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In an industry where model size is often seen as a proxy for intelligence, IBM is charting a different course — one that values efficiency over enormity, and accessibility over abstraction.

The 114-year-old tech giant's four new Granite 4.0 Nano models, released today, range from just 350 million to 1.5 billion parameters, a fraction of the size of their server-bound cousins from the likes of OpenAI, Anthropic, and Google.

These models are designed to be highly accessible: the 350M variants can run comfortably on a modern laptop CPU with 8–16GB of RAM, while the 1.5B models typically require a GPU with at least 6–8GB of VRAM for smooth performance — or sufficient system RAM and swap for CPU-only inference. This makes them well-suited for developers building applications on consumer hardware or at the edge, without relying on cloud compute.

In fact, the smallest ones can even run locally on your own web browser, as Joshua Lochner aka Xenova, creator of Transformer.js and a machine learning engineer at Hugging Face, wrote on the social network X.

All the Granite 4.0 Nano models are released under the Apache 2.0 license — perfect for use by researchers and enterprise or indie developers, even for commercial usage.

They are natively compatible with llama.cpp, vLLM, and MLX and are certified under ISO 42001 for responsible AI development — a standard IBM helped pioneer.

But in this case, small doesn't mean less capable — it might just mean smarter design.

These compact models are built not for data centers, but for edge devices, laptops, and local inference, where compute is scarce and latency matters.

And despite their small size, the Nano models are showing benchmark results that rival or even exceed the performance of larger models in the same category.

The release is a signal that a new AI frontier is rapidly forming — one not dominated by sheer scale, but by strategic scaling.

What Exactly Did IBM Release?

The Granite 4.0 Nano family includes four open-source models now available on Hugging Face:

• Granite-4.0-H-1B (~1.5B parameters) – Hybrid-SSM architecture

• Granite-4.0-H-350M (~350M parameters) – Hybrid-SSM architecture

• Granite-4.0-1B – Transformer-based variant, parameter count closer to 2B

• Granite-4.0-350M – Transformer-based variant

The H-series models — Granite-4.0-H-1B and H-350M — use a hybrid state space architecture (SSM) that combines efficiency with strong performance, ideal for low-latency edge environments.

Meanwhile, the standard transformer variants — Granite-4.0-1B and 350M — offer broader compatibility with tools like llama.cpp, designed for use cases where hybrid architecture isn’t yet supported.

In practice, the transformer 1B model is closer to 2B parameters, but aligns performance-wise with its hybrid sibling, offering developers flexibility based on their runtime constraints.

“The hybrid variant is a true 1B model. However, the non-hybrid variant is closer to 2B, but we opted to keep the naming aligned to the hybrid variant to make the connection easily visible,” explained Emma, Product Marketing lead for Granite, during a Reddit "Ask Me Anything" (AMA) session on r/LocalLLaMA.

A Competitive Class of Small Models

IBM is entering a crowded and rapidly evolving market of small language models (SLMs), competing with offerings like Qwen3, Google's Gemma, LiquidAI’s LFM2, and even Mistral’s dense models in the sub-2B parameter space.

While OpenAI and Anthropic focus on models that require clusters of GPUs and sophisticated inference optimization, IBM’s Nano family is aimed squarely at developers who want to run performant LLMs on local or constrained hardware.

In benchmark testing, IBM’s new models consistently top the charts in their class. According to data shared on X by David Cox, VP of AI Models at IBM Research:

• On IFEval (instruction following), Granite-4.0-H-1B scored 78.5, outperforming Qwen3-1.7B (73.1) and other 1–2B models.

• On BFCLv3 (function/tool calling), Granite-4.0-1B led with a score of 54.8, the highest in its size class.

• On safety benchmarks (SALAD and AttaQ), the Granite models scored over 90%, surpassing similarly sized competitors.

Overall, the Granite-4.0-1B achieved a leading average benchmark score of 68.3% across general knowledge, math, code, and safety domains.

This performance is especially significant given the hardware constraints these models are designed for.

They require less memory, run faster on CPUs or mobile devices, and don’t need cloud infrastructure or GPU acceleration to deliver usable results.

Why Model Size Still Matters — But Not Like It Used To

In the early wave of LLMs, bigger meant better — more parameters translated to better generalization, deeper reasoning, and richer output.

But as transformer research matured, it became clear that architecture, training quality, and task-specific tuning could allow smaller models to punch well above their weight class.

IBM is banking on this evolution. By releasing open, small models that are competitive in real-world tasks, the company is offering an alternative to the monolithic AI APIs that dominate today’s application stack.

In fact, the Nano models address three increasingly important needs:

1. Deployment flexibility — they run anywhere, from mobile to microservers.

2. Inference privacy — users can keep data local with no need to call out to cloud APIs.

3. Openness and auditability — source code and model weights are publicly available under an open license.

Community Response and Roadmap Signals

IBM’s Granite team didn’t just launch the models and walk away — they took to Reddit’s open source community r/LocalLLaMA to engage directly with developers.

In an AMA-style thread, Emma (Product Marketing, Granite) answered technical questions, addressed concerns about naming conventions, and dropped hints about what’s next.

Notable confirmations from the thread:

• A larger Granite 4.0 model is currently in training

• Reasoning-focused models ("thinking counterparts") are in the pipeline

• IBM will release fine-tuning recipes and a full training paper soon

• More tooling and platform compatibility is on the roadmap

Users responded enthusiastically to the models’ capabilities, especially in instruction-following and structured response tasks. One commenter summed it up:

“This is big if true for a 1B model — if quality is nice and it gives consistent outputs. Function-calling tasks, multilingual dialog, FIM completions… this could be a real workhorse.”

Another user remarked:

“The Granite Tiny is already my go-to for web search in LM Studio — better than some Qwen models. Tempted to give Nano a shot.”

Background: IBM Granite and the Enterprise AI Race

IBM’s push into large language models began in earnest in late 2023 with the debut of the Granite foundation model family, starting with models like Granite.13b.instruct and Granite.13b.chat. Released for use within its Watsonx platform, these initial decoder-only models signaled IBM’s ambition to build enterprise-grade AI systems that prioritize transparency, efficiency, and performance. The company open-sourced select Granite code models under the Apache 2.0 license in mid-2024, laying the groundwork for broader adoption and developer experimentation.

The real inflection point came with Granite 3.0 in October 2024 — a fully open-source suite of general-purpose and domain-specialized models ranging from 1B to 8B parameters. These models emphasized efficiency over brute scale, offering capabilities like longer context windows, instruction tuning, and integrated guardrails. IBM positioned Granite 3.0 as a direct competitor to Meta’s Llama, Alibaba’s Qwen, and Google's Gemma — but with a uniquely enterprise-first lens. Later versions, including Granite 3.1 and Granite 3.2, introduced even more enterprise-friendly innovations: embedded hallucination detection, time-series forecasting, document vision models, and conditional reasoning toggles.

The Granite 4.0 family, launched in October 2025, represents IBM’s most technically ambitious release yet. It introduces a hybrid architecture that blends transformer and Mamba-2 layers — aiming to combine the contextual precision of attention mechanisms with the memory efficiency of state-space models. This design allows IBM to significantly reduce memory and latency costs for inference, making Granite models viable on smaller hardware while still outperforming peers in instruction-following and function-calling tasks. The launch also includes ISO 42001 certification, cryptographic model signing, and distribution across platforms like Hugging Face, Docker, LM Studio, Ollama, and watsonx.ai.

Across all iterations, IBM’s focus has been clear: build trustworthy, efficient, and legally unambiguous AI models for enterprise use cases. With a permissive Apache 2.0 license, public benchmarks, and an emphasis on governance, the Granite initiative not only responds to rising concerns over proprietary black-box models but also offers a Western-aligned open alternative to the rapid progress from teams like Alibaba’s Qwen. In doing so, Granite positions IBM as a leading voice in what may be the next phase of open-weight, production-ready AI.

A Shift Toward Scalable Efficiency

In the end, IBM’s release of Granite 4.0 Nano models reflects a strategic shift in LLM development: from chasing parameter count records to optimizing usability, openness, and deployment reach.

By combining competitive performance, responsible development practices, and deep engagement with the open-source community, IBM is positioning Granite as not just a family of models — but a platform for building the next generation of lightweight, trustworthy AI systems.

For developers and researchers looking for performance without overhead, the Nano release offers a compelling signal: you don’t need 70 billion parameters to build something powerful — just the right ones.

Microsoft is launching a significant expansion of its Copilot AI assistant on Tuesday, introducing tools that let employees build applications, automate workflows, and create specialized AI agents using only conversational prompts — no coding required.

The new capabilities, called App Builder and Workflows, mark Microsoft's most aggressive attempt yet to merge artificial intelligence with software development, enabling the estimated 100 million Microsoft 365 users to create business tools as easily as they currently draft emails or build spreadsheets.

"We really believe that a main part of an AI-forward employee, not just developers, will be to create agents, workflows and apps," Charles Lamanna, Microsoft's president of business and industry Copilot, said in an interview with VentureBeat. "Part of the job will be to build and create these things."

The announcement comes as Microsoft deepens its commitment to AI-powered productivity tools while navigating a complex partnership with OpenAI, the creator of the underlying technology that powers Copilot. On the same day, OpenAI completed its restructuring into a for-profit entity, with Microsoft receiving a 27% ownership stake valued at approximately $135 billion.

How natural language prompts now create fully functional business applications

The new features transform Copilot from a conversational assistant into what Microsoft envisions as a comprehensive development environment accessible to non-technical workers. Users can now describe an application they need — such as a project tracker with dashboards and task assignments — and Copilot will generate a working app complete with a database backend, user interface, and security controls.

"If you're right inside of Copilot, you can now have a conversation to build an application complete with a backing database and a security model," Lamanna explained. "You can make edit requests and update requests and change requests so you can tune the app to get exactly the experience you want before you share it with other users."

The App Builder stores data in Microsoft Lists, the company's lightweight database system, and allows users to share finished applications via a simple link—similar to sharing a document. The Workflows agent, meanwhile, automates routine tasks across Microsoft's ecosystem of products, including Outlook, Teams, SharePoint, and Planner, by converting natural language descriptions into automated processes.

A third component, a simplified version of Microsoft's Copilot Studio agent-building platform, lets users create specialized AI assistants tailored to specific tasks or knowledge domains, drawing from SharePoint documents, meeting transcripts, emails, and external systems.

All three capabilities are included in the existing $30-per-month Microsoft 365 Copilot subscription at no additional cost — a pricing decision Lamanna characterized as consistent with Microsoft's historical approach of bundling significant value into its productivity suite.

"That's what Microsoft always does. We try to do a huge amount of value at a low price," he said. "If you go look at Office, you think about Excel, Word, PowerPoint, Exchange, all that for like eight bucks a month. That's a pretty good deal."

Why Microsoft's nine-year bet on low-code development is finally paying off

The new tools represent the culmination of a nine-year effort by Microsoft to democratize software development through its Power Platform — a collection of low-code and no-code development tools that has grown to 56 million monthly active users, according to figures the company disclosed in recent earnings reports.

Lamanna, who has led the Power Platform initiative since its inception, said the integration into Copilot marks a fundamental shift in how these capabilities reach users. Rather than requiring workers to visit a separate website or learn a specialized interface, the development tools now exist within the same conversational window they already use for AI-assisted tasks.

"One of the big things that we're excited about is Copilot — that's a tool for literally every office worker," Lamanna said. "Every office worker, just like they research data, they analyze data, they reason over topics, they also will be creating apps, agents and workflows."

