Apple Reference Image is a hidden iOS 27 feature that could let iPhone owners prove a photo was shot with a real camera, not AI.
(via Cult of Mac - Your source for the latest Apple news, rumors, analysis, reviews, how-tos and deals.)
Recorrendo a uma tecnologia de IA patenteada que adapta o som à sua localização, hora do dia e frequência cardíaca, a Endel é usada por milhões de pessoas em todo o mundo para melhorar o sono, a concentração e o bem-estar.
The post Endel: a app que usa inteligência artificial para criar a paisagem sonora perfeita para dormir, focar ou relaxar appeared first on Tek Notícias.
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Last week, I headed 30 miles south of San Francisco to a hotel in Mountain View, California, to join some of the most accomplished, and some of the most promising, AI researchers in the world. I was hosting roundtable interviews and speaking at a media training for a convening of the Schmidt Sciences AI2050 program, an initiative funded by Eric and Wendy Schmidt that supports academics whose work involves AI. The fellows list is a who’s who of AI luminaries, and though not all of them made it out to the Bay, every time I turned a corner I saw a scientist whom I’d interviewed previously or whose research I admired. (Full disclosure: I received a science communication award funded by Schmidt Sciences in 2024.)
It’s a weird time for university AI researchers, who make up most of the AI2050 group. In the past four years, AI research has reoriented around large language models, and its cutting edge has moved from academic institutions to private companies. Universities simply can’t afford the GPUs required to train and run frontier models, and even if they could, Anthropic and OpenAI aren’t letting anyone else see the inner details of Claude or ChatGPT.
In a conversation over lunch, Nika Haghtalab, a computer science professor at UC Berkeley, said that being an AI academic these days was like being a biologist in a world in which private companies had exclusive control over the gene-editing tool CRISPR. Experts outside the frontier labs can study how ChatGPT and Claude behave, but they can’t do any detailed research on the design and training of those tools, nor can they steer that design or training themselves.
The AI2050 program does offer fellows some funding that they can use to buy GPUs, which some researchers I spoke with said was a major benefit of participating in the program. But money remains a pressing concern, especially given the reduction of federal scientific funding in the United States. Even for researchers who don’t run local models themselves, the cost of repeatedly querying OpenAI’s, Anthropic’s, and Google’s models in order to study them rigorously can be prohibitive.
Rather than focusing on advancing capabilities, many fellows aim their attention at questions that are unlikely to be addressed by Anthropic or OpenAI. “I try not to work on problems that I think are gonna be solved by a tech company,” says Anjalie Field, a computer science professor at Johns Hopkins. Companies need to make money, and research questions that have little promise of profit might not be worth investing in—especially if their answers might make the companies look bad. Recently, for example, Field conducted a study in which she found that language models give less sophisticated responses to prompts that are phrased in ways more commonly used by women than by men. It’s difficult to imagine that kind of research coming out of Anthropic or OpenAI.
There’s also a huge group of AI academics who don’t work with LLMs at all. Many of them are scientists who build specialized AI models that can analyze data, make useful predictions, or even simulate entire physical systems. Those researchers aren’t necessarily competing with the frontier labs—Google DeepMind’s AlphaFold team, which built a Nobel Prize–winning model that predicts the structures of proteins, was disbanded last month. But they face plenty of their own challenges. At the convening, several voiced concerns about how the widespread ignorance of non-LLM AI was affecting their work. Researchers who build specialized AI tools to help address climate change, for example, sometimes struggle to advocate for their work when so many people believe that “AI” means “energy-guzzling LLMs.”
All these challenges are changing the landscape of academia: Several prominent academics have recently taken leave from their universities to join frontier labs, and many AI2050 fellows hold industry positions alongside their academic jobs. And in the past six months, yet another threat has emerged. OpenAI’s models have solved a number of real research problems in mathematics, and some experts are worried that humans might not have a future in pure math. One fellow I spoke with said that she was concerned about the mental health of her mathematician peers.
But it’s not all doom and gloom. For one thing, empirical science may prove much more difficult to automate than mathematics, because collecting data is an intrinsically slow process. And some researchers see AI mathematicians and scientists as a boon rather than a threat—including Tim Dettmers, a computer scientist at Carnegie Mellon who works to make AI models faster and cheaper to run. AI scientists won’t replace humans, Dettmers says. On the contrary, they could make human scientists far more efficient, so that he and his peers have the chance to pursue all the wild and inspired ideas they might otherwise never have gotten around to.
And scientists are a resilient sort. The very resource constraints that prevent them from training frontier models also push them to discover new ways to make models smaller and more efficient, or to explore completely new architectures. If the next big AI breakthrough comes not from a major company but from a scrappy academic lab, I won’t be shocked.
Descubra como o app de weather no Windows 11 usa quase 1GB de RAM, enquanto apps Linux são muito mais leves.
O post Apps de previsão do tempo no Linux consomem muito menos RAM que o Windows 11 apareceu primeiro em Blog do Edivaldo - Informações e Notícias sobre Linux.
Every few decades, someone announces that science has reached its end. In 1903, the revered physicist Albert Michelson wrote that the “facts of physical science have all been discovered.” In the 1980s, Stephen Hawking predicted that theoretical physics might be finished by the end of the century. With the explosive arrival of artificial intelligence, the feeling is in the air again—this time accompanied by a Nobel Prize.
In 2024, Demis Hassabis and John Jumper of Google DeepMind were awarded part of the Nobel in chemistry for their neural network AlphaFold, which predicts the three-dimensional structures of proteins by learning from thousands of experimentally measured shapes. This devilish problem had resisted systematic attacks for half a century; AlphaFold seemed to have solved it once and for all, and the world became fixated on the promise of its approach. Hassabis and his team called AlphaFold “the template for how AI can accelerate all of science to digital speed.” A wave of startups building foundation models for biology, chemistry, and materials discovery raised billions of dollars, buoyed by DeepMind’s success. AlphaFold had shown that the combination of AI and sufficient data could make groundbreaking discoveries (even if we did not understand the underlying mechanisms involved), and it seemed, once again, that a path through the rest of science was laid out before us.
To be sure, AI will bring extraordinary changes to science, but it has become increasingly clear that AlphaFold, and things like it, may not be the best template for that metamorphosis. Though it is a profound achievement, the conditions that produced the likes of AlphaFold are rare, and the time it will take to meet those conditions in other fields will be measured in decades, not years. Instead, the acceleration of science will come about thanks to another approach: AI agents.
The primary condition for AlphaFold’s success was the existence of the Protein Data Bank, a data set of roughly 170,000 experimentally validated protein structures on which DeepMind’s team could train its model. The creation of the Protein Data Bank was not simple: It took 53 years of international scientific cooperation and, by a recent estimate, roughly $21 billion worth of experimental work to assemble. Efforts of that scale are infamously difficult to fund, next to impossible to coordinate, and hugely time-consuming to execute; they have often been unsuccessful as a result.
