MVK Chari, a pioneer in finite element field computation, died on 3 December. The IEEE Life Fellow was 97.

Chari developed a finite element method (FEM) for analyzing nonlinear electromagnetic fields—which is crucial for the design of electric machines. The technique is used to obtain approximate solutions to complex engineering and mathematical problems. It involves dividing a complicated object or system into smaller, more manageable parts, known as finite elements, according to Fictiv.

As an engineer and technical leader at General Electric in Niskayuna, N.Y., Chari used the tool to analyze large turbogenerators for end region analysis, starting with 2D and expanding its use over time to quasi-2D and 3D.

During his 25 years at GE, he established a team that was developing finite element analysis (FEA) tools for a variety of applications across the company. They ranged from small motors to large MRI magnets.

Chari received the 1993 IEEE Nikola Tesla Award for “pioneering contributions to finite element computations of nonlinear electromagnetic fields for design and analysis of electric machinery.”

A career spanning industry and academia

Chari attended Imperial College London to pursue a master’s degree in electrical engineering. There he met Peter P. Silvester, a visiting professor of electrical engineering. Silvester, a professor at McGill University in Montreal, was a pioneer in understanding numerical analysis of electromagnetic fields.

After Chari graduated in 1968, he joined Silvester at McGill as a doctoral student, applying FEM to solve electromagnetic field problems. Silvester applied the method to waveguides, while Chari applied it to saturated magnetic fields.

Chari joined GE in 1970 after earning his Ph.D. in electrical engineering. He climbed the leadership ladder and was a manager of the company’s electromagnetics division when he left in 1995. He joined Rensselaer Polytechnic Institute in Troy, N.Y., as a visiting research and adjunct professor in its electrical, computer, and systems engineering department. Chari taught graduate and undergraduate classes in electric power engineering and mentored many master’s and doctoral students. His strength was nurturing young engineers.

He also conducted research on electric machines and transformers for the Electric Power Research Institute and the U.S. Department of Energy.

In 2008 Chari joined Magsoft Corp., in Clifton Park, N.Y., and conducted advanced work on specialized software for the U.S. Navy until his retirement in 2016.

Remembering a friend

Chari successfully nominated one of us (Hoole) to be elevated to IEEE Fellow at the age of 40. He helped launch Haran’s career when Chari sent his résumé to GE hiring managers for a position in its applied superconductivity lab.

Chari’s commitment to people came from his family background. His father—M.A. Ayyangar—was known throughout India as a freedom fighter, mathematician, and eventually the speaker of the Indian Parliament’s lower house under Prime Minister Nehru. Chari’s wife, Padma, was a physician in New York.

From Chari’s illustrious family, he was at the peak of South India (Tamil) society.

Chari would fondly and cheerfully tell us the story behind his name. Around the time of his birth, it was common in Tamil society not to have formal names. He went by the informal “house name” Kannah (a term of endearment for Krishna). When it was time for Chari to start school, an auspicious uncle enrolled him. But Chari had no formal name, so the uncle took it upon himself to give him one. He asked Chari if he would like a long or short name, to which he said long. So the uncle named him Madabushi Venkadamachari.

When Chari moved to North America, he shortened his name to Madabushi V.K.

He could also laugh at himself.

A stellar scientist, he also was a role model, guide, and friend to many of us. We thank God for him.

A threat actor is compromising NGINX servers in a campaign that hijacks user traffic and reroutes it through the attacker’s backend infrastructure.

NGINX is open-source software for web traffic management. It intermediates connections between users and servers and is employed for web serving, load balancing, caching, and reverse proxying.

The malicious campaign, discovered by researchers at DataDog Security Labs, targets NGINX installations and Baota hosting management panels used by sites with Asian top-level domains (.in, .id, .pe, .bd, and .th) and government and educational sites (.edu and .gov).

Attackers modify existing NGINX configuration files by injecting malicious ‘location’ blocks that capture incoming requests on attacker-selected URL paths.

They then rewrite them to include the full original URL, and forward traffic via the ‘proxy_pass’ directive to attacker-controlled domains.

The abused directive is normally used for load balancing, allowing NGINX to reroute requests through alternative backend server groups to improve performance or reliability; hence, its abuse does not trigger any security alerts.