The integration offers significant technical advantages, he argued. Because Copilot already indexes a user's Microsoft 365 content — emails, documents, meetings, and organizational data — it can incorporate that context into the applications and workflows it builds. If a user asks for "an app for Project Spartan," Copilot can draw from existing communications to understand what that project entails and suggest relevant features.

"If you go to those other tools, they have no idea what the heck Project Spartan is," Lamanna said, referencing competing low-code platforms from companies like Google, Salesforce, and ServiceNow. "But if you do it inside of Copilot and inside of the App Builder, it's able to draw from all that information and context."

Microsoft claims the apps created through these tools are "full-stack applications" with proper databases secured through the same identity systems used across its enterprise products — distinguishing them from simpler front-end tools offered by competitors. The company also emphasized that its existing governance, security, and data loss prevention policies automatically apply to apps and workflows created through Copilot.

Where professional developers still matter in an AI-powered workplace

While Microsoft positions the new capabilities as accessible to all office workers, Lamanna was careful to delineate where professional developers remain essential. His dividing line centers on whether a system interacts with parties outside the organization.

"Anything that leaves the boundaries of your company warrants developer involvement," he said. "If you want to build an agent and put it on your website, you should have developers involved. Or if you want to build an automation which interfaces directly with your customers, or an app or a website which interfaces directly with your customers, you want professionals involved."

The reasoning is risk-based: external-facing systems carry greater potential for data breaches, security vulnerabilities, or business errors. "You don't want people getting refunds they shouldn't," Lamanna noted.

For internal use cases — approval workflows, project tracking, team dashboards — Microsoft believes the new tools can handle the majority of needs without IT department involvement. But the company has built "no cliffs," in Lamanna's terminology, allowing users to migrate simple apps to more sophisticated platforms as needs grow.

Apps created in the conversational App Builder can be opened in Power Apps, Microsoft's full development environment, where they can be connected to Dataverse, the company's enterprise database, or extended with custom code. Similarly, simple workflows can graduate to the full Power Automate platform, and basic agents can be enhanced in the complete Copilot Studio.

"We have this mantra called no cliffs," Lamanna said. "If your app gets too complicated for the App Builder, you can always edit and open it in Power Apps. You can jump over to the richer experience, and if you're really sophisticated, you can even go from those experiences into Azure."

This architecture addresses a problem that has plagued previous generations of easy-to-use development tools: users who outgrow the simplified environment often must rebuild from scratch on professional platforms. "People really do not like easy-to-use development tools if I have to throw everything away and start over," Lamanna said.

What happens when every employee can build apps without IT approval

The democratization of software development raises questions about governance, maintenance, and organizational complexity — issues Microsoft has worked to address through administrative controls.

IT administrators can view all applications, workflows, and agents created within their organization through a centralized inventory in the Microsoft 365 admin center. They can reassign ownership, disable access at the group level, or "promote" particularly useful employee-created apps to officially supported status.

"We have a bunch of customers who have this approach where it's like, let 1,000 apps bloom, and then the best ones, I go upgrade and make them IT-governed or central," Lamanna said.

The system also includes provisions for when employees leave. Apps and workflows remain accessible for 60 days, during which managers can claim ownership — similar to how OneDrive files are handled when someone departs.

Lamanna argued that most employee-created apps don't warrant significant IT oversight. "It's just not worth inspecting an app that John, Susie, and Bob use to do their job," he said. "It should concern itself with the app that ends up being used by 2,000 people, and that will pop up in that dashboard."

Still, the proliferation of employee-created applications could create challenges. Users have expressed frustration with Microsoft's increasing emphasis on AI features across its products, with some giving the Microsoft 365 mobile app one-star ratings after a recent update prioritized Copilot over traditional file access.

The tools also arrive as enterprises grapple with "shadow IT" — unsanctioned software and systems that employees adopt without official approval. While Microsoft's governance controls aim to provide visibility, the ease of creating new applications could accelerate the pace at which these systems multiply.

The ambitious plan to turn 500 million workers into software builders

Microsoft's ambitions for the technology extend far beyond incremental productivity gains. Lamanna envisions a fundamental transformation of what it means to be an office worker — one where building software becomes as routine as creating spreadsheets.

"Just like how 20 years ago you put on your resume that you could use pivot tables in Excel, people are going to start saying that they can use App Builder and workflow agents, even if they're just in the finance department or the sales department," he said.

The numbers he's targeting are staggering. With 56 million people already using Power Platform, Lamanna believes the integration into Copilot could eventually reach 500 million builders. "Early days still, but I think it's certainly encouraging," he said.

The features are currently available only to customers in Microsoft's Frontier Program — an early access initiative for Microsoft 365 Copilot subscribers. The company has not disclosed how many organizations participate in the program or when the tools will reach general availability.

The announcement fits within Microsoft's larger strategy of embedding AI capabilities throughout its product portfolio, driven by its partnership with OpenAI. Under the restructured agreement announced Tuesday, Microsoft will have access to OpenAI's technology through 2032, including models that achieve artificial general intelligence (AGI) — though such systems do not yet exist. Microsoft has also begun integrating Copilot into its new companion apps for Windows 11, which provide quick access to contacts, files, and calendar information.

The aggressive integration of AI features across Microsoft's ecosystem has drawn mixed reactions. While enterprise customers have shown interest in productivity gains, the rapid pace of change and ubiquity of AI prompts have frustrated some users who prefer traditional workflows.

For Microsoft, however, the calculation is clear: if even a fraction of its user base begins creating applications and automations, it would represent a massive expansion of the effective software development workforce — and further entrench customers in Microsoft's ecosystem. The company is betting that the same natural language interface that made ChatGPT accessible to millions can finally unlock the decades-old promise of empowering everyday workers to build their own tools.

The App Builder and Workflows agents are available starting today through the Microsoft 365 Copilot Agent Store for Frontier Program participants.

Whether that future arrives depends not just on the technology's capabilities, but on a more fundamental question: Do millions of office workers actually want to become part-time software developers? Microsoft is about to find out if the answer is yes — or if some jobs are better left to the professionals.

Also, expanded revenue opportunities via creator subscriptions.

Google DeepMind researchers have developed BlockRank, a new method for ranking and retrieving information more efficiently in large language models (LLMs).

• BlockRank is detailed in a new research paper, Scalable In-Context Ranking with Generative Models.
• BlockRank is designed to solve a challenge called In-context Ranking (ICR), or the process of having a model read a query and multiple documents at once to decide which ones matter most.
• As far as we know, BlockRank is not being used by Google (e.g., Search, Gemini, AI Mode, AI Overviews) right now – but it could be used at some point in the future.

What BlockRank changes. ICR is expensive and slow. Models use a process called “attention,” where every word compares itself to every other word. Ranking hundreds of documents at once gets exponentially harder for LLMs.

How BlockRank works. BlockRank restructures how an LLM “pays attention” to text. Instead of every document attending to every other document, each one focuses only on itself and the shared instructions.

• The model’s query section has access to all the documents, allowing it to compare them and decide which one best answers the question.
• This transforms the model’s attention cost from quadratic (very slow) to linear (much faster) growth.

By the numbers. In experiments using Mistral-7B, Google’s team found that BlockRank:

• Ran 4.7× faster than standard fine-tuned models when ranking 100 documents.
• Scaled smoothly to 500 documents (about 100,000 tokens) in roughly one second.
• Matched or beat leading listwise rankers like RankZephyr and FIRST on benchmarks such as MSMARCO, Natural Questions (NQ), and BEIR.

Why we care. BlockRank could change how future AI-driven retrieval and ranking systems work to reward user intent, clarity, and relevance. That means (in theory) clear, focused content that aligns with why a person is searching (not just what they type) should increasingly win.

What’s next. Google/DeepMind researchers are continuing to redefine what it means to “rank” information in the age of generative AI. The future of search is advancing fast – and it’s fascinating to watch it evolve in real time.

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Reaction to a huge round of layoffs rippled across Amazon and beyond on Tuesday as the Seattle-based tech giant confirmed that it was slashing 14,000 corporate and tech jobs.

We’ve rounded up some of what’s being said online and/or shared with GeekWire:

‘Never been laid off before’

A megathread on Reddit served as a collection of comments by impacted employees who posted about their level, location, org and years of service at Amazon.

Workers across ads, recruitment, robotics, retail, Prime Video, Amazon Games, business development, North American Stores, finance, devices and services, Amazon Autos, and more used the thread to vent.

• “TPM II for Amazon Robotics, 6.5 years there. Still processing this, I’ve never been laid off before.”
• “L6 SDEIII, started as SDEI 7 years ago. I went L4 to L6 in 3 years. My last performance review I got raising the bar. Thought I was a top performer but guess I’m expendable.”
• “Never been laid off before feels overwhelming on VISA! Someone please help me understand next steps in terms of VISA, if I am not able to get H1b sponsoring job in next 90 days will I have to uproot everything here and go back?”
• “I heard AWS layoffs come after re:invent to avoid customer disruption and bad press.”
• “It’s heartbreaking how impersonal and abrupt these layoffs have become. People who’ve given years to a company are finding out in minutes that they’re done.”

Bad news via text?

Kristi Coulter, author of Exit Interview: The Life and Death of My Ambitious Career, a memoir about what she learned in her 12 years at Amazon, weighed in about the timing of apparent text messages that were sent to impacted employees.

“Wait, I’m sorry: Amazon made people relocate, switch their kids’ schools, and bookend their days with traffic for RTO only to lay them off via a 3 a.m. text? What happened to the vibe and conversations that only being together at the office could allow?” Coulter wrote on LinkedIn.

‘Reduced functionality’

Some employees shared how they were quickly locked out of work laptops, expressing confusion about whether that was how they were supposed to learn about being terminated.

“I lost access to everything immediately :( ,” one Reddit user said.

Others discussed how they should have found time to transfer important work examples or positive interactions related to their performance over to personal computers.

“One thing I would recommend for everyone is to back up your personal files onto your personal laptop,” one user said on Reddit. “I used to keep all my accolades and praise in a quip file along with all my 2×2 write ups and MBR/QBR write ups cataloging my wins. When I found out I got laid off my head was spinning so I went outside for a walk, by the time I returned I was locked out of my laptop and no longer had access to anything.”

Why layoffs now?

Amazon human resources chief Beth Galetti pinned the layoffs in part on the need to reduce bureaucracy and become more efficient in the new era of artificial intelligence. Others looked for deeper meaning in the cuts.

In a post on LinkedIn, Yahoo! Finance Executive Editor Brian Rozzi said stock price is likely a key consideration when it comes to top execs and the Amazon board signing off on such mass layoffs.

Amazon’s stock was up about 1% on Tuesday to $229 per share.

“If the layoffs keep jacking up the stock price, maybe I can retire instead,” one longtime employee told GeekWire.

Entrepreneur and investor Jason Calacanis posted on X about how AI was coming for middle managers and those with “rote jobs” faster than anyone expected. He encouraged workers to become a founder and do a startup before it’s too late.

Hard-hit divisions

Mid-level managers in Amazon’s retail division were heavily impacted by Tuesday’s cuts, according to internal data obtained by Business Insider.

More than 78% of the roles eliminated were held by managers assigned L5 to L7 designations, BI reported. (L5 is typically the starting point for managers at Amazon, with more seniority assigned to higher levels.)

BI also said that U.S.-focused data showed that more than 80% of employees laid off Tuesday worked in Amazon’s retail business, spanning e-commerce, human resources, and logistics.

Bloomberg and others reported that significant cuts are also being felt by Amazon’s video games unit.

