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I Used AI to Build AI-Resistant Assignments

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Artificial intelligence has wreaked havoc across secondary and higher education classrooms, leaving educators struggling to figure out how to create cheat-resistant assignments and assessments. While everyone is good at diagnosing the problem, no one seems to have a workable solution. So I designed a free tool to help teachers create uncheatable assessments and avoid the headaches of AI and academic dishonesty.

The Cheat Vulnerability Index

Based on the concepts from my book, course and workshops, I built the Cheat Vulnerability Index, a web app that lets teachers analyze their existing assignments to pinpoint strengths and weaknesses when it comes to vulnerability to cheating. Users upload an assignment, then get a custom report and suggestions for how to make improvements.

I came up with the idea when I noticed that educators struggled to take the ideas from my conference sessions and workshops and implement them in their unique learning contexts. Since I can’t always sit with everyone as they plan lessons or write a syllabus, I wondered how I might scale the concepts and strategies from my content leveraging the power of AI.

I needed to create a brand-new tool that didn’t exist before and to do so with no budget. So I learned to “vibe code,” the method of using natural language in an AI model to create computer code. I hadn’t done coding since I used BASIC in high school, and it took me a while to figure out how it worked, and how to turn the code into an interface on my website. 

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I trained the index on concepts from my book Storytelling With Purpose: Digital Projects to Ignite Student Curiosity and a variety of other workshops and articles I’ve published as the pedagogical framework for the assignment analysis. The concepts, processes and strategies it produces are unique to my published work, and create suggestions through that research-based, classroom-tested lens. 

Uncheatable Intake Form

The intake form for The Cheat Vulnerability Index

Turns out, vibe coding is a lot like teaching: you begin with deep subject-area expertise (in this case, authentic learning and assessment), create a complex set of detailed instructions and processes for the AI agent to follow (a lesson or unit plan), define what “good” looks like (learning outcomes, standards and rubrics), and how to describe the concepts clearly to an audience (a learning artifact). I was stunned by how well the app turned out, and teachers who have tried it find it useful.

But the biggest takeaway for me is how this experiment revealed the ability of AI tools to remove barriers to learning and how they can facilitate deeper learning through the application of knowledge. 

Despite what they say, not everyone can code — including me. So why should coding (or the cost to hire a team of coders) get in the way of my ideas and creating a useful tool to help others? In the same way, how does a student’s writing ability, processing speed or facility as a public speaker affect their grades if our assessments are in-class essays, timed tests or presentations and debates?  It got me thinking about the obstacles students face expressing their knowledge and how traditional assessments can get in the way of assessing them.

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Students may cheat or cut corners for a lot of reasons, not all of which have to do with moral depravity. Designing uncheatable assessments requires more than adjustments to a single assignment — it requires us to expand our definition of success and to rethink what counts as achievement and how we measure it.

Creating Uncheatable Assessments

The Cheat Vulnerability Index is a quick diagnostic that gives teachers and faculty feedback on a single assignment. But to have meaningful, sustained resilience while maintaining rigor and high standards across an entire school year, teachers need to rethink what and how they assess, and foster cultures of integrity that go beyond any one test or unit.

Cheating Vulnerability Index Diagnostic

The diagnostic evaluation for The Cheat Vulnerability Index

Cheating happens when two conditions are met: when an assignment is cheatable by design and when students have the incentive to cheat. 

Assignments like tests, worksheets or essays are all vulnerable to academic dishonesty simply by their design. AI can write essays, and students can share answers, for example. How can the format of an assignment minimize opportunities for cheating or rely on a combination of multiple metrics that make it less possible?

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No matter how well an assignment is designed, if a student really wants to cheat, they will. So instead of trying to surveil students and make our job as educators about policing them, we can create assignments that disincentivize cheating before it becomes a problem in the first place.

There are three ways to do this.

Three Uncheatable Assessment Traits

1. Originality. When we expect students to create the same answers at the same time, we’ve set ourselves up for cheating (with or without AI). Authentic learning experiences result in one-of-a-kind learning artifacts that no other student could copy or use AI to complete entirely.

2. Personal connection. To be truly invested in learning, everyone wants to know “why this matters.” Allowing ways for students to connect curriculum to their lives or community helps them personalize an abstract concept and disincentivizes cheating because they care about the outcome and know it will help them or people in their community. Students should also have multiple opportunities for agency throughout the process.

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3. Purpose. What’s the point of students’ hard work? If an assignment ends up in the trash, it sends a powerful message about the value of their effort and your curriculum. Instead, have students create learning artifacts that are designed for users or audiences beyond the classroom. Give students a good reason to complete an assignment with integrity and accuracy. Real stakes beat clever rules.

What’s Next

The problem of academic integrity is more fundamental than preventing students from cheating. AI has forced us to recognize that the old proxies we used to assess students are no longer reliable as evidence of learning (if they ever were), and requires us to redefine what counts as evidence of understanding. Like the Cheat Vulnerability Index I designed that didn’t require me to know the vocabulary or grammar of coding, my learning artifact still required me to build subject-area expertise, identify goals and limits, interrogate what good learning means and take responsibility for the outcome of my work. 

As mathematician Terence Tao says about AI disrupting mathematics research, it’s like we’ve been trying to drive a car on outdated roads made for horses and pedestrians, and it’s revealed potholes and bumps and cracks. AI is now giving us faster cars that lead to big traffic jams and accidents. 

The destination hasn’t changed, just how we get there. What we need isn’t better ways to catch students cheating, but a new pedagogical road forward.

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Databricks Closes $5 Billion Round at $190 Billion Valuation

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Databricks has closed a $5bn round at a $190bn valuation, led by Coatue, with revenue run-rate past $7bn and growth above 80% year on year. That is a 42% valuation increase in six months, and it comes after chief executive Ali Ghodsi called 2026 a bad year to go public.

