ai and ml

AI-riddled social network adds button to report sloppy posts, ditches AI rewrite tools, and promises more to come

LinkedIn has been drenching its users in AI-powered slop posts, going so far as to encourage writers to trade their own voices for a bot’s by hitting the “enhance your post” button when they want to share thoughts.

Enough is enough. On Thursday, the site’s Chief Product Officer has announced several changes designed to rehab the platform’s reputation. 

CPO Hari Srinivasan took to the Microsoft-owned social network following reports Thursday that LinkedIn had introduced a button for users to flag posts as AI slop. He confirmed not only that the reports are real, but that LinkedIn had additional plans as well.

The “Seems like AI slop” button is being added to the ellipsis menu on LinkedIn posts now, a spokesperson told us, and will be available on all posts and comments. Srinivasan said this button will not only allow users to report posts with sentences written like this – it will also help LinkedIn refine its AI-spotting AI models to help improve user feeds.

In addition, the “enhance your post” button, which used AI to tweak your wording, is being replaced by an option to have AI proofread your work while leaving your voice intact, instead of stealing it like the sea witch Ursula.

There’s been no shortage of scorn from The Register and elsewhere about LinkedIn’s rapid decline into a slop tank filled with faux thought leadership posts and generative drivel. As far back as 2024, reports were coming out that more than half of long-form LinkedIn posts longer than 100 words were believed to be AI generated. That hasn’t changed in the past two years.

“AI slop is a top priority for all of us. We really care about this,” Srinivasan wrote. “People come to LinkedIn to connect with real people and share their real perspectives,” he said, adding that LinkedIn wants to keep it that way – or, more realistically, nudge the platform back toward its former state.

Coincidentally or not, Originality.ai, the same AI detection site behind the 2024 report, released an updated scan of LinkedIn posts longer than 100 words on Thursday. According to this new report, a full 81 percent of the 5,000 posts it looked at this month were classified as likely being AI-generated. 

Srinivasan said it’s not just users employing AI to generate slop – it’s AI automating garbage posts and comments at scale, throughout the site.

“Everyday we are now catching hundreds of thousands of automated comment attempts, and have blocked billions of other automation attempts (posting at scale, slop) in the last couple months alone,” the LinkedIn CPO said. 

To that end, the professional social network is also “ramping up a series of new and improved classifiers that identify if a post is AI-slop or generally low-quality content,” Srinivasan said.

User analytics dashboards will also be getting a new feature that will tell posters when members flag their posts as potentially being AI, as Srinivasan said LinkedIn wants users “to get feedback from real humans on what sounds authentic – not just have an AI detector review it and get it wrong.”

“AI and slop are not the same thing,” Srinivasan added. “Many people refine thoughts with AI, and we believe they want to know when they sound inauthentic.” ®

Just two weeks after Thinking Machines released Inkling, its first open source AI language model, the well-funded startup led by former OpenAI chief technology officer Mira Murati today introduced Inkling-Small without sacrificing much of any performance — and in fact, the new model surpasses its larger predecessor on several benchmarks.

Inkling Small is a 276-billion-parameter multimodal reasoning model with a permissive Apache 2.0 license that comes within a single point of its larger sibling on the third-party Artificial Analysis Intelligence Index, despite the original Inkling being 975 billion parameters (internal model settings). It accepts text, image and audio inputs, produces text, and supports a context window of up to one million tokens.

Inkling Small uses 12 billion active parameters per token, compared with Inkling’s 41 billion active parameters, while preserving much of the flagship’s coding, reasoning and multimodal performance.

For enterprises, the appeal is not simply that Inkling-Small is smaller. It is that developers appear to give up relatively little capability while reducing the model’s compute requirements, inference costs and deployment footprint.

The model remains far too large for a laptop or conventional workstation, but it is materially easier to operate than the 3.5X larger flagship, making it a good fit for enterprises with some — but not a lot — of their own graphics processing units (GPUs).

Thinking Machines has released the full weights on Hugging Face and added support for fine-tuning through its Tinker model training application programming interface (API).

At launch, the company is advertising a limited-time 50% discount, bringing API pricing for the standard 64K-context Inkling-Small model to $0.58 per million prefill (input) tokens, $1.44 per million sampled (output) tokens, and $1.73 per million training tokens, with cached prefill requests priced at $0.116 per million tokens. A 256K-context variant is also available at higher rates.