Steve Boom, VP of audio, Twitch, and games said in a memo shared with The Verge that “significant role reductions” would be felt at studios in Irvine and San Diego, Calif., as well on Amazon’s central publishing teams.

“We have made the difficult decision to halt a significant amount of our first-party AAA game development work — specifically around MMOs [massively multiplayer online games] — within Amazon Game Studios,” Boom wrote.

Current titles in Amazon’s MMO lineup include “New World: Aeternum,” “Throne and Liberty,” and “Lost Ark.” Amazon also previously announced that it would be developing a “Lord of the Rings” MMO.

‘Ripple effects throughout the community’

Jon Scholes, president and CEO of the Downtown Seattle Association (DSA), has previously praised Amazon for its mandate calling for employees to return to the office five days per week, saying that the foot traffic from thousands of tech workers in the city is a necessary element to helping downtown Seattle rebound from the pandemic.

On Tuesday, Scholes reacted to Amazon’s layoffs in a statement to GeekWire:

“As downtown’s largest employer, a workforce change of this scale has ripple effects throughout the community — on individual employees and families and our small businesses that rely on the weekday foot traffic customer base. In addition, these jobs buttress our tax base that helps fund the city services we all depend on. Employers have options for where they locate jobs, and we want to ensure downtown Seattle is the most attractive place to invest and grow. We must provide vibrancy and a predictable regulatory environment in a competitive landscape because other cities would welcome the jobs currently based in downtown.”

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Whatnot, a live shopping platform and marketplace, has closed a $225 million Series F round, more than doubling its valuation to $11.5 billion in less than 10 months.

DST Global and CapitalG co-led the financing, which brings the Los Angeles-based company’s total raised to about $968 million since its 2019 inception. Whatnot had raised $265 million in a Series E round at a nearly $5 billion valuation in January.

New investors Sequoia Capital and Alkeon Capital participated in the Series F, alongside returning backers Greycroft, Andreessen Horowitz, Avra and Bond. Other investors include Y Combinator, Lightspeed Venture Partners and Liquid 2 Ventures.

As part of the latest financing, Whatnot says it will initiate a tender offer where select current investors will buy up to $126 million worth of shares.

Funding to e-commerce startups globally so far this year totals $7.1 billion, per Crunchbase data. That compares to $11.3 billion raised by e-commerce startups globally in all of 2024. This year’s numbers are also down significantly from post-pandemic funding totals, which surged to $93 billion in 2021.

‘Retail’s new normal’

Live commerce is the combination of livestreaming and online shopping. Grant LaFontaine, co-founder and CEO of Whatnot, said in an announcement that his startup is “proving that live shopping is retail’s new normal.”

The company says more than $6 billion worth of items have been sold on its platform in 2025 so far, more than twice its total for all of 2024. Its app facilitates the buying and selling of collectibles like trading cards and toys through live video auctions. It also offers items such as clothing and sneakers. It competes with the likes of eBay, which currently does not offer a livestreaming option. It’s also a competitor to TikTok Shop.

“Whatnot brought the live shopping wave to the US, the UK, and Europe and has turned it into one of the fastest growing marketplaces of all time, Laela Sturdy, Whatnot board member and managing partner at CapitalG, Alphabet’s independent growth fund, said in a release.

The company plans to use its new funds to invest in its platform, roll out new features and “evolve” its policies. It is also accelerating its international expansion, adding to its current 900-person workforce by hiring across multiple departments.

Related query:

• Funding to E-Commerce Startups, 2025

Related Reading:

• TikTok Shop Rival Whatnot Bags $265M At $5B Valuation For Live Shopping Platform

Illustration: Dom Guzman

FurMark is a widely trusted GPU stress test built for enthusiasts, overclockers, and system tuners. It delivers a brutal, sustained load to expose thermal and stability limits, giving you a clear picture of your cooling performance and long-term reliability. Despite its intensity, it stays simple to run and produces repeatable, no-nonsense results.

Google appears to be playing it safe with its upcoming budget offering, the Pixel 10a, if the latest CAD renders are anything to go by, with the tech giant eschewing any flashy design changes and opting for a predictable, if a tad boring, overall design language. Almost nothing appears to have changed between the Pixel 9a and the upcoming Pixel 10a, as per the new CAD renders As per the CAD renders published by the X user OnLeaks on behalf of Android Headlines, the following can be easily concluded: As for the budget offering's rumored specs, the following is known […]

Read full article at https://wccftech.com/pixel-10a-cad-renders-show-a-pixel-9a-clone/

Developer and publisher DON'T NOD has published its latest financial release which goes over its half-year results for 2025, which includes a few notable updates from the studio, like how its most recent release, Lost Records: Bloom & Rage performed "below expectations," and that the studio signed a deal with Netflix to make a narrative game based on "a major IP." It's definitely a disappointing result for DON'T NOD, particularly considering the fact that its last major releases last year, Banishers: Ghosts of New Eden and Jusant, also fell below expectations. The studio's total operating revenue took a 5% dip […]

Read full article at https://wccftech.com/lost-records-bloom-and-rage-missed-expectations-dont-nod-signs-deal-with-netflix/

Apple revamped its iPhone 17 lineup this year by introducing ProMotion technology to the base model, making it one of the best decisions it could ever make for its flagship smartphone family. Best of all, it brings a host of other upgrades while retaining that $699 price point, which is probably why the iPhone 17 has garnered immense popularity worldwide, particularly in China. Part of why Apple has been able to keep this price unchanged from the iPhone 16 is by keeping the display costs low. According to the latest report, the OLED panel in the iPhone 17 costs around 42 […]

Read full article at https://wccftech.com/iphone-17-oled-panel-around-42-percent-to-make-than-pro-models/

In the ongoing high-stakes court battle between Oppo and Apple, the former has only a few hours left to complete a transfer of required documents and device forensic reports on an ex-Apple engineer who stands accused of stealing proprietary intellectual property (IP) at the behest of Oppo. Apple accuses Oppo of using its former employee, Chen Shi, to steal Apple Watch secrets Before going further, let's summarize what has happened in this high-stakes saga so far: Apple is asking the court for injunctive relief on four counts: For its part, Oppo maintains that it has conducted a comprehensive search of […]

Read full article at https://wccftech.com/oppo-has-just-a-few-hours-left-to-hand-over-to-apple-evidentiary-documents-on-a-former-engineer-who-stole-apple-watch-secrets/

In a market that is littered with countless options, Sony successfully stands out with its family of wireless headphones that offer comfort, impeccable audio, a boatload of features, and value, though the latter is subjective, especially if you are not on the hunt for the WH-1000XM6, which cost a jaw-dropping $458 on Amazon. Sure, the latter are the crème de la crème of wireless headphones, but if your primary objective is affordability, you will want to pick the WH-1000XM4, which are available at the same online retailer, but at a more affordable $198, or 43 percent off. Despite being two […]

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A new report from GamesIndustry.Biz, based on data provided by Alinea Analytics, shows that in the lead-up to launch, Call of Duty: Black Ops 7 trails "far behind" the numbers that Battlefield 6 was able to pull. Setting the parameters here: this is based on data from Steam pre-order sales for Battlefield 6 and Call of Duty: Black Ops 7, 18 days ahead of their respective launches. Within that 18-day lead-up period, Battlefield 6 was able to sell close to a million copies in pre-orders. Black Ops 7 has only managed 200K pre-order copies sold. These numbers start to look […]

Read full article at https://wccftech.com/call-of-duty-black-ops-7-far-behind-battlefield-6-pre-order-sales/

After Team Group, now Corsair also claims to have reached 14,900 MB/s of read speeds on its latest PCIe 5.0 SSD. CORSAIR Launches MP700 PRO XT and Compact 2242 Form Factor MP700 MICRO PCIe 5.0 SSDs with Blazing Fast Read/Write Speeds One of the leading hardware and peripheral manufacturers, CORSAIR, has released its two new high-performance PCIe 5.0 SSDs for enthusiasts, offering the best-in-class performance for PC builders. The first SSD is the MP700 PRO XT, which is its flagship offering, delivering up to 14,900 MB/s of sequential Read speeds and up to 14,500 MB/s of sequential Write speeds. If […]

Read full article at https://wccftech.com/corsair-unveils-its-flagship-mp700-pro-xt-pcie-5-0-ssd-offering-up-to-14900-mb-s-of-read-speeds/

With each passing month, artificial intelligence creeps into more industries. That does not exclude the gaming industry, which has long used artificial intelligence to populate its virtual worlds. Still, the generative AI that is taking root everywhere offers much more power, and also much greater risk, compared to what gaming developers were used to. Big companies like Microsoft, Amazon, and EA are already laying off (or thinking about laying off) employees to invest further into artificial intelligence. What do the actual developers think about this artificial intelligence revolution? Their takes, as you would expect, are quite varied. The creator of […]

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The latest AIO cooler from Thermaltake will work with Intel's upcoming LGA 1954 platform, as spotted on the official website. Thermaltake Lists LGA 1954 as a Compatible Socket for MINECUBE 360 Ultra ARGB Sync AIO Cooler, Confirming Support for Intel Nova Lake Popular cooler and PC case maker, Thermaltake, has officially listed the Intel LGA 1954 socket as a compatible platform for one of its latest AIO coolers. Thermaltake's MINECUBE 360 Ultra ARGB Sync, which was showcased at Computex this year, lists the LGA 1954 on its compatibility list, which confirms that the cooler won't just be compatible with the […]

Read full article at https://wccftech.com/thermaltake-confirms-one-of-its-existing-aio-coolers-will-be-compatible-with-lga-1954-socket/

Pathea Games, the studio known for games like My Time at Portia, My Time at Sandrock, and the upcoming My Time at Evershine has just shadow-dropped something you'd be more likely to expect from Velan Studios after its game Knockout City, or even Psyonix as a spin-off from Rocket League with Superball, a new free-to-play 3v3 arcade hero football game that's out now on PC and Xbox Series X/S. Announced during the ID@Xbox and IGN Showcase, Superball is described as a mash between Rocket League, something that's made extremely obvious with its giant ball and arena style, and Overwatch with […]

Read full article at https://wccftech.com/rocket-league-minus-cars-f2p-superball-pathea-games/

ClearWork helps companies transform their operations by first automatically discovering and mapping their actual, end-to-end processes. Unlike old-school methods that rely on manual workshops and guesswork, our AI analyzes real user activity to give a precise, objective view of current operations and pinpoint friction points.

From there, we use AI to help you model and plan an optimized future state that's grounded in your operational reality. Finally, we provide an AI co-pilot, powered by your own data, and orchestrate automated, cross-platform workflows to ensure new processes are not only planned but also executed and sustained across the organization.

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Data security company Fortanix Inc. announced a new joint solution with NVIDIA: a turnkey platform that allows organizations to deploy agentic AI within their own data centers or sovereign environments, backed by NVIDIA’s "confidential computing" GPUs.

“Our goal is to make AI trustworthy by securing every layer—from the chip to the model to the data," said Fortanix CEO and co-founder Anand Kashyap, in a recent video call interview with VentureBeat. "Confidential computing gives you that end-to-end trust so you can confidently use AI with sensitive or regulated information.”

The solution arrives at a pivotal moment for industries such as healthcare, finance, and government — sectors eager to embrace AI but constrained by strict privacy and regulatory requirements.

Fortanix’s new platform, powered by NVIDIA Confidential Computing, enables enterprises to build and run AI systems on sensitive data without sacrificing security or control.

“Enterprises in finance, healthcare and government want to harness the power of AI, but compromising on trust, compliance, or control creates insurmountable risk,” said Anuj Jaiswal, chief product officer at Fortanix, in a press release. “We’re giving enterprises a sovereign, on-prem platform for AI agents—one that proves what’s running, protects what matters, and gets them to production faster.”

