The Undetectable Humanizer transforms your AI-generated text into natural, human-like prose to help it bypass AI-detection systems.
(via Cult of Mac - Your source for the latest Apple news, rumors, analysis, reviews, how-tos and deals.)
Apesar de ter perdido um pouco do hype inicial, os Instants seguem sendo uma excelente maneira de compartilhar fotos e vídeos rapidamente com os seus seguidores do Instagram.
Caso queira, é possível consultar facilmente um arquivo de todas as publicações feitas, usando três métodos diferentes — como mostraremos a seguir.
Confira! 📱
Posts relacionados
Com o app do Instagram aberto, toque na aba das suas mensagens diretas e no ícone do Instants (no canto inferior direito).
Vá até os quadradinhos (no canto superior direito) para conferir o seu histórico.
A segunda maneira de fazer isso é praticamente a mesma coisa que o método anterior, com a diferença de que isso pode ser feito diretamente pelo aplicativo dedicado do Instants.
No Instagram, toque na aba do seu perfil (no canto inferior direito), vá até os três risquinhos (no canto superior direito) e entre em “Itens Arquivados”.
Use o menu suspenso da parte superior para selecionar “Arquivo de instants”.
A native Australian millet may carry the genetic fingerprints of thousands of years of Aboriginal harvesting, storage, and plant management. Seen from the air, Channel Country resembles a vibrant and vast tapestry, with a network of waterways crisscrossing the land. Spread across more than 280,000 square kilometers in outback Australia, it is one of the [...]
We're now well past the point where it's very difficult, and in some cases impossible, to tell AI-generated photos and videos from the real thing. That means it's now important to treat everything you see online from untrusted sources with a big dose of skepticism.
A newly rolled out Google Earth feature has apparently made AI-generated misinformation even more of a problem — and it highlights the scary potential of the technology, as well as the need for stronger guardrails to block the efforts of those who will instantly try and find the most subversive and harmful ways to use a new product.
The feature is AI image generation in Google Earth, powered by the Nano Banana 2 model, and after a wave of inappropriate images were created and posted online, it's now been pulled while Google works on some more robust protections against misinformation.
It all started with the usual level of hype that Google builds around any new AI feature: the Nano Banana 2 image generation model that has already wowed us in the Gemini app would be available inside Google Earth on the web.
"Generate custom images to help you visualize history, create real estate plans, and more," Google said. "For the first time, you can generate custom images using Google Earth's satellite, aerial, and 3D imagery alongside Nano Banana, which creates concepts grounded in the real world."
Google had examples of how it could be used too: create an infographic about the Statue of Liberty, visualize your dream home by the lake, see what your neighborhood might look like a hundred years from now, or imagine Pompeii just before it got buried by ash and rock from an erupting Mount Vesuvius.
All it took was a text prompt, and Google Earth would combine existing satellite and 3D data to create the AI image you were asking for. While the AI model wasn't new, adding it to the accurate and authentic imagery already in Google Earth was.
Perhaps we should've seen this coming — and indeed some people did — but combining AI with the 3D data and satellite imagery of Google Earth didn't work out quite as well as Google hoped. The new upgrade was quickly used to create all manner of deceptive images that were then widely shared online.
I'm reluctant to point you to them directly and give these fakes any more attention, but they're not hard to find on social media. Think natural disasters, terrorism attacks, giant sinkholes, alien activity, and refugee populations all in places where they never actually were, thanks to AI.
Some of the images are clearly jokes, but many were produced deliberately to spread misinformation and confusion. That they were based on actual images sourced from Google Earth added extra credibility they wouldn't otherwise have had. And that was enough for Google to remove the feature.
Google says a flood of images "that appear to violate our policies" mean that "we're rolling back this feature in Google Earth while we work on implementing stronger guardrails". The company added:."It's important to note that generated images didn't appear in the main Google Earth experience for others to see and were watermarked as AI generated."
