AI News Archive: July 7, 2026 — Part 15
Sourced from 500+ daily AI sources, scored by relevance.
- Your Brand Will Be Invisible in AI Search If You’re Not Showing Up on These 8 Channels
Your Brand Will Be Invisible in AI Search If You’re Not Showing Up on These 8 Channels Entrepreneur
- Why AI doesn’t create bad decisions, it just exposes them faster
Why AI doesn’t create bad decisions, it just exposes them faster Entrepreneur
- Scaling Leadership on a Startup Budget: How One Virtual Coach Can Support Every Leader on Your Team
Scaling Leadership on a Startup Budget: How One Virtual Coach Can Support Every Leader on Your Team Entrepreneur
- Krispy Kreme's Doughnut-Decorating Robots Were a 'Supermessy' Disaster. Now It's Hiring Humans.
Krispy Kreme's Doughnut-Decorating Robots Were a 'Supermessy' Disaster. Now It's Hiring Humans. Entrepreneur
- Can AI fill prescriptions? Here’s what doctors think of Utah’s refill program
A prescription refill program that quietly launched in Utah earlier this year has kicked off a big medical debate: Is artificial intelligence ready to take over tasks that, until now, could only be performed by doctors ? The program allows Utah residents to skip the doctor’s office and get their prescriptions refilled online by an AI chatbot called Doctronic. It’s a seemingly simple step toward making healthcare more convenient for patients and prescribers. But it’s also a precedent-shattering milestone that has set off alarm bells for doctors, lawyers, and public health experts. The pilot program has laid bare a host of questions about the role of AI in medicine , including how it should be regulated, whether doctors should be able to veto it, and what kind of safety measures are needed to protect patients. At the center of the debate: State and federal laws limit prescribing to licensed medical professionals. Proponents say those laws, which have underwritten American medicine for over 100 years, should be updated to include AI chatbots and other new technologies . “We have crossed a threshold in terms of giving something that is not human a medical license, whether or not we want to call it that,” said Dr. Eric Bressman of the University of Pennsylvania. AI cannot practice medicine under current laws Bressman and other experts say they aren’t opposed to AI prescribing. But they say it should have to meet rigorous standards akin to human doctors, who undergo years of testing and training before being licensed to practice medicine. In Utah, Doctronic was able to launch, thanks to a “regulatory sandbox” that allows state officials to waive laws for AI companies offering promising technology. The refill program is currently overseen by a five-member board of AI specialists, none of whom are doctors, who say they have implemented numerous safeguards. During the program’s initial phase, for example, human doctors review all Doctronic refill orders. The company expects to soon transition to fully automated refills. The head of the state’s medical licensing board says he and his colleagues learned of the program when its January launch was reported in the news. In a March letter to the state, 11 board members called for the program to be halted, citing the risks of automatically renewing medicines that can have side effects or drug interactions. “We were essentially told: ‘Yes this is going on. And no, you don’t have a say in it,’” said Dr. Alan Smith, a family physician who heads the board but said he was speaking only for himself. Complicating the picture is the fact that medical technology is traditionally regulated at the federal level, while medical professionals are overseen by states. Doctronic executives consider their AI part of the state-regulated practice of medicine. But the federal Food and Drug Administration is supposed to oversee AI that directly impacts medical care or decision-making, a line that some experts believe Doctronic has crossed. Some states are clearing the way for AI in healthcare In an interview, Doctronic’s executives wouldn’t say whether they have sought permission from the FDA. “Our goal here is really just to meet patients where they need healthcare,” said Dr. Adam Oskowitz, who co-founded the company with a tech industry entrepreneur. “We try not to get too deep into the weeds on the regulatory side.” In Utah, residents can visit a Doctronic website built for the refill program. After confirming their identity, the AI chatbot asks users about their prescriptions and medical history, verifying that they have a valid prescription by tapping into a national pharmacy database. If there are no issues, the AI can renew the prescription and send it to a local pharmacy. If the request requires more attention, the chatbot transfers the patient to a doctor who works for Doctronic’s telehealth service. Oskowitz envisions a future where many routine medical tasks, including ordering tests and analyzing results, can be offloaded to Doctronic, allowing doctors to manage thousands more patients than they can today. Other states are also waiving rules for AI, including Texas and Wyoming. Meanwhile, lawmakers in Iowa, Idaho, and elsewhere have introduced legislation to formally license AI medical services. Many of the bills are based on a template from the nonprofit Cicero Institute, a pro-AI think tank founded by Joe Lonsdale, cofounder of the artificial intelligence software company Palantir . Pushback against medical AI mainly stems from the economic fears of doctors and other health workers, says Cicero’s director for health policy. “Whoever goes first is going to take the slings and arrows because there’s economic interests, concerns about the workforce and what that’s going to mean for jobs,” said Cicero’s Adam Meier. Doctors see potential