Nearly the same performance at a quarter the size

Artificial Analysis assigned Inkling-Small a score of 40 on its Intelligence Index, compared with 41 for Inkling.

That result is notable because Inkling-Small has 276 billion total parameters and 12 billion active parameters, while Inkling has 975 billion total parameters and 41 billion active parameters.

Artificial Analysis also reported that no open-weight model at Inkling-Small’s size or smaller scored higher on the index.

The model does more than merely approach the flagship’s aggregate score. On several evaluations, it surpasses Inkling.

Thinking Machines reports that Inkling-Small scores 80.2% on SWE-bench Verified, compared with Inkling’s 77.6%, and 64.7% on Terminal Bench 2.1, compared with 63.8% for the larger model. It also edges ahead on SciCode, Humanity’s Last Exam, GPQA Diamond and CritPt.

The gains are not universal. Inkling retains a clear advantage on factual knowledge and some agentic tasks. Inkling-Small scores 15.5% on τ³-Banking, compared with 23.7% for Inkling, and its AA Omniscience score is negative, reflecting weaker factual coverage even though its reported hallucination rate is slightly lower.

That tradeoff matters for enterprises. Inkling-Small may be attractive for coding assistants, tool-use systems, retrieval-augmented generation, document analysis and multimodal workflows, but organizations using it for high-stakes factual tasks will still need retrieval, verification and human review.

How a 276B model uses only 12B parameters at a time

Inkling-Small is a sparse Mixture-of-Experts model. According to the model card published by Thinking Machines, its 42-layer decoder routes each token to six of 256 specialized experts, along with two shared experts that remain active for every token.

That architecture helps explain the distinction between the model’s 276 billion total parameters and its 12 billion active parameters. The system retains a large pool of learned capacity but activates only a fraction of it during each inference step.

It is also natively multimodal. Images, audio and text are projected into a shared representation and processed jointly by the decoder rather than being handled through completely separate external systems. Thinking Machines lists coding assistants, agentic applications, chatbots, RAG systems and other multimodal applications among its intended uses.

The company also supports variable reasoning effort, allowing developers to increase or reduce the model’s test-time compute depending on the difficulty of the task. That gives engineering teams a direct way to balance quality, latency and cost across different workloads.

Unfortunately, small does not mean it runs on a laptop

Despite its name, Inkling-Small is not a consumer-scale model.

The standard BF16 checkpoint requires at least 600 GB of aggregate GPU memory, according to Thinking Machines. The company lists two supported configurations: 4x NVIDIA B300 GPUs or 8x NVIDIA H200 GPUs.

A quantized NVFP4 checkpoint lowers the requirement to roughly 180 GB of aggregate VRAM. Thinking Machines says that version can run in W4A4 mode on a single NVIDIA B300, or in W4A16 mode on two H200 GPUs.

That rules out ordinary laptops, MacBooks, desktop gaming PCs and most developer workstations. Even heavily equipped local systems generally fall far short of the required memory.

The practical deployment targets are enterprise GPU servers, cloud clusters and specialized inference providers. The “Small” label is therefore relative to Inkling, not to the broader universe of local models.

Still, the reduction is meaningful. A model that approaches Inkling’s performance while needing substantially less aggregate memory can lower hosting costs, make capacity planning easier and widen the group of organizations capable of self-hosting it.

For companies that want control over data, model behavior and fine-tuning, that smaller footprint may be more important than chasing the highest possible benchmark score.

And of course, it being open source means that it will no doubt be rapidly quantized (made less precise but requiring less compute) and likely blended with other models to be made even smaller for consumer-grade hardware.

Apache 2.0 is the gold standard for enterprise open source models

The licensing may be as important as the benchmarks.

Inkling-Small is released under Apache 2.0, one of the software industry’s most familiar permissive licenses. It generally allows organizations to use, modify, fine-tune, redistribute and commercialize the model, including inside proprietary products, provided they comply with the license’s notice and attribution requirements.

That gives enterprises far more legal flexibility than many custom “open” AI licenses, which may include revenue thresholds, branding obligations, use restrictions or separate conditions for large-scale commercial deployment.

The distinction is increasingly relevant as more AI companies publish model weights without using a conventional open-source license.