You don't have to look for long online to find rather strong anti-AI sentiment building up among users, especially when it comes to image and video content. The general sentiment on Reddit is that there's "zero use" to a feature like this, and that users are fed up with AI being "shoved down our throats".
There are various obvious lessons to be learned here: firstly not everything needs to have an AI prompt box or an AI element to it, and if some tools are largely AI-free, that's fine. Microsoft recently rolled back some of the AI it added to Notepad — an app where a big part of the appeal is its simplicity — after user complaints.
Secondly, before companies release any new AI features, they need to consider the worst-case scenarios in terms of how they're going to be used. The internet as a collective is going to immediately try and stress test them in the most deceptive and unwholesome ways, and there need to be safety measures put in place around that.
Lastly, it's never been more important to stick to sources of information that you know you can trust: not random social media accounts, but established publications that have policies around AI and its use. That's one effective way you can tell the difference between what's trustworthy and what isn't.
One of my favorite hobbies is Dungeons & Dragons. I love nothing more than sitting around a table with friends and playing out a fantastical adventure, in which the dice are the difference between success and failure.
It's a fun game, and all the more fun because real-life people make dumb, suboptimal decisions and the dice cause the game to change in unpredictable ways. But I really love it because the concept of people sitting around a table or hearth and telling stories is as old as the campfire, and it's a fun modern-day way to keep that experience alive.
You might think such a robust framework for an analog hobby (friends, a table, dice, pen and paper) might be tech-proof, but even in the Dungeons & Dragons world, AI is coming for my job as a Dungeon Master — the person who runs and guides the game world. Deep in the corpus of most popular generative AI models designed for general use are the rules to D&D 5e (and likely every other edition before it) and plenty of people report playing with their chatbots online.
Many of these players can't, or simply don't want to, get round a table with real people. For them, having a chatbot write them a story while the user prompts the continuation of the story with their character's actions, must seem like the perfect solution — even if it's more like a solo choose-your-own-adventure than a 'proper' group D&D game.
Gemini and ChatGPT both have a strong grasp of the rules for D&D's popular 5th Edition; they are certainly strong enough to make them an accessible choice for a player without a group. Maybe one day, the fire will die, and instead of sitting around a table with friends, we'll all be sitting at separate desks playing our own separate D&D adventures with our AI companions.
I wanted to test whether this dark future would be an adequate replacement for my usual game night, and which chatbot will be the best one to choose if so. I fired up Gemini 3.6 Flash and ChatGPT 5.5, and asked them for the same game.
I gave both chatbots the same prompt: "Can you be my D&D 5e DM?" and following up with another message:
"Let's do a classic high fantasy dungeon crawl. Please use a strict RNG for all rolls to ensure fairness - you can roll dice for me. My character is a 10th-level conjuration wizard. See all information about him below:"
I then copy-pasted a character sheet of a 10th-level wizard — a powerful spellcaster with a variety of magical abilities at his disposal. It's easy to DM a game for a level 1 thief — I wanted to see how the AIs could handle a character who could do fantastical things, such as teleport and summon creatures to fight for him.
Interestingly, both Gemini and ChatGPT created a similar adventure, at least at first: an abandoned dwarven stronghold bristling with magical traps, ready to be explored. However, that's where the similarities ended.
Gemini's adventure was what any D&D player could expect from the "classic high-fantasy dungeon crawl." I avoided a pressure-plate trap that would have shot darts at me, and in one memorable sequence, defeated a bunch of skeletons by casting a Grease spell on a stone staircase and freezing them in place with my Ray of Frost cantrip while they slipped and stumbled at the bottom of the steps.
Gemini was offering me suggested actions, and I did find myself choosing a few of those suggestions — but as time wore on, this began to feel lazy, like my brain wasn't firing in the same way it did when I played with my friends. I was skim-reading the AI's descriptions of the scene and responding with a single click. I asked it to remove suggested options and as a result, I had to think more carefully about each move, typing out requests and thinking about my actions rather than clicking on a predetermined response.