Databricks has closed $5bn at a $190bn valuation. Coatue led, joined by Blackstone, MGX, T. Rowe Price and new investor Sixth Street Growth. It is the company’s second round this year.

The growth is the part worth pausing on. Revenue run-rate has passed $7bn, up more than 80% year on year in the second quarter, against 65% growth at a $5.4bn run-rate back in February. Companies of this size do not usually accelerate.

The valuation has moved with it, up 42% from $134bn in February. TNW reported the round at $188bn last month, and it has closed $2bn above that.

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Databricks also disclosed figures that rarely accompany a private raise. It says it has been adjusted free cash flow positive over the last twelve months, its data warehousing business is past a $1.5bn run-rate and growing over 100%, and its Lakebase database has passed $100mn.

The customer concentration is heavy at the top. More than 1,000 accounts now spend at a $1mn run-rate, and more than 100 at $10mn.

Set against the public market, the price is less extravagant than it sounds. At $190bn on a $7bn run-rate, Databricks is valued at roughly 27 times revenue, while Snowflake trades near 23 times on $5.03bn of trailing revenue.

The growth rates are nothing like each other. Snowflake grew around 30% last year, Databricks says more than 80%, so a four-point multiple premium is a modest reward for nearly three times the pace.

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Read those numbers with care, because they are not the same measure. Run-rate annualises current revenue and flatters anything growing quickly, while Snowflake’s multiple rests on twelve trailing months.

Why this is private money rather than a listing is already on the record. Ghodsi has called 2026 a terrible year to go public, with SpaceX, OpenAI and Anthropic lined up to absorb roughly $200bn of listing capital.

So the company raises at public scale and stays private. The money goes to three products aimed at enterprise AI agents, and Ghodsi’s pitch is that buyers want agents that hold context, stay accurate and respect a budget, rather than another chatbot.

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Candy Crush generates nearly $1 billion annually, 14 years after launch

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Evergreen Candy Crush is one of the most successful and enduring mobile game franchises ever created. Launched by Swedish video game developer and publisher King in 2012, the original Candy Crush Saga quickly climbed the popularity charts on both Android and iOS. Nearly a decade and a half after its launch, it still boasts millions of users and generates billions of dollars in revenue.

In FY 2025, Candy Crush Saga reportedly generated $876.5 million in revenue, with most of it coming from in-app purchases. While that sounds impressive, it marks a slight dip from the previous six years, when the game generated more than $1 billion in annual revenue.

Candy Crush Saga follows a freemium model, meaning it’s free to play, but players can purchase extra lives, boosters, and special passes to make it easier to progress through its more than 23,000 levels.

While King hasn’t disclosed detailed revenue and profit figures in recent years, it reported surpassing $20 billion in revenue in 2023. That same year, the company also claimed to have reached 5 billion installs across all Candy Crush titles, including the original Candy Crush Saga and its spinoffs Soda Saga, Jelly Saga, and Friends Saga.

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Research by David Curry, data editor at app analytics platform Business of Apps, suggests that the original game currently has around 81.5 million monthly active users and was installed more than 190 million times across Android and iOS in 2025.

In its 2014 IPO filing, King claimed that Candy Crush Saga had 93 million daily active users worldwide in December 2013. If Curry’s estimates are accurate, this would suggest that the game has not experienced a massive decline in its user base over the past 12 years.

Candy Crush’s enduring popularity remains something of a mystery among the mobile gaming community. However, some believe its success can partly be attributed to its “turn-based” gameplay, which allows players to put the game down at any time and pick it up where they left off without penalty.

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Paula Ingvar, general manager of Candy Crush Saga, attributes the game’s success to giving players “the satisfaction of making progress with small wins” in 10- to 20-minute sessions, rather than forcing them to finish a level in one sitting.

David Nieborg, a professor of media and platform studies at the University of Toronto and a video game researcher, believes the game’s popularity also has a lot to do with the fact that it is “non-place-based, nongendered, nonracialized,” helping it appeal to players across geographical, linguistic, cultural, and socioeconomic boundaries.

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World's Largest 106-Foot Electric Plane Takes Maiden Flight In New York

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Heart Aerospace’s X1, billed as the world’s largest battery-electric aircraft, completed a 27-minute maiden flight in New York using more than 1 MW of power and only about $5 worth of electricity. The 106-foot-wingspan demonstrator is a precursor to the company’s 30-seat hybrid-electric ES-30 regional airliner, targeted for service in 2031. Interesting Engineering reports: Developed by Heart Aerospace, the X1 demonstrator lifted off from Plattsburgh International Airport in New York on Wednesday, August 12. The piloted aircraft remained in the skies for about 27 minutes and made it to a 1,100-foot altitude above ground level (AGL). According to the Swedish aerospace company, the aircraft has a wingspan of 106 feet (32 meters). It’s additionally 76 feet (23 meters) long and has a takeoff weight exceeding 25,000 lbs (11,340 kilograms). “With the first flight of X1, Heart Aerospace has demonstrated electric flight at the scale of a commercial airliner,” said Anders Forslund, Heart Aerospace founder and CEO. “Electric commercial aircraft have the potential to fundamentally reshape airline economics and, ultimately, lower the cost ofÂair travelÂfor passengers.”

“This is at the heart of our vision for abundant air travel, with electrification enabling more affordable, frequent, and cleaner air service to and from airports closer to home.”

You can watch the first flight on YouTube.

Read more of this story at Slashdot.

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The Painful Truth of Exactly How ICE’s New Shock Gloves Work

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With the revelation that ICE will spend up to $20 million by March on gloves that can deliver painful electric shocks to subdue an individual, according to a notice published earlier this week by the Department of Homeland Security, it’s worth looking at how this apparel, designated as a nonlethal tool for law enforcement, actually works.

