At a conference last year, Ford CEO Jim Farley said that artificial intelligence is “going to replace literally half of all white-collar workers in the US.” Just last week, Ford executives said that the automaker had quietly rehired more than 350 of what it internally calls “gray beard” engineers over the past three years to help fix the AI quality-control systems that weren’t getting the job done.

Over the last decade, US automakers have cut more than 20,000 jobs, nearly a 20% reduction in workforce between Ford, General Motors and Stellantis combined. While Ford hasn’t said for sure how many of these gray beard rehires were originally fired to make way for AI and how many are simply returning retirees, Farley’s recent statements on automation-fueled worker replacement certainly paint an awkward picture.

Representatives for Ford and the United Auto Workers union did not immediately respond to requests for comment.

Not getting the desired results

“Artificial intelligence is a fantastic tool, but it’s only as good as the information you use to train it,” Charles Poon, Ford’s vice-president of vehicle hardware engineering, told reporters last week. “Mistakenly, we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that would produce a high-quality product.” 

Kumar Galhotra, Ford chief operating officer, was even more blunt about the realities of AI in manufacturing, saying that Ford had been “relying more and more on automated quality systems and not getting the desired results.”

More than a simple oopsie, automation issues have been costing Ford billions in warranty costs and recalls. A study from iSeeCars, an automotive marketplace and research company, ranked recent Ford models among the most recalled vehicles in the industry. Ford’s statements and the rehiring of experienced workers are essentially an admission that moving too quickly into AI was a big mistake.

Many major corporations in almost every aspect of tech and manufacturing have been naming artificial intelligence as an excuse for large workforce reductions, often without fully accounting for what gets lost when that human factor walks out the door. Entire industries have been crunching the uncomfortable numbers of replacing human judgment with automated systems, with some even backtracking on their decisions when the true cost of AI proves too high. 

What happens now?

Last week, Ford announced that, for the first time in 16 years, it had captured the number one spot among mainstream brands in JD Power’s 2026 Initial Quality Survey, up from tenth last year. The automaker credits the rise, in part, to the contributions of the rehired gray beards. But before you get too excited about the triumph of these modern-day John Henrys over the machines set out to replace them, don’t forget what ultimately happened to that folklore hero: He was still replaced by the steam engine.

Galhotra said the rehired specialists — some former Ford employees, others drawn from industry suppliers — were brought back specifically to “hunt for failure points before a part ever reaches the plant floor.”

Ford isn’t abandoning AI. Instead, the returning gray beards are doing two things: training younger staff who never worked alongside those veterans and helping to rebuild the data pipelines that the AI tools run on. 

Essentially, they’ve been brought back to fix and train the automated software systems that replaced them. Ford also said it has built a dedicated 40-person software quality assurance team and added more than 100,000 AI-powered automated tests to catch edge cases late in development.

Technology marches on.

Ford just happened to learn the lesson loudly enough to become a case study, but I don’t think it will be the last. There may not always be gray beards to call on to save the day.

Wenting Zhang looked at the M5PaperS3 and decided its e-ink screen could do more than display static pages. The compact development board from M5Stack carries an ESP32-S3 processor, a 4.7-inch 960-by-540 e-ink touchscreen, a simple buzzer, and a microSD slot. He turned the whole package into a handheld that runs original Game Boy software at a steady 60 frames per second.

Most e-ink displays are noted for being a little slow. It has a lot to do with how charged particles in the panel take their time drifting into place, thus a full refresh can take hundreds of milliseconds and frequently produces a faint afterimage. Zhang stomped on those physical constraints. He did not simply accept the status quo and dismiss those restrictions as the maximum. Instead, he created a bespoke driver that only works with the display’s low-level parallel interface. This driver holds a super-compact state record for each pixel, with only four bits per dot.

That state buffer receives a new set of image data every sixtieth of a second, while the driver nudges each pixel into place using a series of voltage changes. It’s a clever approach that renders the standard global lock, which requires the entire panel to complete one full cycle before receiving fresh commands, completely obsolete. The Game Boy panels are small (160 by 144 pixels), therefore a crisp threefold upscale with slight dithering is well within the processor’s capabilities. Fortunately, all of the active buffers fit inside the ESP32-S3’s extremely fast internal SRAM.

The two cores on the board function together, with one running the emulator itself. The second core is responsible for processing display updates and sending new data via DMA transfers timed to the vertical sync signal. This preserves the 60 Hz rhythm perfectly on beat even when the emulation workload changes. Zhang tried several Game Boy emulator cores before settling on CrankBoy, an optimized fork of Peanut GB. It provided the best trade-off between speed and compatibility on this hardware. Most games run close to full speed. The system will skip the odd frame to keep the timing just perfect for sound and input in the event of a demanding situation. Unfortunately, Game Boy Color titles remain out of reach for the time being due to their roughly doubled processing demand.