But even in fields with the requisite cohesion and resources, and where the relevant data are not rendered inaccessible by commercial ownership, another barrier is too little discussed: the scientific impossibility of generating comparable data. In the case of protein structures, the key experimental technique—protein crystallography—is an unusually replicable and dependable tool, so much so that over 25 Nobel Prizes have relied on it. But in most of experimental science, results vary more often than not. Cell lines drift. Chemicals have trace contaminants. Lab humidity changes. The creation of measured datasets that will be consistent enough, accurate enough, precise enough, and scalable enough to train a modern neural network in biology or most of chemistry would require new kinds of measurement and new standardized approaches—none of which will be ready anytime soon.
Of course, there are a handful of fields where these requirements are met: weather forecasting, much of genomics, very limited areas of chemistry. These may see AlphaFold-style breakthroughs soon, if they haven’t already. Government support for the production and coordination of those datasets will be critical, as the US National Security Commission on Emerging Biotechnology has argued. But for most open questions in science, we will need a different plan, at least in the short term. Luckily, something quieter and more modest has begun to show promise.
Scientists have always reasoned under uncertainty. Biologists working to identify new drug targets have never had perfect datasets. Instead, they combine docking calculations and known structures, factor in molecular dynamics, run a handful of binding assays, and use their judgment to weigh each method according to its particular strengths and points of failure. The skill of science is not in any single tool; it is synthesizing what many tools produce, and revising the results as the evidence comes in. This is how most working research actually proceeds. But until very recently, no software could do it.
Agents now can. Simply put, an agent is an AI reasoning engine that has been given access to tools—digital or physical—and the capabilities to use them. Over the last few years, a fundamental architectural shift in AI has enabled the rapid proliferation of these programs, which are powered by large language models, dramatically reducing the need for scientifically specialized datasets. For science, this technological advancement represents a foundational change: it has allowed us to create digital tools that can mimic the iterative, highly contingent process of actual research. While tools like AlphaFold apply a powerful approach to a limited question, agents are inherently generalists. They do not represent a new way to do science—instead, they digitally model the human process of discovery.
Consider Google’s AI Co-Scientist, announced in May. Researchers gave it a one-page brief and a goal: Figure out how antibiotic resistance spreads between bacterial species, a key driver of drug-resistant infections. The system spun up sub-agents. One drafted hypotheses from the literature. Another picked them apart like a peer reviewer. A third ran tournaments to rank the strongest candidates. A fourth refined the winning hypothesis. The agent concluded that resistance genes were hitching rides on bacterial viruses, borrowing whichever virus could ferry them into a new host. The hypothesis was correct. Researchers at Imperial College London had spent a decade reaching the same conclusion through painstaking wet-lab work; their paper, previously unseen by Co-Scientist, was still in peer review.
Agents like Co-Scientist are still novel tools, and there are real challenges to overcome before they become a ubiquitous part of the scientific process: They are still liable to hallucinate, their judgment is not consistent, and they have memory and input constraints that limit the time they can run autonomously. But these technical barriers will fall away, and as they do we will begin to notice the compounding effects of scientific agents on the reliability, consistency, and velocity with which science is done.
Perhaps most notably, agents offer a structural fix for science’s “reproducibility crisis,” the widespread problem of researchers’ inability to replicate each other’s results. For decades, the scientific community has begged researchers to share their raw data and exact code in an effort to standardize experimental processes. But researchers have long resisted this tedious administrative work, which happens after the interesting science is already done. Agents, in contrast, automatically log every move they make, creating an exact record of the method that led to their results and allowing for precise replication.
A second consequence will be an amplification of scientific memory. The transfer of knowledge between researchers is a famously murky process; if it isn’t done over years of training and observation, graduate students are left to pore through the messy lab notebooks kept by decades of predecessors, looking for the details that will make or break their protocol. As agents become an increasingly large part of the scientific process, though, a lab’s entire scientific history will be recorded in a central, standardized repository of institutional knowledge.
But the most important impact of agents will be speed. In any field, when testing an idea takes less time than arguing about it in a meeting, people stop debating and just run the test. An agent that can read a thousand papers in an hour, design 500 molecules, and learn from its failed tests by morning will bring down the cost of experimentation and fundamentally change the pace at which science gets done. It will also give researchers the freedom to chase bold, strange questions they never would have risked their time on before, opening scientific doors we have yet to imagine.
While the AlphaFold template will certainly be key to incredible discoveries, it alone will not bring us to the end of science. Instead, the shift toward agentic AI represents a much rarer tier of breakthrough: a tool that envelops every field of science at once. Historically, tools of such scope have arrived just a handful of times: calculus, statistical inference, spectroscopy, the computer. Each revealed a world of problems no one had thought to formulate, and those problems, in turn, defined their fields anew. With agents, another such transformation is upon us.
Eric Schmidt was the CEO of Google from 2001 to 2011. In 2024, with his wife Wendy, he co-founded Schmidt Sciences, a philanthropic venture to fund unconventional areas of exploration in science & tech.
Suhas Mahesh leads AI for Science work at the AI Center of Schmidt Sciences. He is a specialist in AI for materials discovery.
Additional research by Maya Levin, associate and sciences lead, Office of Eric Schmidt.
MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here.
Way back in the summer of 2017, AI researchers at Google put out a paper called “Attention Is All You Need,” in which they described a new type of neural network called a transformer. It proved to be very good at processing long sequences of data, especially text.
Nine years on, transformers are the engines inside every major large language model on the market. “The entire AI industry is built on transformers,” says Justin Dangel, cofounder and CEO of the AI startup Subquadratic. “They are one of the most important innovations in the history of computer science, and they’ve changed the world.”
But transformers are starting to show their age. Many of the recent advances in LLMs, such as the development of so-called reasoning models and their ability to handle large amounts of input at once, are not neat extensions of that core technology but workarounds that patch over some of its fundamental flaws.
A growing number of scientists and engineers are now asking what’s coming next. LLMs are not going anywhere, but the way they get built is up for grabs. (MIT Technology Review dubbed this future generation of models LLMs+ in this year’s list of the 10 things that matter in AI.)
Enter a wave of startups hoping to push the boundaries of this boomtown technology. Some will no doubt fail—but they have everything to play for and far less to lose than the companies at the front of the pack today.
But first, the problem. The key strength of transformers lies in a mechanism called dense attention, which encodes the meaning of a block of text in a series of numbers. The process involves comparing every word (or part of a word, known as a token) in that text with every other word via a form of multiplication.
Dense attention can capture the meaning of text with remarkable accuracy. But as the length of that text grows, the number of computations needed to process it adds up fast. A document 10,000 words long might require a transformer to perform 50 million multiplications. That’s the main reason LLMs suck up so much power.
The costs are huge. OpenAI is set to spend $50 billion on computing this year, according to the company’s president, Greg Brockman. And the International Energy Agency predicts that the total amount of electricity consumed by data centers will double by 2030.
What’s more, transformers struggle with what many of the latest models are designed to do. Because of the way they process text word by word, transformers are not great at keeping track of a lot of information at once (in other words, what’s known as their context window cannot get too large). And yet if LLMs are to carry out harder tasks, they will need to take in larger amounts of data: a whole library of documents, an entire code base, or in the case of agents, output from other LLMs.
As for reasoning models, they work by writing notes to themselves (in a kind of scratch pad known as a chain of thought) and then reading them back, which again adds to the amount of data to stay on top of.