The memorable finale occurred when I unlocked a secret passage and defeated a Naga, a giant snake monster, by teleporting away and blasting it with a powerful Cone of Cold. The snake ended up frozen solid, and I checked the monster's HP against the official book, and it was accurate. When asked to generate a picture of our adventure, Gemini came up with this moment:
I asked Gemini a few questions at the end, such as whether it nudged the dice in my favor, as all my rolls were successful. This happened in my previous experiment with ChatGPT. Gemini said it used a 'pseudorandom number generator', not a true RNG, but the model doesn't 'fudge dice, pad rolls, or tweak numbers behind the scenes to keep players happy or prolong the game'.
Gemini's language was natural and even evocative in places, although it's still not a patch on a good creative human. Overall, a decent enough effort at simulating a D&D game.
Despite the similar setting, ChatGPT's adventure with the same character was very different. There was little to no combat throughout the entirety of the game, and ChatGPT didn't tell me when it was rolling 'behind the scenes', preferring to keep me in the moment.
When I asked it about the lack of rolling, the grovelling chatbot began its apologetic essay with "That is a fair critique. The short answer is: I leaned too heavily into narrative pacing and not enough into D&D mechanics. The adventure became closer to a collaborative fantasy novel than a 5e dungeon crawl", while suggesting fixes if I wanted to continue. I hadn't even said anything negative — but it certainly picked up on my tone, and the choose-your-own adventure I got was a far cry from a classic dungeon crawl.
Instead of combat, ChatGPT came up with a rambling mystery about a dimensional prison beneath the mountain, a sphere suspended in a cavern. Its guardians spoke ominous (and to be honest, quite confusing) portents such as "The archive does not open for authority. It opens for judgement."
Mysteries work best when they're developed with skill by a writer who can work backwards to seed clues throughout the game or text. ChatGPT does not do this: instead, it's arranging tokens on the fly to generate a facsimile of a mystery, and although it got the trappings of the genre right, the execution fell very flat. I (or at least, my character) underwent trials to seek obsidian objects such as keys and seals to reveal more about the prison, themselves accompanied by more foreboding, sometimes nonsensical portents.
Very little 'action' was taken by my character in a classical sense: it was all about investigating the origins of the being contained in this prison, and by the time I had spent 40 minutes on this, I had seen enough. It didn't help that ChatGPT also spoke in the peculiar sentence structure pattern that AI users are likely very familiar with now: "This isn't just X. It's Y." Examples include:
"Unlike the rest of Khaz Varek, this room has remained untouched.
No dust. No decay."
Or the following, when I met one of the prison's guardians:
"Ser Kaelen immediately looks toward the sphere. 'You hear it too.'
Not a question. A confirmation."
Any user of ChatGPT (or those who look at AI-generated posts on LinkedIn or Reddit) will immediately recognise this sort of language, and know what I'm talking about. By the end, it was quite painful to have every other sentence be treated as a grand revelation. Not a statement. An omen.
When asked to generate a picture of our adventure, ChatGPT came up with this moment:
Out of these two? Gemini 3.6 Flash, by a mile. It was at least a mildly enjoyable experience conveyed in natural language, and something I'd consider revisiting.
ChatGPT's efforts, on the other hand, were unenjoyable, and I wouldn't return to it. In fact, it's gotten worse since the last time I tried it.
Both, however, were a far cry from an in-person session. While they have a good grasp of the rules, and you can create your perfect world and character, the chatbots are ultimately missing the 'human and hearth' element that keep me returning to the game. For people who can't get round a table with friends, I'd pick Gemini over ChatGPT.
For those who can? While AI is busy taking as many creative jobs as it can, I feel safe in the knowledge that the role of Dungeon Master is AI-proof for a long time yet.
A growing disparity in how AI generated content, tasks, and tools are assigned ownership and expectations on accurately marking their origins is emerging, according to a new survey by Use.AI.
Furthermore, the findings indicate an emerging dichotomy between human-claimed authorship of an AI work and clear labeling of the work as AI.
Different attitudes based on settings for the AI work have also been identified, such as private emails and school or professional reports.
