brooks argues hands remain the Achilles’ heel. He notes that decades of research have not produced affordable, reliable manipulation for 10k-component systems. Additionally, Brooks questions whether Musk’s $30,000 price target covers warranty costs and liability.  

Other experts note that no approval systems yet exist for robots in surgery or public places. Officials must test for false emergency stops, and cybersecurity testing of robots outside Tesla factories could take years.  

Expert warnings have made people more cautious about humanoid problems, yet their potential remains. Next, we’ll look at how success in this area could change other industries.  

Wider Strategic Industry Impact 

If successful, optimists could change work across the car, logistics, and electronics factories, enabling people and robots to collaborate on production lines. Consultants estimate that 1 million robots would boost productivity 20-40%.  

A $30,000 price could make Optimus cheaper than many traditional systems. Startups like Figure AI and Apptronik seek partnerships before Tesla scales. Older robotics firms target specialized markets rather than high-volume production.  

Government agencies will likely draft new guidelines for the development of humanoid robots in shared spaces. Nevertheless, clear economic incentives would accelerate regulatory harmonization across areas. Upskilling imperatives emerge, examined next.  

Essential Upskilling for Engineers 

Robotics engineers and operations managers must refresh their skills ahead of the mass adoption of humanoid robots. Additionally, cross-disciplinary competence in AI safety and mechanics will drive career mobility. Professionals can enhance their expertise through the AI Robotics certification endorsed by industry groups.  

Moreover, maintenance technicians will need fluency with 10K components, diagnostics, and predictive analytics. Training programs are surfacing at partner colleges and within Tesla’s own academies. Consequently, early adopters may secure leadership roles in factory automation rollouts.  

  • AI, Model, Tuning.  
  • Safety, Certification, Protocols.  
  • Actuator maintenance.  
  • Supply Chain Analytics  

Training programs need to grow as technology advances. To wrap up, here is a summary of the current situation and what comes next for Optimus and the sector.  

Tesla’s Optimus Gen 3 timeline crystallizes a key year for the development of humanoid robotics. The company targets 1M in production capacity, a $30K price target, and seamless integration with factory automation. However, the 10K components, complexity, safety certifications, and uncertainty remain challenges. If Tesla succeeds, the way manufacturing works around the world could change a lot. Professionals should watch for prototype demos and look for ways to keep learning. Getting certified now can help you prepare for the intelligent production lines of the future. 

Source: Tesla Optimus Gen 3 Spurs Humanoid Robotics Development Leap 

Researchers Discover Glassworm Malware Hidden in Invisible Unicode Aikido Security Warns.

The invisible threat we’ve tracked for nearly a year has re-emerged. While the PolinRider campaign compromised hundreds of GitHub repositories, we now see a sharp rise in glassworm activity impacting GitHub, NPM, and VS Code.  

Last October, we urgently warned about how hidden Unicode characters compromised GitHub repositories, a method unmistakably linked to glassworm. Now the situation is critical. Glassworm has resurfaced this month, and high-profile repositories are already affected, including those from Wasmer, Reworm, and OpenCode-Bench from anomalyco, the team behind OpenCode and SST.  

A Year Tracking the Invisible Code Campaign 

  • In March 2025, Aikido first finds malicious NPM packages that hide payloads using PUA Unicode characters.  
  • In May 2025, we will publish a blog post explaining the risks of invisible Unicode and how attackers can use it in supply chain attacks.  
  • On October 17, 2025, we found compromised extensions on OpenVSX that use the same technique.  
  • On October 31, 2025, we discovered that attackers had started targeting GitHub repositories.  
  • In March 2026, a new large-scale attack compromised hundreds of GitHub repositories, and NPM and VS Code were also affected.  

A Quick Reminder 

Before we uncover just how widespread this alarming new wave is, let’s quickly review how the attack works. Even with months of warnings, it continues to catch developers and tools off guard.  

The attack exploits invisible Unicode characters that escape detection in nearly every editor, terminal, and code review tool. Attackers conceal dangerous payloads within what appears to be empty strings. When the JavaScript runtime executes cold code, a disorder instantly extracts the real bytes and sends them straight to eval(), unleashing the full threat.  