As LLMs get bigger and better, transformers have become a bottleneck. The technology’s key strength is now a limitation.
Here are four new ideas for how to solve the transformer problem—innovations that could change LLMs for good, making them faster, far more efficient, and (maybe) even smarter.
An obvious way to make LLMs faster and cheaper is to tackle the problem head on and change the way attention works. Swapping out dense attention for a mechanism called sparse attention, which runs calculations on only some pairings of words in a block of text instead of all of them, can radically reduce the amount of computation LLMs need to do.
Researchers have come up with plenty of sparse attention mechanisms over the years. The problem is that none of them were as good as dense attention at capturing meaning.
That might have changed. Subquadratic, a startup based in Miami, claims it has invented the first sparse attention mechanism that rivals top mainstream LLMs on a handful of tasks, including search and coding. It’s a huge claim (and some people in the industry remain skeptical).
Subquadratic says its model, SubQ, works by figuring out on the fly—for each piece of text it is given—which words matter and which don’t. The company also claims that thousands have signed up to its waitlist and plans to make the model widely available soon.
Meanwhile, Manifest AI, a startup based in San Francisco, is coming at the problem from a different angle. Instead of changing how attention works, it is replacing it with something else.
It has developed a mechanism it calls power retention, which stores only the most relevant information for a given task and ensures that the amount of data an LLM has to keep track of doesn’t blow up.
Attention mechanisms force LLMs to keep track of everything in their context window. A sparse attention model (such as SubQ) throws out a lot of the individual words, but it still retains a rough picture of everything it has seen. In contrast, power retention works by providing the model with a rolling summary of its context window. As new information is added, less relevant information is dropped.
The basic principle of retention has been around for a decade. Manifest AI claims it has updated those techniques to build models that can stand up to transformer-based LLMs for the first time.
The company says it is possible to adapt a transformer model into a power retention model with minimal retraining. To demonstrate this, it has turned an existing open-source coding LLM called StarCoder into a version that uses power retention, called PowerCoder. It has also released a model called Brumby, which it claims rivals some versions of Alibaba’s popular open-source model Qwen.
Manifest AI wants its power retention tech to become the go-to solution when LLMs need to carry out tasks that involve processing huge amounts of data. There are many useful applications, Manifest AI’s cofounder and CTO, Carles Gelada, claimed in a video announcing his company’s technology last year—from analyzing videos that are hours long to building agents that can stay on task for weeks at a time.
Liquid AI, an MIT spinout based in Cambridge, Massachusetts, hasn’t changed or ditched transformers fully but pairs them with its own tech, liquid neural networks, to build what cofounder and CEO Ramin Hasani calls LFMs (liquid foundation models).
Liquid AI’s models are far smaller and use less energy than most LLMs. The firm builds models for car makers, including Mercedes, which run on the small chips inside vehicles. Its latest models can run on a Raspberry Pi, a low-powered hobbyist computer that costs $50.
Its models are available for free to any organization with an annual revenue less than $10 million. And they have proved popular: The company has racked up almost 34 million downloads, says Hasani.
Liquid neural networks were inspired by worm brains. They are an extension of another type of neural network that predates transformers, called convolutional networks. The key innovation is a mechanism that lets a model adapt its behavior to new information, so it can learn as it goes. That’s not possible with transformers: Once a model is trained, its behavior is fixed.
Liquid AI’s first models were pretty basic but could fly drones or drive vehicles. With LFMs, the company is trying to scale up its technology to compete with mainstream LLMs. Its new models match the performance of rivals four times bigger, including versions of Alibaba’s Qwen and Google’s open-source LLM Gemma.
A typical LLM is built from a stack of transformers wired together. Liquid AI’s recent LFMs are hybrid models made up of 20% transformers and 80% liquid neural networks.
That ratio was hit upon by another AI system that Liquid AI has built, which it uses to help design all its models. “It’s the core technology of our company right now,” says Hasani. This designer AI sifts through many different combinations of neural networks—liquid, convolutional, and more, as well as transformers—and comes up with designs that bolt different ones together to hit a sweet spot of performance and efficiency.
Hasani thinks transformers were just the beginning: “Your brain is an AGI system, you know, and it operates with 20 watts of power. How is it possible? We can get a lot more innovative.”
Almost all LLMs produce their output one word at a time. It makes sense, because that is how people speak and write. But for computers, it’s very inefficient.
It is faster and cheaper for LLMs to generate text all at once—spitting out whole sentences or paragraphs in one shot. That’s the approach taken by Inception, a startup based in Palo Alto, California, which is building LLMs using a technique called diffusion.
Diffusion is better known as the technology that drives most image and video generation models. Diffusion models are trained to take a random grid of pixels—like the static on an old TV set—and turn it into an image. They do this by working on all the pixels at the same time, figuring out which need changing to make the static look more like a high-definition photo.
It turns out this process works on text too. Inception has trained its LLMs to take a random string of words and turn it into sentences that make sense. Diffusion LLMs still use transformers to encode meaning, but by producing whole blocks of text at once, they make transformers do more for less. “You’re still using a big transformer model, but you can predict many tokens at the same time,” says Inception’s cofounder and CEO, Stefano Ermon. “That’s why these models are so much faster and cost-efficient compared to what most other people are building today.”
The challenge was to take a technology designed for image generation and apply it to text. With images, if you need to change a blue pixel to a red one you can step through intermediate colors, says Ermon. That doesn’t work with text: “When you have ‘cat’ and ‘dog,’ there is not really something in between.”
Ermon is also a researcher at Stanford University. In 2024, he and a pair of his Stanford colleagues figured out the math to make diffusion models work with text. They trained a diffusion model that matched the performance of GPT-2—an LLM that OpenAI built in 2019—but was 10 times faster. It was enough for Ermon to spin out a company.
Today he has his sights on the big league. Inception claims its latest model, Mercury 2, performs as well as some of OpenAI’s GPT-4 models, released in 2023, but again 10 times faster. “We’re bullish about this approach because it’s the one that is going to scale up,” says Ermon.
The only things that matter are speed and cost, he adds: “Ultimately, the currency is going to be intelligence per dollar.”
Inception is not the only company betting on diffusion. Google is also experimenting with this approach and has built a prototype LLM called Diffusion Gemma. But Ermon is not worried about the competition. “I think it’s validating,” he says. “This is the future.”
Pathway, another startup based in Palo Alto, is perhaps the most extreme of this new bunch. It wants to free LLMs from the constraints of language.
The firm has built a type of LLM called Dragon Hatchling (named after the dragons in Terry Pratchett’s novel Color of Magic, which materialize if you think about them hard enough). Its standout result so far is a high score on a benchmark that pits LLMs against more than 250,000 very hard sudoku puzzles. Dragon Hatchling beat more than 97% of the puzzles; several leading LLMs from the top labs failed to solve any.
The point Pathway wants to make is that despite their remarkable success at many different tasks, there are still crucial classes of problems where LLMs fail. Sudoku is just one example. If we want LLMs to come up with genuine, novel solutions to real problems, we need to move beyond transformers, says Pathway’s cofounder and CEO, Zuzanna Stamirowska.
