Attribution is preliminary, and so far it seems no real damage.

And it seems like this is a campaign that has targeted at least seven states. And, because this is where the US is right now, Trump doesn’t believe it’s Iran and that Minnesota…I guess…hacked itself.

“I think I blame it on Minnesota because they’re grossly incompetent,” Trump said. “I would blame it on Minnesota and the governor, the corrupt governor of Minnesota. They like to say, ‘Oh, it’s Iran.’ Iran should be so lucky. Iran’s got bigger problems than worrying about Minnesota.”...

Lithium-ion batteries are the leading choice in today’s electric vehicle and battery energy storage system industries, but they contain a number of critical minerals — including lithium, cobalt, nickel, and graphite — that are considered essential for economic and national security reasons, and therefore vulnerable to supply chain disruptions. As renewable energy, electrified infrastructure, and high-power digital technologies continue to grow, there is an increasing need for energy storage systems that are low-cost, resource-abundant, and capable of fast charging and discharging. 

That need, among other reasons, has motivated a group of researchers — based at MIT and led by Ju Li, the Carl Richard Soderberg Professor of Power Engineering in the departments of Nuclear Science and Engineering (NSE) and Materials Science and Engineering — to develop complementary energy storage solutions. 

The team is looking, in particular, at sodium-metal batteries, which offer several attractive features. Sodium is about 1,000 times more abundant than lithium and, pound for pound, about one-hundredth the cost. A key challenge, however, is that sodium metal is highly reactive, making it difficult for these batteries to achieve both long-term stability and fast cycling. 

A new paper in the journal Joule — written by 15 members of the MIT team and published online this week — shows how this dilemma can be addressed by finding the right electrolyte for this battery system.

Electrolytes behaving badly

An electrolyte is one of three main components of a battery, along with the negative electrode (the anode) and the positive electrode (the cathode). The electrolyte acts like the “blood” of the battery, allowing electrically charged ions to move between the two electrodes. “The electrolyte is supposed to just transmit those ions,” explains Li. “It’s supposed to be an ion conductor.” But unfortunately, most electrolytes get involved in unwanted chemical reactions with the electrodes, which can greatly undermine battery stability.

The consequences of these “side reactions” can be severe, says Weiyin Chen, a postdoc in NSE and one of four lead authors of the Joule paper. Insoluble compounds produced during the reactions can build up on the electrodes, creating a barrier that blocks ion transport and can eventually cause the battery to fail. 

Until recently, Chen says, no electrolyte used in sodium-metal batteries was fully stable against these unwanted reactions at both the anode and cathode, even though such stability is essential for rechargeable batteries to achieve a long cycle life. An initial breakthrough occurred in 2021, when the Li group and their collaborators identified a “sulfonamide” molecule — consisting of sulfur, oxygen, and nitrogen atoms — that, when used as a solvent, “is magically stable at both electrodes in lithium batteries,” according to Li. This molecule is known as DMTMSA. 

Building on that discovery, Li and his colleagues set out to see if related molecules could improve sodium batteries. The goal was not only to maintain stability, but also to enable fast charging and discharging. If charging is too slow, it could take all night to recharge, and if discharging is too slow, the battery cannot deliver much power when needed.

How did the solvent cross the road?

Chen explains the idea with an analogy: Suppose you need to cross a street jam-packed with pedestrians, much like ions traveling from one electrode to another. “You can move more quickly through the crowd with a small backpack that is snug against your body, rather than dragging a bulky suitcase on wheels,” Chen says. 

A similar situation occurs in batteries: When sodium ions are surrounded by smaller solvents, they can move faster than when they are surrounded by larger, bulkier solvents. Faster ion transport enables more-rapid charging and discharging. The team’s goal, accordingly, was to identify solvent molecules that are small enough to improve ion transport while still maintaining electrolyte stability.

There is, however, a complicating factor — a trade-off to be addressed: Faster ion transport often comes at the expense of electrolyte stability. Many highly conductive electrolytes react more easily with the electrodes, shortening battery life. Fortunately for their plan, Li says, “reducing the size of solvents provides a new pathway to overcome this trade-off.” 