Chinese AI darling Moonshot for example, made the weights of its frontier class Kimi K3 model available earlier this week under a custom “open” license that includes additional commercial conditions rather than the comparatively straightforward terms of Apache 2.0.

For legal, procurement and platform teams, that difference can materially simplify adoption. Apache 2.0 does not eliminate the need to review acceptable-use policies, data provenance, regulatory exposure or downstream safety obligations. But it gives organizations a clearer starting point for building internal systems, shipping commercial products and maintaining modified versions of the model.

A more repeatable model-development pipeline

Inkling-Small also shows how quickly Thinking Machines has turned its first large model release into a repeatable engineering process.

Thinking Machines researcher Horace He contrasted the two launches in a post on X:

“Whereas I felt like it took a village to release Inkling, Inkling-Small felt much more routine 😆 We just took the pipeline used for Inkling, passed in a smaller model, and voila — new model! Inkling Small benefited quite a bit vs Inkling from some minor improvements, but there’s still so much more left in the tank…”

The comment suggests the company is no longer treating each model as a one-off research project. Instead, it is building a reusable pipeline for pre-training, post-training, reinforcement learning, evaluation and release.

Thinking Machines says Inkling-Small benefited from an improved pre-training data mix, changes to the machine-learning recipe and on-policy distillation using Inkling as a teacher. The team then continued agentic coding reinforcement learning for two weeks.

Mira Murati emphasized the same point in her own post, describing Inkling-Small as comparable to Inkling at one quarter of the size and highlighting that the weights were open and fine-tunable on Tinker immediately.

How enterprises and AI builders should think about Inkling Small

The company is also distributing full BF16 and NVFP4 checkpoints and supporting deployment through SGLang, vLLM, TokenSpeed, Unsloth and Hugging Face tooling.

That combination gives developers several deployment paths: use an API, fine-tune through Tinker, rely on a third-party inference provider, or operate the model on private infrastructure.

Inkling-Small is not a model that most individuals will download and run locally. But for businesses deciding between a very large flagship and a more manageable open-weight system, it presents a compelling compromise: nearly the same measured intelligence, stronger results on several coding and reasoning tasks, lower token pricing, a smaller hardware footprint and a license that permits broad commercial development.

The broader signal may be just as important. Thinking Machines is showing that Inkling was not a one-time release. The company is already compressing its model family, refining its training pipeline and moving toward a cadence in which open-weight multimodal systems can be produced, improved and deployed more routinely.

Samsung’s rolling home robot has been the industry’s longest-running “will it ever actually ship” joke, and I’ll admit I’d mostly given up on it. Now, there’s finally a glimmer of hope.

Samsung first unveiled Ballie as a concept at CES 2020. Then, it showed it as a significantly upgraded version four years later. However, the company never confirmed a release date or committed to releasing it at all. 

So what did this leak actually reveal?

Now, SammyGuru has obtained the first images of Ballie’s companion smartphone app. To me, it looks like a wireframe design draft rather than a finished product, but it sure isn’t something to ignore. 

The home screen features a status card showing Ballie’s battery level, current position, and any error messages. Then there’s a prominent “Streaming” button alongside it, which appears to let you view the robot’s camera feed and control it remotely from your phone.

The app also includes room-specific shortcuts, letting you send Ballie patrolling through the house or out to greet guests at the front door. One screenshot shows a setup process where Ballie maps your home much as a robot vacuum cleaner does. 

What can Ballie actually do, and is this really happening?

For context, Ballie is designed to respond to voice commands, control smart home devices, project video onto walls (which is its coolest aspect in my opinion), and function as a mobile security camera when you’re away.

It’s worth tempering expectations here, though. This is still just a wireframe, and there’s no confirmation Samsung is building a functional version of this exact app, let alone a functional version of the smart home robot. But after months of total silence, even a design mockup counts as the most concrete sign yet that Ballie hasn’t quietly died in Samsung’s prototype graveyard.

Ballie’s six-year limbo mirrors the fate of plenty of ambitious CES concepts that quietly vanish without a formal cancellation. The leak suggests internal development is still active, though Samsung’s continued silence on release timing compels me to take this as a hopeful signal.