Request headers such as ‘Host,’ ‘X-Real-IP,’ ‘User-Agent,’ and ‘Referer’ are preserved to make the traffic appear legitimate.

The attack uses a scripted multi-stage toolkit to perform the NGINX configuration injections. The toolkit operates in five stages:

• Stage 1 – zx.sh: Acts as the initial controller script, responsible for downloading and executing the remaining stages. It includes a fallback mechanism that sends raw HTTP requests over TCP if curl or wget are unavailable.
• Stage 2 – bt.sh: Targets NGINX configuration files managed by the Baota panel. It dynamically selects injection templates based on the server_name value, safely overwrites the configuration, and reloads NGINX to avoid service downtime.
• Stage 3 – 4zdh.sh: Enumerates common NGINX configuration locations such as sites-enabled, conf.d, and sites-available. It uses parsing tools like csplit and awk to prevent configuration corruption, detects prior injections via hashing and a global mapping file, and validates changes using nginx -t before reloading.
• Stage 4 – zdh.sh: Uses a narrower targeting approach focused mainly on /etc/nginx/sites-enabled, with emphasis on .in and .id domains. It follows the same configuration testing and reload process, with a forced restart (pkill) used as a fallback.
• Stage 5 – ok.sh: Scans compromised NGINX configurations to build a map of hijacked domains, injection templates, and proxy targets. The collected data is then exfiltrated to a command-and-control (C2) server at 158.94.210[.]227.

These attacks are hard to detect because they do not exploit an NGINX vulnerability; instead, they hide malicious instructions in its configuration files, which are rarely scrutinized.

Also, user traffic still reaches the intended destination, often directly, so the passing through attacker infrastructure is unlikely to be noticed unless specific monitoring is performed.

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Changes — and subsequent confusion and concern — largely defined the early childhood education sector in 2025. Multiple social programs including Head Start and hunger assistance programs were in flux. Rising costs of living were coupled with the rising costs of child care. And many EdSurge readers were left searching for answers, as seen in our most-read stories of the year.

There was also plenty of innovation in the field, from transforming empty school buildings, adding apprenticeship programs and introducing play into teaching math. There will be more of that undoubtedly in 2026 and EdSurge aims to bring you more answers as questions continue to arise about the future of early learning and child care.

Here are the most popular early childhood education stories, in descending order. You can see our most-read stories covering the K-12 sector here.

10. More Than Half of Child Care Providers Have Gone Hungry, New Report Finds

By Lauren Coffey

Child care providers struggling is nothing new, and many left the field postpandemic due to its low pay and long, unstable hours. But the struggle to survive came to a head last year, as the cost of living continued to rise and multiple social programs — namely SNAP, formerly known as food stamps — were temporarily paused. A report from the RAPID Survey Project at the Stanford Center on Early Childhood found that basic needs may be greater than ever, with 58 percent of child care providers stating they experienced hunger in 2025.

9. Could Play Boost Students’ Math Performance?

By Daniel Mollenkamp

Early education often conjures images of games, bright colors and plenty of play time. But often those associations stop when it comes to math class. EdSurge spoke with experts across the nation looking to marry the two. But similar to the curriculum at ever-popular Montessori schools, “play” is not a free-for-all. When it comes to math instruction, there is a fine line between board and dice games and lessons about larger concepts.

8. What Will Kids Lose If PBS Gets Cut?

By Lauren Coffey

Calls to cut funding for PBS began in the spring of 2025, culminating in multiple slashed grants that more than likely spelled the end for many local public broadcasting affiliates. The cut goes beyond easily accessing beloved shows like “Daniel the Tiger” and “Arthur.” Many experts voiced concerns that the loss of programming, which puts educational guidelines at the forefront, could hit rural and lower-income families particularly hard.

7. As Apprenticeships Expand in Early Childhood, These States Are Training the Field’s Future Leaders

By Emily Tate Sullivan

The leap between early childhood educator and director of an early child care center is often so intimidating that many educators do not attempt to move up, despite it often providing better pay and hours. Registered apprenticeship programs began booming to fix that gap, offering a pathway to train educators for leadership roles. Notably, Kentucky, Massachusetts and New Hampshire offer programs specifically made for emerging leaders in the early education field — and the impact is already being seen.