The question then becomes how to find a smaller solvent that has other desirable properties. The idea they adopted is to look for molecules that are “congeneric,” says Li, “meaning that they belong to a similar family and are molecularly similar.” In particular, they searched for molecules related to DMTMSA, hoping to find candidates that were smaller but could retain the stability that made DMTMSA so promising.

Chia-Wei Hsu, an MIT PhD student in materials science and engineering, created an AI-guided algorithm, which designed 100,000 candidate molecules on his computer within 24 hours. Hsu then narrowed down the pool to 200 candidates by applying a set of technical criteria — including similarity in shape to DMTMSA and comparable electronic properties. Twenty-seven representative candidates covering the full range of possibilities were selected for experimental tests. 

“We tested them all under the same conditions to make it a fair, head-to-head competition,” Chen says. A clear winner emerged, a solvent called DMFSA, which was both the smallest and the best.

Small is beautiful

This work, claims Jinhyuk Lee, an associate professor of materials engineering at McGill University who is not part of the study, “addresses one of the most persistent challenges in battery research: improving battery performance at high charging and discharging rates without sacrificing long-term stability. By carefully tailoring the size of solvent molecules, the authors demonstrate a new design strategy that could enable lower-cost, higher performance batteries.” 

The group is not done. A new search is underway to find an even better solvent. This time, the approach is similar, but DMFSA (rather than the larger DMTMSA molecule) serves as the starting point. Chen believes the new solvents they are uncovering could eventually lead to rechargeable sodium-metal batteries that combine low-cost, abundant materials with fast charging and high-power performance, opening the door to broader energy storage applications.

The overriding goal of this work, the authors emphasize, is not only to advance sodium batteries. It’s also to introduce a new approach to electrolyte design that uses solvent size and molecular similarity as the key guideposts. Viewing the research in this light, sodium-metal batteries serve as a model system for demonstrating a more general design principle.

“Because the concept is broadly applicable,” Lee comments, “its impact could extend well beyond sodium batteries and influence the design of a wide range of future energy storage technologies.”

This work was supported, in part, by a National Research Foundation of Korea grant funded by the government of Korea government, as well as U.S. National Science Foundation graduate research fellowship. The characterization equipment used in this project is partly from the MIT.nano Characterization Facilities. 

In the scorching heat of every Las Vegas summer, EFF joins thousands of hackers, makers, policy analysts, and activists for the world's largest computer security gathering. If you're there during this summer security week, be sure to say hello to us at BSides Las Vegas, Black Hat Briefings, and DEF CON 34. While tech companies align with governments to target the people, our community is harnessing technology to fight back. Will you lend your support this year?

JOIN EFF

EFF’s relentless work in the legal system makes a meaningful difference for privacy and free expression everywhere. But we also know that your rights won't wait while the wheels of justice turn.

Sometimes hacking the system means creating tools and resources to protect your rights today. That includes EFF’s Privacy Badger, Certbot, Surveillance Self-Defense guide, and the countless security trainings that our team conducts for vulnerable populations—all thanks to EFF member support.

Technology is inseparable from our workplaces, schools, healthcare, the justice system, and our democratic process. If you think tech should benefit everyone and not just accumulate wealth and control for the powerful, then congratulations: We'd like to welcome you to the team.

Hayley and Joe take a break from EFF’s Activism Team to show off EFF’s DEF CON member t-shirt.

For a limited time only: Get EFF’s “Many Hands Make Light Work” t-shirt designed for the DEF CON 34 hacker conference by EFF artist Hannah Diaz. Don’t miss the link to the online puzzle incorporated into the design! With the strength of community and the spirit of curiosity, we can hack anything.

Many thanks to our puzzlemasters Aaron Steimle (AKA Elegin) and Kevin Hulin (AKA CryptoK). Elegin is our longtime collaborator on the EFF shirt puzzle, and previously a multiyear winner of this very contest. CryptoK is a crypto puzzle enthusiast and also develops challenges for the DEF CON Crypto and Privacy Village's Gold Bug Contest.

Members can also choose from EFF’s puffy stickers, the internet tracker-obsessed Privacy Badger embroidered sweatshirt, and our ALPR-focused “Claw Back” t-shirt.

EFF member t-shirt designs: Claw Back and Many Hands Make Light Work

EFF fights to protect fundamental rights for everyone, and your privacy and free expression have never been more important. Support the cause today! Together we can make sure that technology supports freedom, justice, and innovation for all people.

