Bluesky may still be the underdog in the race for alternatives to X, but the once Twitter-affiliated service is gaining momentum. The app just passed the 15 million user mark after adding more than a million new users over the last week, the company said in an update.

While Bluesky is still considerably smaller than Threads, which with 275 million users is its biggest rival, there are signs that Threads users have been increasingly curious about the upstart. “Bluesky” has been a trending topic on Threads in recent days and an in-app search suggestion shows there are more than 19,000 posts about “Bluesky.” Bluesky itself has also made to win over Threads users in recent weeks by posting regularly on the Meta-owned service.

That effort seems to be working. A month ago, Engadget , the service had just under 9 million users. Its mobile app also has the top spot in Apple’s App Store, followed by Threads and ChatGPT. Its recent success also seems to be driven, at least in part, by frustration with Elon Musk and X following the US presidential election.

A recent report from web analytics company SimilarWeb found that “more than 115,000 US web visitors deactivated their accounts,” on November 7, “more than on any previous day of Elon Musk’s tenure.” The report also noted that “web traffic and daily active users for Bluesky increased dramatically in the week before the election, and then again after election day,” with Bluesky at points seeing more web traffic than Threads. (Threads’ mobile usage, however, is still “far ahead” of Bluesky.)

“In the US, Bluesky got more web visits than Threads in the immediate aftermath of the election,” the report notes. “For context, it’s important to note that both services are app centric, even though they support a web user interface.”

On its part, Bluesky seems intent on distinguishing itself from its larger, billionaire-controlled rivals. The company, which began as an internal project at Twitter before it spun off into an independent entity, has experimented with novel features like , user-created and “” for new users.

“You’re probably used to being trapped in a single algorithm controlled by a small group of people, that’s no longer the case,” Bluesky’s COO Rose Wang shared in aimed at new users Tuesday. “On Bluesky, there are about 50,000 different feeds … these feeds provide a cozy corner for you to meet people with similar interests. And you can actually make friends again, because you’re no longer tied to a dominant algorithm that promotes either the most polarizing posts and or the biggest brands, and that’s the mandate of Bluesky.”

Microsoft has launched a new suite of specialized AI models designed to address specific challenges in manufacturing, agriculture, and financial services. In collaboration with partners such as Siemens, Bayer, Rockwell Automation, and others, the tech giant is aiming to bring advanced AI technologies directly into the heart of industries that have long relied on traditional methods and tools.

These purpose-built models—now available through Microsoft’s Azure AI catalog—represent Microsoft’s most focused effort yet to develop AI tools tailored to the unique needs of different sectors. The company’s initiative reflects a broader strategy to move beyond general-purpose AI and deliver solutions that can provide immediate operational improvements in industries like agriculture and manufacturing, which are increasingly facing pressures to innovate.

“Microsoft is in a unique position to deliver the industry-specific solutions organizations need through the combination of the Microsoft Cloud, our industry expertise, and our global partner ecosystem,” Satish Thomas, Corporate Vice President of Business & Industry Solutions at Microsoft, said in a LinkedIn post announcing the new AI models.

“Through these models,” he added, “we’re addressing top industry use cases, from managing regulatory compliance of financial communications to helping frontline workers with asset troubleshooting on the factory floor — ultimately, enabling organizations to adopt AI at scale across every industry and region… and much more to come in future updates!”

Siemens and Microsoft remake industrial design with AI-powered software

At the center of the initiative is a partnership with Siemens to integrate AI into its NX X software, a widely used platform for industrial design. Siemens’ NX X copilot uses natural language processing to allow engineers to issue commands and ask questions about complex design tasks. This feature could drastically reduce the onboarding time for new users while helping seasoned engineers complete their work faster.

By embedding AI into the design process, Siemens and Microsoft are addressing a critical need in manufacturing: the ability to streamline complex tasks and reduce human error. This partnership also highlights a growing trend in enterprise technology, where companies are looking for AI solutions that can improve day-to-day operations rather than experimental or futuristic applications.

Smaller, faster, smarter: How Microsoft’s compact AI models are transforming factory operations

Microsoft’s new initiative relies heavily on its Phi family of small language models (SLMs), which are designed to perform specific tasks while using less computing power than larger models. This makes them ideal for industries like manufacturing, where computing resources can be limited, and where companies often need AI that can operate efficiently on factory floors.

Perhaps one of the most novel uses of AI in this initiative comes from Sight Machine, a leader in manufacturing data analytics. Sight Machine’s Factory Namespace Manager addresses a long-standing but often overlooked problem: the inconsistent naming conventions used to label machines, processes, and data across different factories. This lack of standardization has made it difficult for manufacturers to analyze data across multiple sites. The Factory Namespace Manager helps by automatically translating these varied naming conventions into standardized formats, allowing manufacturers to better integrate their data and make it more actionable.

While this may seem like a minor technical fix, the implications are far-reaching. Standardizing data across a global manufacturing network could unlock operational efficiencies that have been difficult to achieve.