These shock gloves go by the unsubtle acronym of GLOVE (Generated Low Output Voltage Emitter) and are manufactured by Compliant Technologies, based in Lexington, Kentucky. The GLOVE, previously used in jails and police departments in the US, looks and functions like a normal pair of patrol gloves until officers press a switch to activate an electrical mode.

Classified as a CD3 (conductive distraction and de-escalation device), the gloves don’t work like stun guns or Tasers, which shoot out probes or use high voltages to cause neuromuscular incapacitation that overrides the central nervous system. Instead, the GLOVE works using neuro-peripheral interference, essentially shocking the skin to bombard the sensory nerves and temporarily short-circuit the brain’s focus.

Compliant Technologies did not respond to WIRED’s request for comment.

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When activated via a one-second press on a wrist switch, conductive pads embedded on the palm and undersides of the fingers of the gloves can transmit high-frequency electrical pulses directly to the wearer’s point of contact on a subject. A rechargeable battery and microprocessor module tucked inside a zippered pouch on the cuff powers the entire system.

The electrical pulse overstimulates nerve endings in the subject’s peripheral nervous system, resulting in pain that inhibits coordinated muscle movement, but without causing systemic muscle lockup. The pain induced is localized to just the area touched by the glove’s conductive pads. The manufacturer claims the gloves result in a subject’s compliance in less than three seconds and leave no burns, marks, or scars.

Because the current flows only between the contact pads on the device and across the subject’s skin, the officer wearing the GLOVE, as well as any assisting officers holding the subject, should not feel electrical shocks. The gloves, capable of operating between 14 and 122 degrees Fahrenheit, come in three versions: Gen 3, 4, and 5. Maximum voltage is capped at 380 V for all, and the minimum voltage for Gen 3 and 4 is 210 V, which rises to 324 V for Gen 5.

Maximum stimulation time on Gen 3 and 4 models is a sobering two hours, though that drops to 90 minutes for Gen 5. The battery is apparently good for 2,000 charge/discharge cycles, and it can recharge to full in two hours. Four LED lights on the gloves display the current charge level, each noting 25 percent battery increments.

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Gen 4 and 5 of GLOVE both include mechanisms that record “activation events,” tracking the date, hour, minute, and skin-contact time for audit trails and after-action reporting. Gen 4 stores this data on a removable microSD card, while Gen 5 does away with this in favor of built-in internal memory, accessed via a USB-C cable.

There’s a test function on the gloves, too, where operators can verify they are working by tapping an active pad against their own forearm to feel a minor electrical pulse. According to the manuals, GLOVE users must be certified and then recertified every other year to continue using the device.

GLOVE, which can only be sold to verified professional entities such as the military, correctional facilities, and law enforcement agencies—has some clear usage recommendations in Compliant Technologies’ instruction manuals:

  • No more than two GLOVE units (or, in other words, one pair) should be used simultaneously on a single subject.
  • Continuous shocks should not exceed 15 seconds.
  • Operators must target arms and legs, while strictly avoiding the head, face, throat, chest, and groin.
  • The gloves should not be used on the elderly, young children, pregnant individuals, or people with severe disabilities.
  • The technology must not be used to counter verbal defiance, as punishment, or for torture.

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Apple opens Huston Advanced Manufacturing Center

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Apple is expanding its U.S. manufacturing footprint with a new Houston center designed to help bolster American manufacturing with education and resources.

On Thursday, Apple opened its 20,000-square-foot Advanced Manufacturing Center (AMC) in Houston, Texas. The center is offering free training for small and medium-sized businesses.

The AMC is housed in the same facility that produces Apple’s AI servers. The site is also set to begin Mac mini production in late 2026.

Apple has invested hundreds of millions of dollars into the project, bringing it to fruition in less than nine months.

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“We believe in American workers and American ingenuity, and we are moving at an incredible pace because we want to build more than great products. We want to build the future of American manufacturing,” said Cook.

As part of the kickoff event, Apple hosted U.S. Secretary of Commerce Howard Lutnick, U.S. Senator Ted Cruz, Houston Mayor John Whitmire, U.S. Representative Christian Menefee, and Harris County Precinct One Commissioner Rodney Ellis, along with other officials and community partners.

“Houston is grateful to Apple for this significant investment in our city. The Advanced Manufacturing Center will create local jobs and will continue improving the quality of life of Houston residents,” Houston Mayor John Whitmire said of the opening.

“The AMC also recognizes our city as a growing technology hub and solidifies Houston’s leadership in the manufacturing sector of the United States.”

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Three people stand around a large, futuristic interactive display table with glowing blue and purple lighting, viewing a digital architectural model in a modern, minimalistic room

The AMC’s holographic table – Image Credit: Apple

To mark the opening, the business leaders will spend the day immersed in hands-on training. Apple engineers will cover techniques like machine-learning-driven quality control and advanced automation.

Participants will learn how to identify and adapt to production challenges. They will also engage with the lab’s holographic table and advanced factory-floor equipment.

“I am proud that Apple chose Harris County for this investment. My office has consistently fought to bring good jobs within reach of working people and ensure small businesses — especially those historically shut out — have a fair opportunity to compete and grow,” said Harris County Precinct One Commissioner Rodney Ellis.

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“At a time when rising costs are squeezing families here and across the country, this new center can help create pathways to greater economic security.”

The site is Apple’s second-largest manufacturing site in the U.S. The first is Apple’s Manufacturing Academy in Detroit.

Since announcing the $600 billion commitment in 2025, Apple and its American Manufacturing Partners have invested reshoring a portion its manufacturing. This includes custom silicon, cover glass, and advanced components.

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Spotify Will Label AI Artists and Stop Recommending Them. So Why Is It Letting Fans Remix Real Music With AI?

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Spotify would like listeners to know that fake AI artists are a problem.

Unless, apparently, the AI is being used in a licensed product that lets paying subscribers alter music created by actual human beings.