And it’s personal information (alternate link):

The exposed data includes an AI-powered therapy app that someone appears to have vibe-coded, notes on meetings, and a dashboard someone made apparently to analyze medical billing data. Exposed chats reportedly include private cryptocurrency wallet keys and personal information like peoples’ addresses.

What seems to be the issue is a user setting about data sharing. Anthropic’s position is that it’s not their problem:

“We give people control over sharing their Claude conversations publicly, and in keeping with our privacy principles, we do not share chat directories or sitemaps with search engines like Google,” the company said in a statement. “These shareable links are not guessable or discoverable unless people choose to share them themselves. When someone shares a conversation, they are making that content publicly accessible, and like other public web content, it may be archived by third-party services.”...

A summer surge in power consumption is straining regional grids that are also seeing a boom in data center energy demand.

Requiring companies to disclose their climate risks provides valuable information that otherwise isn't always available, they said.

Former Republican EPA leaders and a conservative scholar join Democrats supporting local efforts to sue for climate-related damages.

House and Senate Democrats are looking for more data on how home insurers calculate their pricing.

The world hit a milestone of 3 terawatts of solar deployed this year.

Member countries are worried that low-carbon quotas in the Industrial Accelerator Act could push up prices and hurt Europe’s competitiveness.

Firefighters continued to battle wildfires in southern Greece and France on Monday, and much of the U.K. is officially in drought.

Illegal land grabbing, cattle ranching and expanding infrastructure continue to put heavy pressure on the Amazon.

A one-size-fits-all approach likely isn’t the best strategy when designing artificial intelligence systems that assist users in disease diagnosis.

A new study by researchers at MIT and elsewhere found that, while AI assistance generally improved the accuracy of non-experts and clinicians in diagnosing skin diseases, AI explainability methods had different impacts depending on the users’ knowledge level. 

Explainable AI methods help users know when to trust a model’s predictions by describing or validating the model’s decision-making. For instance, a model might use a heat map to highlight image regions that were most important in its diagnosis or a large language model (LLM) to explain the prediction in plain language.

In this study, researchers tested non-experts and primary care providers in skin disease diagnosis, with and without the help of different explainable AI systems. 

They found that non-experts’ diagnostic accuracy improved, but it was largely due to deference to the AI system. Non-experts trusted LLM-based explanations whether they were right or wrong, and found explanations more convincing when they were vague or generic.

By contrast, clinicians were not tripped up by incorrect AI assistance and performed best when given only a model’s prediction, with no accompanying explanation. 

“Good AI systems can improve performance in some health settings, but this has to be balanced carefully with algorithmic deference that can lead to more error. We know that both AI and explainability methods can engage automation bias in humans, and this anchoring effect is something that must be accounted for when we design AI systems,” says Marzyeh Ghassemi, an associate professor in MIT’s Department of Electrical Engineering and Computer Science (EECS), a member of the Institute for Medical Engineering and Science, and a principal investigator at the Laboratory for Information and Decision Systems and the Abdul Latif Jameel Clinic for Machine Learning in Health.

“These findings are important as patients increasingly turn to AI to help with their health care. Our findings show that those with the least medical knowledge are most likely to be led astray when explainable AI models give an erroneous output,” says Roxana Daneshjou, a co-author and assistant professor of biomedical data science and dermatology at Stanford University.

These results underscore the importance of building AI systems with users in mind and of developing explainability methods that encourage critical thinking rather than overreliance on the model, the researchers say.

“It’s getting obvious that we cannot just assume a good AI will solve all problems. We need to pay careful attention to the users who will be using the AI system, because the same explanation can help an expert and mislead a beginner. Often the people who could benefit most from AI are the ones most likely to be led astray by it, so how we present a recommendation matters as much as whether it’s correct,” says lead author Orson Xu, an assistant professor in the Department of Biomedical Informatics at Columbia University.

Ghassemi, Xu, and Daneshjou are joined on the paper by many authors, including MIT graduate student Haoran Zhang, undergraduate Reina Wang, and Luis Soenksen PhD ’20, a research affiliate at the Jameel Clinic, along with clinicians and researchers. A description of the work appears today in Nature Medicine.

