Twenty-eight years after a honey bear and a red-crested bird first scrambled across Spiral Mountain, a fresh batch of Unreal Engine 5 footage has arrived that makes the wait for an official return feel almost unbearable. François Montagud, the same creator who previously rebuilt Doom 3 in the engine for his portfolio, has posted seven uninterrupted minutes of his Banjo-Kazooie fan remake. It will never ship. You cannot download it. Yet the short video already does more to answer the quiet question hanging over the series than any corporate teaser has managed in years.

Montagud’s idea with this one was to bring back the original game’s charm while also incorporating all of the improvements that modern hardware would provide. The end result is that the grass swings in the breeze, the leaves catch the light in a realistic way, and the rock faces suddenly have a degree of detail that the old N64 games couldn’t match. The lighting is stunning, casting shadows that stretch and shrink as Banjo moves around, and you can see how clean the water is, whereas the old cartridge merely hinted at how good it could appear. The environments feel lived-in rather than thrown together.

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The camera follows the duo through some big open areas as well as some tighter spots, and you can still see Banjo trudging along in that typical manner, while Kazooie, of course, bursts out of the backpack at the appropriate times, and leaping around still produces all of those cheery little “woohoo”s and “wheeee”s that you have always enjoyed. When they go underwater, the soundtrack turns to bubbles and muted sounds, which is actually very convincing.

Vegetation is arguably the most improved, because plants and trees no longer sit there like flat billboards; they have volume. Light passes through the leaves, creating all these gorgeous soft patterns on the ground. The far hills have a lot more texture, yet it doesn’t take away from the game’s gentle, storybook appeal. The color scheme is still warm and inviting, and the balance is perfect. None of it has been pushed too far into photorealism, which would clash with Banjo’s nicely rounded design or Gruntilda’s appropriate cartoon menace. That’s significant because it helps the game feel like it belongs in this universe.

Montagud has been open about this, stating that he did it for his portfolio and that it would most likely remain offline, similar to his Doom 3 work. There are no plans for a public build, but this does not diminish the footage’s effect. Fans who have been waiting since Nuts & Bolts, through the Smash Bros. debut, and through the numerous speculations finally have a tangible visual reference for what a properly done remake could look like.
[Source]

from the what-would-Jane-Austen-say? dept

Persuasion plays a key role in society. Whether it is political or financial decisions, workplace or family choices, or simply reading a book or article (like this one), often someone is trying to persuade someone else to agree with them, possibly by changing their mind. This raises an interesting question: if persuasion is such an important part of life, how good are the latest AI systems in this domain? Are they, for example, better than humans? That is what a research project has just investigated, and on an impressively large scale:

in a series of four preregistered experiments (n = 18,978 conversations from 6,923 people), we pitted AI systems against a range of human persuaders, including laypeople, winners of a separately preregistered four-round online persuasion tournament, professional canvassers, and world championship debaters.

The results were unequivocal:

We found that AI systems were reliably more persuasive than expert humans, even when expert humans chose their issues, researched in advance, underwent hours of live, structured practice, and were incentivized with £1,000 cash bonuses. In a follow-up study, AI’s advantage persisted after experts received a coaching tool that let them practice against the AI that beat them, review their performance history, and see what AI would have said at key moments.

An arguably more demanding test found that AI systems were not just persuasive when it came to opinions, but also in terms of real-world actions: they managed to elicit substantially more real-money donations to charity than well-paid professional canvassers. The researchers were able to pin down the two key factors that helped AI to out-perform the best human persuaders in all these tests:

We found converging evidence that AI’s advantage stemmed from rapidly deploying larger quantities of information: after coaching, expert humans could tie an AI constrained to respond at human speeds and with human-length messages.

That is, AI systems were more persuasive largely thanks to the range of knowledge they could demonstrate, and the speed with which they could present it — precisely those aspects of AI that are improving all the time. Which means that frontier AI systems are likely to become even more persuasive in the future. That sounds a rather bleak prospect, but a commentary from Tom Stafford, professor of psychology at the University of Sheffield, and co-author of the book Mind Hacks, points out that things may not be as bad as they seem:

fact-based persuasion may indeed be effective, but that is good news for human reasonableness, not bad. The way the AI works isn’t some sinister magic; if it produces more facts, it is more persuasive. The constraint that persuasion requires evidence means that what anyone can be persuaded of will ultimately ground out on what can reasonably be claimed about reality. If AI is a tool which produces better-informed citizens and more respect for facts, that can be a positive thing.