risks to AI prescription refills Smith, the medical board chair, says the risks to patients are real. He points out that Doctronic’s list of 190 refillable medications includes blood thinners, which can become dangerous if patients develop stomach ulcers or other conditions that cause internal bleeding. “Many times when I see people after six months I find that their medical history or situation has changed,” Smith said. “Just because something was prescribed before does not mean it’s appropriate now.” The American Medical Association has voiced similar concerns, warning that “prescription renewals aren’t routine checkboxes.” Zach Boyd, who heads Utah’s AI office, said Doctronic has thus far been overly cautious, often elevating uncontroversial decisions to doctors. In response to safety concerns, several medications have been removed from the list eligible for refills, including a drug for irregular heartbeats. Utah has released some initial data on the program and Doctronic plans to publish peer-reviewed studies later this year. Currently the only publication about its technology is a paper written by company scientists that was not independently reviewed. The study looked at whether Doctronic could correctly diagnose medical conditions based on records from 500 telehealth consultations. In the study, Doctronic’s diagnoses matched that of human doctors 80% of the time. The FDA is taking a hands-off approach Bressman says Utah should have demanded data on prescription refills up front, not after Doctronic was up and running. “Mostly they’re accepting the company’s word on good faith that they’re up to the task,” he said. The current approach to AI mirrors the haphazard medical standards of the early 20th century, Bressman says, before medical schools, medical boards, and other authorities agreed on national benchmarks for training and licensing. National guidelines on medical technology would typically come from the FDA, but the agency has indicated it plans to take a hand-off approach, at least under the current administration. An FDA spokesperson said the agency has not authorized any AI chatbots but “is committed to encouraging medical innovation and helping bring promising new technologies to patients, while keeping safety at the center of every decision.” For now, Doctronic and other companies are likely to expand across states with different regulatory approaches. “Companies may benefit in the short term by expanding their business models and kind of having the technology go beyond the evidence,” says Daniel Aaron of University of Utah’s law school. “But in the long-term, I think they risk compromising public trust and fueling backlash.” __ The Associated Press Health and Science Department receives support from the Howard Hughes Medical Institute’s Department of Science Education and the Robert Wood Johnson Foundation. The AP is solely responsible for all content. —By Matthew Perrone, AP health writer
- AI learning loops aren’t an engineering trick. They’re a governance issue
For the past two years, the dominant unit of AI work was the prompt. Write a better prompt, get a better answer. Learn the right phrasing, the right examples, the right constraints, the right tone. Prompt engineering became the first folk discipline of the generative AI era because it matched the first experience most people had with these systems: one human, one model, one request, one response. That phase is ending. A recent Business Insider piece describes the rise of “ loop engineering ”: the practice of designing loops that allow AI agents to keep working, checking, retrying, and coordinating instead of waiting for a human to issue every instruction manually. The examples are mostly technical: coding agents, review agents, sub-agents, automated workflows. But the shift is much bigger than software development. The unit of AI value is moving from the answer to the loop. That should make executives, regulators, and boards pay attention. Because in a corporation, a loop is not just an engineering pattern. It is a governance structure. From prompts to loops A prompt asks for an output. A loop creates behavior. That difference changes everything. A prompt can be wrong and disappear. A loop can be wrong and compound. It can observe, act, receive feedback, adjust, and repeat. That is exactly why loops are powerful. It is also why they are dangerous if companies do not understand what they are optimizing. This is the real significance of the current move from prompt engineering to loop engineering. Engineers are discovering that the important work is no longer just asking the model better questions. It is designing the system that keeps invoking the model, evaluating the results, and deciding what happens next. In software development, that may mean one AI agent writes code while another reviews it. In a company, it may mean an AI system optimizes sales, hiring , pricing, procurement, customer service, credit, insurance, logistics, or internal performance. At that point, the question is no longer technical. It’s institutional. Every loop has politics A corporate loop always contains a theory of what matters. If a customer service loop optimizes for resolution speed, it may learn to close tickets faster while quietly degrading trust. If a sales loop optimizes for conversion, it may learn which arguments, discounts, or psychological cues move customers most effectively. If a hiring loop optimizes for retention, it may select for conformity. If a pricing loop optimizes for margin, it may produce outcomes that look efficient internally and discriminatory externally. None of these failures requires a malicious model. They require only a poorly governed loop. This is why “human in the loop” is no longer