Exploring explanations

Several FDA-approved AI interfaces are being used to help clinicians identify skin conditions in medical images, as a way to streamline early diagnosis. In addition to providing a prediction of whether disease is present in the image, these tools often use one of several methods that explain the model’s decision-making.

At the same time, non-experts can perform digital diagnosis on their own using AI-powered search engines that predict skin diseases based on user prompts. These systems often use LLMs to explain the model’s prediction in simpler terms.

The researchers explored the effects and potential benefits of these explainable AI tools on primary care physicians and non-experts in dermatological disease detection. They tested users by showing them medical images plus an AI prediction of skin disease, employing different explainable AI approaches. 

These approaches included: an AI prediction and confidence level with no explanation, a method that provides similar images to reinforce its prediction, a heat map-based approach that highlights important image regions, and an LLM that explains the model’s reasoning in plain language.

Non-experts were tasked with deciding whether an image of a skin mole was cancerous, with and without the help of explainable AI. Clinicians were given the more challenging task of providing a differential diagnosis of dermatological disease.

The researchers found that all explainable AI approaches improved the accuracy of non-experts, mostly because the tools helped users diagnose non-cancerous moles. 

In addition, when they employed a fairness-constrained model designed to combat bias against darker skin tones, the system significantly improved accuracy and reduced diagnostic disparities based on skin tone.

“But the reason non-expert users are better is because they are more reliant on the models. When the model is wrong, it hurts performance more than it helps performance when the model is right. We were just able to train very good AI models for this setting,” Ghassemi says.

This deference effect is largest with LLM explanations, and users were more confident about their wrong answers when aided by an LLM.

On the other hand, clinicians were resilient to incorrect AI explanations and, of all the explainability methods, LLMs boost their accuracy the least.

“It really comes down to how each group uses the explanation. A clinician already has a diagnosis in mind and checks the AI against their own training, so a bad explanation gets caught. Meanwhile, a non-expert can use that exact same explanation to form an opinion in the first place, so a plausible, confident-sounding rationale can pull them toward the wrong answer. The same tool ends up being an asset for one user and a liability for another,” Xu says.

Overcoming the deference effect

When the researchers dug deeper, they found that users who were most deferential to AI assistance were the worst performers on the task without the help of AI. 

They also found that the time at which users were presented with AI explanations influenced their behavior. If an explanation is given first, before the user can perform the diagnosis on their own, they tend to become more deferential to the model.

In addition, AI systems outperformed humans when the presentation of disease was subtle, but humans performed much better if there are atypical symptoms or unrelated features in an image.

Taken together, these results indicate that explainable AI can cause overreliance on models and lead users to blindly follow AI recommendations even when they are wrong. 

Rather than using LLMs to generate more detailed explanations, it might be more effective to force users to give a diagnostic hypothesis first, then provide an AI-based suggestion to highlight other possible conditions for consideration. 

“We really want AI to improve creativity and either upskill or fill in gaps where users are missing subtle presentations. Otherwise, we risk engaging automation bias and then, when the model is wrong, users can’t recover,” Ghassemi says. 

This research was funded, in part, by the National Science Foundation, Schmidt Sciences, the National Bureau of Economic Research, and Columbia University.

Nature Climate Change, Published online: 04 August 2026; doi:10.1038/s41558-026-02729-3

Climate change communication shapes how societies perceive risks and respond to them. In this issue of Nature Climate Change we examine the evolving content and methods of climate communication, the challenges of urgency and injustice, and the strategies for building effective and inclusive communication channels.

Nature Climate Change, Published online: 04 August 2026; doi:10.1038/s41558-026-02714-w

Communication is essential for keeping communities safe when climate disasters occur. Here, we explore how disaster communication informs protective actions and examine five challenges: false information, exhaustion from repeated disasters, unequal access to information, polarized climate attitudes and mental health.

Nature Climate Change, Published online: 04 August 2026; doi:10.1038/s41558-026-02680-3

Social media have become an important arena for climate change communication. This Review synthesizes climate-related content on social media, its effects on climate attitudes, knowledge and behaviour, and its role in reshaping the broader communication landscape.

