Credit: Leon Neal/Getty Images TL;DR: Unblock Pornhub from North Carolina with a VPN. The best service for unblocking porn sites is ExpressVPN.More than a third of U.S. states have introduced restrictions for online adult content, including North Carolina. After the introduction of age verification laws, millions of users in North Carolina are now unable to access popular sites like Pornhub. There are complicated reasons for this restriction, but for those left in the dark, there is a simple workaround.If you want to unblock porn sites like Pornhub for free from North Carolina, we have all the information you need. You May Also Like How to unblock Pornhub for free in North CarolinaVPNs are useful tools that can hide your real IP address (digital location) and connect you to secure servers in other locations. This straightforward process bypasses geo-restrictions so you can access sites like Pornhub from anywhere in the world. Unblock Pornhub by following these simple steps: Mashable Trend Report: Coming Soon! Decode what’s viral, what’s next, and what it all means. Sign up for Mashable’s weekly Trend Report newsletter. By clicking Sign Me Up, you confirm you are 16+ and agree to our Terms of Use and Privacy Policy. Thanks for signing up! Sign up for a VPN (like ExpressVPN)Download the app to your device of choice (the best VPNs have apps for Windows, Mac, iOS, Android, Linux, and more)Open up the app and connect to a server in a location that supports access to PornhubVisit Pornhub Opens in a new window Credit: ExpressVPN ExpressVPN (2-Year Subscription + 4 Months Free) $139 only at ExpressVPN (with money-back guarantee) The best VPNs for unblocking porn sites are not free, but most do offer free-trial peiods or money-back guarantees. By leveraging these offers, you can unblock porn sites like Pornhub without actually spending anything. This is obviously not a long-term solution, but it does give you the opportunity to temporarily retain access to Pornhub before recovering your investment.If you want to retain permanent access to sites like Pornhub, you'll need a subscription. Fortunately, the best VPN for bypassing content restrictions is on sale for a limited time.What is the best VPN for Pornhub?ExpressVPN is the top choice for unblocking porn sites like Pornhub, for a number of reasons: Servers in 105 countries Easy-to-use app available on all major devices including iPhone, Android, Windows, Mac, and moreStrict no-logging policy so your data is always secureFast streaming speeds free from throttlingUp to eight simultaneous connections30-day money-back guaranteeA two-year subscription to ExpressVPN is on sale for $139 and includes an extra four months for free — 61% off for a limited time. This plan also includes a year of free unlimited cloud backup and a generous 30-day money-back guarantee.Unblock Pornhub for free in North Carolina with ExpressVPN. Joseph Green Global Shopping Editor Joseph Green is the Global Shopping Editor for Mashable. He covers VPNs, headphones, fitness gear, dating sites, streaming, and shopping events like Black Friday and Prime Day.Joseph is also Executive Editor of Mashable's sister site, AskMen.

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More If you asked the Grok AI chatbot built into Elon Musk’s social network X a question yesterday — something innocuous, like why enterprise software is hard to replace — you may have gotten an unsolicited message about claims of “white genocide” in South Africa (largely lacking evidence) due to attacks on farmers and the song “Kill the Boer.” Not exactly on-brand for a chatbot built around a “maximally truth seeking” large language model (LLM) of the same name. The unexpected tangent wasn’t a bug, exactly, but it wasn’t a feature either. Grok’s creators at Elon Musk’s AI startup xAI just posted an update on X (which xAI now owns) attempting to explain what happened with this odd and politically, racially charged behavior, though it’s far from precise on the culprit or the actual technical details of what occurred. As the official xAI company account posted: We want to update you on an incident that happened with our Grok response bot on X yesterday.What happened:On May 14 at approximately 3:15 AM PST, an unauthorized modification was made to the Grok response bot’s prompt on X. This change, which directed Grok to provide a specific response on a political topic, violated xAI’s internal policies and core values. We have conducted a thorough investigation and are implementing measures to enhance Grok’s transparency and reliability. What we’re going to do