That’s because transformers force LLMs to do everything with text. But language is not the best tool for certain kinds of reasoning. “It’s very difficult to represent a sudoku board word by word,” says Stamirowska.
Pathway’s solution is to change the math behind the transformer, replacing the attention mechanism with a mathematical structure called a state space. Instead of encoding information word by word, state spaces compress it into a more abstract representation. Using this technique, Dragon Hatchling can still process and produce text, but it can also mimic forms of reasoning that do not involve sequences of words. This not only makes Pathway’s model more efficient, but (in theory) it lets it take on tasks that other LLMs cannot do.
Think of chess or mathematics—those kinds of puzzles are not held in your head as a long sentence, says Stamirowska: “The eureka moment that pops up in your brain isn’t necessarily in language. We would argue that if you have to reason in language, you’re somehow constrained.”
Stamirowska admits that a mainstream LLM could read a book about how to solve sudoku and then write code to do it. But we want to build models with more than book smarts, she says: “The hope for AI is not to solve sudoku; it’s to cure cancer. There’s not a book for that.”
“Transformers are an engineering convenience that we fell on,” she adds. “It started a religion, but it’s silly to think that a breakthrough won’t happen again.”
Precisa de substituir uma aplicação paga, um programa descontinuado ou simplesmente procura uma opção open-source? O AlternativeTo ajuda-o a encontrar as melhores alternativas com base nas recomendações da comunidade.
The post AlternativeTo ajuda a encontrar as melhores alternativas de software para PC, ferramentas online e apps appeared first on Tek Notícias.
Desenvolvida por um programador independente, a Helys usa uma fórmula científica que considera o seu tipo de pele, localização, hora do dia, condições meteorológicas e índice UV para calcular, em tempo real, quanto tempo de exposição solar é seguro e suficiente. Disponível gratuitamente apenas para iOS.
The post Helys: a app que calcula o tempo exato que deve passar ao sol para produzir vitamina D sem riscos appeared first on Tek Notícias.
Setting up a safe chat space for your kids is about to get much simpler on your Apple smartphone. The popular messaging app WhatsApp is rolling out a brand new parental controls section inside its main settings menu for the iPhone. While the company launched parent-managed accounts earlier this spring, many families never set them […]
The post WhatsApp Makes Parent-Managed Accounts Easier To Set Up On iPhone appeared first on The Mac Observer.
Creating funny pictures to share with friends might soon become a lot easier. People use the ChatGPT app to generate custom images, but turning those images into messaging stickers takes too many steps. You have to save the picture, crop it, and use another tool to import it. A new report shows that OpenAI is […]
The post ChatGPT App Might Soon Get A Native WhatsApp Sticker Creator appeared first on The Mac Observer.
[Update: See the retraction below.]
On the web, if I build something that works within the standards, it works. Whether people use it is up to them. On iOS, there is another question entirely: whether Apple decides it should exist.
[…]
In the past couple of years I’ve built three apps that were rejected from the App Store on various grounds.
[…]
A while ago I tried to submit an iOS app for Dark Hours, my astronomy website for normal people. It was rejected on the grounds that it was astrology.
[…]
I see countless ChatGPT wrappers with nearly identical icons and eye-watering subscriptions. I see astronomy apps requesting permissions that have nothing to do with looking at the night sky. My kids download games that interrupt play every few minutes to advertise the developer’s other subscription apps, each with their own $20–50 yearly plans.
Via John Gruber (Mastodon, Hacker News):
But if you actually look at Godier’s Dark Hours, for even just a few seconds, it is instantly obvious that it pertains to the science of astronomy and has absolutely nothing — zero, zilch, nada — to do with astrology.
[…]
Godier proceeded through a series of escalations up to the App Review Board and the Review Board responded that they determined the original rejection was valid because, I shit you not, “We understand that the app includes a live tarot reading feature.” Which isn’t even about astrology. It’s straight out of Kafka.
[…]
This isn’t just contrary to the benefit of developers, like Godier. It’s obviously contrary to the benefit of Apple itself, which should not just accept an app like Dark Hours, but celebrate it as an exemplar of the platform.
Mistakes happen. But in a functioning system mistakes get corrected, and mistakes as obvious as this one get corrected almost instantly and include a quick apology for the conflation. The App Store is not a functioning system.
Previously:
Update (2026-08-10): John Gruber:
To the best of my recollection, this is the first post I’ve retracted in the 24 years I’ve been writing Daring Fireball. I hope it’s the last. I was misled, both overtly and through omissions[…]
[…]
The truth is, the app, as originally submitted by Godier to the App Store (under the name “Asterly”, not “Dark Hours”), was entirely dedicated to astrology, not astronomy, and did in fact include a “Tarot card of the day” feature amongst other occultist horseshit.
[…]
I wrongly took Godier at his word, both in his public blog post and in private iMessage correspondence yesterday, that the rejection wasn’t just merely debatable, but completely and rather preposterously ungrounded. Whether Godier ever submitted a build of “Asterly” to the App Store that contained no occult horseshit and only the hard-science astronomy features that were present in his “Dark Hours” website that was available for the last week, I don’t know. But I have no reason to believe that he did.
[…]
In an uncomfortable exchange between Beher and Godier on Bluesky, Beher pointed out that Godier’s Dark Hours had the same bug as Beher’s that routed people to “random fields in Mexico”. Earlier today, Godier took his web app down and redirected his
darkhours.iodomain to Beher’sdarkhours.app.
I’m really unhappy about this, both for my role in spreading a false story and because I think it will hurt the cause of reforming App Review. I know there are many crazy rejections and have experienced some first-hand. This story was believable because of that well-known history and because Godier didn’t seem like a nobody trying to get attention—he was the developer of another highly regarded app. But now people are going to point to true developer stories and accuse them of being fakes, too.
Terry Godier has just published a so-called “Mea Culpa” blaming Claude for cloning an open source app and denying foreknowledge.
There’s no mention of or apology for how Godier deceived Gruber and the world about the astronomy/astrology App Store submission/rejection.
The blog post essentially confirms that this person cannot be trusted.
Update (2026-08-11): Colin Cornaby:
BOTH projects were generated using Claude. So you have two people - both generating the same app from the same training data - having a public throw down over who copied who.
[…]
The “original” developer does claim to know how to code - which wasn’t clear from my research. That still doesn’t necessarily mean the original app was the source of the training data. They both may be a result of the same training data.
Holy shit, so the “Claude plagiarised an app” story is actually “Claude produced the same app twice when prompted to do so by two separate developers” 😅
It’s not clear to me exactly how much each developer relied on Claude or which training data was used, but I wanted to note this angle of the story.
Update (2026-08-14): Rosyna Keller:
With all this talk of an astrology app being rejected by App Store review I’m reminded there’s still a flat Earth/astrology app on the App Store that actively leaks the location of all of its users.
Apple Health data can now move between the Health app and Google Health on an iPhone. The connection lets Google Health import supported information recorded by an iPhone, Apple Watch, and compatible apps. The latest Google Health update can also send supported Fitbit information to Apple Health.
