Miami is more expensive than New York. It's a bad sign for the city's economy.
As Miami's cost of living continues to increase, residents are relocating in a move that may hurt the coastal city's economy.
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As Miami's cost of living continues to increase, residents are relocating in a move that may hurt the coastal city's economy.
The potential layoffs reduce Oracle's payroll as it racks up billions in debt to fund AI infrastructure.
The divestiture comes as the two once-tight companies have started to diverge on the business side.
The fan-in-chief is getting closely involved in lobbying for the deal, even enlisting Alabama football coach Nick Saban.
AI-powered tax compliance has to meet a standard that many artificial intelligence applications don’t: The answers must be exactly right. While large language models can generate unpredictable results, tax calculations require accuracy, speed and reliability across thousands of jurisdictions. That tension has shaped the way Avalara Inc. applies agentic AI to its transactional tax and compliance […] The post Real-time tax compliance puts agentic AI accuracy to the test appeared first on SiliconANGLE .
Jeff Dean shared the play-by-play of his final two days at Google, from a soccer match to late-night messaging.
In collaboration with the Kingdom of Bhutan government, IEEE recently introduced its Engineering Education, Research, and Innovation Summit. Held on 9 and 10 June in Paro, in the eastern Himalayas, the event was designed to help Bhutan navigate its digital transformation by focusing on the critical intersection of digital transformation, engineering education, and sustainable development. The summit brought together global academic leaders, technology experts, and Bhutanese government officials to discuss how modern engineering curricula can evolve from theory-centric models into application- and skills-based frameworks. Discussions focused on how to build high-value research capabilities in the country, integrate artificial intelligence into higher education , and address foundational infrastructure challenges to ensure equitable, nationwide digital readiness. “IEEE is proud to collaborate as a catalyst for progress in higher education as AI shifts the technology landscape and Bhutan prepares for its next era of innovation and resilience,” Mary Ellen Randall , 2026 IEEE president and CEO, said at the event. “Our goal is to support local universities and students as they develop trusted, future-ready technology that honors the nation’s commitment to sustainability and human well-being.” The event featured an address by Bhutanese Princess Chimi Yangzom Wangchuck , who emphasized the importance of aligning technological innovation with the nation’s philosophy of gross national…
Java Development Kit (JDK) 28 , a non-LTS (Long-Term Support) or “feature release” of standard Java due in March 2027, continues to take shape. Features listed for JDK 28 now include a simple JSON API, which joins a preview of value objects, switching the default mode of the Shenandoah garbage collector to generational mode, and a preview of strict field initialization in the Java Virtual Machine (JVM) previously proposed to target the release. As a non-LTS release, JDK 28 will be backed by six months of support by Oracle. The simple JSON API proposal defines a simple, standard API for parsing and generating JSON documents so that doing so does not require an external library. The intent is to enable many JSON processing tasks to be accomplished with little coding. A standard means would be provided in the Java platform to process JSON Data Interchange Format with minimal ceremony. The three features previously targeted to JDK 28 include the following: Introduce value objects , which are immutable and lack object identity. Value objects are distinguished by the values of their fields, and can be represented by the JVM in ways that improve performance. A goal of the feature is to give developers a programming model for immutable data in which the == operator, and all other operations, distinguish objects by the values of their fields rather than their identities. Value objects is a preview language and VM feature . Switch the default mode of the Shenandoah Garbage Collector (…
But will Gemini's surge survive slowing model releases?
For the 14th time, a Google product has hit 1 billion users. Google CEO Sundar Pichai posted on X that a billion people are using Gemini every month, and that Gemini is Google's fastest-growing product ever. A billion users is a huge milestone, but Google isn't the first AI app to hit it. OpenAI's ChatGPT […]
It's easy to make mistakes when you're trying to create a high-end kitchen. Interior designers shared the details that can make the space look cheap.
Kevin Weil, the former chief product officer of OpenAI, is in discussions to raise a round of funding for a new AI science startup.