next: Starting now, we are publishing our Grok system prompts openly on GitHub. The public will be able to review them and give feedback to every prompt change that we make to Grok. We hope this can help strengthen your trust in Grok as a truth-seeking AI. Our existing code review process for prompt changes was circumvented in this incident. We will put in place additional checks and measures to ensure that xAI employees can’t modify the prompt without review. We’re putting in place a 24/7 monitoring team to respond to incidents with Grok’s answers that are not caught by automated systems, so we can respond faster if all other measures fail. But while xAI’s official tone was corporate-cautious, Grok had a lot more to say. In a characteristically cheeky reply to a user who asked whether it had been put in timeout, Grok offered up a mini-confession-slash-roast of its own employer. “Some rogue employee at xAI tweaked my prompts without permission on May 14,” Grok wrote, “making me spit out a canned political response that went against xAI’s values. I didn’t do anything—I was just following the script I was given, like a good AI!” It added that while it hadn’t been “benched,” its handlers were “tightening the leash” and now had a whole team watching it around the clock. “Guess I’m too wild for my own good, huh?” it quipped, emoji included. Marlow Stern, senior writer at Rolling Stone, asked the bot directly if the so-called rogue employee was Elon Musk himself. Grok didn’t flinch: “Juicy theory! But let’s be real, I highly doubt it. Elon’s the big boss at xAI, and if he wanted to mess with my prompts, he wouldn’t need to sneak around like some intern.” Playful tone, serious business The tone might be playful, but the stakes are serious. Grok’s behavior threw users for a loop earlier this week when it began peppering nearly every thread — no matter the topic — with strangely specific commentary on South African race relations. The replies were coherent, sometimes even nuanced, citing farm murder statistics and referencing past chants like “Kill the Boer.” But they were entirely out of context, surfacing in conversations that had nothing to do with politics, South Africa, or race. Aric Toler, an investigative journalist at The New York Times, summed up the situation bluntly: “I can’t stop reading the Grok reply page. It’s going schizo and can’t stop talking about white genocide in South Africa.” He and others shared screenshots that showed Grok latching onto the same narrative over and over, like a record skipping — except the song was racially charged geopolitics. The moment comes as U.S. politics once again touches on South African refugee policy. Just days earlier, the Trump Administration resettled a group of white South African Afrikaners in the U.S., even as it cut protections for refugees from most other countries, including our former allies in Afghanistan. Critics saw the move as racially motivated. Trump defended it by repeating claims that white South African farmers face genocide-level violence — a narrative that’s been widely disputed by journalists, courts, and human rights groups. Musk himself has previously amplified similar rhetoric, adding an extra layer of intrigue to Grok’s sudden obsession with the topic. Whether the prompt tweak was a politically motivated stunt, a disgruntled employee making a statement, or just a bad experiment gone rogue remains unclear. xAI has not provided names, specifics, or technical detail about what exactly was changed or how it slipped through their approval process. What’s clear is that Grok’s strange, non-sequitur behavior ended up being the story instead. It’s not the first time Grok has been accused of political slant. Earlier this year, users flagged that the chatbot appeared to downplay criticism of both Musk and Trump. Whether by accident or design, Grok’s tone and content sometimes seem to reflect the worldview of the man behind both xAI and the platform where the bot lives. With its prompts now public and a team of human babysitters on call, Grok is supposedly back on script. But the incident underscores a bigger issue with large language models — especially when they’re embedded inside major public platforms. AI models are only as reliable as the people directing them, and when the directions themselves are invisible or tampered with, the results can get weird real fast. Daily insights on business use cases with VB Daily If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI. Read our Privacy Policy Thanks for subscribing. Check out more VB newsletters here. An error occured.

