The AI arms race reached a fever pitch on Monday after Chinese e-commerce and cloud provider Alibaba called into question America’s technological lead with the launch of Qwen 3.8-Max, a 2.4 trillion-parameter model that goes toe-to-toe with the best models from Anthropic and OpenAI. The new model comes just days after the launch of DeepSeek V4 Flash 0731, which, according to independent benchmarks by Artificial Analysis, performs within a single point of OpenAI’s budget-friendly GPT-5.6 Luna while costing 40 percent less per task. What’s more, at just 284 billion parameters, it’s small enough to run on relatively modest enterprise servers and workstations. Chinese model devs like Moonshot, Alibaba, and DeepSeek are now attacking their American counterparts on both price and performance. The pincer movement comes as US model devs like Anthropic and OpenAI stoke fears over the origins and safety of China-made AI models. In a recent blog post, Anthropic CEO Dario Amodei insisted he’s not opposed to open models, just ones made in China, ones distilled from proprietary models, and ones that do not meet rigorous safety metrics. In other words, anything that actually competes with Anthropic's own models. The safety bit is particularly disingenuous, as the company’s fearmonger-in-chief has gone out of his way to stoke fears among US government officials. Proprietary models can be controlled, but open weights, once released in the wild, are impossible to claw back. But neither Amodei’s comments nor commitments from major American and European tech giants change the fact that China is providing the only meaningful competition in the open weights arena. China dominates here. Speaking on CNBC Monday, Clément Delangue, CEO of Hugging Face, the biggest and most influential model repo in the world, said as much. “They’re clearly dominating on open models right now, and I wouldn’t be surprised if they start dominating at the frontier either by the end of this year or next year at the rate of progress,” he told the financial TV news network. America’s most capable open weights model, Inkling, is just shy of a billion parameters, and still can’t keep up with DeepSeek’s finest. Models like Moonshot's Kimi K3 and Z.ai's GLM 5.2 are in an entirely different orbit. In fact, an enterprise's only credible alternatives to proprietary models and their dubious security policies are Chinese models. DeepSeek, Alibaba, Moonshot, MiniMax, and Z.ai aren’t going to pass that opportunity up. Alibaba lets its most powerful model loose on the world Of the Chinese model devs, Alibaba is arguably the most like its American competition. Its open-weight models are well regarded, and their range of sizes and permissive licensing have made them attractive for fine-tuning application-specific systems. However, like Google and OpenAI, its top models have been locked behind an API – until now. Seizing the moment, Alibaba is making its most capable model weights available for download for the first time with the launch of Qwen 3.8-Max. The blog post contains the usual array of vaguely intelligible bar charts showcasing how the model compares to OpenAI and Anthropic’s own models, as well as a slew of demos that would have made Billy Mays proud. If you want specifics, we recommend checking out the launch blog here. You don’t have to look that closely to get what Alibaba is selling: anything OpenAI and Anthropic can do, we can do as well, if not better, cheaper, and on the hardware you own. With that said, 2.4 trillion parameters is a rather big lift for most enterprises, likely requiring 48-64 Nvidia B200-class GPUs if deploying in a customer-facing capacity. For internal workloads, 8-16 B300 or AMD MI355X GPUs would be adequate. If that’s a little rich for your blood, Alibaba hasn’t forgotten its roots and will be releasing a 27-billion parameter version of the model alongside the Max variant. With benchmarks, model devs can usually find a collection of tests that paint their model in a positive light. And unsurprisingly, Artificial Analysis’ own Intelligence leaderboard tells a slightly different story than Alibaba’s, with Qwen 3.8-Max matching Anthropic’s less-capable, but still formidable, Claude Sonnet 5. Beyond the marketing and pitch demos, Qwen’s blog post is surprisingly short on detail. What we do know is it’s a multi-modal mixture of experts (MoE) model. That means of the 2.4 trillion total parameters, only 95 billion are actually used to generate tokens for any one request. We can also assume that the model uses the same hybrid Transformer+Mamba architecture as previous Qwen models in order to maintain performance across large contexts. Speaking of which, the model will support context windows up to 1 million tokens, though it’s still not clear whether that relies on techniques like rope scaling to extend it or not. The model is currently available via Alibaba’s API service QwenCloud for $2 per million input tokens, and $6 per million output tokens. Cached tokens are charged on a sliding scale with $0.25 charged per million implicit cached tokens, $0.17 per