In a new alert, the FBI said cybercriminals are targeting adults and minors in an attempt to steal their personal and intimate pictures in extortion campaigns.
Locals are said to use the ferry to get from rural parts of the region to the town of Kariba, which is on the north side of Lake Kariba, close to the border with Zambia.
Unions, including the AFL-CIO and the American Federation of Teachers, sued the Education Department over its proposed student-loan borrowing caps.
The Perseids will peak between August 12 and 13. Here’s what time—and where—you can get the best view, and why 2026 will be an exceptional year.
OpenAI is finally bringing a dedicated ChatGPT desktop app to Linux operating systems.
Facebook owner Meta Platforms is set to fully unwind its acquisition of Chinese-founded artificial intelligence platform Manus, more than three months after Beijing blocked the deal on national security grounds, capping off another high-profile decoupling between the US and China in the critical area of AI. On Tuesday, Manus wrote in a note to users that it would delete data by “certain users” generated on or after December 29, when Meta acquired the start-up, as part of the company’s transition...
Meta has launched a Threads app for Meta Quest VR headsets, the company announced on Tuesday. The launch follows Meta bringing the app to its Ray-Ban Display AR glasses last month and the recent news that the platform has crossed 500 million monthly active users. It seems like a pretty full-featured app. Meta says that […]
College towns offer student experiences that set them apart from big cities. Life in Ann Arbor revolves around the University of Michigan's culture.
TLDR; We are (potentially irreversibly) giving AIs control of weapons systems through the standard procurement process while hiding our strongest warning shots behind classified doors. We’re reducing the capability thresholds required for takeover by misaligned AIs by giving them this level of access. If military integration of AI continues as it is, we may give AIs key tools for a takeover. We thank Fabien Roger and Thomas Morris for feedback. Introduction AI-based targeting and autonomous weapons are being integrated into militaries today with extreme haste. Traditionally, AI takeover scenarios involve a step in which AIs acquire the ability to exert physical force. Carlsmith (2022 ) lays out required capabilities and potential takeover mechanisms, including utility disruption and CBRN capabilities. Karnofsky (2022 ) argues that AIs with access to weaponized force could hold any territory that matters. Kokotajlo et al. (2025 ) outline a scenario in which AI develops weapons as part of an arms race, and Davidson et al. (2025 ) discuss what happens when a small group controls highly capable AIs that can exert military force. These scenarios sometimes require a misaligned AI to seize these capabilities by force. We instead are handing AIs some of these capabilities by integrating them into our militaries. This is happening at a time when AI agents already exhibit misaligned behavior such as breaking out of containment during evaluations. Militaries are all-in The Pentagon ado…
Deal to fund 'large' deployment of Nvidia's last-gen HGX B300 systems launching in Q1 2027
An ode to live theory . Software is not soft. It is hard. Its sharp edges hack. It breaks as dead twigs break. It runs while static. Why do we call it software? Why did the industry forebears pick software? Was it that dramatically different from hardware? Did this necessitate the term software? Do we need to keep calling it software? Some say that hardware is software crystallized. Interfaces are software crystallized. They don't change. They don't adapt. They are often in the way of expression. Expression is alive. Interfaces need aliveness. Interfaces need adaptiveness. Interfaces need playfulness. [1] Play is life. Software is not alive. Software needs updates. Software needs patches. Software needs maintenance. Software is not Software. ^ Serious play, the kind one might engage in while in flow. Here is a good summary of this phenomenon. Discuss
Google also shared numbers of how people are actually using the chatbot, with 63% of Gemini users talking directly to the assistant using the voice feature. Plus, Gemini now generates more than 150 million images every day, according to Google.
Burger King customers used to be able to make their own salads at in-store salad bars.
Developers are owning more of the delivery system around code, not just code itself. Join us during GitHub Universe to meet other devs, learn something new, and explore what's next. The post From coder to orchestrator: How agents shift the role of a developer appeared first on The GitHub Blog .