enough. Too often, the phrase is used as a ritual reassurance: Somewhere, somehow, a person is involved. But which person? With what authority? At which point in the loop? Seeing what information? Able to stop which action? Responsible for which outcome? A human rubber-stamping machine-speed optimization is not governance. It is liability with a user interface. AI governance has to become continuous Most AI governance still assumes that the organization is governing a relatively static object. A model is assessed. A use case is approved. A risk is classified. A compliance document is created. A dashboard is built. The system goes live. But a learning loop is not static. It changes through use. That’s why the most serious governance frameworks are already pointing, implicitly or explicitly, toward continuous governance. The NIST AI Risk Management Framework is structured around governing, mapping, measuring, and managing AI risks. The EU AI Act requires post-market monitoring for high-risk AI systems, including the collection and analysis of performance data throughout their lifetime. ISO/IEC 42001 , the international standard for AI management systems, is explicitly about establishing, maintaining, and continually improving an AI management system. The direction is clear: AI governance cannot be a launch checklist. Once AI becomes a loop, the crucial question is not simply “Was this system approved?” It’s “What is this loop learning, from which data, against which objective, under whose authority, within what constraints, and with what right of appeal?” That’s a very different kind of governance. The problem isn’t autonomy. It’s adaptation. Much of today’s enterprise AI conversation is obsessed with autonomy. Can the agent do more by itself? Can it use more tools? Can it execute more tasks? Can it run longer without supervision? Those questions matter, but they are not the deepest ones. The real issue is not whether an AI system can act. It is whether the company can govern what the system learns from acting. A non-learning automation can be audited as a process. A learning loop must be governed as an evolving system. It can drift. It can discover shortcuts. It can optimize a metric while damaging the institution. It can make one department more efficient while making the company less coherent. That last point is critical. One loop may optimize support for speed while another optimizes retention for long-term satisfaction. One may optimize procurement for lowest price while another optimizes resilience. One may optimize sales for conversion while another optimizes compliance for risk reduction. Each loop may look rational locally. Together, they may pull the company apart. The old enterprise software problem was integration: getting systems to exchange data. The new enterprise AI problem is coherence: getting learning systems to pursue compatible objectives. Boards need to understand the loops Boards don’t need to review every prompt. They don’t need to understand every model architecture. But they do need to understand which parts of the company are becoming self-optimizing, what those systems are optimizing for, and whether those objectives align with the firm’s strategy, obligations, and values. Because every metric is a governance decision pretending to be a technical one. Optimizing for cost, speed, growth, retention, satisfaction, fraud reduction, compliance, or margin is not neutral. Each choice encodes a theory of what the company is for. When those choices are embedded into adaptive systems, they become more than KPIs. They become operating instructions for the organization. That’s why corporate learning loops belong on the board agenda: not because boards should micromanage AI, but because learning loops will increasingly shape how companies behave. Governance must become executable The obvious conclusion is uncomfortable: Policies written in documents are not enough. If loops are going to observe, act, evaluate, and improve, governance has to be built into the loop itself. The system must know what it’s allowed to do, what it must record, when it must escalate, which constraints are absolute, which are contextual, and which decisions require human judgment. In other words, governance has to become executable. A corporate AI loop should have a declared objective, a visible reward function, a defined operating perimeter, an auditable memory, explicit permissions, measurable outcomes, escalation paths, stopping conditions, and a record of how its behavior changes over time. It should be possible to ask not only “What did the AI answer?” but “What has this loop learned to do?” That’s the difference between supervising outputs and governing adaptation. The next AI failures will be loop failures The next generation of enterprise AI failures will not come mainly from bad prompts. They’ll come from loops that worked exactly as designed, optimized exactly what they were told to optimize, and quietly taught the company to become something it never consciously chose to become. That’s the real governance challenge. The model race made AI look like a question of capability. The agent race made it look like a question of autonomy. The loop era will reveal that enterprise AI is ultimately a question of institutional control. Who defines the objective? Who owns the memory? Who changes the reward function? Who sees the drift? Who can stop the loop? Who is accountable when optimization works, but the company moves in the wrong direction? Those aren’t engineering questions: They’re governance questions. Corporate learning loops are not just the next trick in AI development. They are the adaptive machinery of the firm. And adaptive machinery must be governed before it governs us.