Cybersecurity researchers have identified three new extensions linked to the Glassworm campaign, indicating continued targeting of the Visual Studio Code (VS Code) ecosystem.  

These extensions remain active threats and can still be downloaded right now. They are:  

  • AI-driven-dev.ai-driven.dev 3402 Downloads  
  • Adhamu.history-in-sublime-merge 4057 downloads  
  • Yasuyuky.transient.emacs 2431 Downloads  

Glassworm was first reported by Koi Security late last month. Attackers are exploiting VS Code extensions from both the Open VSX Registry and Microsoft Extension Marketplace to steal Open VSX, GitHub, and Git credentials. They actively drain funds from 49 cryptocurrency wallet extensions and install extra remote access tools, escalating the threat to urgent levels.  

This malware is particularly dangerous because it hides its code using invisible Unicode characters in code editors, stolen credentials fuel a self-replicating infection cycle that rapidly spreads across systems, making it difficult to stop the worm-like attack.  

Based on this evidence, Open VSX said it had found and removed all malicious extensions and had changed or revoked related tokens as of October 21, 2025. However, Koi’s Security’s latest report shows the threat has returned, using unusable Unicode characters to avoid detection.  

The attacker submitted a new Solana blockchain transaction that updated the C2 endpoint for malware downloads, according to security researchers Idan Dardikman, Yuval Ronan, and Luton Sery. This shows the resilience of blockchain-based C2 infrastructure. Even if servers shut down, the attacker can post a cheap transaction, and all infected machines get the updated location.  

The security vendor also found an exposed endpoint on the attackers’ server, revealing a partial victim list across the US, South America, Europe, Asia, and a major Middle East government entity.  

Further analysis found keylogger data that seems to come from the attacker’s own machine. This has provided some indications about where glassworms come from. The attacker is believed to be Russian-speaking and uses an open-source browser extension C2 framework called Redext as part of their setup.  

These are real organizations and real people whose credentials are being harvested now, whose machines may be serving as criminal proxy infrastructure and whose internal networks could be compromised at any moment, Koi Security said.  

The alarming news follows reports from Aikido Security that Glassworm is actively targeting GitHub, with stolen credentials being used to push malicious commits and cause immediate harm.

Sources: GlassWorm Malware Discovered in Three VS Code Extensions with Thousands of Installs 

Glassworm Is Back: A New Wave of Invisible Unicode Attacks Hits Hundreds of Repositories

Amazon Pushes ML-KEM Quantum-Safe Encryption for Future Internet, AWS Blog Reveals

Amazon Web Services (AWS) has deployed the latest hybrid post-quantum key agreement standards for TLS for 23 AWS services. AWS Key Management Service (AWS KMS), AWS Certificate Manager (ACM), and AWS Secrets Manager endpoints now support the lattice-based Key Encapsulation Mechanism (ML-KEM) for hybrid post-quantum key agreements in non-FIPS endpoints across all AWS regions. The AWS Secrets Manager Agent, built on the AWS SDK for Rust, now provides optimal support for hybrid post-quantum key agreement. This allows customers to use end-to-end post-quantum–enabled TLS when bringing data into their applications.  

These three services were selected because they are security-critical and require the highest level of post-quantum confidentiality. They previously supported Crystals Kyber, which ML-KEM now replaces. Crystals Kyber will continue until 2025, but will be removed from all AWS service endpoints in 2026 as ML-KEM becomes the standard.  

Our Migration to Post-Quantum Cryptography 

AWS is following its post-quantum cryptographic migration plan as part of this. AWS will add MLKM support to all services with HTTPS endpoints over the next few years. Customers need to update their TLS clients and SDKs to use ML-KEM when connecting to AWS HTTPS endpoints. This helps protect against future threats from quantum computing. AWS endpoints will select ME, ML-KEM when clients offer it.  

Our hybrid pAWS can negotiate hybrid post-quantum key-agreement algorithms thanks to AWS LibCrypto and AWS LC. Our open-source FIPS 143-validated cryptographic library and S2N TLS, our open-source TLS implementation. AWS LC has received several FIPS certificates from NIST: 434631, 4759, and 4816, and was the first open-source cryptographic module to include MLKM in a FIPS 140-3 validation.  