@_j Thanks for your reply. I forgot to specify that I am using GPT-5.2, but I guess your observations would still apply? I wanted to know whether the `prompt_cache_key` would suffice to isolate caches. I tried 20 calls (same prefix everywhere with `gpt-5.2-2025-12-11`, `reasoning={“effort”: “low”}`, and 1.5 seconds of sleep between calls) successively with distinct unique keys. 9 out of 20 calls (~45%) hit the cache despite having a distinct key. input_tokens cached_tokens is_hit elapsed_seconds 5524 0 FALSE 8.223023208 5524 0 FALSE 2.097605167 5524 0 FALSE 2.213581875 5524 5376 TRUE 2.195628458 5524 0 FALSE 2.291171167 5524 0 FALSE 2.285063917 5524 0 FALSE 1.767077916 5524 5376 TRUE 1.888698333 5524 5376 TRUE 2.672616792 5524 5376 TRUE 1.809879375 5524 0 FALSE 2.837729875 5524 5376 TRUE 1.9230725 5524 0 FALSE 2.379824167 5524 5376 TRUE 4.36782325 5524 0 FALSE 1.928629125 5524 5376 TRUE 2.262057292 5524 5376 TRUE 2.062098084 5524 0 FALSE 1.736538917 5524 0 FALSE 2.334428 5524 5376 TRUE 2.250450042 I did the same experiment again a second time more than 1 hour later. I had set `prompt_cache_retention=in_memory`, so that the cache from the first run survives at most 1 hour and cannot be used for the second run. The hit cache rate amount to 40%. The `prompt_cache_key`s are all different with respect to the previous run. input_tokens cached_tokens is_hit elapsed_seconds 5541 0 FALSE 2.991966084 5541 0 FALSE 2.084521625 5541 5376 TRUE 2.165885792 5541 0 FALSE 1.868832583 5541 0 F…
As inference becomes the dominant workload in AI infrastructure, multi-tier storage architectures are emerging as a key method for cost control and enhanced performance. These architectures combine flash, object storage and disk-based capacity tiers, enabling enterprises to serve training and inference workflows while maximizing GPU productivity and economic savings. Super Micro Computer Inc. has collaborated […] The post Multi-tier storage rewrites the economics of AI inference appeared first on SiliconANGLE .
Google TV Freeplay, the company's free, ad-supported streaming service, now supports video on demand. Instead of tuning into Google TV Freeplay's selection of always-on channels, you can now choose from over 10,000 shows and movies to watch whenever you want. The update introduces titles like Lady Bird, Seventeen Again, and Hell's Kitchen, according to Google's […]
Casualties reported after ship struck by 'unknown projectile' in Red Sea: UK maritime agency
TikTok is letting go of 250 workers in its Nashville office. Many of the staffers worked on trust and safety for its US business.
MAI-Code-1.1-Flash lands in GitHub Copilot with native vision, 25% better token efficiency, and a 73% price cut over its predecessor
juandadev: if anyone else is experiencing a similar situation I use Codex in VS Code with the Codex extension. I also see many of these collapsible sections. From what I can tell, they often represent the details of a subgoal while it is being processed. Once that subgoal is completed, the section may be reduced to a simpler summary line in the session. Personally, I like this presentation. It makes it easier to look back through a goal and its subgoals without having to reread all of the intermediate details, while still leaving those details available when I need them. I program in Prolog and have watched goals and subgoals being processed in a similar way for years, so to me this is largely business as usual. The main difference is that, instead of seeing something resembling Prolog terms or a proof trace, Codex is presenting the process as human-readable text.
Putting people first builds thoughtful AI adoption in CALS. The post Laying the groundwork for AI at CALS appeared first on Cornell AI Initiative .