million explicit cache token reads, and $2.5 per million explicit cache tokens created. For comparison, Anthropic’s Claude Sonnet 5 will set you back $2/M input tokens, $0.20/M cache hits, and $10/M output tokens. And Sonnet 5 pricing is set to increase 50 percent starting September 1. Meanwhile OpenAI’s GPT 5.6 Luna, which falls just behind Qwen 3.8-Max and Sonnet 5 on the Artificial Analysis leaderboard, will run you $0.20/M input, $0.02/M cached input, $0.25/M cached write, and $1.20/M output tokens for short context lengths under 272,000 tokens and double that for jobs exceeding that context. The model weights are slated for release on popular model repos, including Hugging Face, starting next week. DeepSeek undercuts OpenAI with flashy new V4 refresh While Alibaba joins Kimi K3-maker Moonshot.AI’s assault on frontier models, DeepSeek has taken a very different tack with its latest open weights model: squeeze every ounce of performance from as few weights as possible. The result is DeepSeek V4-Flash-0731, a 284-billion parameter model that can fit into around 142 GB of GPU memory (at FP4). That means enterprises can easily run this model at scale on a single system. And despite its smaller stature, the Flash model actually outperforms the 1.6 trillion-parameter DeepSeek V4 Pro by nearly 14 percent on Artificial Analysis’ Intelligence leaderboard. With that said, the refinements made to DeepSeek V4 Flash will no doubt find their way into the Pro model before long. Like Qwen 3.8-Max, DeepSeek V4 Flash undercuts OpenAI and Anthropic on pricing – this time by a considerable margin. For API access, DeepSeek is currently asking $0.14/M input tokens, $0.0028/M cached tokens, and $0.28/M output tokens. However, in an agentic world filled with reasoning models, API pricing doesn’t paint a complete picture. A model may appear cheaper, but if it consumes twice as many tokens as another higher priced model, it may not actually be less expensive. Because of this, it's important to look at how efficiently the model solves real world problems. Alarmingly for the US LLM makers, according to Artificial Analysis, DeepSeek V4-Flash isn’t just cheaper per token; it is also incredibly efficient at its job. Compared to OpenAI’s GPT 5.6 Luna, which is among the most efficient and least expensive models in Sam Altman’s current lineup, DeepSeek’s latest model is a full 40 percent less expensive, with a cost to solve of just three cents versus five cents. That difference may feel small, but it’s worth remembering that for vibe coders consuming tens or hundreds of millions of tokens a day, that difference adds up quite quickly. One of the secrets to DeepSeek's efficiency is the integration of DSpark speculative decoding directly into the model weights. We’ve explored speculative decoding in the past, but in a nutshell, it involves using a smaller draft model to predict the outputs of a larger, more capable one. When it works, inference performance increases. When it guesses wrong, it falls back to the base model. More importantly, because the smaller model is only predicting the output of the larger one, it’s entirely lossless and therefore requires no compromise in terms of performance. Alibaba and others have implemented similar speculative decoding mechanisms, like multi-token-prediction (MTP), for this reason. With DSpark, DeepSeek claims it can extract 57–85% more per-user speed on the exact same hardware, which is an impressive claim, and one that at least in our testing rings true. Your own personal frontier model? DeepSeek V4 Flash is just small enough that running it at home is entirely possible if you’ve got some deep pockets, or a heck of a lot of memory lying around. Testing on a 128 GB DGX Spark, we were able to get the model running at a respectable 128,000 token context window using Unsloth’s IQ3-XXS quant in Llama.cpp. Three bits per weight gives us just enough room to pack the DSPARK draft model into memory, but it’s also a bit more compression than we typically recommend for homelab use. Because of this, Unsloth warns that the model’s outputs could show some signs of quality loss, but it does run. Unsloth has a full guide on how to get the model up and running, assuming you’ve got beefy enough hardware. We admit, the DGX Spark is not a cheap box. At $4,699, it’s squarely in workstation territory, as is the $3,999 Ryzen AI Halo we looked at early last month. But the fact that you can even contemplate running a model like DeepSeek V4-Flash at home is impressive in its own right. The original IBM PC kitted out with a monitor and diskette drive cost about $3,735 in 1981. Adjusting for inflation, that works out to about $13,700 in today’s money. If history repeats itself, the hardware necessary to run models like DeepSeek V4-Flash should become much more accessible within the next decade.®
Greg Adams Photography posted a photo:
From my first trip to Sydney almost 20 years ago. I woke up early to get some photographs of the sun rising behind the iconic Sydney Opera House and there was an amazing Tall Ship docked in just the right spot!