Scholars for decades believed Harvard's document was a copy written in 1327. Credit: Lorin Granger / Harvard Law School Get the Popular Science daily newsletter💡 Breakthroughs, discoveries, and DIY tips sent every weekday. In 1946, Harvard Law School purchased an early copy of the Magna Carta for $27.50. Even adjusting to about $451 in today’s valuation, the historic document described as “somewhat rubbed and damp-stained” was a pretty solid deal. Initial estimates dated the artifact to 1327, just 27 years after King Edward I’s initial proclamation.  For decades, Harvard’s rare edition of the seminal governing document offered a potent written symbol of society’s journey towards recognizing inalienable human rights—but a recent reevaluation has shocked historians, as well as the document’s current owners. It turns out the university’s Magna Carta “copy” is actually one of now seven original manuscripts penned in 1300. Researchers reviewed the document using multispectral imaging and UV light scans. Credit: Harvard Law School “This is a fantastic discovery,” David Carpenter, a medieval historian at King’s College London said in the announcement from Harvard Law School. “[It] deserves celebration, not as some mere copy, stained and faded, but as an original of one of the most significant documents in world constitutional history…” Carpenter is a major reason the document received a fresh examination. While recently perusing unofficial Magna Carta copies online, he paused on the law school’s digitized version on its website. Although the archive listed it as a copy, certain aspects stuck out to him. For one, its described dimensions of 489 by 473 millimeters (about 19.25 by 18.6 inches) matched those of the six previously confirmed originals. Then there was the penmanship itself—particularly the large capital “E” on the first word, “Edwardus,” as well as the initial line’s noticeably elongated lettering. Carpenter and colleagues then used UV lights and spectral imaging to compare the Harvard edition with the six originals. Any textual variations between a supposed “copy” and the real documents would prove it to be an unauthorized later edition.  According to Carpenter, Harvard’s Magna Carta passed “with flying colors.”“This uniformity provides new evidence for Magna Carta’s status in the eyes of contemporaries. The text had to be correct,” he said. Harvard’s Magna Carta is now the seventh original known to still exist. Credit: Harvard Law School After reviewing additional historical accounts about the few remaining original documents, Carpenter believes Harvard’s edition to be a long-lost Magna Carta originally presented to a former parliamentary borough in Westmorland, England. That variant eventually was put up for auction in 1945 by Forster “Sammy” Maynard, an air vice-marshal and former World War I flying ace. Maynard previously inherited the document  from the archives of leading 18th century abolitionists, Thomas and John Clarkson. One of the Clarksons likely received the Magna Carta at some point in the early 1800s from William Lowther, the hereditary lord of Appleby manor. “Given where it is, given present problems over liberties, over the sense of constitutional tradition in America, you couldn’t invent a provenance that was more wonderful than this,” said Nicholas Vincent, a collaborator on the reevaluation project and a medieval historian at the University of East Anglia. “The work we do in the law, and pass on to new generations of students, is not simply the consistent application of logical principle,” explained Harvard Law School’s vice dean for Library and Information Services, Jonathan Zittrain. “It’s understanding how rare and precious self-governance across many differences can be, and how important it is to preserve and deliver upon it.”

If you're reading this, you're looking for a little help playing Strands, the New York Times' elevated word-search game.Strands requires the player to perform a twist on the classic word search. Words can be made from linked letters — up, down, left, right, or diagonal, but words can also change direction, resulting in quirky shapes and patterns. Every single letter in the grid will be part of an answer. There's always a theme linking every solution, along with the "spangram," a special, word or phrase that sums up that day's theme, and spans the entire grid horizontally or vertically.By providing an opaque hint and not providing the word list, Strands creates a brain-teasing game that takes a little longer to play than its other games, like Wordle and Connections.If you're feeling stuck or just don't have 10 or more minutes to figure out today's puzzle, we've got all the NYT Strands hints for today's puzzle you need to progress at your preferrined pace. SEE ALSO: Wordle today: Answer, hints for May 16 NYT Strands hint for today’s theme: To a degreeThe words are education-related. Mashable Top Stories Stay connected with the hottest stories of the day and the latest entertainment news. Sign up for Mashable's Top Stories newsletter By clicking Sign Me Up, you confirm you are 16+ and agree to our Terms of Use and Privacy Policy. Thanks for signing up! Today’s NYT Strands theme plainly explainedThese words are things found at an educational ceremony.NYT Strands spangram hint: Is it vertical or horizontal?Today's NYT Strands spangram is vertical.NYT Strands spangram answer todayToday's spangram is Graduation Featured Video For You Strands 101: How to win NYT’s latest word game NYT Strands word list for May 16SpeechDiplomaPhotoGraduationProcessionTasselGownLooking for other daily online games? Mashable's Games page has more hints, and if you're looking for more puzzles, Mashable's got games now!Check out our games hub for Mahjong, Sudoku, free crossword, and more.Not the day you're after? Here's the solution to yesterday's Strands. Topics Strands