6. What Will Districts Do With All Those Empty School Buildings? Some Look to Fill Them With Younger Kids

By Emily Tate Sullivan

Enrollment continues to decline in traditional public schools, due in part to the rise of popularity in virtual schools and charter schools buoyed by voucher programs. The outcome: a lot of large, empty school buildings. But some districts, like in Oklahoma City and Tucson, are overhauling them to house early learning programs instead. What follows is a way to address the rising need for child care and a way to lure in early childhood educators, thanks to district benefits.

5. Head Start’s Future Is Uncertain. Rural Americans Aren’t Ready for What Happens Next.

By Claire Woodcock

As the Head Start program turned 60 in 2025, questions swirled about its future. The program, which has long helped families living at or below the poverty level access affordable child care and services, saw half of its regional offices close this year. For most of the year, the fate of its funding was unknown. While Head Start funding was later approved, there was no increase from previous years — bringing concern from many. There is a particular worry about the consequences for rural communities, where 1 in 3 child care programs is backed by Head Start.

4. Study: Kids Suffer as Nearly Half of U.S. Families Struggle to Meet Basic Needs

By Marianna McMurdock

Similarly to our No. 8 story of the year focusing on child care providers, families themselves also struggled this year to make ends meet. A report showed 4 in 10 families are experiencing material hardship, which goes beyond short-term stress: It can hurt children’s learning long-term. Parents’ stress can seep to their children, causing depression and anxiety. It can also cause an overreliance on screen time. The result: children can have a learning gap of up to a year compared to those not experiencing hardship.

3. Why the Dire State of the Early Learning Workforce Is ‘Alarming and Not Surprising’

By Emily Tate Sullivan

Rising costs, staff shortages and low morale brought the early childhood educator crisis to a head in 2025. According to a report by the National Association for the Education of Young Children, high rents and an uptick in property and liability insurance rates has caused stagnant or low revenue for providers, prompting many programs to shutter. Those working in the early childhood world are not surprised by these findings, but do believe more funding and action — versus inaction — is needed.

2. Idaho Moves to Deregulate Child Care in First-of-Its-Kind Legislation

By Emily Tate Sullivan

Idaho made major waves at the start of the year when it attempted to become the first in the nation to eliminate state-mandated child-to-teacher ratios, in a move it believed would help the severe shortage of child care openings. Many experts were quick to defend the ratios as essential to helping with the health of children and the quality of child care. The amended bill ultimately tweaked the ratio proposals, loosening, versus ridding, the requirements.

1. Why Don’t Early Childhood Programs Have Access to Substitute Teachers?

By Emily Tate Sullivan

As winter swings on, bringing with it inevitable sickness, the K-12 system can rely on its large infrastructure of substitute teachers, but the early childhood sector has no such programming. Beyond cold and flu season, this makes it difficult for the already-burned-out teachers in early learning to take a sick day or vacation. However, there are some efforts under way, with many turning toward future full-time educators to fill the gap.

You may see some of my bylines above, and you’ll be seeing more of those in 2026 as I cover more early childhood education for EdSurge. If you have any tips, or just want to say hello, feel free to shoot me a note at lauren@edsurge.com.

Mistral AI, the Paris-based startup positioning itself as Europe’s answer to OpenAI, released a pair of speech-to-text models on Wednesday that the company says can transcribe audio faster, more accurately, and far more cheaply than anything else on the market — all while running entirely on a smartphone or laptop.

The announcement marks the latest salvo in an increasingly competitive battle over voice AI, a technology that enterprise customers see as essential for everything from automated customer service to real-time translation. But unlike offerings from American tech giants, Mistral’s new Voxtral Transcribe 2 models are designed to process sensitive audio without ever transmitting it to remote servers — a feature that could prove decisive for companies in regulated industries like healthcare, finance, and defense.

“You’d like your voice and the transcription of your voice to stay close to where you are, meaning you want it to happen on device—on a laptop, a phone, or a smartwatch,” Pierre Stock, Mistral’s vice president of science operations, said in an interview with VentureBeat. “We make that possible because the model is only 4 billion parameters. It’s small enough to fit almost anywhere.”

Mistral splits its new AI transcription technology into batch processing and real-time applications

Mistral released two distinct models under the Voxtral Transcribe 2 banner, each engineered for different use cases.