Nature Climate Change, Published online: 04 August 2026; doi:10.1038/s41558-026-02711-z

Individuals and groups shape others’ attitudes and actions through social influence. This Perspective articulates a research agenda of social influence for climate action, synthesizes existing evidence and highlights its practical relevance for designing effective behavioural interventions.

The Senate Commerce Committee is once again considering legislation that would dramatically expand age verification, and undermine privacy for everyone. Alongside the SCREEN Act, the CHATBOT Act, and the Youth AI Privacy Act, the Kids Online Safety Act (KOSA) would push companies to collect more information about their users while creating new incentives to restrict lawful speech.

Take action

Tell Congress: KOSA endangers the privacy of all

KOSA Pushes Platforms Toward Age Verification

The Senate version of KOSA imposes a “duty of care” on online services, including social media, to avoid exposing young people to certain material the law deems harmful. But those obligations only work if online services know which users are minors. That means more platforms will be pressured to implement age verification or age estimation systems.

That’s not a bill that increases privacy—it’s one that creates new privacy problems. Whether companies verify ages by checking government IDs, performing facial analysis, checking your bank records, or collecting other personal information, all of these systems require the handing over of more sensitive data, simply to access lawful online speech and services. They also create new databases of personal information that can be breached, misused, or demanded by governments.

Everyone deserves privacy online. Congress could push for a bill that protects privacy for all users, but that’s not what they’re doing here. Instead, KOSA and the other bills coming up for a vote this week push online services to adopt systems that require people to identify themselves before they can speak, read, or participate online.

KOSA Still Creates Incentives to Censor Lawful Speech

Some online content isn’t appropriate for minors. Families, schools, and communities all have important roles to play in helping children navigate the internet. But KOSA takes those decisions away from families and the young people who have a First Amendment right to speak and access information online. It instead empowers government officials to enforce how online services handle lawful speech. 

And by empowering elected attorneys general in states across the country to enforce KOSA, the bill means those elected officials, rather than your family, deciding what’s appropriate online content for teens. Even more likely, it will lead to limits on what minors and adults are able to see at all, as companies shut down potentially controversial forums in order to avoid legal action from government bureaucrats. 

The latest version of KOSA once again includes a broad "duty of care" requiring platforms to mitigate a wide range of alleged harms to minors.

Whatever disclaimers and exceptions the bill includes, the practical effect is unchanged. When platforms face liability for content that someone later claims contributed to harms like anxiety, eating disorders, or substance use, the safest response is to remove lawful speech or shut down forums discussing those topics altogether. 

More worrisome, the potential liability KOSA creates may push online services to either remove speech well in advance of a young person seeing it, or block young people’s access so they never see it. That will likely include forums where people try to help each other, find community and recovery resources for the exact harms listed in the bill, like gambling and drug addiction. In trying to protect young people, KOSA may actually cut them off from valuable sources of support. 

We've explained these censorship risks in detail before, and they remain just as real in the latest version of the bill.

Congress Should Reject KOSA

Minors deserve meaningful privacy protections online—as do adults. But KOSA moves in the opposite direction by encouraging more age verification, as well as more legal pressure for platforms to monitor and restrict lawful speech.

The Senate Commerce Committee should reject KOSA, along with the other bills in this legislative package, and instead pursue comprehensive privacy legislation that protects everyone—not just minors—without undermining privacy, security, or free expression.

Take action

Congress shouldn't set the rules for what we see online

EFF joined a group of 18 civil society organizations to send a letter encouraging New York Governor Kathy Hochul to Senate Bill 9934A, the New York Stealth Crawler Prohibition Act. The letter states:

While framed as a measure to protect local journalism, this legislation harms free expression and establishes a dangerous precedent by effectively deanonymizing and criminalizing automated access to the open web. By requiring all web crawlers to disclose their identity and explicit purpose, and by granting media outlets unchecked authority to obtain judicial subpoenas to unmask unidentified automated web traffic without any showing of misconduct or actual injury, this bill threatens digital privacy, compromises the foundational architecture of the internet, and will ultimately stifle the very independent journalism it seeks to protect.