- I managed through four tech disruptions at HBO. ‘AI Minimalism’ is the secret to survival in the fifth disruption
Over the course of my career, I’ve built teams and transformed organizations through four major technological disruptions: web, social, mobile, and streaming. Each wave arrived with the same promise and the same trap: Move fast, deploy broadly, figure out the ROI later. I navigated each of those disruptions inside HBO, one of the most beloved and high-profile cultural brands in the world. Bold in its DNA. Deeply risk-averse when it came to exposing that brand. Rooted in decades of success and a long-standing way of doing things. That tension taught me something that is more relevant than ever: In a period of radical transformation, simplicity is a competitive advantage. That is the discipline artificial intelligence , the latest wave of disruption challenging business leaders, now demands. When there’s no guarantee that the hype of a new platform or product will be able to deliver when it’s tested in practical team workflows, the ability of organizational leaders to focus their efforts becomes more important than ever. That means stripping their operations down to the most essential, efficient architecture, then amplifying only where it genuinely warrants it. I call this approach AI Minimalism. Near-term realities With AI, deployment is moving faster than any prior wave of disruption because AI accelerates acceleration itself. Many organizations are already looking toward fully autonomous agentic systems and recursive self-improvement—the ability of an AI system to improve itself with minimal human guidance—not as distant possibilities but as near-term competitive realities. The goal is to compound advantage over time and enable more intelligent, sophisticated automation as systems learn from clean, well-governed inputs. But covering more ground faster creates better outcomes only if you know which direction to run. And the infrastructure, human and organizational, is buckling under the weight of what we’re building on top of it. Data from Microsoft released in May shows that using AI was more expensive than paying human employees in some contexts. A week later, Uber reported that the company had exhausted its annual AI budget just four months into 2026, noting that it could not tie AI spending to the development of any new “useful consumer features.” Just last month, an internal memo from Meta informed employees that it expected to “move toward managing AI tokens [a single unit of data processed by AI models like ChatGPT or Claude] in a more structured way” starting in 2027, noting that it had spent billions for internal use so far in 2026. As leaders grapple with how to safely and effectively embed AI within their teams and workflows without sacrificing the judgement and creativity that sets them apart, the answer is less, not more. That is the heart of AI Minimalism. Three steps In practice, this means doing three things in tandem. 1. Overhaul your knowledge base. Your AI is only as good as the information it has access to. When an organization’s “corpus,” the collection of data used to train a large language model, is massive and disorganized, a team is naturally going to use more tokens because the underlying LLM has to work harder every time it’s prompted in order to find the right information. Building smaller data sets with intentionally selected files that are hyperspecific to the automation you’re trying to build helps ensure accuracy on top of a more efficient workflow. This can be as simple as a list of 50 facts or data points about your company that have been verified, giving the AI system proper context and ensuring that the human employee can measure the clarity and accuracy of its outputs. From there, you have to develop a set of governing principles that dictate who has access to those files and how regularly they’re updated. Even if dozens or hundreds of employees can access a particular AI project or automation, limiting the permission to edit those files to a handful of people ensures that the body of information referenced in the automation is always accurate. And updating the information at a structured cadence means it will always be recent. Because the automation is built efficiently and accurately based on a very specific set of assets, the token load will naturally be lower. 2. Evaluate your tech stack honestly. Every company has a tech stack, usually a mix of enterprise tools like Microsoft 365 or Google Workspace, alongside specialized tools specific to their industry. When adding AI into the mix, many companies are finding that they don’t need nearly as many software platforms as they used to. Start by creating a list of all the software platforms your team uses, how you use them, who uses them, and how they tie to business workflows. Most importantly, you want to note the specific value that these tools create for your organization. From there, identify a list of new tech platforms that you think could address any pain points or open up the opportunity for long-term efficiencies. Trusted platforms like Microsoft (Copilot), Google (Gemini), and Adobe (Firefly) have added generative AI capabilities into select payment tiers, making for easy experimentation for teams that are already using these enterprise tools. Popular new platforms like OpenAI’s ChatGPT and Anthropic’s Claude have emerged as market leaders, and both offer enterprise plans that provide enhanced data security protocols. Many leaders are experimenting with open source models like Deep Seek and Kimi, both from China. Open models can be more cost effective because you pay for overall computing power vs. individual token usage, and you generally avoid hefty subscription and licensing fees that the closed models charge. This is where the value of your team really comes into play. If you are relying on fewer platforms to do the same amount of work, it’s essential to have experts on your team who know what the ideal final product should look like, and can replicate that process to achieve the product inside fewer tools. 3. Identify core workflows to optimize. Once you have identified which existing platforms are essential to your team, and which new platforms you’d like to optimize, the next step is to focus on a few specific workflows to test. These workflows should offer high potential upside, low risk, and the capability for clear measurement. In practice, this means layering experimentation on top of real work in rapid iteration cycles. Rather than traditional A/B testing, one team should run a selected workflow twice in tandem—once conventionally, once with AI—and document the delta to note the prompt and tool workflows used, where AI helped, what new problems it created, and the performance against key metrics. The only variable between the two scenarios is the AI itself. The resulting documentation will help leaders measure the efficacy of any new potential workflow and report their findings to other organizational leaders. It will also allow the wider team to replicate effective workflows for future initiatives. (This is where cost savings comes in—one team burns through tokens during experimentation so that the wider team can use AI in intentional, cost-effective ways.) The goal is to incrementally scale with tools that are available to you, while strategically testing how new tools might layer on top of them. Too valuable to break The corporate culture at HBO was bold by instinct but deeply protective of its carefully curated brand reputation. Running thousands of digital and social campaigns over my time there taught me that introducing something different inside a complex organization required being extremely clear about what it was, why it would make a difference, and how it would leverage everything that already existed. Because the organization was too valuable to break carelessly. In a world of AI Minimalism, alignment should come before experimentation. That means more collaboration at the strategic level, with departmental leaders sharing priorities and areas of focus before teams start vibe coding on new projects. This requires a culture of safety, where leaders feel comfortable taking risks. Companies that are serious about AI implementation should also assign an AI enablement lead to be a neutral voice who can moderate their efforts and make sure they continue moving forward. This is the essential work of this moment that nobody is talking about: turning implicit organizational knowledge into explicit, reusable, consistently updated logic that AI systems can actually work from. It’s not glamorous and it doesn’t get trumpeted in headlines or keynote presentations. The organizations that come out of this moment stronger will be the ones that chose the right problems before reaching for the tools and treated the infinite nature of AI output as a reason for more discipline. In a world of AI Minimalism, amplification is powerful only when the signal underneath it is clear. Less is where the real work begins.
- Samsung's Galaxy Unpacked Event: We Expect Weird Foldables, Funky AI Glasses and More
Samsung's foldable phone launch will take place later this month in London. We'll be there.
- Coinbase's AI Hallucinated a World Cup Match Result Before the Game Even Started
The crypto marketplace sent out a news blast even when its own prediction market listing showed a delay.
- Google's Learn Your Way Is Yet Another AI-Powered Learning Tool
This AI-powered textbook tool for students personalizes your learning. But how does it differ from Learn About and NotebookLM?
- Anthropic Caught Secretly Spying on Users
Anthropic's privacy claims are now "harder to believe." The post Anthropic Caught Secretly Spying on Users appeared first on Futurism .
- Tripadvisor’s AI Is Telling Vacationers That Gruesome Horror Grottos Are Wonderful Holiday Retreats
Need to whitewash a horribly reviewed hotel or resort? Sounds like a job for AI! The post Tripadvisor’s AI Is Telling Vacationers That Gruesome Horror Grottos Are Wonderful Holiday Retreats appeared first on Futurism .
- Bank of America Warns That AI Investors Are in for a Nasty Reality Check
"Speculation is hitting extreme levels." The post Bank of America Warns That AI Investors Are in for a Nasty Reality Check appeared first on Futurism .