ML-KEM on TLS Performance 

Migrating from an elliptic curve Diffie-Hellman (ECDH) only key agreement to an ECDH plus ML-KEM hybrid key agreement necessarily requires that the TLS handshake send more data and perform more cryptographic operations. Switching from a classical to a hybrid post-quantum key agreement will transfer approximately 1,600 additional bytes during the TLS handshake and will require approximately 80 to 150 microseconds more compute time to perform ML-KEM cryptographic operations. This is a one-time TLS connection startup cost or amortized over the lifetime of the TLS connection across the HTTP requests sent over it.  

AWS is working to provide a smooth migration to hybrid post-quantum key agreement for TLS. This work includes benchmarking example workloads to help customers understand the impact of enabling hybrid post-quantum key agreements with ML-KEM.  

Using the AWS SDK for Java v2, AWS measured how many AWS KMS GenerateDataKey requests per second a single thread can send between an Amazon EC2 C6i bare metal client and the public AWS KMS endpoint, both in the US West 2 region. Classical TLS connections used the P-256 elliptic curve, while hybrid post-quantum TLS connections used the X25519 elliptic curve with ML-KEM-768. Your results may vary depending on your environment, including instance type, workload, parallelism, number of threads, and network setup. The tests measured HTTP request rates with TLS connection reuse enabled and disabled. The handshake is never amortized, and every HTTP request must perform a full TLS handshake. Enabling hybrid post-quantum TLS reduces transactions per second (TPS) by about 2.3%, from 108.7 TPS to 106.2 TPS.  

Results show that enabling post-quantum TLS has little impact on performance. For most workloads, the maximum DPS rates dropped by just 0.05%. In the worst case, with each request creating a new TLS handshake, the drop was only 2.3%.   

Removing Support for Draft Post Quantum Standards 

AWS Service Endpoints that currently support Crystals Kyber, the predecessor to ML-KEM, will continue to support it through 2025. AWS will gradually phase out Crystals Kyber after customers switch to ML-KEM. If you are using an AWS SDK for Java version that only supports Crystals Kyber, upgrade to the latest version with ML-KEM support. If your code uses a recent AWS SDK for Java V2 release, no changes are needed for the transition from Crystals Kyber to ML-KEM.  

Customers whose clients currently use Crystals Kyber must upgrade their AWS Java SDK v2 to a version that supports ML-KEM before 2026, as Crystals Kyber will be removed in 2026. Clients that have not updated will automatically revert to using classical key agreements to maintain connectivity but will lose post-quantum confidentiality.  

How to use Hybrid Post Quantum Key Agreement 

To enable hybrid post-quantum key agreement in the AWS SDK for Rust, add rustls to your crate and activate the prefer-hybrid-post-quantum feature flag.  

For AWS SDK for Java 2.x, enable hybrid postquantum key agreement by calling .postquantumtlsenabled(true) when building the AWS common runtime HTTP client.  

Step 1: Add the AWS Common Runtime HTTP client to your Java dependencies 

Add the latest AWS Common Runtime HTTP Client to your Maven dependencies. Use version 2.30.22 or higher for ML-KEM support.  

Step 2: Enable Post-Quantum TRS in your Java SDK client configuration 

Select AWSCRTAsyncHTTPClient in your AWS Client setup. Enable post-Quantum TLS.  

Things to Try 

Here are a few ways you can use this client with Post-Quantum support:  

  • Run, Load, Tests, and Benchmarks: AWSCRTAsyncHTTPClient is high-performing and uses AWS LibCrypto on Linux. If you are new to it, compare its performance to the default SDK client. Afterward, enable Post Quantum TLS and check whether it outperforms the default client without it.  
  • Test connections from various locations: Requests may be made via proxies or firewalls that use Deep Packet Inspection (DPI). If blocked, ask your security team to update rules for these TLS algorithms. Share feedback on how your network handles this traffic.  

Conclusion: We’ve added ML-KM Hybrid Key Agreement to 3 AWS Endpoints with TLS connection reuse, enabling hybrid post-quantum TLS, with minimal impact on performance in our tests. We saw only a 0.05% drop in the maximum transactions per second when using AWS KMS-generated data key.

Source: ML-KEM post-quantum TLS now supported in AWS KMS, ACM, and Secrets Manager