Secure AI, Verified from Chip to Model

At the heart of the Fortanix–NVIDIA collaboration is a confidential AI pipeline that ensures data, models, and workflows remain protected throughout their lifecycle.

The system uses a combination of Fortanix Data Security Manager (DSM) and Fortanix Confidential Computing Manager (CCM), integrated directly into NVIDIA’s GPU architecture.

“You can think of DSM as the vault that holds your keys, and CCM as the gatekeeper that verifies who’s allowed to use them," Kashyap said. "DSM enforces policy, CCM enforces trust.”

DSM serves as a FIPS 140-2 Level 3 hardware security module that manages encryption keys and enforces strict access controls.

CCM, introduced alongside this announcement, verifies the trustworthiness of AI workloads and infrastructure using composite attestation—a process that validates both CPUs and GPUs before allowing access to sensitive data.

Only when a workload is verified by CCM does DSM release the cryptographic keys necessary to decrypt and process data.

“The Confidential Computing Manager checks that the workload, the CPU, and the GPU are running in a trusted state," explained Kashyap. "It issues a certificate that DSM validates before releasing the key. That ensures the right workload is running on the right hardware before any sensitive data is decrypted.”

This “attestation-gated” model creates what Fortanix describes as a provable chain of trust extending from the hardware chip to the application layer.

It’s an approach aimed squarely at industries where confidentiality and compliance are non-negotiable.

From Pilot to Production—Without the Security Trade-Off

According to Kashyap, the partnership marks a step forward from traditional data encryption and key management toward securing entire AI workloads.

Kashyap explained that enterprises can deploy the Fortanix–NVIDIA solution incrementally, using a lift-and-shift model to migrate existing AI workloads into a confidential environment.

“We offer two form factors: SaaS with zero footprint, and self-managed. Self-managed can be a virtual appliance or a 1U physical FIPS 140-2 Level 3 appliance," he noted. "The smallest deployment is a three-node cluster, with larger clusters of 20–30 nodes or more.”

Customers already running AI models—whether open-source or proprietary—can move them onto NVIDIA’s Hopper or Blackwell GPU architectures with minimal reconfiguration.

For organizations building out new AI infrastructure, Fortanix’s Armet AI platform provides orchestration, observability, and built-in guardrails to speed up time to production.

“The result is that enterprises can move from pilot projects to trusted, production-ready AI in days rather than months,” Jaiswal said.

Compliance by Design

Compliance remains a key driver behind the new platform’s design. Fortanix’s DSM enforces role-based access control, detailed audit logging, and secure key custody—elements that help enterprises demonstrate compliance with stringent data protection regulations.

These controls are essential for regulated industries such as banking, healthcare, and government contracting.

The company emphasizes that the solution is built for both confidentiality and sovereignty.

For governments and enterprises that must retain local control over their AI environments, the system supports fully on-premises or air-gapped deployment options.

Fortanix and NVIDIA have jointly integrated these technologies into the NVIDIA AI Factory Reference Design for Government, a blueprint for building secure national or enterprise-level AI systems.

Future-Proofed for a Post-Quantum Era

In addition to current encryption standards such as AES, Fortanix supports post-quantum cryptography (PQC) within its DSM product.

As global research in quantum computing accelerates, PQC algorithms are expected to become a critical component of secure computing frameworks.

“We don’t invent cryptography; we implement what’s proven,” Kashyap said. “But we also make sure our customers are ready for the post-quantum era when it arrives.”

Real-World Flexibility

While the platform is designed for on-premises and sovereign use cases, Kashyap emphasized that it can also run in major cloud environments that already support confidential computing.

Enterprises operating across multiple regions can maintain consistent key management and encryption controls, either through centralized key hosting or replicated key clusters.

This flexibility allows organizations to shift AI workloads between data centers or cloud regions—whether for performance optimization, redundancy, or regulatory reasons—without losing control over their sensitive information.

Fortanix converts usage into “credits,” which correspond to the number of AI instances running within a factory environment. The structure allows enterprises to scale incrementally as their AI projects grow.

Fortanix will showcase the joint platform at NVIDIA GTC, held October 27–29, 2025, at the Walter E. Washington Convention Center in Washington, D.C. Visitors can find Fortanix at booth I-7 for live demonstrations and discussions on securing AI workloads in highly regulated environments.

About Fortanix

Fortanix Inc. was founded in 2016 in Mountain View, California, by Anand Kashyap and Ambuj Kumar, both former Intel engineers who worked on trusted execution and encryption technologies. The company was created to commercialize confidential computing—then an emerging concept—by extending the security of encrypted data beyond storage and transmission to data in active use, according to TechCrunch and the company’s own About page.

Kashyap, who previously served as a senior security architect at Intel and VMware, and Kumar, a former engineering lead at Intel, drew on years of work in trusted hardware and virtualization systems. Their shared insight into the gap between research-grade cryptography and enterprise adoption drove them to found Fortanix, according to Forbes and Crunchbase.

Today, Fortanix is recognized as a global leader in confidential computing and data security, offering solutions that protect data across its lifecycle—at rest, in transit, and in use.

Fortanix serves enterprises and governments worldwide with deployments ranging from cloud-native services to high-security, air-gapped systems.

"Historically we provided encryption and key-management capabilities," Kashyap said. "Now we’re going further to secure the workload itself—specifically AI—so an entire AI pipeline can run protected with confidential computing. That applies whether the AI runs in the cloud or in a sovereign environment handling sensitive or regulated data.

Another powerful creation option for your short-form video clips. 

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Australia's under 16 social media restrictions will come into effect in December.

Microsoft and OpenAI signed a deal extending Microsoft's rights to OpenAI models through 2032, confirming a 27% stake worth $135B

The post Microsoft Locks In OpenAI Partnership Through 2032 appeared first on Search Engine Journal.

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AccessGrid raised $4.4M to expand its mobile access control system that turns smartphones into secure digital keys for buildings.

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Despite widespread anxieties about artificial intelligence decimating the workforce, Steve Odland, CEO of The Conference Board, offers a more nuanced, and perhaps more optimistic, perspective: AI is not primarily a job killer, but a catalyst for productivity. He contends that while AI will profoundly reshape the professional landscape, current large-scale layoffs stem more from broader […]

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Google's new accelerator program is investing in 11 AI cybersecurity startups in Latin America, aiming to fortify the region's digital defenses.

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Dan Dolev, Mizuho’s managing director and senior analyst covering the fintech and payments space, spoke with the host of CNBC’s “The Exchange” following the announcement of a strategic partnership between PayPal and OpenAI. The discussion centered on the potential total addressable market for “agentic commerce” and the specific upside for PayPal in this burgeoning domain, […]

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Fireworks AI secured $250 million to expand its platform that makes AI inference faster and more cost-effective for developers.

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Google DeepMind’s Nano Banana, the image model that recently captivated the internet, represents a pivotal moment in the democratization and evolution of digital creativity. Its creators, Principal Scientist Oliver Wang and Group Product Manager Nicole Brichtova, recently sat down with a16z partners Yoko Li and Guido Appenzeller to unravel the model’s origins, its unexpected viral […]

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The post Google Gemini for Home: The AI Assistant’s Next Evolution appeared first on StartupHub.ai.

Google Gemini for Home is rolling out in early access, upgrading smart assistants with advanced conversational AI and introducing a premium subscription for enhanced features.

The post Google Gemini for Home: The AI Assistant’s Next Evolution appeared first on StartupHub.ai.

The post Mem0 raises $24M to cure AI’s digital amnesia appeared first on StartupHub.ai.

Mem0 is tackling AI's "digital amnesia" with a universal memory layer, aiming to become the foundational database for the next generation of intelligent agents.

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The post Amazon’s AI-Driven Efficiency Reshapes Big Tech Workforce appeared first on StartupHub.ai.

The transformative power of artificial intelligence, while heralding unprecedented innovation, is simultaneously catalyzing a profound restructuring of the tech workforce, a reality starkly illustrated by Amazon’s recent corporate layoffs. As CNBC’s MacKenzie Sigalos reported on “Money Movers,” Amazon is embarking on a multi-year efficiency drive, predominantly focused on “hollowing out layers of middle management.” This […]

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The convergence of advanced artificial intelligence and a uniquely poised global economy is setting the stage for an unprecedented era of mergers and acquisitions, fundamentally altering how companies operate and how value is created. This transformative period, characterized by both immense opportunity and inherent risks, was a central theme in Ken Moelis’s discussion with CNBC’s […]

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Google Labs' new Pomelli AI aims to democratize on-brand social media campaign generation for SMBs by leveraging AI to understand and replicate brand identity.

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A stark warning echoes from the latest CNBC Fed Survey: nearly 80% of respondents believe AI stocks are currently overvalued, with a quarter deeming them “extremely overvalued.” This sentiment, highlighted by CNBC Senior Economics Reporter Steve Liesman on “Squawk on the Street,” paints a picture of growing apprehension within the investment community regarding the sustainability […]

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“I cannot believe that they are doing it this way.” This sentiment, articulated by Jake Heller, co-founder and CEO of Casetext, encapsulates the entrepreneurial spark that ignited his $650 million AI legal startup, CoCounsel, recently acquired by Thomson Reuters. His candid talk at the AI Startup School on June 17th, 2025, offered a masterclass in […]

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Corsair extends its PCIe 5.0 offerings with its MP700 PRO XT and MP700 Micro Corsair has just added two new SSDs to its PCIe 5.0 storage lineup, promising high-end SSD performance and Microsoft DirectStorage support. Catering to the high-end market, Corsair’s new MP700 PRO XT SSD promises performance levels that reach the limits of the […]

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NVIDIA has shown off its next-gen Vera Rubin Superchip for the first time at GTC in Washington, primed to spark the next wave of AI. NVIDIA Has Received Its First Rubin GPUs In The Labs, Ready For Vera Rubin Superchip Mass Production Next Year, Around The Same Time or Earlier At GTC October 2025, NVIDIA's CEO Jensen Huang showcased the next-gen Vera Rubin Superchip. This is the first time that we are seeing an actual sample of the motherboard, or Superchip as NVIDIA loves to call it, featuring the Vera CPU and two massive Rubin GPUs. The motherboard also hosts […]

Read full article at https://wccftech.com/nvidia-shows-next-gen-vera-rubin-superchip-two-massive-gpus-production-next-year/

NVIDIA has announced a surprise partnership with Nokia to bring 6G connectivity by utilizing the firm's new AI-RAN products, involving Grace CPUs and Blackwell GPUs. NVIDIA's Collaboration With Nokia Allows Merging CUDA & Computing Tech With Existing RAN Infrastructure Team Green has managed to integrate AI into everything mainstream, and it seems that the telecommunications industry is now expected to benefit from the next wave of AI's computing capabilities. At the GTC 2025 keynote, NVIDIA's CEO announced a pivotal partnership with Nokia, formally entering the race for achieving 6G connectivity through a new suite of AI-RAN products combined with Nokia's […]

Read full article at https://wccftech.com/nvidia-announces-a-massive-partnership-with-nokia-bringing-next-gen-6g-connectivity/

Amazon is laying off more than 14,000 corporate jobs today, and per a report from Bloomberg, the video games division, Amazon Game Studios, is not immune to the cuts. While Amazon doesn't specify exactly how many people from its video games division will be laid off, a statement from Steve Boom, Amazon's head of audio, Twitch, and games, does call the cut "significant," and says that the cuts are happening despite Amazon being "proud" of the success it has had. While the studio's MMO, New World, isn't mentioned by name, the statement does say that Amazon is halting its game […]