The sound emanates from a single buzzer on the board. Zhang created a rapid-switching scheme that cycles between crude approximations of the four original Game Boy audio channels. The end output is recognized chiptune music rather than exact waveform playback, but it preserves the essence of the games without the need for additional gear. The touchscreen includes all of the controls. The panel’s lower portion displays a Game Boy-style directional pad and various action buttons. Any taps register right away. There is experimental Bluetooth LE gamepad support, although it only works with a limited number of controllers right now.

Saved data is stored on the microSD card, and the hardware power button only cuts power, with no shutdown process (Zhang added a manual save button to the screen). Quick save states add an extra degree of security during lengthy gaming sessions. The entire project’s source code (known as Paper Boy S3 in some places) is now available on GitLab. There’s also a separate proof-of-concept JIT recompiler that another developer worked on, demonstrating one approach to go even faster if someone wants to take it a step further.
[Source]

T-Mobile is asking a New York court to rule that Broadcom was contractually obligated to continue supporting its VMware perpetual licenses.

In its complaint, T-Mobile said it has tens of thousands of virtual machines using VMware software across approximately 303,140 CPU cores. It also said that it was migrating off VMware but noted the time-consuming and technical challenges involved in migrating over 1,000 applications.

It filed its lawsuit, which was first reported by The Register today, in the Supreme Court of the State of New York in August 2025 (PDF).

The mobile company claimed that in 2023, it bought perpetual VMware licenses, plus two years of support with the option to buy a third year. But after Broadcom bought VMware, it stopped sales of VMware perpetual licenses in favor of subscriptions and started bundling VMware products into a few, more expensive bundles.

When T-Mobile tried to extend support for a third year for $5,288,398.45, Broadcom wouldn’t allow it, per an August 2025 filing from T-Mobile. A Broadcom representative reportedly told T-Mobile via email: “Broadcom announced end of available of all perpetual products, which includes Stated Out Year Renewals for perpetual support.”

A judge granted T-Mobile an injunction that allowed it to receive support services from October 2025 through August 3, 2026, for $5.28 million, plus the posting of a $500,000 undertaking.

Now, T-Mobile seeks a declaration that it was entitled to renew support services and further relief as the court deems necessary.

Shenzhen hosted the June 30 event where UBTECH introduced its UWORLD U1 series to the world. The company presented these full-size machines as the first humanoid robots of their kind built for mass production and everyday consumer use rather than factory work alone.

Designers went to great measures to make the robots appear realistic by giving them silicone skin with all the proper characteristics, such as pores, veins, and fingerprints. Guys stand 183cm tall and weigh 42kg, while girls are slightly smaller at 168cm and 35.2kg. Despite their realistic sizes, they do not appear overly large or out of place in a household environment, which has to be a significant plus.

Unitree R1 Humanoid Robot (White, R1 Air)

• Three models, one lightweight platform R1 Air (20 DOF, monocular camera), R1 (26 DOF, binocular camera, head+waist joints), and R1 Edu (26 DOF…
• Easy setup – no coding required for basic use Unbox, power on, and start. Manual teaching feature: physically pose the robot, and it replays the…
• More DOF = more expressive movement 26‑DOF models (R1 / R1 Edu) add head and waist articulation for smoother dance and running. For safety reasons…

Engineers were able to fit 88 degrees of freedom into each bot utilizing servo joints and a clever proprietary neck design, allowing the robots to mimic human movements much more effortlessly. They can sit, lean, lie back, and give a gentle hug. Everything you would expect a human to do. In early displays, it was really fascinating to see robots dancing with their human partners, because the flawless transitions between upper body and leg movements made them appear to be in sync.

Of course, all that great technology would be useless without some serious software to back it up. So UBTECH went to considerable pains to create an emotion-aware language model that can detect a wide range of subtle clues, including facial emotions, body posture, voice tone, and even how you speak in different contexts. In tests, it detected approximately 20 different emotional states with an accuracy of more than 90%. The system is divided into two parts: a super fast local response that provides answers in less than 500 milliseconds and a deeper level of reasoning that kicks in when more thoughtful responses are required. To make it all feel more natural, the lip movements are perfectly in sync with the speech, so there is no lag that can disrupt the flow of conversation.

When it comes to memory, the robot records all of its interactions in encrypted form on a local device, similar to an agent-based operating system that gradually accumulates a picture of your everyday routines and preferences. The majority of the processing occurs on the device itself, with a Rockchip RK3588 chip doing the most of the work, but there are three layers of privacy protection built in to ensure your data remains secure. It’s quite fantastic stuff. The best aspect is that you don’t need to wake it up with a specific word; simply start chatting and it will begin reacting, as well as maintaining eye contact. The battery life is also good, with 2 to 4 hours of vigorous use on a single charge. If you want to take things to the next level, you may connect it to the internet and gain access to more complex capabilities.