Trump ‘disappointed’ with US attorney amid reports he’s considering removing her from her post
Donald Trump accused US attorney Jeanine Pirro of having “folded like an umbrella” amid reports that he was considering removing her from her post.
Trump criticized Pirro on Monday after she dropped vandalism charges against a former Olympian accused of damaging the recently renovated Lincoln Memorial reflecting pool. Trump nominated Pirro to serve as interim US attorney for the District of Columbia last year, and the Senate later confirmed her in August.
Continue reading...Celebrated writer, poet and novelist for young adults drew upon his sexuality and life on US southern border
Benjamin Alire Sáenz, an award-winning fiction writer and poet who drew upon his sexuality and his life on the southern border, has died at age 71. He was the first Latino to win the PEN/Faulkner prize.
Sáenz, who produced a story collection that won the PEN/Faulkner prize and a young adult novel that received acclaim from readers and reviewers and condemnation from censors, died on 28 July in El Paso, Texas, after a lengthy illness, according to a spokesperson for Simon & Schuster.
Continue reading...Coalition of 25 states ask the US Court of International Trade to halt tariffs, calling them unlawful
A coalition of 25 US states sued the Trump administration on Monday over new tariffs pegged at 10% to 12.5% on goods from 60 trading partners, calling them a pretext for replacing import taxes struck down by the supreme court in February.
The states are asking the US Court of International Trade to halt the tariffs, declare them unlawful and order refunds of duties that have already been paid.
Continue reading...Andy Burnham will also delay release of thousands of prisoners across England and Wales a month, until October
Rapists, child sexual abusers and groomers will be excluded from early release plans to ease critical prison overcrowding in a major intervention by Andy Burnham.
The prime minister said on Tuesday that he will delay the planned early release of thousands of prisoners across England and Wales by a month until October.
Continue reading...This near-hour of frothing wildness, in aid of Stand Up to Cancer, is utter chaos – interspersed with awareness-raising videos full of dignity and grace
If it’s early August, it must be Celebrity Hunted’s annual series in aid of the charity Stand Up to Cancer. The dozen famouses must evade capture for two weeks, which is a shorter time than civilians are required to manage it. Is it because celebrity schedules are fuller or their brains emptier? By the end of the first couple of episodes, I know which way I’m leaning.
The contestants, working in six pairs, include Britain’s last vaudevillian Brian Conley (“I’m like a coiled sponge!”) and his daughter Lucy (“Dad! Stop being funny”), TV property presenter siblings – a sort of hyphenate from hell – Scarlette and Stuart Douglas, plus comedians Jen Brister and Laura Smyth. In a game where predictability is the greatest – well, predictor – of failure, they may have the best plan. “I’m just not going to take my HRT for a week and let’s see what happens,” says Jen.
Celebrity Hunted is on Channel 4
Continue reading...Democrats say agreement – which clears way for Blanche’s confirmation – makes promises president can easily reverse
A compromise between Todd Blanche and two Republican senators that potentially cleared the way for Blanche’s confirmation as attorney general is weak, Democrats and several experts said, as it makes promises Donald Trump can easily reverse.
Blanche, the acting attorney general, posted two documents on Sunday evening showing he was withdrawing an 18 May order allowing for the creation of a $1.8bn weaponization fund and clarifying that broad tax immunity the justice department granted to Trump only applied retroactively to the president, his sons, and businesses.
Continue reading...
Apple's practice of mixing employees' work files with personal iCloud accounts reportedly left some former staff with continued access to confidential documents, messages, and even new updates after leaving the company. "The former employees said many Apple files they had been shared on over their careers at the company -- including planning documents for product launch events -- continued to sync to their personal devices through the iCloud storage service after they left Apple," reports The Information. "In some cases, they even received notifications about fresh updates to the documents. Some former employees said they were petrified to delete the files for fear that doing so would attract Apple's attention." MacRumors reports: Apple files get mixed in with employees' accounts because the company encourages them to use their personal Apple Accounts with their work iCloud account. Employees are given a 2TB iCloud plan and are able to merge the storage with their existing Apple Account or create a new account. Since only one primary account can be signed in at a time, most opt to use their existing account to avoid having to carry two iPhones. Apple has a managed folder for workplace files that it revokes access to when an employee leaves, but some internal documents aren't saved there automatically and shared files end up mixed in with personal content. Employees can also retain access to iMessage chats and files shared via the Messages app.