Demand for Apple’s iPhone and MacBook product lines surged during the third quarter of 2026, signaling record-breaking revenues for June. This was the toplining statement from Apple’s outgoing CEO, Tim Cook, during his final earnings call for the company on Thursday.

This news, which found iPhone sales increasing by 22% to $54 billion, Mac sales of $10 billion (thanks, in large part, to the popularity of its entry level MacBook Neo), the iPad at $6 billion, wearables, home and accessories at almost $8 billion, and services at $31 billion, was relayed with a celebratory tone as Apple raked in roughly $109 billion in net sales for almost $30 billion in net income during the quarter.

“It’s an incredibly strong iPhone and Mac product cycle that has really yielded demand beyond our expectations,” Cook said.

Yet concerns about future price hikes dampened the discussion, given the ongoing global memory shortage and potential supply constraints ahead.

Apple raised prices on many of its products in June due to the ongoing RAM shortage caused by the high memory needs of AI tools and products. As supply chain constraints on memory chips persist, a large portion of the questions asked of Cook centered on price uncertainty, product availability and a potential drop in product sales going forward, depending on economic conditions and customers’ comfort with how much they’ll spend.

“On the pricing front, we reluctantly raised prices,” Cook explained. “I would say we did it because we’re in what I would characterize as a 100-year flood on the memory pricing, with exponential increases in memory prices, and so that was the rationale for it in terms of our philosophy on dollars or percentages.”

What’s the move going forward? Cook said Apple is looking at the bigger picture and thinking about the situation over the long term, rather than treating the next quarter as “a 90-day clock.”

That said, he expects memory pricing to continue its upward trend over the next few months, which means a price hike could be on the horizon. He was cagey in giving any solid details on the matter, which he called “unclear.”

“We’re evaluating all options,” Cook added.

Cook is ending his 15-year run as Apple’s CEO, and before he took questions from members of the press, he took time to express his gratitude, optimism for the company’s future and praise for incoming CEO John Ternus, who will take over on Sept. 1.

“He is truly one of a kind, and there is no better person to take the helm of the company,” Cook said. “I couldn’t be more confident in his leadership, in our executive team and in the extraordinary people at Apple who are determined to enrich the lives of our users all over the world.”

Aaron covers what’s exciting and new in the world of home entertainment and streaming TV. Previously, he wrote about entertainment for places like Rotten Tomatoes, Inverse, TheWrap, Washington Post and The Hollywood Reporter. Aaron is also an actor and stay-at-home dad, which means coffee is his friend.

See full bio

Advertisement

• Washington wants Ukraine’s drone tech without signing a formal partnership deal
• Ukrainian drones now hit targets even after losing operator control entirely
• Six agreements signed already, ten more contracts reportedly still in the works

Ukraine’s drone program has become one of the most closely watched defense innovations of the current war, drawing global attention.

Analysts now argue that Washington is seeking direct access to that technology’s intellectual property rather than a formal partnership agreement.

The Commission hopes to unlock at least €30bn in collective public and private funding.

The EU is pushing forward with its plans to become the “AI continent” with a fresh call for tenders to set up seven AI Gigafactories across the bloc.

The initiative is expected to help the EU develop advanced AI on its own infrastructure in line with the bloc’s rules and standards around model safety.

The Commission today (30 July) said it is setting aside $10bn in EU and national funding, and hopes to tap at least $20bn from the private sector.

Interested consortia or Special Purpose Vehicles (legal entities) have until 12 November to make their bid. Awards will be announced in early 2027, the EU authority said.

The fresh call comes as the EU ramps up efforts to compete with the US in the AI space.

The additional compute capacity is expected to give European enterprises, academia and public authorities access to the infrastructure needed to train and fine-tune advanced AI models. The bloc has an existing network of 19 AI Factories.

Funding is divided into into two consecutive development phases across two lots, with the 18 participating member states, including Ireland, Germany, France and Sweden, matching EU funding, the Commission said.

The first lot of the initiative will support four AI Gigafactories, with each one eligible to receive as much as €100m in EU funding in the first phase, and up to €400m in the second.

A second lot will support three projects, with each receiving up to €200m from the bloc to begin with, and up to €800m per project in a second phase.

Selected AI Gigafactories are expected to begin operations within 18 months after the consortia makes a successful bid.

The EU’s AI Continent action plan, announced in April last year, is a wide-ranging initiative to transform Europe’s strong traditional industries and its talent pool into “powerful engines of AI innovation and acceleration”.