This two-way connection is useful for people who wear a Fitbit but prefer Apple’s Health app as the central place for viewing activity, sleep, steps, and other wellness information. It also reduces the need for third-party synchronization apps.
The feature requires a recent version of Google Health. Google began introducing support for sending data back to Apple Health with Google Health version 5.05. Because app updates can roll out gradually, the new sharing options may not appear until the latest version reaches your account.
The Fitbit app became Google Health in 2026. Existing Fitbit users should find that the installed app has changed its name and design after updating. Fitbit accounts must also be moved to an adult Google Account to use the redesigned service.
Google says the Apple Health connection requires iOS 16.4 or later. The iPhone must have internet access while the connection is being created.
Settings > General > About > iOS Version
Install the latest compatible iOS update if the phone is running an older release.
Settings > General > Software Update
Next, confirm that Google Health is current. Version 5.05 or later is required for the newer option that sends supported Google Health and Fitbit information to Apple Health.
App Store > Profile Picture > Available Updates > Google Health > Update
If Google Health does not appear under Available Updates, search for the app manually. The App Store page may show an Update button if a newer release is available.
App Store > Search > Enter Google Health > Google Health (Fitbit)
People who previously used Fitbit should sign in with the Google Account connected to their Fitbit information. Creating a different account may produce an empty Google Health profile.
Connecting the apps allows Google Health to read supported information from Apple Health. This can include data collected by the iPhone, Apple Watch, and other apps that already share information with Apple Health.
Google Health > Connections > Apps and Services > Apple Health > Get Started
Depending on the Google Health version and interface shown on the iPhone, Connections may appear behind the profile icon.
Google Health > Profile Icon > Partner Apps > Apple Health
Google Health will display a consent screen explaining the connection. Read the information before continuing.
Sync Google Health With Apple Health > Agree
The iPhone will then display Apple’s health permission screen. This screen lets you decide which information Google Health can read.
Apple Health Permission Screen > Turn All Categories On > Allow
You do not have to share every available category. Selecting individual permissions provides more control over sensitive health information.
Apple Health Permission Screen > Select Each Health Category > Allow
Google Health can import supported information such as steps, distance, active energy, exercise records, routes, sleep sessions, sleep stages, heart rate, resting heart rate, heart rate variability, blood oxygen, respiratory rate, body temperature, weight, body fat percentage, water, nutrition information, and some cycle-tracking data.
Available categories depend on the devices and apps connected to Apple Health. An iPhone may provide steps and walking information, while an Apple Watch can contribute heart rate, workouts, sleep, and other measurements supported by the model.
After the permissions are approved, Google Health should confirm that the connection is complete.
Google Health Confirmation Screen > Done
New Apple Health information should begin appearing automatically in Google Health. Google has said that imported data can be used across relevant Google Health metrics, although not every external data type is used for every calculation.
Google Health > Health > Select a Metric
Google Health may display information from several connected sources together. Its value for a metric can differ slightly from the number shown by Apple Health because the apps may process available data differently.
Google Health version 5.05 adds the reverse direction that many iPhone and Fitbit users previously lacked. Supported information recorded through Fitbit can now be written into Apple Health.
Before enabling this option, make sure the Fitbit is properly connected to Google Health. Fitbit devices normally synchronize automatically throughout the day when Bluetooth is available and the wearable is near the iPhone.
Google Health > Profile Icon > Devices > Select Your Fitbit
The device page should show the wearable, its battery level, and its most recent synchronization time. Opening Google Health can also prompt a nearby Fitbit to transfer recently collected information.
Once the wearable is updating Google Health correctly, open the Apple Health connection settings.
Google Health > Profile Icon > Partner Apps > Apple Health
The interface may use Apps and Services instead of Partner Apps on some versions.
Google Health > Connections > Apps and Services > Apple Health
Follow the prompts for sharing Google Health information with Apple Health. The iPhone may display separate permissions for data that Google Health can read and data that it can write.
Apple Health Permission Screen > Allow Google Health to Write > Select Health Categories > Allow
Google says supported information can include exercise records, steps, sleep, vitals, and other health data. The exact selection can vary according to the Fitbit model, region, Google Health version, and information stored in the account.
Selecting every category is convenient, but it can create overlapping sources when an iPhone, Apple Watch, and Fitbit record the same type of activity. Choose only the categories you want Google Health to add.
A person who uses a Fitbit mainly for sleep, for example, can allow sleep information while leaving steps disabled. Someone who wears a Fitbit instead of an Apple Watch may prefer to enable steps, workouts, heart rate, sleep, and other supported categories.
Apple Health keeps permission controls on the iPhone. You can change them after the first connection without deleting either app.
Health > Profile Picture > Privacy > Apps > Google Health
The page shows the categories Google Health is permitted to read or update. Turn off any category you no longer want to share.
Some iOS versions provide the same controls through the Sharing tab.
Health > Sharing > Apps and Services > Google Health
Turning off a permission stops future sharing for that category. It does not necessarily remove information that has already been written to Google Health or Apple Health.
The Google Health app also provides controls for the connection.
Google Health > Connections > Apps and Services > Connected > Apple Health
If Google Health displays a message that new data types are available, open the connection and approve the additional categories you want to use. Newly supported information is not automatically shared without permission.
An iPhone, Apple Watch, and Fitbit can record similar information at the same time. Apple Health keeps each source attached to its data and uses a priority order when presenting combined measurements.
You can inspect the sources contributing to a category and choose which one Apple Health should prefer.
Health > Search > Activity > Steps > Data Sources and Access
The screen shows the apps and devices that provide step records. If Google Health, an Apple Watch, and the iPhone all appear, you can adjust their priority.
Health > Search > Activity > Steps > Data Sources and Access > Edit > Drag Sources Into the Preferred Order > Done
Move the source you trust most toward the top. Apple Health generally gives higher priority to sources placed above others when similar records overlap.
Repeat the same process for other categories if their totals do not look right. Exercise, sleep, active energy, and distance can each have their own sources and permissions.
Synchronization is not always immediate. A Fitbit first sends its information to Google Health, and Google Health then writes supported records to Apple Health. Bluetooth availability, Background App Refresh, internet access, and battery-saving behavior can affect this process.
Begin by opening Google Health with the Fitbit close to the iPhone. Wait for the device to complete its normal synchronization before checking Apple Health.
If Apple Health information is missing from Google Health, confirm that the connection remains active.
Google Health > Connections > Apps and Services > Apple Health
If Google Health displays an error or a request for new permissions, follow the message shown on the screen.
Background App Refresh can help Google Health update when the app is not open.
Settings > General > Background App Refresh > Google Health > On
Bluetooth must remain available for a Fitbit to communicate with the iPhone.
Settings > Bluetooth > Bluetooth On
If the connection continues to fail, disconnecting and reconnecting Apple Health may restore the permission request.
Google Health > Connections > Apps and Services > Apple Health > Disconnect > Confirm Disconnect
Disconnecting the service does not automatically remove every permission already stored by Apple Health. Review the Google Health entry in the Health app if you want to remove those permissions as well.
Health > Profile Picture > Privacy > Apps > Google Health > Turn Off All
After reconnecting the apps, allow time for new records to appear. Previously stored information may not transfer in exactly the same way as activity recorded after the connection was enabled.