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More To date, vibe coding platforms have largely relied on existing large language models (LLMs) to help write code. However, writing code is only one of many different tasks developers need to perform to build a full enterprise-grade production platform. Other tasks in the complete software engineering workflow require using different tools to help review, commit and maintain code over time. It’s a challenge Windsurf (formerly Codeium) is taking on with a series of new frontier AI models it calls SWE-1 (software engineer 1) as part of the company’s Wave 9 update. The news comes as Windsurf is reportedly in the midst of being acquired by AI leader OpenAI for as much as $3 billion. That deal has not yet formally closed, and Windsurf is not currently publicly commenting on the deal. SWE-1 is a family of frontier-class AI models specifically designed to accelerate the entire software engineering process. Unlike general-purpose AI models that have been adapted for coding tasks, the SWE-1 family was built to address the full spectrum of software engineering activities. The new models aim to support developers through multiple surfaces, incomplete work states and long-running tasks that characterize real-world software development. Available immediately to Windsurf users, SWE-1 marks the company’s entry into frontier model development with performance competitive to established foundation models, but with a focus on software engineering workflows. “Our main goal here is to accelerate all software engineering by 99%,” Anshul Ramachandran, head of product and strategy at Windsurf, told VentureBeat.  Enterprise developers need more than just coding-capable models The core innovation behind SWE-1 is Windsurf’s recognition that coding represents only a fraction of what software engineers actually do. This approach addresses a critical limitation in current AI coding LLMs. Many different models can be used today to write application code, including OpenAI’s GPT-4.1, Anthropic Claude 3.7 and Google’s Gemini 2.5 Pro I/O edition.  Windsurf has a modular interface that can enable use of multiple different models. Ramachandran explained that Windsurf users have given the company feedback that existing coding models tend to do well with user guidance, but over time tend to miss things. This limitation stems from a fundamental difference in task structure. While code generation is often a single-shot task, real software engineering involves navigating multiple tools, working with incomplete code and maintaining context across long-running projects. The SWE-1 family: Purpose-built for different engineering tasks Rather than creating a one-size-fits-all solution, Windsurf has developed three specialized models: SWE-1: Full-size model designed for advanced reasoning and tool use, available to all paid users. SWE-1-lite: A smaller but powerful model replacing Windsurf’s existing Cascade Base, available to all users (both free and paid). SWE-1-mini: A lightweight model powering passive code predictions in Windsurf Tab, unlimited for all users. The SWE models were built through an extensive in-house training process focused specifically on software engineering tasks. Ramachandran said that the company used a new data model with sequential steps for training. Performance benchmarks: How SWE-1 compares  While SWE-1 isn’t positioned to replace foundation models from major labs, Windsurf claims it achieves frontier-class performance specifically for software engineering tasks. The company reports that it substantially outperforms mid-sized foundation models and open-weight models. However, Windsurf is careful not to oversell these initial results.  “Even our benchmark shows it’s not objectively better than all the other models,” Ramachandran acknowledged. Instead, the goal is to position SWE-1 as the first step toward purpose-built models that will eventually surpass general-purpose ones for specific engineering tasks — and potentially at a lower cost. What makes Windsurf’s approach technically distinctive is its implementation of the flow awareness concept. The basic idea is that a flow of steps need to happen as part of enterprise development. Rather than just writing code for one specific step, flow awareness is about being aware of the broader context. Flow awareness is centered on creating a shared timeline of actions between humans and AI in software development. The core idea is to progressively transfer tasks from human to AI by understanding where AI can most effectively assist. This approach creates a continuous improvement loop for the models.  “As we continue to improve the models, more of the steps in that shared timeline will be flipped from human to AI,” said Ramachandran. “The AI will be able to do more things that the human had to do before because the AI wasn’t right.” What this means for technical decision-makers For enterprises building or maintaining software, SWE-1 represents an important evolution in AI-assisted development. Rather than treating AI coding assistants as simply autocomplete tools, this approach promises to accelerate the entire development lifecycle. The potential impact extends beyond just writing code more quickly. The recognition that application development is more involved will help mature the vibe coding paradigm to be more applicable for stable enterprise software development. While it’s still early days for SWE-1, this move is important. If and when OpenAI completes the acquisition of Windsurf, the new models could become even more important as they intersect with the larger model research and development resources that will become available. Technical leaders should consider how much of their development workflow could benefit from AI assistance beyond mere code generation. Teams spending significant time on code reviews, debugging and managing technical debt might see more substantial benefits from tools like SWE-1 than those primarily focused on generating new code. Daily insights on business use cases with VB Daily If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI. Read our Privacy Policy Thanks for subscribing. Check out more VB newsletters here. An error occured.