That may be true in general, but the original researchers note that there are other factors at play here. For example, access to resources is clearly important:

power could flow to whoever can most readily access and deploy the most capable systems. In practice, that could mean the actors who already command the most resources, such as large private corporations, political campaigns, or nation states. These actors spend heavily to influence public opinion and consumer behaviour, and although the per-message effects of such efforts can be modest, such AI could raise their effectiveness, deepening existing imbalances in who can sway the public.

Another issue is that the persuasive power that comes with the deployment of leading AI systems could increase the clout of top AI companies:

in persuasion contests where both sides can secure access to the most capable systems, such AI could consolidate power by giving significant leverage to the actors that build and control those systems. These actors could tilt the outcome of such contests by, for example, deciding which positions their models will, and will not, argue for. In this case, power would flow not to the users of persuasive AI but to its suppliers, and consolidation of their influence would occur even when access among users is perfectly equal.

More positively, the researchers point out that as constant improvements in technology push down the cost of using persuasive AI

it could help under-resourced actors (e.g., pro se litigants and public defenders, small charities, grassroots activists) compete against more established and better-funded rivals, narrowing long-standing gaps in access to justice and assisting civic advocacy more broadly.

In his blog post, Stafford mentions another factor to consider:

In a world where every surface becomes filled with persuasive text, I don’t think it is inevitable that people will open themselves to being pulled in every direction. Not only do people have a significant degree of native scepticism, tending to resist persuasive efforts as they seek to maintain stability in their existing views, but they also have agency to open themselves, or not, to persuasive effects. The studies reported in this paper asked for an average of 14 minutes of conversation from participants. 14 minutes of sincere engagement might be a lot more than most of us give to alternative points of view in our daily lives.

In other words, we don’t really know yet what impact these highly-persuasive AI systems will have on politics, business, and everyday life. But given their superior ability to convince it seems likely that we will be encountering them more frequently in their role of indefatigable persuader, whether we want that or not.

Follow me @glynmoody on Mastodon and on Bluesky.

Filed Under: ai, canvassing, charities, coaching, donations, facts, grassroots, investment, persuasion, politics, pro se, public defender, tom stafford, university of sheffield

The end-of-cycle updates are often devoid of anything user-facing, but that doesn’t mean you should delay updating to visionOS 26.6. Security patches and preparation for Siri AI are worth the update.

Not every operating system update is going to have some flashy headlining feature. As we await the OS 27 releases in the fall, the OS 26 cycle is still getting needed updates.

Apple has released visionOS 26.6 today alongside the other operating systems with minor changes. The release notes don’t mention anything beyond the usual bug fixes and performance enhancements.

The release build for visionOS 26.6 is number 23O770.

Even though users won’t get any new user-facing features, there has been some evidence that Apple will begin indexing users’ devices before releasing Siri AI in September. The index would occur in the background and not be accessible to the user in visionOS 26, but would be ready for visionOS 27.

Indexing every piece of data available across all of a user’s devices is time-consuming and can affect performance. It is also not an ideal situation given that users hoping to try the new Siri AI might be disappointed to find errors when their data isn’t indexed.

Outside of that, there isn’t anything of note in visionOS 27. Since the update offers the usual bug fixes and performance enhancements, it is best to install the update as soon as is reasonable.

It is rather refreshing that, in an era of artificial intelligence and faceless machine made music, there is not only interest in, but genuine demand for, a multidisc, 118 track boxed set dedicated to recordings by Gertrude “Ma” Rainey, the “Mother of the Blues,” including four previously unreleased alternate takes.

Ma Rainey: Mother of the Blues, The Complete Paramount Recordings 1923 to 1928 promises improved sound while offering a compelling portrait of this underappreciated artist. The set also includes a 100 page book featuring unpublished photographs, detailed biographies, notes for each track, news clippings, period advertisements, and a complete discography.