• Voxtral Mini Transcribe V2 handles batch transcription, processing pre-recorded audio files in bulk. The company says it achieves the lowest word error rate of any transcription service and is available via API at $0.003 per minute, roughly one-fifth the price of major competitors. The model supports 13 languages, including English, Mandarin Chinese, Japanese, Arabic, Hindi, and several European languages.

• Voxtral Realtime, as its name suggests, processes live audio with a latency that can be configured down to 200 milliseconds — the blink of an eye. Mistral claims this is a breakthrough for applications where even a two-second delay proves unacceptable: live subtitling, voice agents, and real-time customer service augmentation.

The Realtime model ships under an Apache 2.0 open-source license, meaning developers can download the model weights from Hugging Face, modify them, and deploy them without paying Mistral a licensing fee. For companies that prefer not to run their own infrastructure, API access costs $0.006 per minute.

Stock said Mistral is betting on the open-source community to expand the model’s reach. “The open-source community is very imaginative when it comes to applications,” he said. “We’re excited to see what they’re going to do.”

Why on-device AI processing matters for enterprises handling sensitive data

The decision to engineer models small enough to run locally reflects a calculation about where the enterprise market is heading. As companies integrate AI into ever more sensitive workflows — transcribing medical consultations, financial advisory calls, legal depositions — the question of where that data travels has become a dealbreaker.

Stock painted a vivid picture of the problem during his interview. Current note-taking applications with audio capabilities, he explained, often pick up ambient noise in problematic ways: “It might pick up the lyrics of the music in the background. It might pick up another conversation. It might hallucinate from a background noise.”

Mistral invested heavily in training data curation and model architecture to address these issues. “All of that, we spend a lot of time ironing out the data and the way we train the model to robustify it,” Stock said.

The company also added enterprise-specific features that its American competitors have been slower to implement. Context biasing allows customers to upload a list of specialized terminology — medical jargon, proprietary product names, industry acronyms — and the model will automatically favor those terms when transcribing ambiguous audio. Unlike fine-tuning, which requires retraining the model, context biasing works through a simple API parameter.

“You only need a text list,” Stock explained. “And then the model will automatically bias the transcription toward these acronyms or these weird words. And it’s zero shots, no need for retraining, no need for weird stuff.”

From factory floors to call centers, Mistral targets high-noise industrial environments

Stock described two scenarios that capture how Mistral envisions the technology being deployed.

The first involves industrial auditing. Imagine technicians walking through a manufacturing facility, inspecting heavy machinery while shouting observations over the din of factory noise. “In the end, imagine like a perfect timestamped notes identifying who said what — so diarization — while being super robust,” Stock said. The challenge is handling what he called “weird technical language that no one is able to spell except these people.”

The second scenario targets customer service operations. When a caller contacts a support center, Voxtral Realtime can transcribe the conversation in real time, feeding text to backend systems that pull up relevant customer records before the caller finishes explaining the problem.

“The status will appear for the operator on the screen before the customer stops the sentence and stops complaining,” Stock explained. “Which means you can just interact and say, ‘Okay, I can see the status. Let me correct the address and send back the shipment.’”

He estimated this could reduce typical customer service interactions from multiple back-and-forth exchanges to just two interactions: the customer explains the problem, and the agent resolves it immediately.

Real-time translation across languages could arrive by the end of 2026

For all the focus on transcription, Stock made clear that Mistral views these models as foundational technology for a more ambitious goal: real-time speech-to-speech translation that feels natural.

“Maybe the end goal application and what the model is laying the groundwork for is live translation,” he said. “I speak French, you speak English. It’s key to have minimal latency, because otherwise you don’t build empathy. Your face is not out of sync with what you said one second ago.”

That goal puts Mistral in direct competition with Apple and Google, both of which have been racing to solve the same problem. Google’s latest translation model operates at a two-second delay — ten times slower than what Mistral claims for Voxtral Realtime.

Mistral positions itself as the privacy-first alternative for enterprise customers

Mistral occupies an unusual position in the AI landscape. Founded in 2023 by alumni of Meta and Google DeepMind, the company has raised over $2 billion and now carries a valuation of approximately $13.6 billion. Yet it operates with a fraction of the compute resources available to American hyperscalers — and has built its strategy around efficiency rather than brute force.