Early adopters like Swire Coca-Cola USA, which plans to use this technology to streamline its production data, likely see the potential for gains in both efficiency and decision-making. In an industry where even small improvements in process management can translate into substantial cost savings, addressing this kind of foundational issue is a crucial step toward more sophisticated data-driven operations.

Smart farming gets real: Bayer’s AI model tackles modern agriculture challenges

In agriculture, the Bayer E.L.Y. Crop Protection model is poised to become a key tool for farmers navigating the complexities of modern farming. Trained on thousands of real-world questions related to crop protection labels, the model provides farmers with insights into how best to apply pesticides and other crop treatments, factoring in everything from regulatory requirements to environmental conditions.

This model comes at a crucial time for the agricultural industry, which is grappling with the effects of climate change, labor shortages, and the need to improve sustainability. By offering AI-driven recommendations, Bayer’s model could help farmers make more informed decisions that not only improve crop yields but also support more sustainable farming practices.

The initiative also extends into the automotive and financial sectors. Cerence, which develops in-car voice assistants, will use Microsoft’s AI models to enhance in-vehicle systems. Its CaLLM Edge model allows drivers to control various car functions, such as climate control and navigation, even in settings with limited or no cloud connectivity—making the technology more reliable for drivers in remote areas.

In finance, Saifr, a regulatory technology startup within Fidelity Investments, is introducing models aimed at helping financial institutions manage regulatory compliance more effectively. These AI tools can analyze broker-dealer communications to flag potential compliance risks in real-time, significantly speeding up the review process and reducing the risk of regulatory penalties.

Rockwell Automation, meanwhile, is releasing the FT Optix Food & Beverage model, which helps factory workers troubleshoot equipment in real time. By providing recommendations directly on the factory floor, this AI tool can reduce downtime and help maintain production efficiency in a sector where operational disruptions can be costly.

The release of these AI models marks a shift in how businesses can adopt and implement artificial intelligence. Rather than requiring companies to adapt to broad, one-size-fits-all AI systems, Microsoft’s approach allows businesses to use AI models that are custom-built to address their specific operational challenges. This addresses a major pain point for industries that have been hesitant to adopt AI due to concerns about cost, complexity, or relevance to their particular needs.

The focus on practicality also reflects Microsoft’s understanding that many businesses are looking for AI tools that can deliver immediate, measurable results. In sectors like manufacturing and agriculture, where margins are often tight and operational disruptions can be costly, the ability to deploy AI that improves efficiency or reduces downtime is far more appealing than speculative AI projects with uncertain payoffs.

By offering tools that are tailored to industry-specific needs, Microsoft is betting that businesses will prioritize tangible improvements in their operations over more experimental technologies. This strategy could accelerate AI adoption in sectors that have traditionally been slower to embrace new technologies, like manufacturing and agriculture.

Inside Microsoft’s plan to dominate industrial AI and edge computing

Microsoft’s push into industry-specific AI models comes at a time of increasing competition in the cloud and AI space. Rivals like Amazon Web Services and Google Cloud are also investing heavily in AI, but Microsoft’s focus on tailored industry solutions sets it apart. By partnering with established leaders like Siemens, Bayer, and Rockwell Automation, Microsoft is positioning itself to be a key player in the digitization of industries that are under growing pressure to modernize.

The availability of these models through Azure AI Studio and Microsoft Copilot Studio also speaks to Microsoft’s broader vision of making AI accessible not just to tech companies, but to businesses in every sector. By integrating AI into the day-to-day operations of industries like manufacturing, agriculture, and finance, Microsoft is helping to bring AI out of the lab and into the real world.

As global manufacturers, agricultural producers, and financial institutions face increasing pressures from supply chain disruptions, sustainability goals, and regulatory demands, Microsoft’s industry-specific AI offerings could become essential tools in helping them adapt and thrive in a fast-changing world.

Lyten, a Silicon Valley battery startup, announced today that it’s acquiring manufacturing assets from Northvolt, a Swedish battery manufacturer that’s facing a cash crunch.

As part of the deal, Northvolt is selling manufacturing equipment the company inherited in its 2021 acquisition of Cuberg, another battery startup. Lyten will also assume the lease of Cuberg’s old manufacturing facility in San Leandro, California. Lyten will invest $20 million next year to expand facilities in San Leandro and its existing operations in San Jose.

Neither Lyten nor Northvolt immediately replied to questions about the deal’s financial terms.

Unlike many other battery manufacturers, Lyten isn’t relying on nickel, cobalt, manganese, or even iron for its cathode materials. Instead, it’s using cheap and abundant sulfur mixed into a graphene matrix. On the anode side, it doesn’t use any graphite, a material that faces export restrictions from China. The company says the combination results in cells that have greater energy density than nickel-manganese-cobalt flavors but are cheaper to produce than low-cost lithium-iron-phosphate.

Northvolt has been struggling lately. The company has struggled to scale up production of lithium-ion batteries, and it missed delivery of a large order from BMW, which nudged the automaker to nullify a €2 billion contract. 