As we’ve previously explained, so-called “stealth crawlers” are simply automated tools to access and collect public web data—without disclosing the user’s identity. Private crawlers like these facilitate all kinds of important work that benefits the public, including investigative reporting, academic research, cybersecurity protection, and EFF’s own Privacy Badger. As we illustrate in the letter: 

Anonymous crawling fuels important investigative journalism. For example, The Markup, a non-profit news site, used anonymous crawlers to investigate potentially anti-competitive practices by tech companies, such as Amazon’s tendency to prioritize Amazon brands and Amazon-exclusive products over competitors with higher ratings. The crawlers identified themselves as ordinary Firefox browsers to web servers, which allowed The Markup to understand how Amazon search results pages would appear to ordinary users. Similarly, ProPublica used an automated tool designed to simulate an ordinary Amazon customer to reveal that the site steered shoppers to more expensive products over cheaper alternatives. 

Anonymous web scraping is also crucial for cybersecurity professionals, who use automated tools to monitor the web for information that helps them protect against malicious attackers. Privacy tools, including EFF’s Privacy Badger, also crawl sites anonymously to identify trackers without compromising user privacy.

Laws like S9934A sweep far beyond AI, targeting anonymity rather than the real technical issue: overaggressive crawling that can overtax technological infrastructure. Unmasking crawlers won't fix these server strains, but it will chill vital public-interest research and compromise digital privacy. Addressing the harms of web scraping requires narrow technical solutions—not policies that give publishers veto power over the open web. This is why we are calling on Governor Hochul to veto S9934A.

You can read the full letter here. For a deeper dive into why crawlers and scrapers are vital for the open web, check out this blog post.

The Federal Trade Commission (FTC) in July issued a proposed policy statement “concerning the suppression of accuracy in artificial intelligence systems.” We urge the FTC to withdraw this misguided proposal and instead focus on its core strengths and mission to protect consumers. 

The new proposed policy builds on, and directly references, the Trump administration’s  “Preventing Woke AI in the Federal Government” executive order—a nightmare for civil liberties that seeks to strong-arm AI companies into modifying their models to conform with the its ideological agenda. In recently filed comments, EFF,  Public Knowledge, and Fight for the Future call for the FTC to stop its unconstitutional efforts to regulate lawful speech, override state laws, and intimidate AI developers into ideological alignment with the Trump administration.

The government may not install itself as the arbiter of truth.

In the joint comments, we outline three critical flaws within the latest proposed policy. First, it violates the First Amendment. The policy calls for the Commission to become the judge of which AI outputs meet an undefined standard of accuracy. Installing the FTC as the authority of this sort of viewpoint-based judgment is a prior restraint on speech. Additionally, the policy’s proposed solution to address speech concerns compounds, rather than properly limits, the likely harms to speech. As we say in our comments: the government may not install itself as the arbiter of truth. 

Second, it exceeds the FTC’s legal authority by claiming that its federal regulatory rules can override, or “preempt,” laws in states that have passed to regulate artificial intelligence use. This is clearly an attempt to target state laws the administration disagrees with. For example, the policy specifically criticizes Colorado's automated decisionmaking law, which applies when automated technology is used to consider consequential decisions such as those around employment, access to housing, health care, and insurance. We noted to the FTC that characterizing this law as one that requires AI companies to “suppress accuracy,” or encourages deception, is itself inaccurate. In any case, the FTC lacks the authority to put its rules in place over state law, unless Congress directly delegates it that power. It has been given no such power here.

Third, the policy is vague and sets the stage for improper jawboning of AI developers and companies that use AI tools (deployers). Jawboning is a term for situations in which the government urges private companies or people to censor another's speech. The proposal, as written, creates an enforcement regime that would put a thumb on the scale in favor of certain partisan speech and ideals. This will lead companies to censor only what the administration interprets as biased or untruthful. Yet, in our filing, we note that the FTC itself can't define an objective standard for what “bias” means, conceding the “exact line of what constitutes bias may be difficult to draw.”

There is work the FTC should be doing to protect consumers in the age of AI. In our comments, we conclude by saying:

[We] implore the Commission to focus on its core strengths and the mission for which it is so urgently needed—promoting structural market competition and protecting consumers from real unfair and deceptive acts and practices—in both the burgeoning and critically important AI industry and across the broader technology marketplace.

EFF and our partners have always urged the FTC to police genuine deception in technology markets. We have also consistently opposed government efforts to dictate what private speakers may say. That’s why we urge the FTC to withdraw this proposal. 

You can read our full comments here.