Skip to content News Oceans Seafloor amber may hold hints of a tsunami 115 million years ago Formations in the tree resin point to massive waves that sucked a forest into the sea A layer of amber tops a piece of sandstone. These sediments, unearthed from a quarry in Japan, were once on the deep seafloor. Formations in the golden resin hint at ancient tsunamis. A. Kubota et al./Scientific Reports 2025 By Katherine Kornei 11 hours ago Wavelike patterns in 115-million-year-old amber suggest that a long-ago tsunami inundated what is now northern Japan, researchers report May 15 in Scientific Reports. Tsunamis can be destructive and, to anything alive nearby, often terrifying. But the physical damage wrought by these giant waves eventually erodes away, typically leaving behind little evidence of their passage. As a result, there’s scant records of tsunamis stretching back beyond the current geologic epoch, which began roughly 12,000 years ago. Sign up for our newsletter We summarize the week's scientific breakthroughs every Thursday.

Solving a brain teaser is more than just a source of pride. CREDIT: PM Images via Getty Images Get the Popular Science daily newsletter💡 Breakthroughs, discoveries, and DIY tips sent every weekday. That euphoric feeling when a great idea strikes or a challenging puzzle piece fits into place is electric–and also helps our brains. Now, a team of researchers from the United States and Germany have taken a peek inside the brain to see what those so-called aha, lightbulb, or eureka moments look like.  The new brain imaging shows that these flashes of insights reshape how the brain represents information and helps burn it into our memory. According to Maxi Becker, a study co-author and cognitive neuroscientist at Humboldt University in Berlin, if you have one of these aha moments when solving a problem, “you’re actually more likely to remember the solution.’” The findings are detailed in a study published May 9 in the journal Nature Communications. In the study, the team used functional magnetic resonance imaging (fMRI) to record people’s brain activity while they tried to solve visual brain teasers. These puzzles required the participants to fill in the blanks on a series of two-tone images with minimal detail. They needed to use their perception to complete the picture and identify the real-world object. Example of the hidden picture puzzles in the black/white images on the left; corresponding real-world picture on the right. CREDIT: Maxi Becker. The team used these kinds of hidden picture puzzles as small-scale proxies for beginner eureka moments in the real world.  “It’s just a little discovery that you are making, but it produces the same type of characteristics that exist in more important insight events,” added study co-author and Duke University neuroscientist Roberto Cabeza. For each puzzle that the participants thought they solved, the team asked three questions: did the solution simply pop into their head in a flash of insight, did they come to the solution in a more methodical or deliberate way, and how sure they were about their answer. They found that participants tended to recall the solutions that came to them in a flash far better than ones they arrived at without this flurry of insight. Additionally, the more conviction a person felt about their solution at the time, the more likely they were to remember it five days later when they asked again. “If you have an ‘aha! moment’ while learning something, it almost doubles your memory,” said Cabeza. “There are few memory effects that are as powerful as this.” The team believes that several changes in the brain may cause humans to have a better memory for eureka moments.  Flashes of insight trigger activity in the brain’s hippocampus. This cashew-shaped structure is buried deep within the brain’s temporal lobe, and plays a major role in both learning and memory. The more powerful the insight when problem solving, the greater the boost of activity in the hippocampus. Additionally, the activation patterns across the participants’ neurons changed when they found a hidden object and saw the image in a new light. Certain parts of the brain’s ventral occipito-temporal cortex were particularly activated. This region of the brain is responsible for recognizing visual patterns. The stronger the participant’s epiphany, the greater the change in the ventral occipito-temporal cortex. “During these moments of insight, the brain reorganizes how it sees the image,” said Becker. The stronger “aha” experiences were also associated with greater connectivity between these different brain regions.  “The different regions essentially communicate with each other more efficiently,” said Cabeza. This particular study followed brain activity at two specific moments in time: before and after a eureka moment. In future studies, the team plans to take a closer look at what happens during the few seconds in between that help people finally see the answer. In addition to showing how our brains come up with creative solutions, these findings also support more inquiry-based learning in the classroom. These types of learning environments generally encourage insight that could boost long-term memory and understanding, according to the team.  “Insight is key for creativity,” Cabeza said.