Read full article at https://wccftech.com/amazon-video-game-division-hit-significant-cuts-amid-mass-14000-layoff/

Qualcomm will keep pace with Apple and announce its first 2nm chipset in late 2026, the Snapdragon 8 Elite Gen 6, directly succeeding the Snapdragon 8 Elite Gen 5. A tipster now shares some partial specifications of the chipset, claiming that it will feature LPDDR6 RAM and UFS 5.0 storage, bringing in a wave of improvements. However, the rumor also mentions that the Snapdragon 8 Elite Gen 6 will utilize TSMC’s more advanced ‘N2P’ process, which has been refuted on a previous occasion. Based on TSMC’s 2nm production timeline, its N2 wafers will be available in higher volume for customers like […]

Read full article at https://wccftech.com/snapdragon-8-elite-gen-6-to-get-lpddr6-and-ufs-5-0-support-but-will-stick-with-tsmc-n2-process/

Zack Fair's gameplay in Final Fantasy VII Rebirth is set to be significantly expanded by a new mod introducing new mechanics and skill for an overhauled combat experience. This Zack gameplay overhaul mod is being developed by NSK, the modder behind the Zack and Sephiroth Combat Fix mod whichaddressed some issues for the two characters and expanded their possibilities when added to the regular combat party outside their small playable segments. Judging from the video showcase shared a few days ago on YouTube, the changes being made to Zack's gameplay are going to be significant, leveraging his unique Charge mechanics […]

Read full article at https://wccftech.com/final-fantasy-vii-rebirth-zack-gameplay-mod/

It's a big day for Battlefield 6, with both its Season 1 update now live for players to jump into, and its new free-to-play battle royale mode, Battlefield REDSEC, also now available. EA and Battlefield Studios confirmed yesterday what was already rumored, that REDSEC would be revealed and launched today, and now it's here for all players on PC, PS5, and Xbox Series X/S. Once the gameplay trailer that was teased yesterday was over, the mode and the new season was officially live for all players to jump into, and we got our first major question of the day answered. […]

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The Pro 27Q-10 is probably the cheapest QHD OLED gaming monitor available on the market and is currently available for just 2,399 Yuan in China. Lenovo Debuts Legion Pro Series OLED Monitors, Starting at $337; Available in Both 2K and 4K Variants with Up To 280Hz Refresh Rate Competition in the OLED display category is getting aggressive, and while we already have some QHD OLED gaming monitors available for as low as $450-$500, Lenovo just brought the price to under $350. Lenovo is the most popular PC brand on earth, isn't just involved in desktops and laptops; it is also […]

Read full article at https://wccftech.com/lenovo-launches-legion-pro-27q-10-the-cheapest-qhd-oled-monitor-at-just-337/

President Trump is expected to meet with NVIDIA's CEO, Jensen Huang, during his visit to South Korea, where he will congratulate him on the firm's recent achievements. President Trump Will Congratulate NVIDIA On Producing The First Blackwell Chip Wafer In the US Well, the timing of a meeting between President Trump and Jensen Huang is indeed a 'massive' coincidence, to say the least, especially since both the US and China have agreed on a trade deal framework, which is expected to reduce hostilities between the two nations. While speaking with business leaders in Tokyo, Japan, President Trump announced his meeting […]

Read full article at https://wccftech.com/president-trump-to-meet-nvidia-ceo-jensen-huang/

OpenAI has been working for quite a while now with the famous Apple designer, Jony Ive, to come up with a consumer AI device, one that would supposedly render smartphones obsolete, devastating Apple's legendary moat around its iPhones in the process. Now, we have just received the clearest sign yet that OpenAI is indeed working on such a device. What's more, Microsoft will no longer exercise any influence over the upcoming "Apple iPhone killer." OpenAI and Microsoft have successfully renegotiated their tie-up, removing the latter's influence over the former's upcoming "Apple iPhone killer" consumer device, among other things Microsoft and […]

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Wrekcreation, the sandbox open-world arcade racing game from Three Fields Entertainment, a studio founded by former Criterion developers who worked on the Burnout series, is out now on PC, PS5, and Xbox Series X/S. Published by THQ Nordic, Wreckreation gives players the freedom to create whatever kinds of tracks they want, from the kinds of things you'd only expect to see in Hot Wheels Unleashed to something super realistic if that's more your speed, and race the wide variety of vehicles on them to your heart's content. With more than 400 square kilometres of space to create tracks in and […]

Read full article at https://wccftech.com/sandbox-racer-wreckreation-out-now-pc-ps5-xbox-series/

The advent of AI and Meta's launch of its smart glasses have injected fresh air into the sector after Google decided to shelve its smart glasses in 2023, the sector has seen increased interest. In fact, Meta CEO Mark Zuckerberg has gone as far as to suggest that courtesy of AI, users who do not use smart glasses can find themselves at a cognitive disadvantage. To understand the smart glasses industry and how the gadgets can impact consumer electronics manufacturing, semiconductor fabrication and AI GPU production, we decided to talk to Vuzix Corporation's President, Enterprise Solutions Dr. Chris Parkinson. Vuzix […]

Read full article at https://wccftech.com/smart-glasses-can-be-the-future-of-chip-manufacturing-and-smartphone-ai-gpu-production-says-vuzixs-enterprise-solutions-head/

TSMC's former SVP, known for his key role in driving the Taiwan giant's chip technologies, is reportedly being pursued to join Intel Foundry, which could be a significant hiring move for Team Blue. Intel's Pursuit of TSMC's Former Executive Shows the Firm's 'Hunger' Towards a Comeback in the Chip Industry Intel has been scaling up its chipmaking ambitions since the change in leadership, and under CEO Lip-Bu Tan, the foundry division has vowed to gain recognition in the semiconductor industry. Structural changes are being made within the department, including adjustments to the management hierarchy and the approach towards specific chip […]

Read full article at https://wccftech.com/intel-foundry-reportedly-pursuing-former-tsmc-executive/

Developer ArenaNet has launched the sixth major expansion for Guild Wars 2 today, with Visions of Eternity now available to players on PC. Visions of Eternity adds a new island to explore called Castora, with two new maps to explore, a new storyline, and plenty more. The new storyline kicks off with whispers and rumors of the island of Castora, with the Tyrian Alliance stepping in to uncover more about the magical island once they discover that the Inquest has begun sniffing around for Castora. Alongside two new maps included with the new expansion, Shipwreck Strand and Starlit Weald, players […]

Read full article at https://wccftech.com/guild-wars-2-visions-of-eternity-expansion-out-now-pc/

Tampo is a modern task and team management platform built for startups and growing teams. It helps you organize projects, assign tasks, and collaborate seamlessly—all in one place. With features like multi-user assignments, real-time tracking, and smart filters, Tampo simplifies team coordination without sacrificing power. Designed to be fast, intuitive, and mobile-friendly, Tampo is the productivity partner your team needs to get more done, together.

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Less than two weeks ahead of the United Nations climate conference, Bill Gates posted a memo on his personal blog encouraging folks to just calm down about climate change.

“Although climate change will have serious consequences — particularly for people in the poorest countries — it will not lead to humanity’s demise. People will be able to live and thrive in most places on Earth for the foreseeable future,” Gates wrote.

The missive seems to run counter to earlier climate actions taken by the Microsoft co-founder and billionaire, but also echoes Gates’ long-held priorities and perspectives. In some regards, it’s the framing, timing and broader political context that heighten the memo’s impact.

What the world needs to do, he said, is to shift the goals away from reducing carbon emissions and keeping warming below agreed-upon temperature targets.

“This is a chance to refocus on the metric that should count even more than emissions and temperature change: improving lives,” he wrote. “Our chief goal should be to prevent suffering, particularly for those in the toughest conditions who live in the world’s poorest countries.“

More than four years ago, Gates published “How to Avoid a Climate Disaster,” a book highlighting the urgency and necessity of cutting carbon emissions and promoting the need to reduce “green premiums” in order to make climate friendly technologies as cheap as unsustainable alternatives.

“It’ll be tougher than anything humanity’s ever done, and only by staying constant in working on this over the next 30 years do we have a chance to do it,” Gates told GeekWire in 2021. “Having some people who think it’s easy will be an impediment. Having people who think that it’s not important will be an impediment.”

Gates’ clean energy efforts go back even earlier. In 2006 he helped launch the next-gen nuclear company TerraPower, which is currently building its first reactor in Wyoming. In 2015 he founded Breakthrough Energy Ventures, a $1 billion fund to support carbon-cutting startups, which evolved into Breakthrough Energy, an umbrella organization tackling clean tech policies, funding for researchers and data generation.

Earlier this year, however, Gates began taking steps that suggested a cooling commitment to the challenge.

Roughly two months after President Trump took office in January, and as clean energy policies and funding began getting axed, Breakthrough Energy laid off staff. In May Gates announced he would direct nearly all of his wealth to his eponymous global health foundation, deploying $200 billion through the organization over two decades.

At the same time, many of the key points in the memo published today reflect statements that Gates has made in the past.

In both his new post and at a 2022 global climate summit organized in Seattle by Breakthrough Energy, Gates urged people to focus on reducing green premiums more than on cutting emissions as a key benchmark.

“If you keep the primary measures, which is the emissions reductions in the near term, you’re going to be very depressed,” Gates said. At his summit talk, he shared optimism that new innovations were arriving quickly and would address climate challenges.

A curious paradox in Gates’ stance is the reality that people living in lower-income nations and in regions important to the Gates Foundation are often hardest hit by the rising temperatures and natural disasters that are stoked by increased carbon emissions.

Gates acknowledged that truth in his post this week, and said that solutions such as engineering drought tolerant crops and making air conditioning more widespread can address some of those harms. At the Seattle summit three years ago, one of the Breakthrough Energy executives likewise said the organization was going to increase its investment into technologies for adapting to climate change.

On Nov. 10, global climate leaders will meet in Brazil for COP30 to discuss climate progress and issues. Gates has often attended the event, but the New York Times reported that won’t be the case this year.

UN efforts meanwhile continue to emphasize the importance of reducing emissions. A statement today from the organization notes that while carbon emissions are curving downward, it’s not happening fast enough.

The world needs to raise its climate ambitions, the statement continues, “to avoid the worst climate impacts by limiting warming to 1.5°C this century, as science demands.”

GitHub is making a bold bet that enterprises don't need another proprietary coding agent: They need a way to manage all of them.

At its Universe 2025 conference, the Microsoft-owned developer platform announced Agent HQ. The new architecture transforms GitHub into a unified control plane for managing multiple AI coding agents from competitors including Anthropic, OpenAI, Google, Cognition and xAI. Rather than forcing developers into a single agent experience, the company is positioning itself as the essential orchestration layer beneath them all.

Agent HQ represents GitHub's attempt to apply its collaboration platform approach to AI agents. Just as the company transformed Git, pull requests and CI/CD into collaborative workflows, it's now trying to do the same with a fragmented AI coding landscape.

The announcement marks what GitHub calls the transition from "wave one" to "wave two" of AI-assisted development. According to GitHub's Octoverse report, 80% of new developers use Copilot in their first week and AI has helped to lead to a large increase overall in the use of the GitHub platform.

"Last year, the big announcements for us, and what we were saying as a company, is wave one is done, that was kind of code completion," GitHub's COO Mario Rodriguez told VentureBeat. "We're into this wave two era, [which] is going to be multimodal, it's going to be agentic and it's going to have these new experiences that will feel AI native."

What is Agent HQ?