You can inspect an individual entry to confirm whether it came from Google Health, a Fitbit, an Apple Watch, or the iPhone.
Health > Search > Select a Health Category > Show All Data > Select the Latest Entry
The post How to Sync Apple Health Data With Google Health on Your iPhone appeared first on AppleMagazine.
Com mais de 1.500 títulos de não-ficção condensados em cartões de leitura e clipes de áudio, a Headway é a aplicação mais popular para quem quer aprender mais mas tem pouco tempo para ler.
The post Headway: os livros mais importantes do mundo resumidos em 15 minutos e com versão áudio para ouvir a caminho do trabalho appeared first on Tek Notícias.
Apple faces a $32.5 billion lawsuit over how its Photos app scans and stores faces. A federal judge cleared the case to proceed as a class action late last month, allowing millions of Illinois iPhone owners to join.
The case has been years in the making, and Apple has fought to shut it down at nearly every turn. Now it heads back to the court, with the company’s privacy practices on trial.
[…]
Court records have shown that the update applies to users with iCloud Photos turned on. Those accounts also need to use at least 10GB of storage and contain at least 5,000 photos and videos. Plaintiffs say all this amounts to collecting biometric data without consent, putting Apple squarely at odds with Illinois law.
Again, I find it odd that features are apparently gated based on the capacity of your iCloud account rather than the available storage.
The lawsuit accuses Apple of collecting biometric data without user consent in the Photos app, reports The Times. It is alleged that Photos uses facial recognition technology to scan individuals who appear in images, creating a “faceprint” for each person in the photo library.
After collecting enough samples, an algorithm is allegedly used to identify the iPhone user. That data is then stored on the iPhone within the Photos app.
I don’t see what the issue is here. My understanding is that it only uploads vectors after you’ve opted in by enabling iCloud Photos and manually assigning names. Also, the vectors don’t seem like the sort of biometric information that the law was meant to protect.
Assuming things are working properly, what should happen is that the people you have specifically named and the photos you have specifically verified sync their info via iCloud. However, the actual face detection for the rest of the photos is done on a per device basis and is not synced via iCloud, primarily for security/privacy reasons.
So, when you first set up a new device, it will pull down the names and info for the photos where you have identified a particular person, but then it still needs to go through the entire library on-device, identify the faces in each photo, and compare them to the synced information to tell which person is in which photo. So once it’s done, you should end up with mostly the same photos identified as each person, but each device still needs to do its own face detection locally.
You can delete stickers on iPhone without removing your entire collection, but the correct procedure depends on whether you want to erase a custom sticker, remove a Genmoji, hide keyboard stickers, or detach a sticker from a conversation.
For custom photo stickers and Live Stickers, iOS provides an individual Remove command. This lets you clean the sticker drawer while leaving every other saved sticker untouched.
Apple does not provide a protected Favorites folder inside the system sticker drawer. The Frequently Used section is created automatically from your activity and should not be treated as a permanent collection. If you have stickers you cannot afford to lose, save recoverable copies before beginning the cleanup.
The safest backup is to send your favorite stickers to a conversation with yourself. The sent copies remain inside the conversation even if you later remove the reusable versions from your sticker collection.
Messages > New Message > Your Contact > Add > Stickers
Select each favorite sticker and send it as a separate message. You can also create a private conversation with a trusted contact, but messaging yourself keeps the backup separate from ordinary conversations.
If you accidentally delete one of these stickers from the drawer, return to the self-conversation and restore it.
Messages > Your Self-Conversation > Touch and Hold Sticker > Save to Stickers
For a sticker attached to the corner of a message bubble, open its details before saving it.
Messages > Your Self-Conversation > Touch and Hold Sticker > Sticker Details > Save
This procedure is especially useful for stickers received from other people. A photo sticker you created yourself can usually be recreated from the original image, but a saved sticker from another person may be difficult to replace if the original conversation is deleted.
The easiest way to remove an individual custom sticker is through the Messages app. You can open any existing conversation because you do not need to send a message.
Messages > Conversation > Add > Stickers > Live Stickers
The Live Stickers tab uses an icon resembling a sticker being peeled from a surface. It contains stickers created from your photos, animated Live Stickers, and compatible stickers saved from conversations.
Touch and hold the unwanted sticker until the contextual menu appears, then release your finger and select Remove. Do not drag the sticker toward the conversation because that action may attach it to a message instead of opening the menu.
Touch and Hold Sticker > Remove
Only the selected sticker is deleted. Other photo stickers, saved designs, and favorite items remain in the collection.
Some iOS versions also display a Rearrange command after you touch and hold a sticker. This mode allows you to move the stickers you use most often toward the beginning of the collection.
Messages > Conversation > Add > Stickers > Live Stickers > Touch and Hold > Rearrange
Drag the stickers you want to preserve toward the first positions. You may also see a removal control beside each sticker while rearranging. Use it carefully because iOS may remove the sticker immediately without presenting a second confirmation.
A selective cleanup is safer than trying to reset the complete keyboard or sticker database. Removing and reinstalling the Emoji keyboard is unnecessary and may alter frequently used emoji without providing better sticker controls.
The same sticker collection is accessible from the system keyboard in Messages, Notes, Mail, and other supported apps.
Any Text Field > Emoji Keyboard > Stickers > Live Stickers
Touch and hold the sticker you want to erase, then tap Remove. If the menu does not appear, confirm that you are viewing the Live Stickers collection rather than Frequently Used.
Frequently Used combines recently selected emoji, Memoji, Genmoji, and stickers. Items shown there are shortcuts to content stored in another section. Opening the Live Stickers tab gives iOS the correct context for managing your custom collection.
If the keyboard is compressed or several categories are hidden, swipe across the sticker controls until the Live Stickers icon becomes visible. The exact position can change as Apple adjusts the keyboard, but the touch-and-hold removal gesture remains the same.
You can also access the system sticker drawer through the Markup tools in Photos. This is useful when Messages tries to drag a sticker rather than displaying its menu.
Photos > Open Photo > Edit > Markup > Add > Add Sticker
Touch and hold the unwanted sticker, then tap Remove. You are managing the same synchronized collection, so a sticker deleted through Photos also disappears from Messages and the system keyboard.
Removing a sticker does not delete the source photograph. If you created the sticker by lifting a person, pet, or object from an image, the original photo remains in your library unless you delete it separately.
To recreate the sticker later, open the original image and lift its subject again.
Photos > Open Photo > Touch and Hold Subject > Add Sticker
You can then apply an effect such as Outline, Comic, Puffy, or Shiny. Live Photos can produce animated Live Stickers when the isolated subject contains usable motion.
Genmoji created with Apple Intelligence are stored with the stickers accessible through the emoji keyboard. They can be removed individually without affecting your photo stickers or other generated designs.
Any Text Field > Emoji Keyboard > Stickers > Genmoji
Touch and hold the Genmoji you no longer want, then tap Remove. You can perform the same procedure from Messages.