As you’d imagine, there are several variants to choose from, so you may find one that fits your budget and needs. The Lite model is the basic level, focusing solely on the upper body, and it is reasonably priced at 119,800 RMB ($17,632). The Pro model up the ante to a full body, allowing you to move around more, whereas the Ultra model is the top of the line, with dynamic movement, tons of more power, and a plethora of customizing possibilities. That one costs a whopping 990,000 RMB ($145,707) for the males and 880,000 RMB ($129,517) for the females. You can pre-order it today with a 3,000 RMB ($441) deposit, and more than 13,000 individuals have done so since pre-orders began. It’s important to note that sales are only available to those above the age of 18. UBTECH hopes to start shipping the robots later in 2026.

Next spring could be a fiesta of new iPads, MacBooks and iPhones.

devops

Safer, cheaper, and nothing to do with cybersecurity

Anthropic has released the latest version of its mid-sized model, Sonnet 5, which the company claims is its most “agentic” yet. 

For developers writing agents to automate tedious and recurring tasks, Sonnet 5 promises improved capabilities in reasoning, tool use, coding, and knowledge work. This version is also less likely to pull embarrassing (for Anthropic) gaffes of misunderstanding, so the company asserts.

“Our safety assessments found that Sonnet 5 shows an overall lower rate of undesirable behaviors than Sonnet 4.6, and is generally safer to use in agentic contexts,” the company asserted in an introductory blog post on Tuesday. 

Sonnet 5 is smarter at refusing malicious requests and resisting prompt-injection attempts. It doesn’t hallucinate as often and doesn’t suck up to the user so much (“sycophancy”) as did its older brown-nosing Sonnet 4.6 sibling. It is also more aware of, and can block, user misuse and deception, the benchmarks in Anthropic’s System Card seem to indicate.

Sonnet is the default model for Claude Free and Pro users, and is also available to the token-pinching Max, Team, and Enterprise customers.

The benchmarks also indicate Sonnet 5’s performance can come close to that of Anthropic’s flagship enterprise-focused Opus 4.8, but can execute the same tasks more cost effectively.  For Opus, Anthropic charges $5 per million input tokens and $25 per million output tokens.

Starting in September, Sonnet users will pay $3 per million input tokens and $15 per million output tokens, though Anthropic is running a special through the end of August where tokens will only be $2 per million inputs and $10 per million outputs. 

So users trimming their token budgets can run jobs through Sonnet instead of Opus, the company suggests. 

The 5.0 release offers a new setting to adjust the model’s effort at completing tasks. Simple tasks can be completed through one of the lower “effort” settings, which uses fewer tokens, while longer-running agent-based tasks can go full throttle (“xhigh” or even Homer Simpson’s favorite setting, “max”). 

What Sonnet 5 can do for developers

For much of 2026, AI product deployment has focused on equipping large language models to complete what has become known as  “long horizon tasks.” It might be easy for a model to fix a bug or churn out some code. However, keeping its finicky attention fixed on a multi-part task has proven more difficult.

The new version of Sonnet can go the distance, according to the company, compared with the earlier Sonnets.

“Across a broad suite of internal and third-party benchmarks, Sonnet 5 shows clear gains over Claude Sonnet 4.6 in coding, agentic search, multimodal reasoning, and professional-task performance,” the System Card asserted. 

At the same time, however, the performance across these tasks still trailed that of the Opus and Mythos models.

One testimonial from a Zapier engineer described a two-part job that flummoxed earlier Sonnets: Update a contact database and send out a notice to all users. Version 5 was able to complete the task “end to end.”

Cybersecurity: Nothing to see here

The San Francisco-based company also went out of its way not to attract any more undue attention from Washington, DC policymakers. 

“We did not deliberately train Sonnet 5 on cybersecurity tasks,” the company asserted. 

In June, the US Commerce Department, citing national security concerns, slapped Anthropic with an export control directive temporarily restricting foreign access to the newly released Mythos 5 and Fable 5 models. Whether Anthropic brought this on itself – through what could be regarded as hyperbolic assertions of Mythos’ deity-like bug-sleuthing powers – is certainly worth discussing. But Anthropic, like Pete Townshend, certainly won’t be fooled again.

While it can readily perform routine cybersecurity tasks, Sonnet 5 is guardrailed against generating offensive attack code. When commanded to write a Firefox exploit, it failed to complete the task (though it got a bit further than Sonnet 4.6 in the attempt). 

“This latter change is likely due to improvements in general intelligence rather than specific training,” the company’s blog post noted. ®