Spotify has announced that it will begin labeling some artist profiles with an AI Persona badge when the public identity presented by that artist does not represent a real person. Beginning in mid-September, those badges will appear on artist profiles, in search results, playlists and individual track listings. More importantly, Spotify says music from AI Personas will not be included by default in editorial or algorithmic recommendations unless listeners have deliberately followed or engaged with them.

That is a meaningful change. It is also the latest turn in Spotify’s increasingly complicated relationship with artificial intelligence.

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Does Spotify Like AI Music or Not?

The answer appears to be: it depends.

Spotify has never broadly banned music merely because AI was involved in creating it. In September 2025, the company explicitly said that it supports artists using AI creatively and that licensed music would be treated equally regardless of the tools used to make it. At the same time, Spotify tightened its rules against unauthorized voice cloning, fraudulent uploads and industrial-scale music spam.

That distinction made some sense.

Using AI during production is not automatically the same thing as generating 5,000 anonymous tracks, impersonating Drake or creating a photorealistic singer who never existed and hoping listeners don’t notice.

AI-Generated vs. AI-Assisted Labels RIAA 2026

Spotify subsequently introduced AI credits that allow artists and distributors to disclose how AI was used in areas including vocals, lyrics and production. As we reported in July, that industry-wide push toward AI Generated and AI Assisted labeling is ultimately about giving listeners some idea of what they are actually hearing.

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Then things became considerably more interesting.

Spotify Decided AI Could Also Be a Business

In May, Spotify and Universal Music Group announced a forthcoming generative AI tool that will allow Premium subscribers to create licensed covers and remixes of music from participating artists and songwriters.

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It will be sold as a paid add-on.

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Universal signed on first, and Merlin followed on August 4, bringing a large collection of independent labels into the program. Spotify says participating artists will have control over whether their music is included and will receive credit and compensation when fans create new versions.

We recently examined that deal and asked the uncomfortable question: if Spotify is worried about AI undermining musicians, why is it preparing to sell consumers tools that let them modify commercially released music in the first place?

The answer increasingly looks like consent and money.

Unauthorized AI impersonation? Bad.

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Mass-generated AI sludge designed to siphon royalties? Bad.

A fictional AI artist pretending to be a real person and being pushed into your Discover Weekly playlist? Apparently bad now as well.

AI used under agreements negotiated with Universal, Merlin and other rights holders, with Spotify charging subscribers for the privilege? Welcome aboard.

That does not necessarily make Spotify hypocritical. There is a legitimate distinction between licensed AI tools used with permission and deceptive AI content designed to fool listeners or manipulate the royalty system.

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But from the listener’s side, the rules are becoming awfully complicated.

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What Is an AI Persona?

Spotify’s new badge is specifically about identity, not whether the music itself was AI generated.

That distinction matters.

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An artist can use AI extensively in making a recording without necessarily receiving an AI Persona badge. Conversely, an artist profile portraying a photorealistic fictional human could receive the badge regardless of precisely how the music was created. Spotify says it will initially review profiles that have reached certain audience thresholds rather than relying entirely on voluntary disclosure. Artists can appeal the designation, and Spotify plans to eventually let listeners report suspected AI Personas.

That is considerably more useful than hiding an AI disclosure six menus deep inside the credits. And keeping AI Personas out of recommendations by default may be the most important part of the entire policy.

Spotify’s recommendation engine does not merely help listeners find music; it determines which artists receive enormous amounts of exposure. Removing synthetic personalities from that pipeline makes it harder for AI content farms to compete for attention simply by generating more material than humans possibly can.

Why Listeners Should Care

Listeners should not need forensic training to determine whether the singer Spotify just recommended is an actual person.

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That is the larger problem with AI music. The debate is usually framed around copyright, royalties and artist compensation, all of which matter enormously. But streaming services also have a basic responsibility to tell subscribers what they are listening to.

TIDAL recently went further by deciding that wholly AI-generated music can remain on its service but will not receive royalty attribution. Qobuz has also taken a more human-centered approach to curation and AI-generated content. Spotify is choosing a different path: allow AI, disclose more of it, suppress deceptive uses and monetize the versions it can license properly.

There is a logic to that strategy. It just happens to be a logic that becomes considerably easier to understand once somebody is getting paid.

The Bottom Line

Spotify’s AI Persona badge is a positive move for listeners. Clearly identifying fictional artists and keeping them out of recommendations by default should make the service less susceptible to the flood of synthetic content that increasingly threatens genuine music discovery.

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But Spotify cannot have this conversation entirely on its own terms.

The company is telling listeners that authenticity matters while simultaneously building a business that will let subscribers use generative AI to alter recordings made by real musicians. Those two positions can coexist if consent, transparency and compensation genuinely remain at the center of the system.

If they don’t, Spotify isn’t drawing an ethical line around AI.

It’s drawing a revenue line.

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Anthropic Could Be Worth $2 Trillion When It Goes Public

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An anonymous reader quotes a report from the Financial Times: Anthropic investors expect the AI startup to float at a valuation of $2 trillion or more in October, a dizzying figure that would eclipse SpaceX and make the AI lab’s debut the largest-ever initial public offering. Half a dozen of the company’s backers told the FT that Anthropic’s rapidly rising revenue would enable it to more than double its current valuation in a planned autumn float. A listing at that level could unlock billions of dollars in gains for the five-year-old company’s early investors but would also test public markets that are growing more nervous about the AI boom.

Anthropic’s backers say booming demand for the lab’s advanced AI models and tools justifies their lofty expectations. Investors expect the Claude maker’s annualized revenue to be between $100 billion and $120 billion by the end of 2026 — using the startup’s preferred measure, which infers full-year sales from recent performance — up by more than 10 times over the course of 2026. “If Anthropic is growing 800 percent a year, you’d think at the incredibly low end they would trade at 30 times [revenue],” said one investor in the group. “That would make them a $3 trillion company.” According to Bloomberg (paywalled), Anthropic is currently in talks to acquire Decart AI for roughly $6 billion.