Messages > Conversation > Add > Stickers > Genmoji > Touch and Hold > Remove
Removing one Genmoji does not disable Apple Intelligence or erase the other Genmoji you created. If you later want a similar design, open the Genmoji creator and enter the description again, although the generated image may not be identical.
Memoji stickers operate differently because iOS automatically creates a complete sticker pack from each custom Memoji character. You cannot normally delete one expression while keeping the remaining expressions from the same character.
Deleting the underlying Memoji removes its generated sticker pack. Before selecting Delete, confirm that you do not want any of that character’s poses or expressions.
Any Text Field > Emoji Keyboard > Memoji > More > Delete
If you only want Memoji and other stickers to stop appearing in the keyboard, use the keyboard setting instead of deleting the character.
Settings > General > Keyboard > Stickers
Turning off Stickers hides sticker content from the emoji keyboard. It does not provide a selective cleanup and should not be used as a substitute for removing individual items. Your saved stickers remain available when the setting is enabled again.
A sticker attached to a message bubble is separate from the reusable copy stored in your collection. Deleting it from the conversation does not erase the sticker from your drawer.
Messages > Conversation > Touch and Hold Message Bubble > Sticker Details
Find the sticker you want to detach, swipe left across it, and tap Delete. This removes that placement from the conversation on your iPhone.
Use this procedure when a sticker covers text, was attached accidentally, or no longer belongs in the conversation. If you instead open the sticker drawer and select Remove, you erase the reusable sticker while leaving previously sent copies in their conversations.
Understand What Happens Through iCloud
Custom stickers sync through iCloud to supported Apple devices using the same Apple Account. Removing a sticker from the iPhone can therefore remove it from the sticker drawers on an iPad, Mac, or Apple Vision Pro.
There is no Recently Deleted album for the system sticker collection. Keep the original source photo or send valuable stickers to yourself before removing anything.
After a deletion, another Apple device may continue displaying the sticker briefly while synchronization completes. Closing and reopening the sticker drawer usually refreshes the collection. A favorite preserved in your self-conversation can be restored at any time through Touch and Hold Sticker > Save to Stickers.
The post How to Delete Stickers on iPhone Without Losing Favorites appeared first on AppleMagazine.
Google Health (previously the Fitbit app) collects fitness, sleep, and wellness data from a connected wearable device. Steps, distance traveled, heart rate, sleep, and more can now be added to Apple Health. As noted on Reddit, some data such as HRV doesn't transfer over.
[…]
Syncing can be enabled after downloading the update by selecting Connections, choosing Apps and services, and then adding Apple Health.
My wife is a longtime Fitbit user. A few weeks ago, some family and friends started a friendly competition to see who could get the most steps. They’re all Fitbit users, too. Then she wanted to start comparing step counts with me, but I have an Apple Watch. I thought this would be no problem because the Health app has a sharing feature, and Fitbit and HealthKit have both been around forever. I was shocked to learn that there has never been any syncing. I don’t really understand the reason for this, as it seems like it would make the Fitbit more valuable.
It looks like the syncing part is now solved, but we still can’t automatically compare our steps. We’ve both tried inviting each other to share step counts and some other metrics multiple times over the last several weeks, and it has never worked. Either the invitation doesn’t arrive or the app just spins forever when trying to accept it.
Previously:
Apple briefly removed Telegram from the App Store on Monday night after a review found content that violated its guidelines against child sexual abuse material (CSAM), but restored the app a short while later, according to Reuters.
It seems like this was a symbolic gesture. Removing the app didn’t affect any of the customers who had already downloaded it. Probably none of them saw the image. And restoring the app doesn’t mean that it’s completely free from CSAM—probably none of the major platforms is. It just means that Telegram did the obvious thing after the violation was reported, which presumably they would have done, anyway.
They removed an entire app for ONE app user???
And before even contacting the developer? Telegram banning the account is what actually prevented users from potentially seeing the CSAM. Apple’s action seems like either a mistake or trying to send them a message.
I would note, by the way, that the Telegram app was apparently never removed from the Mac App Store. Which goes to show how little Apple cares about the Mac App Store.
I’m sure this stance will be applied equally to all other apps in the store, in the future right? @Apple
Pavel Durov (Telegram, MacRumors):
Because Telegram quickly removes illegal content from public groups using all kinds of moderation tools, the attacker had to resort to a technical trick. He inserted AI-modified illegal content by editing an old message in an active group chat. As a result the content was effectively hidden from the group’s members, preventing them from seeing/reporting it.
The attacker was a takedown extortionist: someone who demands ransom from group owners in exchange for not targeting their communities. These extortionists use automated accounts to plant illegal content in public groups and then report it directly to Apple, attempting to trigger the removal of legitimate communities whose owners refused to pay them.
[…]
Extortionists have found a way to manipulate Apple into overreacting. Apple removed Telegram from the App Store before contacting us. This creates a potential systemic risk for every mobile app that hosts user-generated content. If an app used by more than a billion people can be removed from the App Store without prior warning, any app can be.
During the time the app was unavailable, many speculated that the move was politically motivated, or perhaps in response to the recent discovery that Telegram has allegedly been using an obfuscated private UIKit API to animate its emoji picker.
It’s sad but kind of hilarious that both of these so disparate theories were considered possible. Durov really does have political enemies, and Apple really does seem to have it out for Telegram. But, also, using private API to animate emojis totally seems like something that Telegram would do and that Apple would overreact to.
Previously:
Update (2026-08-06): Matt Burgess (Hacker News):
Over the last nine months, Mark Zuckerberg’s Meta has run dozens of paid ads that include explicit AI-generated child sexual abuse material (CSAM) and images of minors alongside sexually suggestive statements, according to details of the ads shared with WIRED. The ads, which in some cases reached several thousand accounts, were targeted at people living in the United States, United Kingdom, and more than a dozen European countries.
This is much worse than what happened with Telegram, yet Apple didn’t overreact.
There’s also the Twitter/Grok case.
Previously:
At the end of August, NASA is set to launch the Nancy Grace Roman Space Telescope from Kennedy Space Center in Florida. Its goal is to help us better understand how the universe works, from the glue-like dark matter that keeps galaxies together to the elusive dark energy that drives the expansion of the cosmos. But Roman could also serve another purpose: defending Earth from killer asteroids. In September, a multi-institutional team of planetary scientists and astronomers will outline how the space telescope is uniquely placed to scan asteroids and provide information about their trajectories, sizes, and compositions.
Roman, named after NASA’s first chief astronomer, is equipped with a super-wide-angle, 300-megapixel infrared camera, allowing it to see a large patch of space at any one time—roughly 100 times larger than is possible with the Hubble Space Telescope. That will allow it to discover thousands of new planets and tens of thousands of exploding stars, and survey over a billion galaxies in remarkable detail.
While doing so, it’ll be looking “through” our solar system, and this is what makes it uniquely well placed to spot asteroids.
Roman was not built for this purpose, but last summer, it was (once again) threatened with significant funding cuts by the Trump administration. “My colleague Rick Cosentino [a planetary scientist at NASA] said to me in July 2025 that we need to show what Roman can do for planetary defense as a way to further increase the visibility of the mission with lawmakers and taxpayers,” says Bryan Holler, a researcher at the Space Telescope Science Institute in Baltimore.