Lingering access to Apple systems is central to Apple's lawsuit against OpenAI. In its filing, Apple alleged that former employee Chang Liu breached Apple's systems using a "rare, previously unknown authentication bug" to download files while he was working at OpenAI. Apple told The Information that the OpenAI lawsuit is unrelated to any files left available on iCloud and that it does not pursue legal claims against former employees who accidentally have Apple documents in their personal iCloud accounts. "This case is about OpenAI employees wrongfully taking Apple's secret and confidential information regarding our unreleased technologies, processes, and products. Nothing in the filing relates to documents shared by, or stored in, iCloud."
Lingering access to Apple systems is central to Apple's lawsuit against OpenAI. In its filing, Apple alleged that former employee Chang Liu breached Apple's systems using a "rare, previously unknown authentication bug" to download files while he was working at OpenAI. Apple told The Information that the OpenAI lawsuit is unrelated to any files left available on iCloud and that it does not pursue legal claims against former employees who accidentally have Apple documents in their personal iCloud accounts. "This case is about OpenAI employees wrongfully taking Apple's secret and confidential information regarding our unreleased technologies, processes, and products. Nothing in the filing relates to documents shared by, or stored in, iCloud."
Read more of this story at Slashdot.
theodp writes: During Microsoft's 2026Q4 earnings call, CEO Microsoft Satya Nadella took time to tout a dashboard he personally created using AI from a Morgan Stanley analyst's PDF research report, which suggested a rosy payback for the so-called MAG7's ('Magnificent 7' companies) massive capital expenditures on AI (to which Nadella later added a "not financial advice" disclaimer). "It would be fun for you, Adam. I think one of your colleagues put out an ROIC [Return on Invested Capital] document. I took that document to Copilot, which is a PDF, and I said, 'Build me a new Power BI dashboard, essentially.' But here is the thing. It built a rich semantic model that went into my Fabric with OneLake that brought all the data in from the external sources. In fact, it was current with all the SEC filings of all the MAG7. And then on top of that, the repo itself is in GitHub, but the artifact is sitting in my Copilot as a site. That, to me, is a classic example of an enterprise-wide workflow. I, as a knowledge worker, could go create a dashboard. The data engineer can go to Fabric and find the artifact. The professional developer can go to the repo and find it in GitHub. And by the way, it's all registered with Agent 365. That's a little bit of what Amy is describing as the coming together of a new way to work, even while at the same time, bringing IT, security and manageability of it."
After Nadella's show-and-tell drew an underwhelming response during the call ("That's very helpful. Thank you." said the Morgan Stanley analyst whose team's work Nadella scraped with AI), Nadella turned to social media with posts on LinkedIn (12M followers) and Twitter/X (8M followers) to make the case for why his DIY project was such a brilliant demonstration of how AI enables governance, controls, security, development, testing, deployment, maintenance, data analysis/modeling, visualization, usability, and value. "Some more detail on the ROIC Intelligence App I built yesterday and mentioned on today's earnings call," Nadella wrote on LinkedIn. "I took the PDF that Brian Nowak at Morgan Stanley put together for Hyperscale ROIC this week and used Copilot code (coming in our new superapp) with a single prompt + skill (/drill-me) to create the plan, then used autopilot in auto to create the full app (with history, lookups, scenarios, what-ifs, etc). And /rubber-duck to test. And the best part is that all the artifacts are in my enterprise environment. My app is in Copilot, my code is in GitHub Enterprise; all my data pipelines/lake/semantic models are in Fabric. And everything is under Agent 365 IT/Sec/FinOps control! So this is not about Tokenmaxxing or vibe coding. Every step of the way the rails are engineered to create value, making everything a long-term reusable asset, with governance/security, and cost controls. This is the full system to drive business value. Disclosures: This is all pulled from public sources, and for illustrative purposes only...not financial advice! :) Here is the app and architecture..."