To conserve cash, the company announced in August that it would shutter research and development at the Cuberg site, laying off nearly 200 employees. Then in September, it said that it was laying off an additional 1,600 employees, about 20% of its workforce, and that it had halted two planned factory expansions.

It’s unclear whether that cost-cutting and deal with Lyten will be enough to help Northvolt get through the coming year. Last week, Bloomberg reported that Northvolt needs to raise nearly $1 billion to give it some breathing room; the company’s operations reportedly burn through about $100 million a month.

While Northvolt is on the skids, Lyten appears ascendent.

The San Jose-based startup is planning to break ground next year on a factory in Nevada with a planned capacity of 10 gigawatt-hours. When complete, the $1 billion facility will produce lithium-sulfur batteries destined for micromobility vehicles like scooters and e-bikes, and defense and space applications like drones and satellites. The company expects it to come online in 2027.

Lyten’s purchase of Northvolt’s Cuberg assets give it the equipment and space to produce up to 200 megawatt-hours of lithium-sulfur batteries in the Bay Area. That should give the company some revenue while it prepares its larger factory in Nevada.

Lyten has raised $476 million to date at a $1.17 billion valuation, according to PitchBook, including a $200 million round that closed last year.

OpenAI is preparing to release an autonomous AI agent that can control computers and perform tasks independently, code-named “Operator.” The company plans to debut it as a research preview and developer tool in January, according to Bloomberg.

This move intensifies the competition among tech giants developing AI agents: Anthropic recently introduced its “computer use” capability, while Google is reportedly preparing its own version for a December release. The timing of Operator’s eventual consumer release remains under wraps, but its development signals a pivotal shift toward AI systems that can actively engage with computer interfaces rather than just process text and images.

All the leading AI companies have promised autonomous AI agents, and OpenAI has hyped up the possibility recently. In a Reddit “Ask Me Anything” forum a few weeks ago, OpenAI CEO Sam Altman said “we will have better and better models,” but “I think the thing that will feel like the next giant breakthrough will be agents.” At an OpenAI press event ahead of the company’s annual Dev Day last month, chief product officer Kevin Weil said: “I think 2025 is going to be the year that agentic systems finally hit the mainstream.”

AI labs face mounting pressure to monetize their costly models, especially as incremental improvements may not justify higher prices for users. The hope is that autonomous agents are the next breakthrough product — a ChatGPT-scale innovation that validates the massive investment in AI development.

Watching old episodes of ER won’t make you a doctor, but watching videos may be all the training a robotic surgeon’s AI brain needs to sew you up after a procedure. Researchers at Johns Hopkins University and Stanford University have published a new paper showing off a surgical robot as capable as a human in carrying out some procedures after simply watching humans do so.

The research team tested their idea with the popular da Vinci Surgical System, which is often used for non-invasive surgery. Programming robots usually requires manually inputting every movement that you want them to make. The researchers bypassed this using imitation learning, a technique that implanted human-level surgical skills in the robots by letting them observe how humans do it.

Strava, a popular app for tracking fitness activities, is expanding its Hatmaps feature to help improve the safety of its users. The update should be especially useful now for users in the Northern Hemisphere, which is heading into winter with reduced daylight.

The new Night and Weekly Heatmaps were announced by the San Francisco-based company on Wednesday and are available to all Strava subscribers. As the name of the feature suggests, the Heatmaps show where Strava users are choosing to exercise, with dark thick lines showing well-used routes, and light thin lines showing less popular ones.

First up, the new Night Heatmaps feature is ideal for those who are doing their activities in the late evening or early morning hours, when there’s less light. They show the most popular areas for outdoor activities from sunset to sunrise, helping athletes to better plan their outdoor activities during this time frame. If it’s a new area for you, you may also want to cross-check the Night Heatmap data with Google Street View images to get a better understanding of the place.

Weekly Heatmaps, on the other hand, show data for recent heat from the last seven days so that users can see which trails and roads are currently active, particularly during seasonal transitions when conditions may be impacted by weather.

“Our global community powers ourHeatmaps and now we’ve made it easier for our community members to build routes with confidence, regardless of the season or time of day,” Matt Salazar, Strava’s chief product officer, said in Wednesday’s announcement about the new features. “We are continually improving our mapping technology to make human-powered movement easier for all skill levels.”

Strava has also shared a useful at-a-glance guide to all four of its Heatmaps, Night, Weekly, Global, and Personal:

Night (new): Discover the most frequented areas between sunset and sunrise; ideal for evening or early morning users.

Weekly (new): Stay updated with the latest data from the past seven days; perfect for adjusting plans around seasonal changes or unexpected closures.

Global (existing): Viewable by anyone regardless of whether you have a Strava account, the Global Heatmap allows you to see what areas are most popular around the world based on community activity uploads.

Personal (existing): A one-of-a-kind illustration showing the record of everywhere you’ve logged a GPS activity. This heatmap is private and only available to you.