“The models we release are enterprise grade, industry leading, efficient — in particular, in terms of cost — can be embedded into the edge, unlocks privacy, unlocks control, transparency,” Stock said.

That approach has resonated particularly with European customers wary of dependence on American technology. In January, France’s Ministry of the Armed Forces signed a framework agreement giving the country’s military access to Mistral’s AI models—a deal that explicitly requires deployment on French-controlled infrastructure.

Data privacy remains one of the biggest barriers to voice AI adoption in the enterprise. For companies in sensitive industries — finance, manufacturing, healthcare, insurance — sending audio data to external cloud servers is often a non-starter. The information needs to stay either on the device itself or within the company’s own infrastructure.

Mistral faces stiff competition from OpenAI, Google, and a rising China

The transcription market has grown fiercely competitive. OpenAI’s Whisper model has become something of an industry standard, available both through API and as downloadable open-source weights. Google, Amazon, and Microsoft all offer enterprise-grade speech services. Specialized players like Assembly AI and Deepgram have built substantial businesses serving developers who need reliable, scalable transcription.

Mistral claims its new models outperform all of them on accuracy benchmarks while undercutting them on price. “We are better than them on the benchmarks,” Stock said. Independent verification of those claims will take time, but the company points to performance on FLEURS, a widely used multilingual speech benchmark, where Voxtral models achieve word error rates competitive with or superior to alternatives from OpenAI and Google.

Perhaps more significantly, Mistral’s CEO Arthur Mensch has warned that American AI companies face pressure from an unexpected direction. Speaking at the World Economic Forum in Davos last month, Mensch dismissed the notion that Chinese AI lags behind the West as “a fairy tale.”

“The capabilities of China’s open-source technology is probably stressing the CEOs in the US,” he said.

The French startup bets that trust will determine the winner in enterprise voice AI

Stock predicted that 2026 would be “the year of note-taking” — the moment when AI transcription becomes reliable enough that users trust it completely.

“You need to trust the model, and the model basically cannot make any mistake, otherwise you would just lose trust in the product and stop using it,” he said. “The threshold is super, super hard.”

Whether Mistral has crossed that threshold remains to be seen. Enterprise customers will be the ultimate judges, and they tend to move slowly, testing claims against reality before committing budgets and workflows to new technology. The audio playground in Mistral Studio, where developers can test Voxtral Transcribe 2 with their own files, went live today.

But Stock’s broader argument deserves attention. In a market where American giants compete by throwing billions of dollars at ever-larger models, Mistral is making a different wager: that in the age of AI, smaller and local might beat bigger and distant. For the executives who spend their days worrying about data sovereignty, regulatory compliance, and vendor lock-in, that pitch may prove more compelling than any benchmark.

The race to dominate enterprise voice AI is no longer just about who builds the most powerful model. It’s about who builds the model you’re willing to let listen.

Health care technology company Abbott has recalled certain FreeStyle Libre 3 and FreeStyle Libre 3 Plus continuous glucose monitoring systems because the sensors are displaying incorrect glucose readings that are lower than the body’s actual levels. This could lead to diabetic users making the wrong treatment decisions.

As of Jan. 7, Abbott reported that the recalled sensor has caused 860 serious injuries and been associated with seven deaths. 

On Nov. 24, 2025, Abbott sent a letter to all affected customers about this issue, and the US Food and Drug Administration notified the public on Dec. 2, 2025. Today, the FDA updated its alert to classify it as a Class I recall, meaning that the use of the affected FreeStyle Libre 3 and FreeStyle Libre 3 Plus CGMs could cause serious health consequences or death.

The FreeStyle Libre 3 Plus is on CNET’s list of the best continuous glucose monitors, which has been updated to include a note about the recall.

How to find out if your Libre 3 CGM has been recalled

The FreeStyle Libre 3 model numbers that have been recalled are 72081-01 and 72080-01. The recalled FreeStyle Libre 3 Plus model numbers are 78768-01 and 78769-01.

If you have a FreeStyle Libre 3 or 3 Plus, you can check whether it was recalled at www.freestylecheck.com. There, you will be asked to confirm your sensor’s serial number, which can be located in or on the following:

• The FreeStyle Libre 3 app: On the main menu, click “About.” The serial number will be under “Last 3 Sensors.”
• Libre app: From the bottom menu, click “Profile,” then “About.” It will be under “Last 3 Sensors.”
• FreeStyle Libre 3 reader: In the settings menu, click “System Status,” then “System Info.”
• Sensor applicator or carton: You can find the serial number on the bottom label.