Not unexpectedly, the accompanying screenshot of a splash page for the BI app and a buzzword-laden complicated architecture diagram drew universal praise from LinkedIn fans, but also a few barbs from less-than-impressed commenters on Nadella's Twitter/X post, some of whom suggested Nadella's project might even represent a jump-the-shark moment for AI mania. "That he doesn't see whats wrong with saying 'My app is in Copilot, my code is in GitHub Enterprise; all my data pipelines/lake/semantic models are in Fabric' is exactly why MS is failing at AI,'" replied @PassingPixels on X/Twitter. "Dude is having to run 5 different systems to emulate babies first vibe code." @zigmund_ignatov added, "Why do we call glorified slide show an app?" @Mathupiriyan quipped, "Looks like Copilot just turned a PDF into a profit crystal ball." Unimpressed, @Markusndnb remarked, "So you created a web page using tons of proprietary MS tools." And @FishyAccounting called on Nadella to show-his-work, saying "Post the prompt or it didn't happen." (btw, Microsoft President Brad Smith similarly declined to provide the prompt for his own self-described amazing AI DIY reporting project that he touted at Microsoft's Shareholder Meeting last December).
So, does Nadella's self-promoted AI reworking of someone else's PDF research report strike you as an amazing example of everything that's good about AI, or does it conjure up memories of The Emperor's New Clothes?
After Nadella's show-and-tell drew an underwhelming response during the call ("That's very helpful. Thank you." said the Morgan Stanley analyst whose team's work Nadella scraped with AI), Nadella turned to social media with posts on LinkedIn (12M followers) and Twitter/X (8M followers) to make the case for why his DIY project was such a brilliant demonstration of how AI enables governance, controls, security, development, testing, deployment, maintenance, data analysis/modeling, visualization, usability, and value. "Some more detail on the ROIC Intelligence App I built yesterday and mentioned on today's earnings call," Nadella wrote on LinkedIn. "I took the PDF that Brian Nowak at Morgan Stanley put together for Hyperscale ROIC this week and used Copilot code (coming in our new superapp) with a single prompt + skill (/drill-me) to create the plan, then used autopilot in auto to create the full app (with history, lookups, scenarios, what-ifs, etc). And /rubber-duck to test. And the best part is that all the artifacts are in my enterprise environment. My app is in Copilot, my code is in GitHub Enterprise; all my data pipelines/lake/semantic models are in Fabric. And everything is under Agent 365 IT/Sec/FinOps control! So this is not about Tokenmaxxing or vibe coding. Every step of the way the rails are engineered to create value, making everything a long-term reusable asset, with governance/security, and cost controls. This is the full system to drive business value. Disclosures: This is all pulled from public sources, and for illustrative purposes only...not financial advice! :) Here is the app and architecture..."
Not unexpectedly, the accompanying screenshot of a splash page for the BI app and a buzzword-laden complicated architecture diagram drew universal praise from LinkedIn fans, but also a few barbs from less-than-impressed commenters on Nadella's Twitter/X post, some of whom suggested Nadella's project might even represent a jump-the-shark moment for AI mania. "That he doesn't see whats wrong with saying 'My app is in Copilot, my code is in GitHub Enterprise; all my data pipelines/lake/semantic models are in Fabric' is exactly why MS is failing at AI,'" replied @PassingPixels on X/Twitter. "Dude is having to run 5 different systems to emulate babies first vibe code." @zigmund_ignatov added, "Why do we call glorified slide show an app?" @Mathupiriyan quipped, "Looks like Copilot just turned a PDF into a profit crystal ball." Unimpressed, @Markusndnb remarked, "So you created a web page using tons of proprietary MS tools." And @FishyAccounting called on Nadella to show-his-work, saying "Post the prompt or it didn't happen." (btw, Microsoft President Brad Smith similarly declined to provide the prompt for his own self-described amazing AI DIY reporting project that he touted at Microsoft's Shareholder Meeting last December).
So, does Nadella's self-promoted AI reworking of someone else's PDF research report strike you as an amazing example of everything that's good about AI, or does it conjure up memories of The Emperor's New Clothes?
Read more of this story at Slashdot.
Uniscent, a pioneer of professional scent marketing in Sweden and Norway, approached Lundgren+Lindqvist to unify its visual identity into one cohesive system for digital and physical use. The redesigned monogram merges a 'U' and 'S' to evoke scent escaping its vessel, paired with a monospaced typeface reflecting the brand's precise, lab-based methodology. Magnified photographs of natural ingredients add an emotional layer, grounding the identity in the physical world scent inhabits.
mnrolvr has added a photo to the pool:
“Long live Hyrule jazz!” The Z Suites is “a modern jazz reinterpretation of the music from The Legend of Zelda”.