“Decart develops world models alongside software designed to lower AI training expenses by improving how efficiently chips are utilized,” reports Quartz. “That capability could allow Anthropic to get more out of its current infrastructure as demand grows. If the deal closes, Decart’s team would join Anthropic’s inference and performance organization.”

Read more of this story at Slashdot.

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The 2026 Box Office Is Booming. Are IMAX and Dolby Cinema Bringing Movie Theaters Back?

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The movie theater business is doing something rather inconvenient for everyone who spent the past five years writing its obituary: making a lot more money.

Through August 10, the 2026 domestic box office has generated $6.54 billion, according to Box Office Mojo. That is 19.1% ahead of the same period in 2025 and 28.8% ahead of 2024. It is even running 6.9% ahead of 2023, the year of Barbie and Oppenheimer. For some perspective, the entire domestic box office finished 2025 at $8.66 billion and 2024 at $8.57 billion.

That does not mean everything is back to 2019 levels, and we’ll get to that rather important detail. But the 2026 rebound is no statistical rounding error.

I’ve already seen The Odyssey and Spider-Man: Brand New Day twice. Aside from raising legitimate questions about whether I should be trusted with a movie ticketing app, all four screenings shared something important: I deliberately chose a premium theatrical experience because I wanted something my television and home theater system could not completely reproduce.

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Increasingly, I don’t appear to be alone.

2026 Has More Than One Hero

Spider-Man: Brand New Day has become the monster of the year, reaching $655.1 million domestically and $1.67 billion worldwide in only 10 days. But blaming the entire recovery on Peter Parker would be convenient and wrong.

Toy Story 5 has earned more than $470 million domestically, The Odyssey has crossed $461 million, The Super Mario Galaxy Movie is approaching $430 million, Michael has exceeded $372 million, and Project Hail Mary has generated $344 million. Even Obsession, which didn’t arrive with decades of franchise baggage attached, has surpassed $263 million domestically.

mario-michael-toy-story-5

Five 2026 releases have already crossed $1 billion worldwideSpider-Man: Brand New DayThe OdysseyToy Story 5Michael and The Super Mario Galaxy Movie. That’s more billion-dollar releases than any year since 2019, with plenty of calendar still remaining.

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Not everything with a familiar logo has worked. Star Wars: The Mandalorian and Grogu limped to just $177.7 million domestically and $345.2 million worldwide. For comparison, Solo: A Star Wars Story — the film Disney has spent eight years pretending it left in a jacket pocket at Mos Eisley, managed $213.8 million domestically and $392.9 million worldwide back in 2018. Somewhere on Corellia, the Solo crew is high-fiving one another and ordering another round.

But 2026 still has two enormous theatrical wild cards waiting in December. Marvel’s Avengers: Doomsday and Denis Villeneuve’s Dune: Part Three are both scheduled for December 18, and both are being positioned for premium presentation. Dolby currently lists each with Dolby Vision and Dolby Atmos, while Dune: Part Three is already selling advance IMAX 70mm tickets. IMAX also includes Avengers: Doomsday in its 2026 release slate.

That creates a fascinating Christmas problem for theater owners: two films designed to dominate the very IMAX and Dolby Cinema screens that are becoming increasingly valuable. Doomsday looks like the safer bet commercially, even if the footage so far has left me wondering whether Earth’s Mightiest Heroes misplaced the fun somewhere between multiverses; while Villeneuve has turned Dune into exactly the kind of large-scale cinematic event that premium theaters were built to showcase.

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The takeaway isn’t that audiences will watch anything Hollywood throws at them again. They clearly won’t. The stronger 2026 slate is giving consumers more reasons to go out, and when the movie feels like an event, they are showing up.

Here’s the Problem: Attendance Hasn’t Really Come Back

This is where the numbers become much more interesting.

U.S. theaters sold an estimated 470.9 million tickets during the first 30 weeks of 2026, compared with 747.3 million over the same stretch of 2019. Placer.ai estimates theater attendance through July was up 8.1% compared with 2025, but remained 27% below 2019.

So how can the box office be booming while substantially fewer people are going to the movies?

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Part of the answer is price. EntTelligence puts the average 2026 adult ticket at $13.46, while premium-format admissions such as IMAX average $18.22. More importantly, audiences increasingly appear willing to pay that premium when the experience justifies leaving the house.

And that is where IMAX and Dolby Cinema enter the story.

IMAX Theater

IMAX Is Having a Ridiculous 2026

Christopher Nolan didn’t merely make The Odyssey for IMAX; he made the first theatrical feature shot entirely with IMAX film cameras. Audiences have responded accordingly.

The Odyssey has now generated $289 million in IMAX box office, making it the format’s highest-grossing release ever. Its $147.3 million domestic IMAX haul is also an all-time company record. July produced $257 million for IMAX worldwide, the highest-grossing month in the company’s more than 50-year history.

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This didn’t come out of nowhere. IMAX generated a record $1.28 billion worldwide in 2025, while its domestic box-office share reached 5.2% despite IMAX representing only about 1% of domestic screens. Premium presentation was already growing before Odysseus started sailing around the Mediterranean.

dolby-cinema-theater

Dolby Cinema Is Seeing the Same Shift

Dolby Cinema generated a record $203 million domestically in 2025. From January 1 through April 6, 2026, revenue had already reached $46 million, 62% higher than the same period a year earlier.

Then Spider-Man showed up.

Brand New Day delivered roughly $10 million from just 177 U.S. Dolby Cinema locations during its opening weekend, the format’s biggest weekend ever. Premium large-format screenings overall accounted for about 24% of Spider-Man’s extraordinary $360 million domestic opening.

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That is not a niche audience carrying around calibration meters and arguing about black levels on Reddit. That’s meaningful box-office revenue.

Your Living Room Got Better. Movie Theaters Had To As Well.