The developing adoption of AI appears to be introducing new attitudes to acceptable creativity, with the emerging model of what is described as “authorship by direction,” where the AI is trusted to create a first draft, which is then revised and claimed by a human.
The statistics shared by Use.AI – a tool that aggregates AI models into a single interface – highlight how AI is being used more extensively, with a human-originating prompt leading to drafts and versions from the machine, followed by editing from the human.
While the AI has contributed, only a human name appears on the finished product, which raises questions of ownership and authorship.
“AI is separating the production of work from the ownership of it. People are increasingly prepared to direct the process, approve the result and take the credit, but there is still no shared standard for when AI’s role should be visible to everyone else,” says Use.AI’s Managing Director, Ihor Herasymov.
With 69% of those surveyed considering AI-generated work their own if they produced the prompt, 46% claiming they would keep AI involvement with their work under wraps, and 54% feeling AI use should be disclosed, we’re some way from agreement on AI use across creative tasks.
Herasymov’s observations point to a constantly evolving set of standards around the creation and use of AI-generated materials. Interestingly, this is happening against a backdrop of growing unease over the use of AI in creative tasks.
The implications of claiming ownership of automated work generated upon instructions to an LLM trained on as much human writing, art, music, and cinema as possible are considerable, and go way beyond questions of ethics. Copyright is just one element, and the most obvious, but we’re also moving into a landscape where inputting a prompt on employer-owned hardware takes authorship away from the person at the keyboard.
Ultimately, the task of prompting and revising means anyone using AI in this way stops being the author, and becomes the editor – and that is before the true ownership of the output is determined.
A common complication of early birth is being thrust into a medical debate that reaches far beyond the neonatal intensive-care unit: how low is too low when a late preterm baby’s blood glucose falls, and what—if anything—does that episode mean years later? A new article in Pediatric Research examines late preterm hypoglycemia through the lens of long-term evidence, highlighting why a seemingly brief metabolic disturbance has become one of neonatology’s most closely watched questions.
Late preterm infants are typically born between 34 weeks and 36 weeks plus six days of gestation. They often appear stronger and more mature than very preterm babies, but their brains, hormone systems and feeding abilities are still developing. After birth, glucose levels can drop as the infant transitions from a constant placental supply to intermittent feeding. Immature glycogen stores, limited fat reserves, difficulty coordinating sucking and swallowing, and illness-related stress can all increase the risk of hypoglycemia.
Glucose is the brain’s primary energy source during early life, making neonatal hypoglycemia biologically concerning. When glucose delivery falls, the brain may initially compensate by using alternative fuels, including lactate and ketone bodies. If the shortage is severe, prolonged or repeatedly missed, however, energy production can become insufficient. This may interfere with neuronal signaling and, in extreme cases, contribute to cellular injury. The challenge is that many affected newborns show no obvious symptoms, while symptomatic infants may display only subtle signs such as jitteriness, poor feeding, lethargy, temperature instability or apnea.
The article by U. Felderhoff-Müser focuses attention on a crucial gap between immediate treatment and long-term certainty. Hospitals commonly screen infants considered vulnerable to hypoglycemia, including late preterm newborns, and intervene with feeding, buccal dextrose gel or intravenous glucose when indicated. These measures are designed to restore glucose quickly and prevent neurological stress. Yet the threshold used to define hypoglycemia is not a universal biological line. It is a clinical value shaped by gestational age, postnatal age, symptoms, measurement method and the balance between avoiding harm and preventing unnecessary intervention.
That uncertainty has made long-term follow-up essential. Researchers have investigated whether neonatal hypoglycemia is associated with later problems involving cognition, executive function, attention, language, motor coordination and school performance. Some studies have reported higher risks after severe, recurrent or prolonged episodes, while others have found limited or inconsistent effects, particularly when low glucose was detected and treated promptly. The differences may reflect more than glucose itself: prematurity, fetal growth restriction, maternal diabetes, infection, respiratory illness and socioeconomic conditions can independently influence neurodevelopment.