A pioneer on many levels, Rainey was a successful Black female musician who maintained control over her work in an industry that continues to exploit and mislead artists. The official materials accompanying the set explain:

“By the time Paramount signed her in 1923, she had the standing to negotiate on her own terms. She managed her own career for nearly thirty years and, according to registered copyrights, wrote or co wrote at least a third of what she recorded, at a time when almost no performer, and few women anywhere in the business, held that kind of control.”

Related Reviews:

While collectors understandably prize original 78 RPM recordings such as these, the reality is that discs produced before tape and digital technology, during the nascent and effectively direct to disc era of recorded music, are exceptionally rare. It is therefore especially encouraging to see this music preserved and improved through the latest technology and restoration expertise. Although the original discs have survived for more than a century, no doubt because of careful collectors, they will eventually deteriorate.

Featuring audio restoration by engineer Doug Benson, the set compiles the best surviving version of each 78 RPM side, as the individual recordings were known. Modern demixing software apparently does not work particularly well with early acoustic recordings, which account for roughly half of this collection, so the producers instead chose to remaster the material while remaining faithful to the sound of the original releases. Some surface noise is inevitable, but preserving it ultimately helps retain the music’s overall fidelity.

So how does it sound, you ask? While the label could not send me the entire boxed set for review, I did receive a very useful 22 song advance sampler CD that provides a strong overview. In short, it is highly enjoyable and, considering that these recordings are now roughly 100 years old, the sound quality is remarkably good. Do not expect Abbey Road by The Beatles or the latest Billie Eilish album, but this remains a vivid and authentic listening experience.

The recordings appear to be in remarkably good condition, given their age and scarcity. The music itself is often astonishing, particularly in its explicit, honest, and direct treatment of sexuality and independence on songs such as the groundbreaking “Prove It on Me,” “Shave ’Em Dry,” and “Dead Drunk Blues.”

Available now from Black Swan Records, named in tribute to the pioneering Black owned label founded in the 1920s, the set also reflects an important historical connection. J. Mayo Williams, who distributed the original Black Swan label, became Paramount’s recording director during the year Rainey signed with the company. Ma Rainey: Mother of the Blues, The Complete Paramount Recordings 1923 to 1928 can be ordered through the official website or Jazzology for $125.

Our Ratings

★★★★★★★★★★ Music

★★★★★★★★★★ Sound Quality

Mark Smotroff is a deep music enthusiast / collector who has also worked in entertainment oriented marketing communications for decades supporting the likes of DTS, Sega and many others. He reviews vinyl for Analog Planet and has written for Audiophile Review, Sound+Vision, Mix, EQ, etc.  You can learn more about him at LinkedIn.

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Apple has released macOS 26.6 to the public, but don’t expect much in the way of big changes to features this time around.

Apple has concluded its beta testing program for updates to its current-gen operating systems. After five developer betas and a release candidate, it has now shipped macOS Tahoe 26.6.

At the same time, Apple has also brought out updates for earlier macOS versions. There are updates available for macOS Sequoia 15.7.8 and macOS Sonoma 14.7.7.

As usual for any system update, it is advisable to install the update, due to Apple including various bug fixes, performance improvements, and security refinements. However, unlike the macOS 27 Golden Gate beta program, you’re not going to see any real feature-based additions.

Instead, users may be forgiven for not seeing any real updates to macOS 26.6 at all. However, release notes for the betas and the RC edition show that there are some changes.

The release notes for the RC build mention some small improvements, including the deprecation of encrypted HFS+, which won’t be supported in macOS 28.

It also resolves a problem with deprecation notifications where it incorrectly identifies an app as Intel-only. This occurred when a system plugin loader like the Color Picker loads x86 code into the host process.

HealthKit also adjusted temporally-weighted average statistics queries for discrete quantity types, such as resting heart rate. It was occasionally returning high values in cases when sample times overlapped.

For Messages, the build fixes HDR screenshots that can appear garbled when sent to other users.

How to update to macOS 26.6

To update, open System Preferences, then General, followed by Software Update. If there is a software update available, select to install it.