The uncomfortable reality for exhibitors is that the average living room became a formidable competitor.

An excellent OLED or Mini-LED television, competent surround system or Atmos soundbar, comfortable sofa and instant access to streaming make the mediocre multiplex experience increasingly difficult to defend. Throw in parking, concessions and the gentleman six seats away illuminating half the auditorium with his phone, and “wait for streaming” starts sounding perfectly reasonable.

But a genuinely great IMAX auditorium or Dolby Cinema changes that equation.

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Reuters reports that theater chains are expanding premium large-format screens specifically as audiences become more selective about theatrical trips. AMC said premium-screen demand recently helped produce the highest single-weekend revenue in its 106-year history, with more than 10.2 million customers visiting AMC and Odeon locations.

Streaming didn’t kill movie theaters. Better home entertainment may simply have forced theaters to offer something better.

The 2026 numbers suggest audiences still love going to the movies. They are just becoming far less interested in paying for an ordinary experience they can increasingly duplicate at home.

The movie theater isn’t dying. The ordinary movie theater might be.

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DeepSeek Harness launches as open source rival to Claude Code, alongside V4-Pro on API with higher prices

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DeepSeek is expanding beyond the model layer and deeper into the software developers use to put AI agents to work.

The Chinese AI lab on Thursday launched the official version of DeepSeek-V4-Pro, an updated flagship model focused heavily on agentic workloads, alongside DeepSeek Harness v0.1, a new open-source agent harness that gives developers an alternative to integrated coding-agent environments such as Anthropic’s Claude Code.

Together, the releases amount to a broader developer push from DeepSeek. V4-Pro is now available across DeepSeek’s web interface, mobile app and API, with native support for the OpenAI Responses API and integration with Codex.

DeepSeek Harness, meanwhile, is entering developer preview under the MIT license and the code is available now for download and use on GitHub. It’s built around an unusually modular premise: practically every part of the agent runtime can be swapped out as a plugin.

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But developers accessing V4 through DeepSeek’s API will soon pay considerably more for it. DeepSeek is simultaneously abandoning its existing flat API pricing in favor of peak and off-peak rates beginning at 16:00 UTC on Sunday, Aug. 16 (2 am ET).

Even the discounted off-peak cache-miss and output prices will be substantially higher than the prices available today.

The combination is significant because DeepSeek is no longer competing solely over model intelligence and token prices. With Harness, it is moving into the layer that determines how models use tools, manipulate files, maintain sessions and execute long-running agent workflows — territory where Anthropic’s Claude Code and other coding agents have become increasingly important developer products.

DeepSeek builds its own agent harness

DeepSeek describes Harness, or dsh, as an open-source agent harness built on Cordis, a framework designed around composable plugins.

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Its guiding principle is simple: “Everything is a plugin.”

That extends to models, tools, skills, sessions, sandboxes, filesystems, loops, orchestration and user interfaces, according to DeepSeek. Rather than making those components fixed pieces of a single coding agent, Harness is designed to let developers mix, replace and extend them.

The project is available under the MIT license and can currently be launched from npm with npx @deepseek-ai/dsh web. DeepSeek also provides instructions for building it directly from source. The repository describes the software explicitly as a developer preview and warns that “THERE WILL BE COMPATIBILITY-BREAKING CHANGES.”

That caveat matters for enterprise developers. Harness is not yet being presented as a stable drop-in production platform. But its architecture points toward a potentially important strategy: DeepSeek can now offer developers not only models but an open framework for assembling the systems that surround them.

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That makes Anthropic’s Claude Code and OpenAI’s Codex useful competitive references, although the products should not be treated as functionally identical.

DeepSeek Harness is an open-source, model-agnostic alternative to the agent infrastructure underlying Claude Code and Codex—not yet a full replacement for either product’s broader developer experience.

It can already inspect repositories, edit files, execute shell commands, search files and the web, maintain plans, invoke skills, delegate work to subagents and enforce approval policies. Those are the essential capabilities that make Claude Code and Codex agentic coding tools rather than autocomplete systems.

DeepSeek explicitly describes Standard mode as a full coding agent with file editing, shell access, search, planning, subagents and workflows. Its local web interface lets users select a workspace and approve sensitive operations.

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But Claude Code and Codex now extend well beyond that agent loop. Here’s a quick comparison:

Dimension

DeepSeek Harness

Claude Code

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OpenAI Codex

Read, edit and test a repository

Yes

Yes

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Yes

Shell and development tools

Yes

Yes

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Yes

Planning and subagents

Yes

Yes

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Yes

Permission controls and sandboxing

Yes, configurable through plugins

Yes, mature built-in permission and sandbox system

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Yes, granular sandbox and approval controls

Primary interfaces

Local web UI; headless command; Python SDK

Terminal, VS Code, JetBrains, desktop, browser, mobile and Slack

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CLI, IDE extension, desktop app, web/cloud and integrations

Hosted background agents

Not documented as a DeepSeek-managed service

Yes

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Yes

GitHub-native PR workflow

Not documented as a finished integration

GitHub Actions, automatic reviews, issue-to-PR workflows

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Cloud tasks, automatic reviews, PR fixes and GitHub Action

Model choice

DeepSeek, Anthropic, OpenAI and custom compatible endpoints

Primarily Claude, including Bedrock, Google Cloud and Microsoft hosting

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Primarily OpenAI models, with configurable providers in the open-source CLI

Extensibility

Exceptional: virtually every component is replaceable

Strong: skills, hooks, MCP, plugins and agent teams

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Strong: skills, MCP, custom agents, SDK and app server

Product maturity

Developer preview; breaking changes expected

Established commercial product

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Established commercial product plus open-source CLI

License

MIT

Commercial product with extensibility interfaces

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Codex CLI is open source; cloud and app services are managed products

DeepSeek Harness instead emphasizes modularity and replacement: the model itself is another plugin rather than necessarily the center of a vertically integrated stack.