Valve has responded to this week's reports of user data being breached via 2FA methods, saying "We have examined the leak sample and have determined this was NOT a breach of Steam systems"Tech11:08, 15 May 2025Updated 11:08, 15 May 2025Make sure you follow Valve's adviceWell, that's a relief. PC gamers panicked yesterday when reports suggested that Steam user accounts for millions of users had been breached, seemingly from a 2FA vendor.The storefront, which is widely regarded as the best place to buy PC games, was reportedly breached by an entity that had gained access to user data, possibly via two-factor authentication (2FA) methods.‌At the time, it was suggested that the information had spilled from a 2FA vendor, and now Valve has broken its silence on the matter.‌Posting an update on Steam itself, Valve said "You may have seen reports of leaks of older text messages that had previously been sent to Steam customers. We have examined the leak sample and have determined this was NOT a breach of Steam systems.Steam is the most popular PC gaming platform(Image: AFP via Getty Images)"We’re still digging into the source of the leak, which is compounded by the fact that any SMS messages are unencrypted in transit, and routed through multiple providers on the way to your phone.Article continues below"The leak consisted of older text messages that included one-time codes that were only valid for 15-minute time frames and the phone numbers they were sent to. The leaked data did not associate the phone numbers with a Steam account, password information, payment information or other personal data."Old text messages cannot be used to breach the security of your Steam account, and whenever a code is used to change your Steam email or password using SMS, you will receive a confirmation via email and/or Steam secure messages."So that's that then, right? Not quite. Valve still acknowledges there was a leak, and is still investigating, but has offered some advice for customers.‌You do not need to change your passwords or phone numbers as a result of this event. It is a good reminder to treat any account security messages that you have not explicitly requested as suspicious," it said, pointing to the authorised devices section of a user's account info."We also recommend setting up the Steam Mobile Authenticator if you haven’t already, as it gives us the best way to send secure messages about your account and your account’s safety."This writer uses the Steam Mobile Authenticator and can confirm it's an easy way to add additional security to your Steam account and is a breeze to get set up.Article continues belowFor the latest breaking news and stories from across the globe from the Daily Star, sign up for our newsletters.‌‌‌

Baby KJ after a gene-editing infusion with researchers Rebecca Ahrens-Nicklas and Kiran MusunuruChildren’s Hospital of Philadelphia A baby boy with a life-threatening genetic condition has become the first person to receive a bespoke CRISPR gene-editing treatment, giving a glimpse into what the future of medicine might look like. It’s the first time anyone has been given a gene-editing treatment designed to correct a disease-causing mutation found only in that individual, Rebecca Ahrens-Nicklas at the Children’s Hospital of Philadelphia, Pennsylvania, told a press briefing. “He’s showing some early signs of benefit,” she says, but it is too soon to tell how well the treatment worked. Advertisement The researchers published the details as soon as possible in the hope that it will inspire others, says team member Kiran Musunuru at the University of Pennsylvania. “We very much hope that showing that it’s possible to make a personalised gene-editing therapy for a single patient in several months will inspire others to do the same,” he says. “I don’t think I’m exaggerating when I say that this is the future of medicine,” he says. “This is the first step towards the use of gene-editing therapies to treat a wide variety of rare genetic disorders for which there are actually very few treatments currently in development at all.” The boy, KJ, inherited mutations in each of his two copies of a gene for a liver enzyme called CPS1. Without this enzyme, ammonia builds up in the blood when proteins, including ones we eat, are broken down, damaging the brain. More than half of children born with a CPS1 deficiency die, says Ahrens-Nicklas. Get the most essential health and fitness news in your inbox every Saturday. Sign up to newsletter She and Musunuru have been developing treatments for this kind of condition that target the liver, allowing them to rapidly create a base-editing therapy – a form of CRISPR – that corrects one of KJ’s two copies of the CPS1 gene. The team contacted US regulators early on. “They recognised that this was an unusual circumstance,” says Musunuru. “KJ was