Chinese AI startup Moonshot AI, creators of the increasingly famous “Kimi K” family of powerful, open AI models, today released the full weights for its largest and most performant version yet — Kimi K3. But enterprises evaluating the model should read the attached custom Kimi K3 usage license as carefully as the benchmark charts.

VentureBeat previously covered Kimi K3 when it debuted through Moonshot’s hosted API earlier this month, including its 2.8 trillion-parameter architecture, one million-token context window and frontier benchmark performance. Today’s announcement completes that rollout with the release of the full model weights, a 47-page technical report documenting its training innovations and obstacles, and much of the infrastructure required to run the model independently.

That includes the complete 2.8 trillion-parameter Mixture-of-Experts model, inference infrastructure, optimized attention kernels, MoE communication libraries and deployment components aimed at researchers and enterprise developers who want to self-host the system rather than consume it exclusively through an API.

Moonshot is also releasing implementation support for ecosystems including vLLM and SGLang, alongside a technical report detailing the architectural innovations behind the model, including Kimi Delta Attention, Attention Residuals and Stable LatentMoE. Together, those techniques underpin what the company describes as the world’s first open 3T-class model, activating 104 billion parameters from a pool of 896 experts while supporting native multimodal reasoning and a one million-token context window.

While Kimi K3’s license grants broad rights to developers and even enterprises to download, modify and deploy the model for commercial purposes — a win for those seeking low-cost, frontier-level AI they can control and run offline — it also imposes some added obligations on larger companies and AI service providers not found in traditional open-source licenses like Apache 2.0 or MIT.

The key restrictions enterprises should understand

Here’s the text of the new Kimi K3 License in full:

Permission is hereby granted, free of charge, to any person (the “Licensee”) obtaining a copy of this software — including the model weights, parameters, configuration files, inference and training code, and associated documentation (collectively, the “Software”) — to deal in the Software without restriction. This includes, without limitation, the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software; to run, deploy, fine-tune, or otherwise modify the Software and create derivative works from it; and to permit persons to whom the Software is furnished to do so, in each case subject to the following conditions:

1. The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. Licensee’s use of the Software must comply with applicable laws and regulations.

2. “Model as a Service” means giving a third party access to language model inference or fine-tuning (e.g., via API) in a manner that allows such third party to exercise meaningful control over the inputs, parameters, or training data. This does not include (a) end-user products with model capabilities solely embedded within specific features or harnesses, or (b) mere relaying of requests to models hosted by others.

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If the Licensee or any of its affiliates operates a Model as a Service business, and the aggregate revenue of the Licensee and its affiliates exceeds 20 million US dollars (or the equivalent in other currencies) in total over any consecutive 12 months, the Licensee must enter into a separate agreement with Moonshot AI before using the Software or its derivative works for any commercial purpose.

3. If the Software (or any derivative works thereof) is used for any of the Licensee’s commercial products or services that have more than 100 million monthly active users, or more than 20 million US dollars (or equivalent in other currencies) in monthly revenue, “Kimi K3” must be prominently displayed on the user interface of such product or service.

4. The requirements set forth in Sections 2 and 3 do not apply to: (a) internal use of the Software, defined as any use that does not make the Software, its outputs, or its underlying capabilities available to third parties; or (b) any use of the Software accessed through Moonshot AI’s official products or certified inference partners.

The most significant clause is 2., as it requires a separate commercial license from companies earning $20 million in annual revenue and operating what Moonshot calls a “Model as a Service,” which it defines as “giving a third party access to language model inference or fine-tuning (e.g., via API) in a manner that allows such third party to exercise meaningful control over the inputs, parameters, or training data” but not ” end-user products with model capabilities solely embedded within specific features or harnesses” or “mere relaying of requests to models hosted by others.”

That would seem to allow for free use of the model for most non-tech focused enterprises: banks and consumer brands seeking to use it as a front-end chatbot or customer service agent should be fine, since they’re not using Kimi K3 as a “Model as a Service.” But firms like they hyperscalers or startups offering model training tools might fall under the commercial license terms.

The wording is further notable because it does not limit the revenue calculation to products built on Kimi K3.