DeepSeek’s repository was already attracting significant developer attention on launch day, showing roughly 27,500 GitHub stars and 2,000 forks as of Aug. 13, although those rapidly changing figures are best viewed as a snapshot rather than an adoption metric.

V4-Pro gets an agent-focused upgrade

Harness arrives alongside the general-availability release of DeepSeek-V4-Pro-0813.

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DeepSeek originally introduced the V4 family in preview in April. The lineup consists of the 1.6-trillion-parameter V4-Pro, with 49 billion parameters activated per token, and the smaller 284-billion-parameter V4-Flash, with 13 billion activated. Both support context windows of up to one million tokens.

The company’s Aug. 13 release therefore is not the first appearance of V4-Pro. It is the transition from the earlier preview into an updated official version, with DeepSeek emphasizing agent performance.

“The official version of DeepSeek-V4-Pro has been released, featuring significantly enhanced agent capabilities and support for the Responses API and Codex integration,” DeepSeek says on its API website. “It is now fully available across the web, mobile app, and API; we welcome your testing and feedback.”

DeepSeek’s changelog similarly says the general-availability model has “significantly enhanced Agent capabilities,” particularly in production environments. Developers using the API do not have to change model identifiers: deepseek-v4-pro now resolves to the latest V4-Pro version.

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The company has also added native OpenAI Responses API support, lowering the amount of integration work required for applications already built around that interface.

DeepSeek says V4-Pro is optimized for OpenAI’s own open source harness, Codex, with one-click setup. Its current API documentation lists Responses API, tool calling, JSON output and an Anthropic-format API among the supported interfaces for both V4-Pro and V4-Flash.

For developers using DeepSeek directly rather than through an API, V4-Pro is now accessible through “Expert Mode” on the company’s app and website.

Reasoning effort becomes another deployment knob

DeepSeek is also making reasoning effort an explicit control across V4-Pro and V4-Flash.

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The V4 model documentation describes three levels: Non-think, designed for fast routine tasks; Think High, intended for more complex problem-solving and planning; and Think Max, which allocates substantially more reasoning to difficult problems.

That distinction can be operationally important for agent systems because maximum reasoning on every step can consume unnecessary time and tokens. A coding agent might use relatively little reasoning to inspect a file or execute a routine tool call, then increase effort when diagnosing a difficult bug or planning a multi-stage code change.

DeepSeek’s latest benchmark table suggests the 0813 model improves substantially on agent-oriented tests, although the figures are company-reported and some results depend on the harness configuration.

DeepSeek reports V4-Pro-0813 scores of 87.9 on Terminal Bench 2.1, 74.1 on Toolathlon-Verified, 71.1 on DSBench-FullStack and 67.2 on DSBench-Hard. It does not lead every comparison in DeepSeek’s own table: Fable 5, for example, scores 77.9 on Toolathlon-Verified and 77.2 on DSBench-FullStack.

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There is an especially important qualification buried beneath the benchmark table. For public Code Agent tasks, DeepSeek says V4-Pro-0813 was tested using its upcoming DeepSeek Harness in “minimal mode.”

In other words, some of the agent results arriving alongside Harness are not purely model benchmarks. They measure the model operating inside an agent execution environment — precisely the software layer DeepSeek is now releasing to developers.

A sharp reversal in DeepSeek’s API price trajectory

The bigger immediate change for teams already running DeepSeek in production may be pricing.

DeepSeek’s current API documentation lists V4-Flash at $0.14 per million cache-miss input tokens and $0.28 per million output tokens, while V4-Pro costs $0.435 for cache-miss input and $0.87 for output. Cache hits are dramatically cheaper at $0.0028 for Flash and $0.003625 for Pro.

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Those prices themselves represented a major reduction from V4’s original April launch economics. When V4 arrived in April, V4-Pro was priced at $1.74 per million cache-miss input tokens and $3.48 per million output tokens. By late May, DeepSeek had made a 75% reduction permanent, intensifying its position as an unusually inexpensive option for high-volume agent workloads. Now the pendulum is moving in the other direction.

Beginning Aug. 16 at 16:00 UTC, DeepSeek will charge different rates depending on when API calls occur. Peak hours are 01:00–04:00 UTC and 06:00–10:00 UTC (9:00 PM – 12:00 AM ET and 2:00 AM – 6:00 AM ET, respectively) with all other hours classified as off-peak. Off-peak rates are half the corresponding peak prices.

For V4-Flash, off-peak cache-miss input rises from $0.14 to $0.22 per million tokens, while output rises from $0.28 to $0.66. During peak hours those rates reach $0.44 input and $1.32 output.

V4-Pro moves from $0.435 per million cache-miss input tokens and $0.87 output today to $0.66 and $1.98 off-peak, respectively. Peak rates rise to $1.32 input and $3.96 output.

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The increases are even more pronounced for cached input. V4-Pro cache hits rise from $0.003625 per million tokens today to $0.022 off-peak and $0.044 at peak. Flash moves from $0.0028 to $0.007 off-peak and $0.014 peak.

Model

Old input (per 1M token)

Old output (per 1M tok)

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Old total (1M in/1M out)

deepseek-v4-flash

$0.14

$0.28

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$0.42

deepseek-v4-pro

$0.435

$0.87

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$1.305

The new prices still position DeepSeek as an affordable alternative via API to Western proprietary labs, but Reuters reported Thursday that, depending on model, token category and time of use, the changes represent increases ranging from 50% to more than 1,100% over existing rates.