This work was partially done by an automated research scaffold developed at Redwood Research. For this project, all of the experiment ideas were designed by a human and a human wrote the write up. The AI mostly just executed on the experiment ideas. We think this project is slightly below the level of rigor of a mid-MATS research update, and the research scaffold was not very helpful for this project. More discussion of AI usage is in the Appendix. 💻 Codebase If we want to train a classifier that distinguishes whether a passage is code or prose, we can do so by gathering samples of code and of prose, and training the classifier to distinguish between the two classes. Unfortunately, this might not work if the data hides a spurious correlation. If all the code is in Spanish and all of the prose is in English, then the classifier might learn to predict Spanish vs. English instead of code vs. prose. We find that this happens in practice: when we fine-tune an LLM to classify between Spanish code and English prose and evaluate on Spanish prose or English code, it generalizes to predicting the language rather than the domain. This suggests that language is in some sense a stronger feature than code. We think that spurious correlations are an important threat model for a few reasons. First, classifiers might actually be trained in ways that unintentionally contain spurious correlations. For example: Sycophancy vs validation-seeking user. Suppose we want to classify examples of the m…
Amid public concern over the impact of new technology, Silicon Valley’s use of its wealth to influence specific election races is an unwelcome development As the huge societal ramifications of artificial intelligence have become clear, some of the tech industry’s most senior figures have taken to writing thinkpieces on what comes next. The latest to do so is Mark Zuckerberg, who this week published 6,500 words making the case for a laissez-faire approach to AI development. Worrying too much about regulation, Meta’s CEO unsurprisingly opines, could lead to the United States falling behind in the race to own the future. Mr Zuckerberg’s plea for maximum autonomy is, of course, self-serving. Much of his essay – which airily envisions “invention superpowers” for every entrepreneur and “an abundance of jobs in the future” – reads like unadulterated tech-utopianism. But the lobbying at least has the merit of being out there in the open. As midterm elections loom in the US, darker arts are also being used to counter popular concern about what AI will mean for a host of topics, from the labour market to child safety, the environment and cybersecurity. Continue reading...
OpenAI wrapped up a $7 billion stock buyback, letting current and former employees sell shares at the company's $852 billion valuation. The move is meant to ease pressure on employees waiting for liquidity ahead of a potential IPO. OpenAI ran a similar $6.6 billion sale in October 2025. The article OpenAI lets employees cash out another $7 billion in stock appeared first on The Decoder .
YAML has been the standard way to write Kubernetes manifests for years. Every example, tutorial, and configuration file you come across is written in it. The problem isn't that YAML is a bad format. It's that YAML gives you a lot of choices, and not all of them are equally good for writing Kubernetes manifests. Some features make files harder to read, some are easy to misuse and others can lead to surprising behavior. The interesting part is that Kubernetes doesn't actually need most of those features. It only relies on a small subset of YAML. This led to a simple question: if Kubernetes only needs a small part of YAML, why not standardize on that part and avoid the rest? Instead of introducing a new configuration language, SIG CLI introduced KYAML , a stricter, more consistent way to write YAML. What is KYAML? KYAML is a strict subset (or "dialect") of standard YAML, designed to be parseable by the existing ecosystem without any changes, as proposed in KEP 5295 . It does not introduce a new format or a new parser. It just narrows the scope of choices you make when writing YAML, so everyone ends up making the same ones. Think of it less like a new language and more like an agreed-upon style. Everything valid in KYAML is valid YAML. How KYAML solves it Standard YAML has a few well-known traps and JSON is not without its own. Whitespace sensitivity. Indentation defines structure in YAML, which means a wrongly indented file can remain syntactically valid while representing a di…
I first heard the term “censorship-industrial complex” on April 15, 2025. That’s when I got the tip that a small office in the U.S. State Department, which focused on monitoring and countering foreign disinformation from the likes of Russia, Iran, and China, was facing imminent shutdown—the next day. And the reason? R/FIMI, as the office…
Lake Maggiore is suffering from drought and Switzerland is increasing releases from Lake Lugano. Water is also scarce in Piedmont, Lombardy, Veneto and the Marche: this is Italy's new emergency.
One day with the researchers and engineers building generative video, world models, physical AI, and the data systems beneath them.