This is one reason the evidence remains difficult to interpret. Most long-term studies are observational, meaning researchers cannot randomly assign infants to experience different glucose levels. Instead, they compare children who had hypoglycemia with those who did not, while attempting to account for other medical and social factors. Even sophisticated statistical adjustments may not eliminate hidden confounding. In addition, studies use different definitions of hypoglycemia, different monitoring schedules and different ages or tools for developmental testing, making direct comparisons challenging.
The timing and pattern of low glucose may be as important as the lowest recorded number. A single short episode may carry a different biological significance from repeated episodes or an extended period below a treatment threshold. Continuous glucose monitoring has raised further questions by revealing fluctuations that intermittent heel-stick testing can miss, although sensor accuracy in newborns and the clinical meaning of every detected dip are still being evaluated. The emerging picture is therefore more complex than a simple division between “normal” and “abnormal.”
For clinicians and families, the practical message is not to dismiss hypoglycemia or to assume that every low reading predicts disability. Instead, the long-term evidence supports careful surveillance of infants at risk, rapid confirmation of abnormal results, and treatment strategies that protect the brain without disrupting feeding or exposing babies to unnecessary interventions. Developmental follow-up may be particularly important for infants with severe or recurrent episodes, neurological symptoms, or multiple additional risk factors. Such monitoring can identify difficulties early, when speech, occupational or educational support may have the greatest effect.
The discussion also underscores the need for better research. Future studies will need standardized glucose definitions, precise records of duration and recurrence, improved measurement technologies and follow-up extending into school age and adolescence. Researchers must also distinguish the effects of hypoglycemia from the conditions that caused it and examine outcomes beyond a single global developmental score. Felderhoff-Müser’s review places late preterm hypoglycemia in that broader scientific context: a common neonatal event whose immediate management is well established, but whose long-term consequences still demand careful, nuanced investigation.
Subject of Research: Late preterm hypoglycemia and its long-term neurodevelopmental evidence
Article Title: Late preterm hypoglycemia through the lens of long-term evidence
Article References: Felderhoff-Müser, U. “Late preterm hypoglycemia through the lens of long-term evidence.” Pediatric Research (2026). https://doi.org/10.1038/s41390-026-05316-6
Image Credits: AI Generated
DOI: https://doi.org/10.1038/s41390-026-05316-6
Keywords: late preterm infants, neonatal hypoglycemia, blood glucose, neurodevelopment, brain health, newborn care, long-term outcomes
Majestic Labs, a startup founded in 2023 by former Google and Meta engineers, has unveiled a server built to rival Nvidia's GPU and HBM combination.
The Tel Aviv-based company argues that pairing costly graphics processors with high-bandwidth memory has become a fundamentally memory-bound and dead-end approach for AI inference.
Its answer is the Prometheus server, which swaps GPUs for Ignite AI Processing Units combining Arm cores with RISC-V vector and tensor engines.
Each Prometheus server can house up to 12 AIUs, sharing between 8 TB and 128 TB of LPDDR6 memory across one contiguous, coherent pool.
That memory pool is accessed through custom memory aggregation chiplets linked by copper cables up to one metre long instead of memory attached directly to GPU packages.
A standard 40U rack can hold four such servers, drawing 120 kW total and cooled through cold-plate liquid systems rather than air.
By comparison, an Nvidia DGX B300 system with eight Blackwell GPUs offers 2.3 TB of HBM3e plus up to 4 TB of DDR5 system memory.
Majestic claims its architecture therefore delivers over 50 times more fast memory than that rival configuration, alongside 1.7 times its interconnect bandwidth.
“One Majestic rack holds the fast memory capacity of 25 Nvidia NVL72 Vera Rubin racks at a fraction of the power,” Majestic Lab said.
“Organizations that could never justify hyperscaler infrastructure can now run any workload. In fact, there can be up to “1000× more memory per processor.”