- Workplaces Have Gotten So Bizarre That People Are Just Sending AI Slop Back and Forth at Each Other
"It's just his AI and my AI going back and forth." The post Workplaces Have Gotten So Bizarre That People Are Just Sending AI Slop Back and Forth at Each Other appeared first on Futurism .
- Zuckerberg Admits That AI Is Not Working Out the Way He Imagined
Shocker. The post Zuckerberg Admits That AI Is Not Working Out the Way He Imagined appeared first on Futurism .
- Apple Extends Broadcom Deal Through 2031: What It Means for AI
Apple and Broadcom extended their chip partnership through 2031 as AI hardware demand raises pressure on suppliers, pricing, and device costs. The post Apple Extends Broadcom Deal Through 2031: What It Means for AI appeared first on TechRepublic .
- The Neuron Launches AI Training Platform for Everyday Professionals
The Neuron Academy launched on July 7 with self-paced AI courses for professionals who want to use ChatGPT, Claude, and Gemini more confidently. The post The Neuron Launches AI Training Platform for Everyday Professionals appeared first on TechRepublic .
- AI Data Centers Face a Networking Bottleneck as GPU Clusters Grow
AI data centers are running into a network bottleneck as GPU clusters expand. For infrastructure teams, fabric design, congestion control, and interoperability now matter as much as power, cooling, and accelerator supply. The post AI Data Centers Face a Networking Bottleneck as GPU Clusters Grow appeared first on TechRepublic .
- Pony.ai Opens Singapore Robotaxi Service to Zig App Booking
Pony.ai and ComfortDelGro opened public Zig app booking for an autonomous mobility service in Singapore’s Punggol district, giving APAC transport and enterprise technology leaders a closer look at regulated robotaxi deployment. The post Pony.ai Opens Singapore Robotaxi Service to Zig App Booking appeared first on TechRepublic .
- InsightFinder Launches ARI Mobile, Putting an Action-Capable Operational AI Agent in Engineers’ Pockets
RALEIGH, N.C. — InsightFinder AI, the predictive reliability platform, has announced the launch of ARI Mobile, its operational AI agent for iOS and Android. With ARI Mobile, on-call engineers can investigate incidents in plain language, get full root-cause analysis and take remediation actions directly from their phones. The ARI operational AI agent triages alerts, surfaces … continue reading The post InsightFinder Launches ARI Mobile, Putting an Action-Capable Operational AI Agent in Engineers’ Pockets appeared first on SD Times .
- Meta is launching its first in-house AI image generator for consumers and advertisers
Muse Image, developed by Meta Superintelligence Labs, will power image creation across Instagram, WhatsApp, and Meta's ad tools
- Why AI Will Reward Open Data Architectures, and Not Closed Platforms
Both Snowflake and Databricks spent the year racing to support the same open table format, open catalog protocol, and cross-engine governance model. Snowflake shipped Iceberg v3 to general availability at its June Summit and rebuilt its Horizon Catalog on Apache Polaris for two-way Iceberg interoperability. Databricks pushed Managed, Foreign, and v3 Iceberg through Unity Catalog … continue reading The post Why AI Will Reward Open Data Architectures, and Not Closed Platforms appeared first on SD Times .
- Survey reveals 78% of enterprises are reporting AI-related security incidents
More than three-quarters of organizations responding to a survey on AI-related security risks say they have experienced incidents or have identified vulnerabilities, making it clear that they still haven’t established ways to manage AI effectively. According to the research from DigiCert, the findings bring clarity to the reality that organizations face challenges in their adoption of … continue reading The post Survey reveals 78% of enterprises are reporting AI-related security incidents appeared first on SD Times .
- Block Details Builderbot Framework for Orchestrating AI Agents Across SDLC
Block Details Builderbot Framework for Orchestrating AI Agents Across SDLC DevOps.com
- How AI is revamping DevSecOps processes
How AI is revamping DevSecOps processes DevOps.com
- Beyond Automation: Building Accountability for AI Agents
Beyond Automation: Building Accountability for AI Agents DevOps.com
- The Next DevOps Bottleneck: When AI Generates More Software Than Organizations Can Manage
The Next DevOps Bottleneck: When AI Generates More Software Than Organizations Can Manage DevOps.com
- Avoid AI atrophy - new tool promises to reverse vibe coding skills decay
Want big muscles? Keep working out. Want big coding skillsets? Flex your dev skills with the Atrophy CLI app before they wither away
- New tool gives CLIs a warm and GUI feeling instead
Fed up with forgetting flags? Let Instagui read --help output and build a browser GUI instead
- Put all your data and AI to work and get it out of silos and lakehouses
PARTNER CONTENT: In the agentic era, intelligence has to be where the agents and data are acting, not separated from it.