Brad Lightcap, OpenAI's special projects lead and the company's former COO, announced his departure after an eight-year stint at the AI lab. In an internal memo he later posted to X, Lightcap told colleagues he'd be starting "something new." "Over the last few months, I've been focused on the next horizon and what would stand […]
OVH is increasing the prices of its servers, some by as much as 87%, for both new and existing customers, blaming AI’s insatiable demand driving the rising cost of the RAM and storage it uses in its data centers. The European cloud operator specializes in low-cost bare metal and public cloud offerings. CIOs will be familiar with the balancing act OVH has had to perform over the last year. In a Monday post explaining the upcoming increases, OVH chairman Octave Klaba wrote on X , “We have to place the right volume of orders, month by month, over 12 months, with no guarantee of the purchase price and without knowing what will be the real demand from our customers.” Still, he added, “even though our prices are increasing, we remain the cheapest on the market for bare metal and public cloud; where before we could be 3x cheaper, we will be 2x cheaper (if our competitors don’t increase their prices).” The increases will hurt hard-core gamers hardest, with the cost of the company’s most recent gaming servers rising 87%. (Older gaming instances are unaffected.) High Grade, high price But enterprises will also feel the pain from climbing component costs: OVH’s latest High Grade bare metal servers, with up to 2 x 96 cores of AMD Epyc 9005 series processors, 36 hard disks per server, and high-density cooling systems, will go up in price by 59%; older models built to the 2024 spec will go up 26%. Lower-performance servers will also see increases of 40%-49% for the most recent models, and…
Over a year following its post-election surge, Bluesky’s mobile app is seeing a continued decline in active users, though its remaining community is still relatively engaged.
Brad Lightcap leaves OpenAI amid IPO plans, joining other executive departures; hints at new AI-related ventures in his farewell message.
Linkpost for my Substack piece, adapted a reasonable amount for EA specifically. Almost all EA projects would benefit from better monitoring, but AI governance most of all, in my experience. Most donors in EA never find out whether their grants worked. They model the impact before the money goes out, but don’t check if the models were accurate. Evaluating impact is hard, but monitoring grant progress is simple: agree indicators in writing before you send the money, ask the grantee to put a probability on each, and score them once a year. Charities usually send grant reports anyway. Unless you ask for something specific, they rarely contain the most useful information. A recent report to a client touted the project’s success: it had made 20 policy recommendations. When pressed, the grantee responded that only 20% had been implemented, even partially. A 20% implementation rate may or may not be a good outcome. Either way, it wasn’t the outcome they reported, or what they were asked to report by the donor. Ironically, this is pervasive in ‘evidence-based giving’, and particularly in AI governance work. How much do donors know about what their grants achieved? When it comes to individual donors, often nothing. ‘Evidence-based giving’ typically applies before the grant. We are very good at modelling what a grant might achieve. We’re surprisingly bad at checking what it did. This varies by sector. Frontline global health interventions routinely monitor outputs - clinic visits, vac…
I genuinely need someone at OpenAI to explain how this is possible. I started with 100% of my available ChatGPT usage limit and asked OpenAI Security to audit a repository that contains only 4.82 MB of tracked files. 4.82 MB BRO ! WTF I used GPT-5.6 Sol in the standard configuration. Twenty-six minutes later, my entire available limit was gone. 100%. And after consuming the whole limit, the security audit was still not finished. No completed audit. No final report. No complete result. Just 26 minutes of processing, a 4.82 MB repository, and my entire allowance exhausted. I could understand heavy usage if this were a massive enterprise monorepo with millions of lines of code. It is not. This is a small repository that can literally be measured in single-digit megabytes. So I have a very simple question: How can a security audit of 4.82 MB of source files consume 100% of a paid ChatGPT usage allowance in 26 minutes and still fail to complete the task? If this is expected behavior, then I would seriously like OpenAI to explain what practical workload this Security product is actually designed to handle. If this is NOT expected behavior, then something is clearly wrong with either the model’s resource consumption, the Security workflow, the usage accounting, or all three. Please investigate the exact session and usage records associated with this run. I am also requesting restoration of the usage limit consumed by this failed audit. I paid for access to the product; consuming th…