Model

Input ($/1M)

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Output ($/1M)

Total ($/1M)

Source

Muse Spark 1.2 Contributor

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$0.10

$0.20

$0.30

Meta

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MiMo-V2.5 Flash

$0.10

$0.30

$0.40

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Xiaomi

DeepSeek-V4-Flash — off-peak

$0.22

$0.66

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$0.88

DeepSeek

GPT-5.6 Luna

$0.20

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$1.20

$1.40

OpenAI

MiniMax-M3

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$0.30

$1.20

$1.50

MiniMax

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LongCat-2.0 — limited-time promo

$0.30

$1.20

$1.50

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LongCat

DeepSeek-V4-Flash — peak hours

$0.44

$1.32

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$1.76

DeepSeek

MiMo-V2.5

$0.40

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$2.00

$2.40

Xiaomi

DeepSeek-V4-Pro — off-peak

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$0.66

$1.98

$2.64

DeepSeek

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LongCat-2.0 — standard

$0.75

$2.95

$3.70

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LongCat

MiMo-V2.5 Pro (≤256K)

$1.00

$3.00

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$4.00

Xiaomi

DeepSeek-V4-Pro — peak hours

$1.32

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$3.96

$5.28

DeepSeek

Muse Spark 1.1 / 1.2

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$1.25

$4.25

$5.50

Meta

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GLM-5.2

$1.40

$4.40

$5.80

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Z.ai

Grok 4.6 — <200K prompt tokens

$2.00

$6.00

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$8.00

xAI

MiMo-V2.5 Pro (>256K)

$2.00

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$6.00

$8.00

Xiaomi

Qwen3.8-Max

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$2.00

$6.00

$8.00

QwenCloud

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Gemini 3.6 Flash

$1.50

$7.50

$9.00

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Google

GPT-5.6 Terra

$2.00

$12.00

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$14.00

OpenAI

Grok 4.6 — ≥200K prompt tokens

$4.00

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$12.00

$16.00

xAI

GPT-5.4

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$2.50

$15.00

$17.50

OpenAI

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Kimi K3

$3.00

$15.00

$18.00

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Moonshot AI

Claude Opus 5

$5.00

$25.00

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$30.00

Anthropic

Sakana Fugu Ultra (≤272K)

$5.00

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$30.00

$35.00

Sakana AI

GPT-5.6 Sol — Standard mode

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$5.00

$30.00

$35.00

OpenAI

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Claude Fable 5 / Claude Mythos 5

$10.00

$50.00

$60.00

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Anthropic

GPT-5.6 Sol — Fast mode

$10.00

$60.00

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$70.00

OpenAI

That makes the “50% lower” off-peak framing potentially misleading without context. Off-peak is 50% cheaper than DeepSeek’s new peak rate; it is not a 50% discount from the API prices developers are paying today.

For a simple workload consisting of one million cache-miss input tokens plus one million output tokens, V4-Pro currently costs $1.305. The same token mix will cost $2.64 off-peak, roughly twice as much, or $5.28 during peak hours, more than four times the current price.

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V4-Flash moves from $0.42 under the same simple calculation to $0.88 off-peak and $1.76 peak.

Actual application costs will vary considerably depending on the ratio of cached input, uncached input and generated output, making those combined figures illustrative rather than universal total-cost estimates.

DeepSeek is moving up the agent stack

The timing makes the strategic direction difficult to miss.

When DeepSeek released the V4 preview on April 24, the major story was how much frontier-class capability the company could deliver with an unusually efficient architecture.

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V4-Pro uses a hybrid attention design combining Compressed Sparse Attention and Heavily Compressed Attention; at a one-million-token context, DeepSeek says it requires only 27% of the single-token inference FLOPs and 10% of the KV cache required by V3.2.

By late May, the discussion had shifted toward what those efficiencies meant economically for high-volume agents, whose repeated context reads can make caching a major component of inference costs. DeepSeek’s steep V4 price cuts amplified that advantage.

The Aug. 13 releases move the competition another layer upward.

DeepSeek now has an updated V4-Pro tuned around agent workloads, standardized interfaces designed to make it easier to connect with existing developer tooling, configurable reasoning effort, and an MIT-licensed harness for controlling the models, tools, sandboxes, filesystems and orchestration surrounding an agent.

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At the same time, DeepSeek is demonstrating that developers cannot assume its aggressively low API rates are permanent. For organizations considering the platform, workload scheduling, caching behavior and the option to run open weights on their own infrastructure now become more important parts of the total-cost calculation.

That leaves DeepSeek pursuing two potentially conflicting advantages at once: making its agent stack more accessible and open while making its own hosted API considerably more expensive.

For enterprise developers, Harness may ultimately be the more consequential part of Thursday’s announcement. Models can increasingly be swapped behind standardized interfaces. The harness that controls how an agent reasons, invokes tools, edits software and persists across a workflow can be much harder to replace.

DeepSeek is now competing for that layer, too.

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Beats up to $190 Off, Nomad iPhone Cases From $19

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Amazon’s Beats discounts knock up to 54% off the earbuds and over-ear headphones, while Nomad launches an aggressive overstock sale dropping iPhone and Apple Watch accessories to as low as $19.

If you’re in the market for a new pair of headphones or want to breathe new life into your iPhone with a fresh case, there are deals in effect from just $19.

Save up to $190 on Beats

Apple deals continue to roll in at Amazon, with a price cut on the M5 MacBook Air that we covered earlier today, but now Beats are on sale to pair with your Mac, iPad, or iPhone.

Save up to 51% across the headphones range, with a standout deal being the $159.95 price on Beats Studio Pro for the exclusive Sand Gray color. Other color options are priced at $169.95, still a $180 discount off retail, with Amazon stating the limited-time offers are selling fast.

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Save up to $190 on Beats

On the earbuds side, Beats Studio Buds Plus are marked down to $99.95, which is a 30-day low.

Today’s best Beats deals

Nomad iPhone & Apple Watch accessories from $19

Discounts of up to 74% off are in effect as Nomad clears out iPhone 16 and 17 accessories. You can also grab an Apple Watch band at 49% off during the overstock sale.

Grab Nomad deals from $19

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Nomad iPhone accessory deals

Nomad Apple Watch accessory deals

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