Instead, it references the aggregate revenue of the licensee and its affiliates, potentially bringing even smaller companies that are part of larger parents into the requirement that they must commercially license the model from Moonshot, pending the parent firm generates revenue of $20 million or more per year as is using it somewhere, in some capacity, as a “Model as a Service.”

Regardless of this requirement, in clause 3, the license also requires attribution once a commercial deployment reaches significant scale:

“If the Software… is used for any of the Licensee’s commercial products or services that have more than 100 million monthly active users, or more than 20 million US dollars in monthly revenue, ‘Kimi K3’ must be prominently displayed on the user interface.”

For enterprise software vendors, AI copilots and consumer applications, that branding requirement may prove just as significant as the revenue threshold. Companies that typically abstract away the underlying model may instead need to disclose it directly within their products.

But, if your enterprise is sticking to using Kimi K3 internally as an employee tool for information retrieval, creating spreadsheets and documents, or answering employee questions, Moonshot offers a significant carve out, stating that clauses 2 and 3 “do not apply to: (a) internal use of the Software, defined as any use that does not make the Software, its outputs, or its underlying capabilities available to third parties.”

Developers immediately zeroed in on the license

The licensing provisions quickly became one of the dominant topics of discussion following the release of the model weights.

AI researcher Nathan Lambert, previously the co-leader of the Olmo family of open models at AI startup Ai2, summarized the issue in a post on X:

“Kimi K3 license. It’s inspired by MIT but distinctly non-commercial, where any company making over $20M/yr must get a specific commercial deal (and display Kimi K3 if over 100M users or $20M/mo revenue).”

Lambert’s assessment reflected what many developers noticed as they dug into the newly published license: while Kimi K3 offers unrestricted access to the model weights for researchers, startups and many enterprises, commercial obligations become significantly more complex for larger organizations.

The broader community reaction was largely positive toward the release itself. Developers praised Moonshot for publishing not only the weights but also supporting infrastructure, including attention kernels, MoE communication libraries and agent tooling, viewing the release as a significant contribution to the open-weight AI ecosystem.

Others highlighted the rapid pace of ecosystem support, with inference projects such as vLLM and SGLang, along with cloud providers and infrastructure partners, moving quickly to support Kimi K3 deployments.

At the same time, discussion centered on two practical caveats. One was licensing: many developers argued the model is more accurately described as open weight than fully open source, given the commercial conditions attached to larger deployments. The other was operational.

At roughly 1.5 TB of model weights, Kimi K3 remains a system aimed primarily at well-resourced organizations capable of operating large-scale inference infrastructure, even as reports emerged of successful deployments on clusters of consumer RTX 5090 GPUs.

Not the first to customize open AI licensing

Moonshot is far from the first frontier AI developer to embrace an “open, but not completely open” licensing strategy.

Meta’s Llama family, for example, has long been distributed under its own community license requiring a commercial agreement for those building with the model and exceeding 700 million monthly users, rather than a traditional open-source software license. Other frontier model developers have likewise adopted bespoke licensing terms governing redistribution, commercial use or attribution.

Kimi K3 follows that broader trend, albeit with a different mechanism. Rather than broadly restricting redistribution, Moonshot ties certain commercial rights to company scale.

Organizations operating a Model-as-a-Service business above specified revenue thresholds must negotiate a separate commercial agreement, while the largest commercial deployments must visibly attribute Kimi K3 within their products.

For enterprises, the practical implication is that “open weights” and “open source” are becoming increasingly distinct concepts. Downloading and modifying frontier models may be straightforward; understanding the legal conditions attached to commercial deployment increasingly is not.

What enterprise leaders should do next

For CIOs, chief AI officers and engineering leaders, the first step is to determine how the organization intends to use Kimi K3 before evaluating its technical performance.

If the model will remain entirely inside the organization—for example, supporting developers, researchers, legal teams or internal productivity workflows—the published license appears substantially more permissive. Those deployments may qualify as internal use under Moonshot’s terms, avoiding the commercial licensing provisions that apply to customer-facing AI services.

Organizations planning to build products on top of Kimi K3 should take a different approach.

Legal, engineering and product leaders should determine whether the planned deployment constitutes “Model as a Service” under the license, whether the company or its affiliates exceed the $20 million revenue threshold, and whether future growth could trigger the requirement to negotiate a commercial agreement with Moonshot.