Holler and his colleagues’ proposal, which will be presented at the Europlanet Science Congress at The Hague, reveals that Roman’s epic field of view and infrared vision allow it to spot small asteroids up to 60 feet long. This is comparable to the asteroid that exploded above the Russian city of Chelyabinsk in 2013, unleashing the force of 500,000 tons of TNT and sending 1,500 people to the hospital.
Roman’s software will need some tweaking to spy space rocks. The telescope, as designed, will see through—and beyond—the solar system in order to gather the clearest possible pictures of the rest of the universe. “Streaks, whether caused by cosmic rays or glitches or asteroids, are caught by the software and discarded,” says Andy Rivkin, a planetary scientist and planetary defense researcher at the Johns Hopkins Applied Physics Laboratory in Laurel, Maryland. But astronomers could go in, study those streaks, and pick out those they identify as asteroids.
Even with those potential adjustments, Roman won’t be an asteroid-finding wunderkind on its own. Its strength lies in its ability to complement the James Webb Space Telescope (JWST), another observatory that’s built to peer at galaxies and stars in the far reaches of the universe. It can also intensely focus on a single asteroid when needed—as it did last year, playing a key role in tracking 2024 YR4, which was briefly the most dangerous asteroid ever discovered. “But Roman’s field of view is much bigger,” says Rivkin.
That means it could look at multiple questionable asteroids very quickly. “Roman can provide infrared observations of more asteroids than JWST could hope to observe in a reasonable amount of observing time,” says Holler.
If those asteroids are found to be benign travelers, we can relax. But if they might collide with Earth, other telescopes—including JWST—can follow up from Roman’s observations. Those observations could give experts the information they need to assess the likely damage of an upcoming asteroid strike—or to launch a mission to attempt to swat an asteroid away.
“Roman will sample such a large volume of the cosmos that we’ve long known it will offer vast opportunities for a range of additional science,” says Alise Fisher, the astrophysics communications lead at NASA headquarters in Washington, DC.
NASA’s Planetary Defense Coordination Office, and its partners across the world, are chiefly worried about asteroids 460 feet long and larger. Around 25,000 of those are estimated to have near-Earth orbits, and just over half have yet to be found. Should one hit a city, much of it would be destroyed or irreversibly damaged in a heartbeat. Astronomers estimate that there are also 230,000 or so 165-foot-long asteroids orbiting close to Earth, and fewer than 10% have been located. A strike on a city from one of those might not annihilate it, but it would unleash a force comparable to that of a large atomic bomb, albeit without the radiation.
Such an asteroid could theoretically be deflected (by ramming a spacecraft into it) or vaporized (perhaps using a nuclear weapon). But planetary defenders need to know where they are first, which is why NASA funds a network of ground-based telescopes designed to seek them out. They work well, but there’s only so much of the night sky they can see, and Earth’s atmosphere peskily gets in their way.
That’s why NASA is launching the Near-Earth Object (NEO) Surveyor space telescope in 2027. By positioning itself between Earth and the sun, it’ll find many elusive asteroids that ground-based telescopes cannot see. And unlike many of its asteroid-seeking cousins, it’ll see in infrared, not visible light. Not only do asteroids show up more clearly in infrared, but seeing them through this lens gives scientists a considerably better measure of their size. In a matter of years, it could find 90% of the city-killer-size asteroids in near-Earth orbits.
NEO Surveyor is explicitly a planetary defense observatory. But it’ll work with other telescopes with more science-minded missions, including Roman and JWST (both of which conveniently have infrared scopes too) as well as the Vera Rubin Observatory, which just began its 10-year survey of the entire night sky from atop a mountain in Chile. As part of its inventory of the cosmos, it’s expected to discover 89,000 near-Earth asteroids.
Here’s how they might all work together. Say NEO Surveyor spies an asteroid that, to judge from a few observations, has a chance of impacting Earth. Then it finds five more just like it. Those initial observations leave a lot of uncertainty about their orbits. Roman, with its huge field of view, could be commanded to look at the corner of the night sky that includes all those asteroids, and in a matter of days it could improve the precision by several orders of magnitude.
Roman also occupies a different part of space from both NEO Surveyor and the Rubin observatory. “Those slightly different viewing angles will also help narrow orbits down more quickly than if all objects were looking from the same place,” says Holler.
Perhaps five of those potentially hazardous asteroids are found to stand no chance of colliding with Earth for the foreseeable future. One, however, might not be able to be ruled out—and that’s when other telescopes, including JWST, could be asked to track it down and study it further.
“Telescope resources, whether in space or on the ground, are typically oversubscribed and will not be available to follow up on all [near-Earth asteroids] with a non-zero impact probability when they are first discovered,” says Holler. Roman, then, will help scientists “make sure we follow up on the correct targets.”
Roman’s infrared scope also allows it to offer a decent estimate of an asteroid’s size and can even tell whether it’s a stony rock, a puffy and watery carbon-rich rock, or a metallic one. “This in turn provides strong clues to the composition and thereby the density and mass of the asteroid, which are important when estimating the impact damage or, less ghoulishly, the effort required to nudge it out of its current orbit,” says Holler.
Roman won’t play the lead role in protecting Earth in the way NEO Surveyor will. But while it’s seeking out supernovas and planets scooting around other stars, it will also be doing its part to protect all 8 billion of us from a cosmic catastrophe.
Telegram was temporarily removed from Apple’s App Store worldwide after the iPhone maker identified content from one user that violated its rules against child sexual abuse material, the messaging service said on Tuesday.
According to a report from Reuters, the episode mirrors a similar brief suspension in 2018, when Apple pulled the app over what Telegram founder Pavel Durov at the time described as “inappropriate content.” The app was restored then after Telegram strengthened its safeguards.
An Apple spokesperson confirmed the recent short-lived removal, stating that its review found content violating guidelines that prohibit child sexual abuse material. “The app was subsequently restored after the developer promptly removed the content and banned the user who posted it,” the spokesperson added. The removal and restoration occurred on the same day once Telegram acted.
Telegram, which counts more than a billion monthly active users, maintains a zero-tolerance policy on such material. It said it has blocked nearly 338,000 groups and channels for related content this year.
Epic Games CEO Tim Sweeney, whose company has long clashed with Apple, also weighed in on X. “Apple has stated they removed the app from availability to a billion users because a Telegram user posted something horrible and illegal,” Sweeney wrote, according to Reuters. He continued: “They did not elaborate on how, if that is the standard for app removal, any communications app could operate at scale, including iMessage.”
An Apple spokesperson did not respond to further questions about the guideline violation, Reuters reported.
MacDailyNews Note: The incident comes amid ongoing regulatory attention on Telegram’s handling of illegal content. Britain’s Ofcom opened an investigation in April over concerns about the sharing of child sex abuse material; Telegram has categorically denied the accusations and said it has virtually eliminated the public spread of such material since 2018 through detection algorithms. In February, Australia’s online safety regulator also fined the company for delays in responding to queries about measures against child abuse material and violent extremism.
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The post Telegram briefly pulled from Apple’s App Store over single user’s prohibited content, then quickly restored appeared first on MacDailyNews.
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