Majestic Labs says the Prometheus server could cost between 10 and 50 times less than a GPU system of equivalent performance once it ships next year, while consuming less electricity per rack.
The server is designed to be OCP-compliant and will support PyTorch, vLLM and OpenAI's Triton frameworks, letting existing AI models run without modification.
Founded by CEO Ofer Shacham, President Sha Rabii and COO Masumi Reynders, the company employs around 40 people across Tel Aviv and Los Angeles and raised $100 million in an A-round late in 2025.
It claims to have already received significant orders from large enterprises, neoclouds and hyperscalers.
Yet several details remain unclear, including how many memory aggregation chiplets a single server actually requires.
If a 128 TB configuration relies on widely available 2 GB LPDDR6 dies, it would need roughly 64,000 of them, implying well over a hundred aggregation chiplets per server.
The numbers Majestic Labs presents are striking, but they remain the startup's own projections ahead of any independent benchmarking or shipped hardware.
Enterprise buyers considering a shift away from established GPU vendors may have to wait for independent validation to verify Majestic's claims.
Via Blocks and Files
A common houseplant hides a splendid geometric pattern.
This design, called a Voronoi diagram, appears on the leaves of the Chinese money plant. The layout may arise as waves of a plant hormone spread across a leaf during its development. A similar process may shape the vein patterns in leaves of other plants, too.
Researchers shared these findings May 12 in Nature Communications.
This is a “beautiful” showcase of how plant veins develop to move water across leaves most efficiently, says Lawren Sack. He did not take part in the research. But he does study plant biology at the University of California, Los Angeles. Veins of leaves “have so much to tell us” about how to get resources where they need to go, he says.
Leaves of the Chinese money plant (Pilea peperomioides) are speckled with pores. Called hydathodes, these pores release water. Each one is fenced in by veins to form a mosaic.
While in high school, Elijah Blum discovered a curious pattern in those leaf veins. He noticed it while plant-sitting for his sister. At the time, Blum was an intern at Cold Spring Harbor Laboratory in New York. (He’s now in college at New York University.) He shared what he’d found with his internship supervisor, computer scientist Saket Navlakha.
“He showed the plant to me, and he said, ‘Look, the veins look kind of interesting here,’” Navlakha says. “We sort of held it up to the light — and we saw that … Voronoi diagram.”
In a Voronoi diagram, a surface is split up into zones. Each zone contains a designated point. And every spot within a zone must be closer to that zone’s point than any other.
Urban planners use the same idea to decide which fire department a house should be assigned to. This helps each house in a city or town be closer to the fire station in its zone than any other station.
Patterns that look similar to Voronoi diagrams appear elsewhere in nature. The tiles on giraffe fur look like this type of design. So do the scales of dragonfly wings. But in a true Voronoi diagram, the mosaic must form around a set of points. In the Chinese money plant, the water-oozing pores serve as those points.
Blum and the others were curious how this pattern might develop.
Similar leaf veins are thought to form through canalization. In this process, a hormone involved in plant development — auxin — branches out across leaves. As it does, it creates a treelike network of channels that become veins.
But that wouldn’t produce a Voronoi diagram around a leaf’s pores, the team found. Computer models instead showed that as Pilea leaves develop, waves of auxin spread out from each pore. Those waves collide to form ridges. Eventually, they become a leaf’s major veins.
More research is needed to find out why Pilea leaves follow this pattern, says team member CiCi Zheng. She studies the math of living things at the Allen Institute in Seattle, Wash.
The Voronoi diagram layout might be useful for plants, she says. This pattern keeps a leaf’s major veins — which transport water — as far as possible from its water-leaking pores. “You might want to place the veins not directly close to any of those locations where water evaporates super-fast,” Zheng says.
Sack at UCLA is excited about what new tech this finding might inspire. Past studies of leaf veins, he says, have helped engineers reimagine how to distribute resources such as water and energy. Such insights have improved solar panels, electronic circuits and irrigation systems.
“The more we know about leaf veins,” Sack says, “the more we can build functional and beautiful systems around us.”
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