Companies expecting products to reach more than 100 million monthly active users—or more than $20 million in monthly revenue—should also evaluate the license’s attribution requirement and how it fits with existing branding, contractual commitments and white-label offerings.

More broadly, Kimi K3 illustrates a transition taking shape across frontier AI. As the industry’s most capable models increasingly become available as downloadable weights instead of exclusively through hosted APIs, enterprises will need to evaluate licensing terms with the same rigor they apply to benchmarks, security reviews and infrastructure planning.

The next competitive battleground may not simply be whether AI models are open or closed, but the increasingly nuanced legal frameworks that determine who can commercialize them, under what conditions and at what scale.

T-Mobile’s Monday outage is officially over. After thousands of customers spent hours dealing with dropped signals and iPhones stuck in SOS mode, the carrier confirmed that service has been fully restored.

So what exactly happened?

Reports started flooding into Downdetector around 4 PM ET, and within an hour, more than 140,000 users had flagged an issue. Most people reported they are receiving no signal or a dead mobile connection.

The trouble didn’t stop at T-Mobile’s own network either. StatusGator confirmed that MVNOs like Mint Mobile and Boost Mobile, which run on T-Mobile’s towers, also felt the impact.

Things got bad enough that plenty of iPhone owners watched their devices switch to Emergency SOS mode, and Google searches for “SOS mode” spiked as people tried to figure out what was happening. New York, Washington D.C., Chicago, Detroit, Minneapolis, and Los Angeles saw the heaviest concentration of complaints, according to Downdetector’s outage map. 

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Over on Reddit and X, posts about the outage blew up, racking up thousands of comments as users traded theories and potential fixes.

Is everything back to normal now?

After first saying its teams were “actively working” on the issue, T-Mobile followed up with a full resolution update on X. “We have fully restored service for all customers and sincerely apologize for the difficulties to those who experienced network impacts today,” the company said. “At T-Mobile, we know that customers rely on us to stay connected to what matters most, and we appreciate their understanding as we prioritized resolution.”

We have fully restored service for all customers and sincerely apologize for the difficulties to those who experienced network impacts today. At @TMobile, we know that customers rely on us to stay connected to what matters most, and we appreciate their understanding as we…

— T-Mobile Newsroom (@tmobilenews) July 28, 2026

T-Mobile still hasn’t shared what actually caused the outage in the first place, so we don’t know if this was a one-time hiccup or something worth keeping an eye on. For now, if your phone was stuck on SOS earlier today, a quick restart should get you back to full bars.

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In 2026, there are so many devices to choose from when it comes to building a comprehensive smart home, and one that’s equipped with great security credentials to make sure that you, your loved ones and any prized possessions are kept safe. While it might be tempting to start with a security camera or even a smart speaker, one of the most feature-packed devices you can buy is a video doorbell, and thanks to the work of our tech experts, we know which ones are worth buying.

Take a walk down any suburban street nowadays and there’s a good chance that you’ll spot at least a handful of video doorbells on your route. Part of their charm is that, even if you’re not the most tech-savvy person in the world, video doorbells have been designed to be highly intuitive to use, and they offer tons of features in return so you get plenty for your money.

At their core, video doorbells do exactly what it says on the tin, by offering you a live video feed of everything that’s happening outside of your front door. This is perfect for when you’re at the office but you want to make sure that a parcel has reached your home safely, and you can use two-way communication to instruct the delivery driver on where’s the best place to leave the parcel in question.

Because video doorbells are smart devices and often connected to a companion app on your smart phone, there’s also less chance of you missing an important delivery. Even if you’re in the garden and out of earshot of the doorbell, you’ll still get a notification on your phone so you can get to the door in time.

Thanks to motion detection and the ability to record the footage that’s been captured, video doorbells also work as a security camera to capture anything you might want to keep an eye on outside of your home. You can get a lot of use out of these devices, and our team of tech experts have whittled down the competition into this comprehensive list so that you can find the right one for your needs. If you want to expand your smart home ecosystem then check out our alternative write-ups for the best outdoor security cameras and the best indoor security cameras too.

Best Video Doorbell at a glance

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