GitHub already updated its GitHub Copilot coding tool for the agentic era with the debut of GitHub Copilot Agent in May.

Agent HQ transforms GitHub into an open ecosystem that unites multiple AI coding agents on a single platform. Over the coming months, coding agents from Anthropic, OpenAI, Google, Cognition, xAI and others will become available directly within GitHub as part of existing paid GitHub Copilot subscriptions.

The architecture maintains GitHub's core primitives. Developers still work with Git, pull requests and issues. They still use their preferred compute, whether GitHub Actions or self-hosted runners. What changes is the layer above: agents from multiple vendors can now operate within GitHub's security perimeter, using the same identity controls, branch permissions and audit logging that enterprises already trust for human developers.

This approach differs fundamentally from standalone tools. When developers use Cursor or grant repository access to Claude, those agents typically receive broad permissions across entire repositories. Agent HQ compartmentalizes access at the branch level and wraps all agent activity in enterprise-grade governance controls.

Mission Control: One interface for all agents

At the heart of Agent HQ is Mission Control. It's a unified command center that appears consistently across GitHub's web interface, VS Code, mobile apps and the command line. Through Mission Control, developers can assign work to multiple agents simultaneously. They can track progress and manage permissions, all from a single pane of glass.

The technical architecture addresses a critical enterprise concern: Security. Unlike standalone agent implementations where users must grant broad repository access, GitHub's Agent HQ implements granular controls at the platform level.

"Our coding agent has a set of security controls and capabilities that are built natively into the platform, and that's what we're providing to all of these other agents as well," Rodriguez explained. "It runs with a GitHub token that is very locked down to what it can actually do."

Agents operating through Agent HQ can only commit to designated branches. They run within sandboxed GitHub Actions environments with firewall protections. They operate under strict identity controls. Rodriguez explained that even if an agent goes rogue, the firewall prevents it from accessing external networks or exfiltrating data unless those protections are explicitly disabled.

Technical differentiation: MCP integration and custom agents

Beyond managing third-party agents, GitHub is introducing two technical capabilities that set Agent HQ apart from alternative approaches like Cursor's standalone editor or Anthropic's Claude integration.

Custom agents via AGENTS.md files: Enterprises can now create source-controlled configuration files that define specific rules, tools and guardrails for how Copilot behaves. For example, a company could specify "prefer this logger" or "use table-driven tests for all handlers." This permanently encodes organizational standards without requiring developers to re-prompt every time.

"Custom agents have an immense amount of product market fit within enterprises, because they could just codify a set of skills that the coordination can do, then standardize on those and get really high quality output," Rodriguez said.

The AGENTS.md specification allows teams to version control their agent behavior alongside their code. When a developer clones a repository, they automatically inherit the custom agent rules. This solves a persistent problem with AI coding tools: Inconsistent output quality when different team members use different prompting strategies.

Native Model Context Protocol (MCP) support: VS Code now includes a GitHub MCP Registry. Developers can discover, install and enable MCP servers with a single click. They can then create custom agents that combine these tools with specific system prompts.

This positions GitHub as the integration point between the emerging MCP ecosystem and actual developer workflows. MCP, introduced by Anthropic but rapidly gaining industry support, is becoming a de facto standard for agent-to-tool communication. By supporting the full specification, GitHub can orchestrate agents that need access to external services without each agent implementing its own integration logic.

Plan Mode and agentic code review

GitHub is also shipping new capabilities within VS Code itself. Plan Mode allows developers to collaborate with Copilot on building step-by-step project approaches. The AI asks clarifying questions before any code is written. Once approved, the plan can be executed either locally in VS Code or by cloud-based agents.

The feature addresses a common failure mode in AI coding: Beginning implementation before requirements are fully understood. By forcing an explicit planning phase, GitHub aims to reduce wasted effort and improve output quality.

More significantly, GitHub's code review feature is becoming agentic. The new implementation will use GitHub's CodeQL engine, which previously largely focused on security vulnerabilities to identify bugs and maintainability issues. The code review agent will automatically scan agent-generated pull requests before human review. This creates a two-stage quality gate.

"Our code review agent will be able to make calls into the CodeQL engine to then find a set of bugs," Rodriguez explained. "We're extending the engine and we're going to be able to tap into that engine also to find bugs."

Enterprise considerations: What to do now

For enterprises already deploying multiple AI coding tools, Agent HQ offers a path to consolidation without forcing tool elimination.

GitHub's multi-agent approach provides vendor flexibility and reduces lock-in risk. Organizations can test multiple agents within a unified security perimeter and switch providers without retraining developers. The tradeoff is potentially less optimized experiences compared to specialized tools that tightly integrate UI and agent behavior.

Rodriguez's recommendation is clear: Begin with custom agents. This allows enterprises to codify organizational standards that agents follow consistently. Once established, organizations can layer in additional third-party agents to expand capabilities.

"Go and do agent coding, custom agents and start playing with that," he said. "That is a capability available tomorrow, and it allows you to really start shaping your SDLC to be personalized to you, your organization and your people."

Building AI for financial software requires a different playbook than consumer AI, and Intuit's latest QuickBooks release provides an example.

The company has announced Intuit Intelligence, a system that orchestrates specialized AI agents across its QuickBooks platform to handle tasks including sales tax compliance and payroll processing. These new agents augment existing accounting and project management agents (which have also been updated) as well as a unified interface that lets users query data across QuickBooks, third-party systems and uploaded files using natural language.

The new development follow years of investment and improvement in Intuit's GenOS, allowing the company to build AI capabilities that reduce latency and improve accuracy.

But the real news isn't what Intuit built — it's how they built it and why their design decisions will make AI more usable. The company's latest AI rollout represents an evolution built on hard-won lessons about what works and what doesn't when deploying AI in financial contexts.

What the company learned is sobering: Even when its accounting agent improved transaction categorization accuracy by 20 percentage points on average, they still received complaints about errors.

"The use cases that we're trying to solve for customers include tax and finance; if you make a mistake in this world, you lose trust with customers in buckets and we only get it back in spoonfuls," Joe Preston, Intuit's VP of product and design, told VentureBeat.

The architecture of trust: Real data queries over generative responses

Intuit's technical strategy centers on a fundamental design decision. For financial queries and business intelligence, the system queries actual data, rather than generating responses through large language models (LLMs).

Also critically important: That data isn't all in one place. Intuit's technical implementation allows QuickBooks to ingest data from multiple distinct sources: native Intuit data, OAuth-connected third-party systems like Square for payments and user-uploaded files such as spreadsheets containing vendor pricing lists or marketing campaign data. This creates a unified data layer that AI agents can query reliably.

"We're actually querying your real data," Preston explained. "That's very different than if you were to just copy, paste out a spreadsheet or a PDF and paste into ChatGPT."

This architectural choice means that the Intuit Intelligence system functions more as an orchestration layer. It's a natural language interface to structured data operations. When a user asks about projected profitability or wants to run payroll, the system translates the natural language query into database operations against verified financial data.

This matters because Intuit's internal research has uncovered widespread shadow AI usage. When surveyed, 25% of accountants using QuickBooks admitted they were already copying and pasting data into ChatGPT or Google Gemini for analysis.

Intuit's approach treats AI as a query translation and orchestration mechanism, not a content generator. This reduces the hallucination risk that has plagued AI deployments in financial contexts.

Explainability as a design requirement, not an afterthought

Beyond the technical architecture, Intuit has made explainability a core user experience across its AI agents. This goes beyond simply providing correct answers: It means showing users the reasoning behind automated decisions.

When Intuit's accounting agent categorizes a transaction, it doesn't just display the result; it shows the reasoning. This isn't marketing copy about explainable AI, it's actual UI displaying data points and logic.

"It's about closing that trust loop and making sure customers understand the why," Alastair Simpson, Intuit's VP of design, told VentureBeat.

This becomes particularly critical when you consider Intuit's user research: While half of small businesses describe AI as helpful, nearly a quarter haven't used AI at all. The explanation layer serves both populations: Building confidence for newcomers, while giving experienced users the context to verify accuracy.

The design also enforces human control at critical decision points. This approach extends beyond the interface. Intuit connects users directly with human experts, embedded in the same workflows, when automation reaches its limits or when users want validation.

Navigating the transition from forms to conversations

One of Intuit's more interesting challenges involves managing a fundamental shift in user interfaces. Preston described it as having one foot in the past and one foot in the future.

"This isn't just Intuit, this is the market as a whole," said Preston. "Today we still have a lot of customers filling out forms and going through tables full of data. We're investing a lot into leaning in and questioning the ways that we do it across our products today, where you're basically just filling out, form after form, or table after table, because we see where the world is headed, which is really a different form of interacting with these products."

This creates a product design challenge: How do you serve users who are comfortable with traditional interfaces while gradually introducing conversational and agentic capabilities?

Intuit's approach has been to embed AI agents directly into existing workflows. This means not forcing users to adopt entirely new interaction patterns. The payments agent appears alongside invoicing workflows; the accounting agent enhances the existing reconciliation process rather than replacing it. This incremental approach lets users experience AI benefits without abandoning familiar processes.

What enterprise AI builders can learn from Intuit's approach

Intuit's experience deploying AI in financial contexts surfaces several principles that apply broadly to enterprise AI initiatives.

Architecture matters for trust: In domains where accuracy is critical, consider whether you need content generation or data query translation. Intuit's decision to treat AI as an orchestration and natural language interface layer dramatically reduces hallucination risk and avoids using AI as a generative system.

Explainability must be designed in, not bolted on: Showing users why the AI made a decision isn't optional when trust is at stake. This requires deliberate UX design. It may constrain model choices.

User control preserves trust during accuracy improvements: Intuit's accounting agent improved categorization accuracy by 20 percentage points. Yet, maintaining user override capabilities was essential for adoption.

Transition gradually from familiar interfaces: Don't force users to abandon forms for conversations. Embed AI capabilities into existing workflows first. Let users experience benefits before asking them to change behavior.

Be honest about what's reactive versus proactive: Current AI agents primarily respond to prompts and automate defined tasks. True proactive intelligence that makes unprompted strategic recommendations remains an evolving capability.

Address workforce concerns with tooling, not just messaging: If AI is meant to augment rather than replace workers, provide workers with AI tools. Show them how to leverage the technology.

For enterprises navigating AI adoption, Intuit's journey offers a clear directive. The winning approach prioritizes trustworthiness over capability demonstrations. In domains where mistakes have real consequences, that means investing in accuracy, transparency and human oversight before pursuing conversational sophistication or autonomous action.

Simpson frames the challenge succinctly: "We didn't want it to be a bolted-on layer. We wanted customers to be in their natural workflow, and have agents doing work for customers, embedded in the workflow."

Washington state Gov. Bob Ferguson is threading the needle when it comes to artificial intelligence.

Ferguson made a brief appearance at the opening reception for Seattle AI Week on Monday evening, speaking at AI House on Pier 70 about his approach to governing the consequential technology.

“I view my job as maximizing the benefits and minimizing harms,” said Ferguson, who took office earlier this year.

Ferguson called AI one of the “top five biggest challenges” he thinks about daily, both professionally and personally.

In a follow-up interview with GeekWire, the governor said AI “could totally transform our government, as well as the private sector, in many ways.”

His comments came just as Amazon, the largest employer in Washington state, said it would eliminate about 14,000 corporate jobs, citing a need to reduce bureaucracy and become more efficient in the new era of artificial intelligence.

Ferguson told the crowd that the future of work and “loss of jobs that come with the technology” is on his mind.

The governor highlighted Washington’s AI Task Force, created during his tenure as attorney general, which is studying issues from algorithmic bias to data security. The group’s next set of recommendations arrives later this year and could shape upcoming legislation, he said.