If you determine that your sensor is included in the recall, immediately discontinue use. On www.freestylecheck.com, you can report that your sensor is affected and a replacement will be sent to you at no cost. 

If you’re currently wearing a recalled sensor, Abbott recommends that you stop using it and remove it from your arm. Until your replacement arrives, you can use a blood glucose meter, your FreeStyle Libre 3 reader’s built-in meter or another sensor.

Today’s LLMs excel at reasoning, but can still struggle with context. This is particularly true in real-time ordering systems like Instacart. 

Instacart CTO Anirban Kundu calls it the “brownie recipe problem.”

It’s not as simple as telling an LLM ‘I want to make brownies.’ To be truly assistive when planning the meal, the model must go beyond that simple directive to understand what’s available in the user’s market based on their preferences — say, organic eggs versus regular eggs — and factor that into what’s deliverable in their geography so food doesn’t spoil. This among other critical factors. 

For Instacart, the challenge is juggling latency with the right mix of context to provide experiences in, ideally, less than one second’s time. 

“If reasoning itself takes 15 seconds, and if every interaction is that slow, you’re gonna lose the user,” Kundu said at a recent VB event. 

Mixing reasoning, real-world state, personalization

In grocery delivery, there’s a “world of reasoning” and a “world of state” (what’s available in the real world), Kundu noted, both of which must be understood by an LLM along with user preference. But it’s not as simple as loading the entirety of a user’s purchase history and known interests into a reasoning model. 

“Your LLM is gonna blow up into a size that will be unmanageable,” said Kundu. 

To get around this, Instacart splits processing into chunks. First, data is fed into a large foundational model that can understand intent and categorize products. That processed data is then routed to small language models (SLMs) designed for catalog context (the types of food or other items that work together) and semantic understanding. 

In the case of catalog context, the SLM must be able to process multiple levels of details around the order itself as well as the different products. For instance, what products go together and what are their relevant replacements if the first choice isn’t in stock? These substitutions are “very, very important” for a company like Instacart, which Kundu said has “over double digit cases” where a product isn’t available in a local market. 

In terms of semantic understanding, say a shopper is looking to buy healthy snacks for children. The model needs to understand what a healthy snack is and what foods are appropriate for, and appeal to, an 8 year old, then identify relevant products. And, when those particular products aren’t available in a given market, the model has to also find related subsets of products. 

Then there’s the logistical element. For example, a product like ice cream melts quickly, and frozen vegetables also don’t fare well when left out in warmer temperatures. The model must have this context and calculate an acceptable deliverability time. 

“So you have this intent understanding, you have this categorization, then you have this other portion about logistically, how do you do it?”, Kundu noted.

Avoiding ‘monolithic’ agent systems

Like many other companies, Instacart is experimenting with AI agents, finding that a mix of agents works better than a “single monolith” that does multiple different tasks. The Unix philosophy of a modular operating system with smaller, focused tools helps address different payment systems, for instance, that have varying failure modes, Kundu explained. 

“Having to build all of that within a single environment was very unwieldy,” he said. Further, agents on the back end talk to many third-party platforms, including point-of-sale (POS) and catalog systems. Naturally, not all of them behave the same way; some are more reliable than others, and they have different update intervals and feeds. 

“So being able to handle all of those things, we’ve gone down this route of microagents rather than agents that are dominantly large in nature,” said Kundu. 

To manage agents, Instacart has integrated with OpenAI’s model context protocol (MCP), which standardizes and simplifies the process of connecting AI models to different tools and data sources.

The company also uses Google’s Universal Commerce Protocol (UCP) open standard, which allows AI agents to directly interact with merchant systems. 

However, Kundu’s team still deals with challenges. As he noted, it’s not about whether integration is possible, but how reliably those integrations behave and how well they’re understood by users. Discovery can be difficult, not just in identifying available services, but understanding which ones are appropriate for which task.

Instacart has had to implement MCP and UCP in “very different” cases, and the biggest problems they’ve run into are failure modes and latency, Kundu noted. “The response times and understandings of both of those services are very, very different I would say we spend probably two thirds of the time fixing those error cases.” 