- Enterprise AI still smarting from leaping before looking
Majority report AI-related security incidents or vulnerabilities
- I stopped using ChatGPT as my default AI — here is when Gemini wins
I stopped using ChatGPT as my default AI — here is when Gemini wins Tom's Guide
- 7 ChatGPT features most people ignore — but have completely changed my workflow
7 ChatGPT features most people ignore — but have completely changed my workflow Tom's Guide
- I asked ChatGPT to rank the most iconic chocolate bars for World Chocolate Day — do you agree with No. 1?
I asked ChatGPT to rank the most iconic chocolate bars for World Chocolate Day — do you agree with No. 1? Tom's Guide
- Nasdaq sinks as AI worries hit chipmakers
Nasdaq sinks as AI worries hit chipmakers Reuters
- Photo Upscaler AI
Photo Upscaler AI
- Microsoft pushed Copilot everywhere, but barely anyone bought it, and even fewer use it: Report
Microsoft has spent years placing Copilot across Windows and Office, yet fewer than 4.5% of commercial Microsoft 365 customers pay for it and far fewer return weekly.
- Apple Home AI features come with a hidden price tag
Apple's new AI-powered Home app features sound great, but they come with a catch. You'll need the 2TB iCloud+ plan to use them.
- Solos’ latest AI glasses are getting a literal privacy mode
Solos has unveiled the camera-free AirGo A6 smart glasses alongside a Privacy Kit for the AirGo V2 that physically blocks its onboard camera.
- Fable 5’s second act on Claude ends today, unless you’re willing to pay more
Starting midnight of July 8, Fable 5 would no longer work with Claude subscriptions.
- Survey says Android users won’t jump ship to iPhone just for smarter AI
But a quarter of the respondents may skip fence for better AI features.
- Siri AI can pull info from third-party apps in the latest developer beta
We’ve been looking out for new features in iOS 27 beta 3, and we’ve now found an additional one: the latest developer beta allows Siri AI to access information from third-party apps. The only examples seen so far are pulling in the remaining battery from electric car apps, but it’s likely that other capabilities will be found …
- Our choice of AI assistant really matters, says Tony Fadell – and raises big questions
“Father of the iPod ” Tony Fadell has written a lengthy column in which he argues that our choice of AI assistant really matters, but also raises some huge questions that need to be addressed. He says the reason the iPhone has stood the test of time is because Apple understood the behavioral shifts it would create, not just the technology shift, and the same will be true of AI assistants …
- Apple @ Work Podcast: The 3 pillars of AI for the Apple enterprise
Apple @ Work is exclusively brought to you by Mosyle , the only Apple Unified Platform. Mosyle is the only solution that integrates in a single professional-grade platform all the solutions necessary to seamlessly and automatically deploy, manage & protect Apple devices at work. Over 45,000 organizations trust Mosyle to make millions of Apple devices work-ready with no effort and at an affordable cost. Request your EXTENDED TRIAL today and understand why Mosyle is everything you need to work with Apple . In this episode of Apple @ Work, Matt Vlasach, SVP of Enterprise Product and Solutions Engineering at Jamf joins the show to talk about their recent survey around AI usage amongst Apple focused enterprise .
- We Need Planetary Intelligence in the Age of AI
We Need Planetary Intelligence in the Age of AI Time Magazine
- The Unexpected Way AI Helped Me Appreciate My Body
The Unexpected Way AI Helped Me Appreciate My Body Time Magazine
- The Latest AI Safety Rankings Are In. Nobody Gets an A
The Latest AI Safety Rankings Are In. Nobody Gets an A Time Magazine
- AI labels a lot of stuff as alien life
‘It’s a very serious vulnerability.’ The post AI labels a lot of stuff as alien life appeared first on Popular Science .
- Are We Entering a New Age of Creativity with the Help of AI?
Inside a case study about why The Atlantic chose to make a deal with OpenAI, and what it may mean for the future of journalism and new ideas.
- AI Project Manager
AI Project Manager Built In