States are moving ahead with their own AI rules in the absence of a comprehensive federal framework. Washington appears to sit in the pragmatic middle of this fast-moving regulatory landscape — using executive action and an expert task force to build guidelines, while watching experiments in states such as California and Colorado.

Seattle city leaders also getting involved. Seattle Mayor Bruce Harrell last month announced a “responsible AI plan” that provides guidelines for Seattle’s use of artificial intelligence and its support of the AI tech sector as an economic driver.

Ferguson said he’s aware of how AI can “really revolutionize our economy and state in so many ways,” from healthcare to education to wildfire detection.

But he also flagged his concerns — both as a policymaker and parent. The governor, who has 17-year-old twins, said he worries about the technology’s impact on young people, referencing reports of teen suicides linked to AI chatbots.

Despite those concerns, Ferguson maintained an upbeat tone during his remarks at Seattle AI Week, citing the region’s technical talent and economic opportunity from the technology.

He noted that the state, amid a $16 billion budget shortfall this year, kept $300,000 in funding for the AI House, the new waterfront startup hub that hosted Monday’s event.

“There is no better place anywhere in the United States for this innovation than right here in the Northwest,” he said.

Related: A tale of two Seattles in the age of AI: Harsh realities and new hope for the tech community

EVERETT, Wash. — In an industrial stretch of Everett is a boxy, windowless building called Ursa. Inside that building is a vault built from concrete blocks up to 5 feet thick with an additional layer of radiation-absorbing plastic. Within that vault is Polaris, a machine that could change the world.

Helion Energy is trying to replicate the physics that fuel the sun and the stars — hence the celestial naming theme — to provide nearly limitless power on earth through fusion reactions.

The company recently invited a small group of journalists to visit its headquarters and see Polaris, which is the seventh iteration of its fusion generator and the prototype for a commercial facility called Orion that broke ground this summer in Malaga in Central Washington.

Few people outside of Helion have been provided such access; photographs were not allowed.

“We run these systems right now at 100 million degrees, about 10 times the temperature of the sun, and compress them to high pressure… the same pressure as the bottom of the Marianas Trench,” said Helion CEO and co-founder David Kirtley, referencing the deepest part of the ocean.

Polaris and its vault occupy a relative small footprint inside of Ursa. The majority of the space is filled with 2,500 power units. They’re configured into 4-foot-by-4-foot pallets, lined up in rows and stacked seven high. The units are packed with capacitors that are charged from the grid to provide super high intensity pulses of electricity — 100 gigawatts of peak power — that create the temperatures and pressure needed for fusion reactions.

All of that energy is carried through miles and miles of coaxial cables filled with copper, aluminum and custom-metal alloys. End-to-end, the cables would stretch across Washington state and back again — roughly 720 miles. They flow in thick, black bundles from the pallets into the vault. They curl on the floor in giant heaps before connecting to the tubular-shaped, 60-foot-long Polaris generator.

The ultimate goal is for the generator to force lightweight ions to fuse, creating a super hot plasma that expands, pushing on a magnetic field that surrounds it. The energy created by that expansion is directly captured and carried back the capacitors to recharge them so the process can be repeated over and over again.

And the small amount of extra power that’s produced by fusion goes into the electrical grid for others to use — or at least that’s the plan for the future.

‘Worth being aggressive’

Helion is a contender in a global race to generate fusion power for a rapidly escalating demand for electricity, driven in part by data centers and AI. No one so far has been able to make and capture enough energy from fusion to commercialize the process, but dozens of companies — including three other competitors in the Pacific Northwest — are trying.

The company aims by 2028 to begin producing energy at the Malaga site, which Microsoft has agreed to purchase. If it hits this extremely ambitious target — and many are highly skeptical — it could be the world’s first company to do so.

“There is a level of risk, of being aggressive with program development, new technology and timelines,” Kirtley said. “But I think it’s worth it. Fusion is the same process that happens in the stars. It has the promise of very low cost electricity that’s clean and safe and base load and always on. And so it’s worth being aggressive.”

Some in the sector worry that Helion will miss the mark and cast doubt on a sector that is working hard to prove itself. At a June event, the head of R&D for fusion competitor Zap Energy questioned Helion’s deadline.

“I don’t see a commercial application in the next few years happening,” said Ben Levitt. “There is a lot of complicated science and engineering still to be discovered and to be applied.”

Others are willing to take the bet. Helion has raised more than $1 billion from investors that include SoftBank, Lightspeed Venture Partners and Sam Altman, who is OpenAI’s CEO and co-founder, as well as Helion’s longtime chair of its board of directors. The company is able to unlock an additional $1.8 billion if it hits Polaris milestones.

The generator has been operating since December, running all day, five days a week, creating fusion, Kirtley said.

Energy without ignition

Helion is highly cautious — some would say too cautious — in sharing details on its progress. Helion officials say they must hold their tech close to the vest as Chinese competitors have stolen pieces of their intellectual property; critics say the secrecy makes it difficult for the scientific community to verify their likelihood of success in a very risky, highly technical field.

In August, Kirtley shared an online post about Helion’s power-producing strategy, which upends the conventional approach.

Most efforts are trying to achieve ignition in their fusion generators, which is a condition where the reactions produce more power than is required for fusion to occur. This feat was first accomplished at a national lab in California in 2022 — but it still wasn’t enough energy that one could put electricity on the grid.

Helion is not aiming for ignition but rather for a system that is so efficient it can capture enough energy from fusion without reaching that state.

Kirtley compares the strategy for producing power to regenerative braking in electric vehicles. Simply put, an EV’s battery gets the car moving, and regenerative braking by the driver puts energy back into the battery to help it run longer. In the fusion generator, the capacitors provide that initial power, and the fusion reaction resupplies the energy and a little bit more.

“We can recover electricity at high efficiency,” Kirtley said. Compared to other commercial fusion approaches, “we require a lot less fusion. Fusion is the hard part. My goal, ironically, is to do the minimum amount of fusion that we can deliver a product to the customer and generate electricity.”

Google has expanded the What’s happening feature within Google Business Profiles to restaurants and bars in the United Kingdom, Canada, Australia, and New Zealand. It is now available for multi-location restaurants, not just single-location restaurants.

The What’s happening feature launched back in May as a way for some businesses to highlight events, deals, and specials prominently at the top of your Google Business Profile. Now, Google is bringing it to more countries.

What Google said. Google’s Lisa Landsman wrote on LinkedIn:

How do you promote your “Taco Tuesday” in Toledo and your “Happy Hour” in Houston… right when locals are searching for a place to go?

I’m excited to share that the Google Business Profile feature highlighting what’s happening at your business, such as timely events, specials and deals, has now rolled out for multi-location restaurants & bars across the US, UK, CA, AU & NZ! (It was previously only available for single-location restaurants)

This is a great option for driving real-time foot traffic. It automatically surfaces the unique specials, live music, or events you’re already promoting at a specific location, catching customers at the exact moment they’re deciding where to eat or grab a cocktail.

What it looks like. Here is a screenshot of this feature:

More details. Google’s Lisa Landsman added, “We’ve already seen excellent results from testing and look forward to hearing how this works for you!”

Availability. This feature is only available for restaurants & bars. Google said it hopes to expand to more categories soon. It is also only available in the United States, United Kingdom, Canada, Australia, and New Zealand.

The initial launch was for single-location Food and Drink businesses in the U.S., UK, Australia, Canada, and New Zealand. It is now available for multi-location restaurants, not just single-location restaurants.

Why we care. If you manage restaurants and/or bars, this may be a new way to get more attention and visitors to your business from Google Search. Now, if you manage multi-location restaurants or bars, you can leverage this feature.

Marketing, technology, and business leaders today are asking an important question: how do you optimize for large language models (LLMs) like ChatGPT, Gemini, and Claude? 

LLM optimization is taking shape as a new discipline focused on how brands surface in AI-generated results and what can be measured today. 

For decision makers, the challenge is separating signal from noise – identifying the technologies worth tracking and the efforts that lead to tangible outcomes.

The discussion comes down to two core areas – and the timeline and work required to act on them:

• Tracking and monitoring your brand’s presence in LLMs.
• Improving visibility and performance within them.

Tracking: The foundation of LLM optimization

Just as SEO evolved through better tracking and measurement, LLM optimization will only mature once visibility becomes measurable. 

We’re still in a pre-Semrush/Moz/Ahrefs era for LLMs. 

Tracking is the foundation of identifying what truly works and building strategies that drive brand growth. 

Without it, everyone is shooting in the dark, hoping great content alone will deliver results.

The core challenges are threefold:

• LLMs don’t publish query frequency or “search volume” equivalents.
• Their responses vary subtly (or not so subtly) even for identical queries, due to probabilistic decoding and prompt context.
• They depend on hidden contextual features (user history, session state, embeddings) that are opaque to external observers.

Why LLM queries are different

Traditional search behavior is repetitive – millions of identical phrases drive stable volume metrics. LLM interactions are conversational and variable. 

People rephrase questions in different ways, often within a single session. That makes pattern recognition harder with small datasets but feasible at scale. 

These structural differences explain why LLM visibility demands a different measurement model.

This variability requires a different tracking approach than traditional SEO or marketing analytics.

The leading method uses a polling-based model inspired by election forecasting.

The polling-based model for measuring visibility

A representative sample of 250–500 high-intent queries is defined for your brand or category, functioning as your population proxy. 

These queries are run daily or weekly to capture repeated samples from the underlying distribution of LLM responses.

Tracking tools record when your brand and competitors appear as citations (linked sources) or mentions (text references), enabling share of voice calculations across all competitors. 

Over time, aggregate sampling produces statistically stable estimates of your brand visibility within LLM-generated content.

Early tools providing this capability include:

• Profound.
• Conductor.
• OpenForge.

Consistent sampling at scale transforms apparent randomness into interpretable signals. 

Over time, aggregate sampling provides a stable estimate of your brand’s visibility in LLM-generated responses – much like how political polls deliver reliable forecasts despite individual variations.

Building a multi-faceted tracking framework

While share of voice paints a picture of your presence in the LLM landscape, it doesn’t tell the complete story. 

Just as keyword rankings show visibility but not clicks, LLM presence doesn’t automatically translate to user engagement. 

Brands need to understand how people interact with their content to build a compelling business case.

Because no single tool captures the entire picture, the best current approach layers multiple tracking signals:

• Share of voice (SOV) tracking: Measure how often your brand appears as mentions and citations across a consistent set of high-value queries. This provides a benchmark to track over time and compare against competitors.
• Referral tracking in GA4: Set up custom dimensions to identify traffic originating from LLMs. While attribution remains limited today, this data helps detect when direct referrals are increasing and signals growing LLM influence.
• Branded homepage traffic in Google Search Console: Many users discover brands through LLM responses, then search directly in Google to validate or learn more. This two-step discovery pattern is critical to monitor. When branded homepage traffic increases alongside rising LLM presence, it signals a strong causal connection between LLM visibility and user behavior. This metric captures the downstream impact of your LLM optimization efforts.

Nobody has complete visibility into LLM impact on their business today, but these methods cover all the bases you can currently measure.

Be wary of any vendor or consultant promising complete visibility. That simply isn’t possible yet.

Understanding these limitations is just as important as implementing the tracking itself.

Because no perfect models exist yet, treat current tracking data as directional – useful for decisions, but not definitive.

Dig deeper: In GEO, brand mentions do what links alone can’t

Estimating LLM ‘search volume’

Measuring LLM impact is one thing. Identifying which queries and topics matter most is another.