On Wednesday, Anthropic announced that its AI chatbot, Claude, will remain free of advertisements, drawing a sharp line between itself and rival OpenAI, which began testing ads in a low-cost tier of ChatGPT last month. The announcement comes alongside a Super Bowl ad campaign that mocks AI assistants that interrupt personal conversations with product pitches.

“There are many good places for advertising. A conversation with Claude is not one of them,” Anthropic wrote in a blog post. The company argued that including ads in AI conversations would be “incompatible” with what it wants Claude to be: “a genuinely helpful assistant for work and for deep thinking.”

The stance contrasts with OpenAI’s January announcement that it would begin testing banner ads for free users and ChatGPT Go subscribers in the US. OpenAI said those ads would appear at the bottom of responses and would not influence the chatbot’s actual answers. Paid subscribers on Plus, Pro, Business, and Enterprise tiers will not see ads on ChatGPT.

“We want Claude to act unambiguously in our users’ interests,” Anthropic wrote. “So we’ve made a choice: Claude will remain ad-free. Our users won’t see ‘sponsored’ links adjacent to their conversations with Claude; nor will Claude’s responses be influenced by advertisers or include third-party product placements our users did not ask for.”

Competition between OpenAI and Anthropic has been fierce of late, due to the rise of AI coding agents. Claude Code, Anthropic’s coding tool, and OpenAI’s Codex have similar capabilities, but Claude Code has been widely popular among developers and is closing in on OpenAI’s turf. Last month, The Verge reported that many developers inside long-time OpenAI benefactor Microsoft have been adopting Claude Code, choosing Anthropic products over Microsoft’s Copilot, which is powered by tech that originated at OpenAI.

In this climate, Anthropic could not resist taking a dig at OpenAI. In its Super Bowl commercial, we see a thin man struggling to do a pull-up beside a buff fitness instructor, who is a stand-in for an AI assistant. The man asks the “assistant” for help making a workout plan, but the assistant slips in an advertisement for a supplement, confusing the man. The commercial doesn’t name any names, and OpenAI has said it will not include ads in chat text itself, but Anthropic’s implications are clear.

The Google Pixel 9a is without a doubt the best option for anyone wishing to purchase a flagship smartphone without breaking the bank in 2026. At $399 for the 128 GB model, which is $100 less than the original price, this device far exceeds its price in terms of what it offers, pushing the limits of how much a phone should cost.

Google equipped the Pixel 9a with the same Tensor G4 processor that powers the flagship Pixel 9 series. Whether you’re switching apps, looking through feeds, or streaming your favorite shows, everyday chores are simple and often exceed expectations. Sure, the CPU is geared for light gaming and multitasking, and you can count on 8GB of RAM to keep things moving along smoothly.

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The Pixel 9a has a big 6.3-inch display with a resolution of 1080 x 2424. The Actua screen can achieve a remarkable 2700 nits, making outdoor use possible even on the brightest of days, and with a 120Hz refresh rate, the scrolling and animations are quite fluid.

The battery is a beast, a 5100mAh cell that can easily last a whole day and sometimes even two, as long as you don’t do anything unusual. Google’s Adaptive Battery learns your usage patterns and adds extra juice when needed, while Extreme Battery Saver goes above and above. In terms of charging power, 23W cable and 7.5W wireless are totally enough given the pricing.

Let’s talk about the cameras, because they’ve always been one of the Pixel’s best features. The 48MP primary sensor captures images with colors that are so genuine they almost appear real. Furthermore, AI capabilities such as Add Me for grouping selfies, Best Take for selecting the perfect photo, and Macro Focus make it incredibly simple to achieve great results with little effort. The 13MP ultrawide and 13MP selfie cameras do not disappoint.

Google has you covered with seven years of OS upgrades and security patches. So you don’t have to worry about your phone getting left behind. Android 16 is swift and responsive, and you get all the exclusive Pixel perks, such as Call Assist to keep telemarketers at bay and Gemini AI for lightning-fast responses to your questions across all your apps.

In terms of build quality, it’s really sturdy, with an IP68 classification indicating that it can withstand some major water and dust exposure. Furthermore, the aluminum frame and plastic back keep it lightweight at 186g while still feeling excellent in the palm.