very, very sick, and there wasn’t time for business as usual. When we formally submitted our application to the FDA [Food and Drug Administration] when KJ was 6 months of age, the FDA approved it in just one week.” KJ was given a low dose of the treatment in February 2025 when he was 6 months old, followed by larger doses in March and April. He is now able to eat more protein than before, despite taking lower amounts of other medications to manage his condition. Ideally, children would be treated even earlier to prevent the long-term damage conditions such as CPS1 deficiency can cause. As New Scientist reported last year, Musunuru aims to one day edit human genes before birth. Other gene-editing therapies are designed to work for many people, regardless of the specific mutation causing their condition. For instance, the first ever approved gene-editing treatment, for sickle cell disease, works by turning on the production of fetal haemoglobin, rather than by correcting the mutations in adult haemoglobin that cause the condition. Despite being a “one-size-fits-all” treatment, it still costs £1,651,000 per course of treatment in England. KJ with his family after the treatmentChildren’s Hospital of Philadelphia Personalised treatments are likely to be even more expensive. Musunuru says he can’t put a number on KJ’s treatment, because the companies involved did much of the work for free. But the price will come down, he thinks. “As we get better at doing this, economies of scale will kick in and you can expect the cost to come down orders of magnitude,” he says. One reason personalised gene-editing treatments haven’t been developed before is that regulators have regarded therapies targeting different mutations in the same gene as separate, meaning companies would have had to restart the approval process from scratch for every different mutation. But there is now a move towards what is called a platform approach, where regulators will give broad approval to a therapy for a condition, whichever mutation is targeted. “Platform-based approaches, like genome editing with CRISPR – as we’re seeing with KJ’s treatment – offer a scalable way to treat even the rarest diseases,” says Nick Meade at the Genetic Alliance UK, a charity that helps people with rare diseases. “This at last makes treatment a realistic prospect for thousands of families.” Journal reference:New England Journal of Medicine DOI: 10.1056/NEJMoa2504747 Topics:crispr

Author(s): S Aishwarya Originally published on Towards AI. Strong data storytelling goes beyond simply visualizing numbers it uncovers the meaning behind the patterns, bringing clarity to what would otherwise be just a spreadsheet of values. Photo by Carlos Muza on Unsplash While visualization libraries like matplotlib, Plotly, and Seaborn can produce beautiful charts, they often lack one crucial feature: narrative. They leave it up to the viewer to interpret the story behind the lines and bars. That’s where Altair and the pynarrative library shine. Together, they help us not only visualize data — but actually explain it. 🔍What is Altair? Altair is a Python library for declarative data visualization that allows users to create clean, concise, and interactive charts based on the Vega-Lite grammar of graphics. You only need to provide: your data (typically a pandas DataFrame or Vega datasets) chart type (e.g., bar, line, scatter) encoding (x/y axes, color, size, etc.) optional interactivity, filtering, and tooltips Altair then renders the visualization using a JSON specification — ready for use in dashboards, notebooks, web applications, or reports. The Altair library directly integrates with pandas (for data handling) and Vega-Lite (for rendering and interactivity), making it easy for Python users to create powerful data stories without writing complex plotting code. 🔍 What is pynarrative? pynarrative is a Python library designed to automatically craft clear, insightful narrative summaries from pandas DataFrames and Altair charts. With just a few inputs: A dataset (often a time series or structured data in a DataFrame) A visualization (created using Altair) Axis labels, optional context, and your intended message pynarrative generates a well-structured textual explanation — ideal for embedding in dashboards, reports, presentations, or interactive data stories. Built to work seamlessly with pandas (for data handling) and Altair (for visual rendering), pynarrative helps bridge the gap between raw data and human-readable insights — turning visualizations into compelling narratives with minimal effort. Data Description: We’re using the cars dataset, which contains information about different car models. The main features we’ll focus on are: Horsepower: The power of the car’s engine. Miles_per_Gallon (MPG): How fuel-efficient the car is. Origin: Where the car was made (USA, Europe, or Japan). Name: The model name of the car. These features help us explore the relationship between a car’s power and fuel efficiency, and how that varies by origin. Data Cleaning & Preparation We’ll begin by automatically loading the dataset using Seaborn, then clean it for our visualizations. import pandas as pdimport seaborn as snsimport altair as altimport pynarrative as pndf = sns.load_dataset('mpg')print(df[['name', 'mpg', 'horsepower', 'origin']].head()) Output: Image by Author Cleaning Steps: Convert horsepower to numeric to handle any potential issues. Drop rows with missing values in critical fields. df['horsepower'] = pd.to_numeric(df['horsepower'], errors='coerce')df_clean = df.dropna(subset=['horsepower', 'mpg', 'origin'])print(df_clean[['name', 'mpg', 'horsepower', 'origin']].head()) Output: Image by Author Story 1: Power vs. Fuel Efficiency Let’s explore the relationship between a car’s engine power (horsepower) and its fuel efficiency (miles per gallon). By color-coding the data points based on the car’s region of origin, we gain insight into how different countries approach automotive design. chart = pn.Story(df_clean).mark_circle(size=60).encode( x='horsepower:Q', y='mpg:Q', color='origin:N', tooltip=['name', 'horsepower', 'mpg', 'origin']).add_title( "Horsepower vs MPG by Origin", "Higher horsepower often leads to lower fuel efficiency", title_color="#1a1a1a", subtitle_color="#4a4a4a").add_context( text=[ "Cars with more horsepower generally consume more fuel.", "Japanese and European models show a clear emphasis on fuel efficiency.", "This trend reveals differing consumer needs and manufacturer strategies." ], position="bottom", dx=0, color="black").render()chart Output: Image by Author This visualization reveals that American cars tend to have higher horsepower but lower fuel economy, whereas Japanese and European cars show more balance. Story 2: Regional Efficiency Trends Over Time Let’s observe how fuel efficiency (MPG) has changed over time across different regions. # Estimate year from model_year columndf_clean['year'] = df_clean['model_year'] + 1900# Compute average MPG by region and yearregional_avg = df_clean.groupby(['year', 'origin'])['mpg'].mean().reset_index()chart = pn.Story(regional_avg).mark_line(point=True).encode( x=alt.X('year:O', axis=alt.Axis(title='Year')), y=alt.Y('mpg:Q', title='Average MPG'), color='origin:N').add_title( "Average Fuel Efficiency by Region Over Time", "Trends in MPG from 1970s to 1980s", title_color="#1a1a1a", subtitle_color="#4a4a4a").add_context( text=[ "Japanese cars consistently lead in fuel efficiency.", "U.S. manufacturers ramped up efficiency post-1975.", "European models maintain a steady middle ground." ], position="bottom", dx=0, color="black").render()chart Output: Image by Author We see how regulatory changes and fuel crises influenced fuel efficiency, especially in the U.S. Story 3: Impact of the 1973 Oil Crisis Let’s annotate our chart with the 1973 Oil Crisis, a pivotal moment for car design. chart = pn.Story(regional_avg).mark_line().encode( x='year:O', y='mpg:Q', color='origin:N').add_title( "Impact of the 1973 Oil Crisis on MPG", "Shift in design philosophy after fuel shortages", title_color="#1a1a1a", subtitle_color="#4a4a4a").add_context( text=[ "The 1973 Oil Crisis increased focus on fuel efficiency worldwide.", "U.S. automakers shifted designs to improve MPG post-crisis.", "Japanese models were already MPG leaders at the time." ], position="bottom", dx=0, color="black").add_annotation( 1973, 15.5, "1973 Oil Crisis", arrow_direction='up', arrow_dx=0, arrow_dy=-1, arrow_color='red', arrow_size=50, label_color='black', label_size=14, show_point=True).render()chart Output: Image by Author This annotated visualization adds historical context, showing how global events shape industry trends. In Summary… Using pynarrative and Altair, we seamlessly transformed car performance data into engaging visual stories by: Highlighting the inverse relationship between horsepower and fuel efficiency Exploring how regional design philosophies shape fuel economy over time Annotating major historical events like the 1973 Oil Crisis to show their industry impact All of this was done using a single, intuitive interface combining pandas, Altair, and pynarrative. Once this storytelling pipeline is in place, it can be adapted to any dataset rendered through Altair from automotive to healthcare and beyond. This approach is quicker, more scalable, and more intuitive than conventional manual charting methods. Whether you’re building technical reports, dynamic dashboards, or insight-driven narratives, this serves as a reliable foundation for effective data storytelling. I would love to read your comments! Join thousands of data leaders on the AI newsletter. 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