Compared to SEO or PPC, marketers have far less visibility. While no direct search volume exists, new tools and methods are beginning to close the gap.

The key shift is moving from tracking individual queries – which vary widely – to analyzing broader themes and topics. 

The real question becomes: which areas is your site missing, and where should your content strategy focus?

To approximate relative volume, consider three approaches:

Correlate with SEO search volume

Start with your top-performing SEO keywords. 

If a keyword drives organic traffic and has commercial intent, similar questions are likely being asked within LLMs. Use this as your baseline.

Layer in industry adoption of AI

Estimate what percentage of your target audience uses LLMs for research or purchasing decisions:

• High AI-adoption industries: Assume 20-25% of users leverage LLMs for decision-making.
• Slower-moving industries: Start with 5-10%.

Apply these percentages to your existing SEO keyword volume. For example, a keyword with 25,000 monthly searches could translate to 1,250-6,250 LLM-based queries in your category.

Using emerging inferential tools

New platforms are beginning to track query data through API-level monitoring and machine learning models. 

Accuracy isn’t perfect yet, but these tools are improving quickly. Expect major advancements in inferential LLM query modeling within the next year or two.

Optimizing for LLM visibility

The technologies that help companies identify what to improve are evolving quickly. 

While still imperfect, they’re beginning to form a framework that parallels early SEO development, where better tracking and data gradually turned intuition into science.

Optimization breaks down into two main questions:

• What content should you create or update, and should you focus on quality content, entities, schema, FAQs, or something else?
• How should you align these insights with broader brand and SEO strategies?

Identify what content to create or update

One of the most effective ways to assess your current position is to take a representative sample of high-intent queries that people might ask an LLM and see how your brand shows up relative to competitors. This is where the Share of Voice tracking tools we discussed earlier become invaluable.

These same tools can help answer your optimization questions:

• Track who is being cited or mentioned for each query, revealing competitive positioning.
• Identify which queries your competitors appear for that you don’t, highlighting content gaps.
• Show which of your own queries you appear for and which specific assets are being cited, pinpointing what’s working.

From this data, several key insights emerge:

• Thematic visibility gaps: By analyzing trends across many queries, you can identify where your brand underperforms in LLM responses. This paints a clear picture of areas needing attention. For example, you’re strong in SEO but not in PPC content. 
• Third-party resource mapping: These tools also reveal which external resources LLMs reference most frequently. This helps you build a list of high-value third-party sites that contribute to visibility, guiding outreach or brand mention strategies. 
• Blind spot identification: When cross-referenced with SEO performance, these insights highlight blind spots; topics or sources where your brand’s credibility and representation could improve.

Understand the overlap between SEO and LLM optimization

LLMs may be reshaping discovery, but SEO remains the foundation of digital visibility.

Across five competitive categories, brands ranking on Google’s first page appeared in ChatGPT answers 62% of the time – a clear but incomplete overlap between search and AI results.

That correlation isn’t accidental. 

Many retrieval-augmented generation (RAG) systems pull data from search results and expand it with additional context. 

The more often your content appears in those results, the more likely it is to be cited by LLMs.

Brands with the strongest share of voice in LLM responses are typically those that invested in SEO first. 

Strong technical health, structured data, and authority signals remain the bedrock for AI visibility.

What this means for marketers:

• Don’t over-focus on LLMs at the expense of SEO. AI systems still rely on clean, crawlable content and strong E-E-A-T signals.
• Keep growing organic visibility through high-authority backlinks and consistent, high-quality content.
• Use LLM tracking as a complementary lens to understand new research behaviors, not a replacement for SEO fundamentals.

Redefine on-page and off-page strategies for LLMs

Just as SEO has both on-page and off-page elements, LLM optimization follows the same logic – but with different tactics and priorities.

Off-page: The new link building

Most industries show a consistent pattern in the types of resources LLMs cite:

• Wikipedia is a frequent reference point, making a verified presence there valuable.
• Reddit often appears as a trusted source of user discussion.
• Review websites and “best-of” guides are commonly used to inform LLM outputs.

Citation patterns across ChatGPT, Gemini, Perplexity, and Google’s AI Overviews show consistent trends, though each engine favors different sources.

This means that traditional link acquisition strategies, guest posts, PR placements, or brand mentions in review content will likely evolve. 

Instead of chasing links anywhere, brands should increasingly target:

• Pages already being cited by LLMs in their category.
• Reviews or guides that evaluate their product category.
• Articles where branded mentions reinforce entity associations.

The core principle holds: brands gain the most visibility by appearing in sources LLMs already trust – and identifying those sources requires consistent tracking.

On-page: What your own content reveals

The same technologies that analyze third-party mentions can also reveal which first-party assets, content on your own website, are being cited by LLMs. 

This provides valuable insight into what type of content performs well in your space.

For example, these tools can identify:

• What types of competitor content are being cited (case studies, FAQs, research articles, etc.).
• Where your competitors show up but you don’t.
• Which of your own pages exist but are not being cited.

From there, three key opportunities emerge:

• Missing content: Competitors are cited because they cover topics you haven’t addressed. This represents a content gap to fill.
• Underperforming content: You have relevant content, but it isn’t being referenced. Optimization – improving structure, clarity, or authority – may be needed.
• Content enhancement opportunities: Some pages only require inserting specific Q&A sections or adding better-formatted information rather than full rewrites.

Leverage emerging technologies to turn insights into action

The next major evolution in LLM optimization will likely come from tools that connect insight to action.

Early solutions already use vector embeddings of your website content to compare it against LLM queries and responses. This allows you to:

• Detect where your coverage is weak.
• See how well your content semantically aligns with real LLM answers.
• Identify where small adjustments could yield large visibility gains.

Current tools mostly generate outlines or recommendations.

The next frontier is automation – systems that turn data into actionable content aligned with business goals.

Timeline and expected results

While comprehensive LLM visibility typically builds over 6-12 months, early results can emerge faster than traditional SEO. 

The advantage: LLMs can incorporate new content within days rather than waiting months for Google’s crawl and ranking cycles. 

However, the fundamentals remain unchanged.

Quality content creation, securing third-party mentions, and building authority still require sustained effort and resources. 

Think of LLM optimization as having a faster feedback loop than SEO, but requiring the same strategic commitment to content excellence and relationship building that has always driven digital visibility.

From SEO foundations to LLM visibility

LLM traffic remains small compared to traditional search, but it’s growing fast.

A major shift in resources would be premature, but ignoring LLMs would be shortsighted. 

The smartest path is balance: maintain focus on SEO while layering in LLM strategies that address new ranking mechanisms.

Like early SEO, LLM optimization is still imperfect and experimental – but full of opportunity. 

Brands that begin tracking citations, analyzing third-party mentions, and aligning SEO with LLM visibility now will gain a measurable advantage as these systems mature.

In short:

• Identify the third-party sources most often cited in your niche and analyze patterns across AI engines.
• Map competitor visibility for key LLM queries using tracking tools.
• Audit which of your own pages are cited (or not) – high Google rankings don’t guarantee LLM inclusion.
• Continue strong SEO practices while expanding into LLM tracking – the two work best as complementary layers.

Approach LLM optimization as both research and brand-building.

Don’t abandon proven SEO fundamentals. Rather, extend them to how AI systems discover, interpret, and cite information.

Seattle is looking to celebrate and accelerate its leadership in artificial intelligence at the very moment the first wave of the AI economy is crashing down on the region’s tech workforce.

That contrast was hard to miss Monday evening at the opening reception for Seattle AI Week 2025 at Pier 70. On stage, panels offered a healthy dose of optimism about building the AI future. In the crowd, buzz about Amazon’s impending layoffs brought the reality of the moment back to earth.

A region that rose with Microsoft and then Amazon is now dealing with the consequences of Big Tech’s AI-era restructuring. Companies that hired by the thousands are now thinning their ranks in the name of efficiency and focus — a dose of corporate realism for the local tech economy.

The double-edged nature of this shift is not lost on Washington Gov. Bob Ferguson.

“AI, and the future of AI, and what that means for our state and the world — each day I do this job, the more that moves up in my mind in terms of the challenges and the opportunities we have,” Ferguson told the AI Week crowd. He touted Washington’s concentration of AI jobs, saying his goal is to maximize the benefits of AI while minimizing its downsides.

Seattle AI Week, led by the Washington Technology Industry Association, was started last year after a Forbes list of the nation’s top 50 AI startups included none from Seattle, said the WTIA’s Nick Ellingson, opening this year’s event. That didn’t seem right. Was it a messaging problem?

“A bunch of us got together and said, let’s talk about all the cool things happening around AI in Seattle, and let’s expand the tent beyond just tech things that are happening,” Ellingson explained.

So maybe that’s the best measuring stick: how many startups will this latest shakeout spark, and how can the Seattle region’s startup and tech leaders make it happen? Can the region become less dependent on the whims of the Microsoft and Amazon C-suites in the process? 

“Washington has so much opportunity. It’s one of the few capitals of AI in the world,” said WTIA’s Arry Yu in her opening remarks. “People talk about China, people talk about Silicon Valley — there are a few contenders, but really, it’s here in Seattle. … The future is built on data, on powerful technology, but also on community. That’s what makes this place different.”

And yet, “AI is a sleepy scene in Seattle, where people work at their companies, but there’s very little activity and cross-pollinating outside of this,” said Nathan Lambert, senior research scientist with the Allen Institute for AI, during the opening panel discussion.

No, we don’t want to become San Francisco or Silicon Valley, Lambert added. But that doesn’t mean the region can’t cherry-pick some of the ingredients that put Bay Area tech on top.

Whether laid-off tech workers will start their own companies is a common question after layoffs like this. In the Seattle region at least, that outcome has been more fantasy than reality. 

This is where AI could change things, if not with the fabled one-person unicorn then with a bigger wave of new companies born of this employment downturn. Who knows, maybe one will even land on that elusive Forbes AI 50 list. (Hey, a region can dream!)

But as the new AI reality unfolds in the regional workforce, maybe the best question to ask is whether Seattle’s next big thing can come from its own backyard again.

Related: Ferguson’s AI balancing act: Washington governor wants to harness innovation while minimizing harms

Microsoft and OpenAI announced the long-awaited details of their new partnership agreement Tuesday morning — with concessions on both sides that keep the companies aligned but not in lockstep as they move into their next phases of AI development.

Under the arrangement, Microsoft gets a 27% equity stake in OpenAI’s new for-profit entity, the OpenAI Group PBC (Public Benefit Corporation), a stake valued at approximately $135 billion. That’s a decrease from 32.5% equity but not a bad return on an investment of $13.8 billion.

At the same time, OpenAI has contracted to purchase an incremental $250 billion in Microsoft Azure cloud services. However, in a significant concession in return for that certainty, Microsoft will no longer have a “right of first refusal” on new OpenAI cloud workloads.

Microsoft, meanwhile, will retain its intellectual property rights to OpenAI models and products through 2032, an extension of the timeframe that existed previously. 

A key provision of the new agreement centers on Artificial General Intelligence (AGI), with any declaration of AGI by OpenAI now subject to verification by an independent expert panel. This was a sticking point in the earlier partnership agreement, with an ambiguous definition of AI potentially triggering new provisions of the prior arrangement. 

Microsoft and OpenAI had previously announced a tentative agreement without providing details. More aspects of the deal are disclosed in a joint blog post from the companies.

Shares of Microsoft are up 2% in early trading after the announcement. The company reports earnings Wednesday afternoon, and some analysts have said the uncertainty over the OpenAI arrangement has been impacting Microsoft’s stock.