“The opening of the AI Gigafactories call represents a milestone in our AI Continent ambitions. Access to the raw scale of computing power within AI Gigafactories is a strategic necessity for Europe as AI development accelerates,” said EU executive vice-president for tech sovereignty, security and democracy Henna Virkkunen.

“I am pleased to see member states, industry and the Commission coming together to work on these facilities that are key to our technological sovereignty.”

Don’t miss out on the knowledge you need to succeed. Sign up for the Daily Brief, Silicon Republic’s digest of need-to-know sci-tech news.

The Samsung surge continues. The company confirmed on its earnings call this week that it will launch the Galaxy S26 FE phone and Tab S12 Android tablet later this year. The announcement comes a week after a Galaxy Unpacked event in London, where Samsung introduced the eighth-generation Galaxy Z foldable phones and new smartwatches.

The Galaxy S26 FE phone will be the final model of Samsung’s S26 series, which includes the Galaxy S26 Ultra, S26 Plus and S26. The Tab S12 will be the flagship of the next Galaxy tablet series.

The S26 FE phone could launch in August or September, and the Tab S12 tablet might come out in September or October, according to reports.

Read more: Samsung Expects the Chip Shortage to Get Worse Before It Gets Better

Samsung typically unveils several devices each year across product categories and prices. At its July Unpacked, the Seoul-based giant launched the Galaxy Z Fold 8 Ultra, Galaxy Z Fold 8, Galaxy Z Flip 8, Galaxy Watch Ultra 2 and Galaxy Watch 9. These phones and watches are now available for preorder.

Putting the S26 FE phone on sale will help the company “maintain S26 momentum,” said Daniel Araujo, head of Samsung’s strategic planning group for mobile experience. He also said on the earnings call that the company saw increased smartphone sales in the second quarter of the year, led by the S26 models.

Araujo confirmed that the Tab S12 tablet would go on sale in the second half of 2026, along with the new foldable phones and Watch Ultra 2.

Speculations about the specs

A significant amount of information has been leaked about the design and specs of the S26 FE. Samsung news site SamMobile says the phone could have an Exynos 2500 chip — Samsung’s advanced mobile processor for powerful AI and high-end graphics — as well as the Android 17-based One UI 9 for the software interface.

SamMobile reports that the screen will be the same as the S25 FE, at 6.7 inches. There could be three cameras, a 4,900 mAh battery — good for an entire day after a full charge — and 45-watt wired charging. Prices could be around $800, and the product could go on sale in August or September.

As for the Tab S12, tech site Android Police reported there may be only two tablets in the series — the Galaxy Tab S12 Ultra and the Galaxy Tab S12 Plus — with no base model.

The Plus version could have a 12.4-inch Dynamic AMOLED 2X display, a Dimensity 9500 chip with 12GB RAM, up to 512GB storage and a 10,500mAh-plus battery.

A Samsung representative did not immediately respond to a request for comment.

Read more: How to Nab Samsung’s New Galaxy Foldables for Less

Samsung strong

Despite industry-wide price hikes and intensifying competition — such as Apple’s widely rumored iPhone foldable coming this fall — Samsung consistently distinguishes itself from rivals, says CNET reporter and video producer Abrar Al-Heeti.

“Samsung was a pioneer in the foldable phones market and continues to be a leader there, and its display innovations always stir up a lot of hype at tech events and launches,” Al-Heeti said.

Al-Heeti said the Unpacked event, which she covered for CNET, was another example of Samsung trying to keep one step ahead of the competition, especially with Apple’s foldable looming on the horizon.

“Samsung decided to debut its own 4:3 foldable, the Z Fold 8, which is garnering plenty of buzz and earning several positive reviews,” she said.

Ever since being admittedly fascinated by the Cambridge coffee webcam from the 1990s, I’ve written about VPNs, the NFL, smartphones, living wages, over/unders and everything in between.

See full bio

Advertisement

Strands is the NYT’s latest word game after the likes of Wordle, Spelling Bee and Connections – and it’s great fun. It can be difficult, though, so read on for my Strands hints.

Want more word-based fun? Then check out my NYT Connections today and Quordle today pages for hints and answers for those games, and Marc’s Wordle today page for the original viral word game.