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Amazon will present its framework for engineering trustworthy AI agents at VB Transform 2026

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AI agents are increasingly proficient at executing business tasks autonomously, but IT leaders are cautious about granting permissions to access enterprise systems. 

Part of the challenge lies in how AI reliability is measured. Industry standards often rely on EVAL scores, which provide a static snapshot of performance rather than a measure of overall reliability. These metrics can fail to capture predictability across prompts, environments, and input types, said Bryan Silverthorn, director of the AGI Autonomy research lab at Amazon.

Amazon’s AGI autonomy research lab is moving beyond raw performance benchmarks, focusing instead on a structured framework centered on consistency, robustness, predictability, and safety, Silverthorn told VentureBeat during an interview ahead of his session at VB Transform 2026.

Rather than assuming that models can be harnessed into safety, Amazon’s approach emphasizes decoupled systems, such as sandboxed environments where agents propose changes that are reviewed by humans before implementation. 

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This strategy aims to bridge the trust gap by prioritizing verifiable interactions, even in highly sensitive domains like finance, where the potential damage an agent can cause is significant.

In VentureBeat’s Q2 Pulse Research survey of over 100 senior technology leaders and buyers, just 4% said they are comfortable relying on model guardrails alone. When asked what worries them most about model guardrails, 40% said unauthorized access to tools or data and 27% cited prompt manipulation or injection.

At VB Transform, Silverthorn will share details of Amazon’s approach to trustworthy agentic AI and how companies can move from single-agent wrappers to multi-tool architectures that can self-correct mid-execution during his session titled Closing the capability-reliability gap: Inside Amazon’s framework for engineering trustworthy agents.

Another agentic ops and evals-focused session at VentureBeat’s flagship conference, happening July 14 and 15 in Menlo Park, is Intelligence at scale: How Waymo builds safe, efficient AI for the physical world with speaker Manasi Joshi, director of systems intelligence and machine learning at Waymo. 

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Interested in attending VB Transform 2026? A select number of complimentary passes are also available to senior technology leaders. Contact us to get yours. You can also purchase tickets here.

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Judge Says Florida’s Social Media Law Is “Literally Impossible” To Obey. Thanks To The Supreme Court, It Gets A Trial Anyway.

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from the the-moody-reality dept

Remember a few years ago when both Texas and Florida passed laws trying to tell social media companies they couldn’t moderate political content? Those cases eventually made their way to the Supreme Court, where the Court (as it’s been known to do) kinda punted: sending the cases back to the lower courts on technical legal grounds, claiming that the plaintiffs, NetChoice and CCIA, had mistakenly filed the cases as “facial” challenges, rather than “as applied.” It’s not worth going into the legal weeds again about the difference here, especially since the ruling had tons of important and useful language making it clear that content moderation is protected by the First Amendment in the case dubbed “Moody” after Florida’s former Attorney General.

But, the two cases have continued to bounce around the courts over the past few years, and the district court in Florida has rejected both the plaintiffs and the state’s motions for summary judgment, but in doing so has again made some great arguments about how content moderation gets First Amendment protections. The case is still before Judge Robert Hinkle, who made the original ruling finding the law unconstitutional five years ago.

Now, after discovery, Hinkle is reviewing the amended complaint that tries to deal with Moody’s limits on “facial” challenges. He starts out by reinforcing that content moderation is obviously protected by the First Amendment:

Collecting third-party speech content into a single speech product is what social-media platforms do. As the Supreme Court said, the collection “is itself expressive, and intrusion into that activity must be specially justified under the First Amendment.” Id. The defendants’ contrary assertion was rejected in NetChoice I (this court’s preliminary-injunction order), and in NetChoice II (the Eleventh Circuit’s opinion affirming that order in relevant part), and in Moody (the Supreme Court’s opinion agreeing with the Eleventh Circuit in relevant part).

Florida tried to argue that because recommendation engines try to keep users on the platform, and that decisions are made by “algorithms” that somehow changes the equation. The court points out that this is technically illiterate, because humans still set the editorial guidelines:

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The defendants say platforms decide which content to show any given user primarily based on the user’s viewing habits, showing the user the content most likely to keep the user on the platform longer. Perhaps so. See Moody, 603 U.S. at 735 (“The selection and ranking is most often based on a user’s expressed interests and past activities.”). And the defendants say this decision is made by algorithms, devoid of human involvement. Not so. Humans adopt the standards and guidelines, establish algorithms that incorporate them, and keep a great deal of content off the platforms on this basis, even though, as the defendants emphasize, the remaining content is organized to a substantial extent by algorithms based on a user’s viewing habits. This record establishes without genuine dispute that the six platforms specifically addressed in the plaintiffs’ motion have standards or guidelines that have a significant role in selection and organization of content. “And because that is true, they receive First Amendment protection.” Moody, 603 U.S. at 740.

Moody reiterated this point in discussing Texas’s similar legislation. The Court said the parties treated Facebook’s News Feed and YouTube’s homepage as the heartland applications of the Texas law—much as those and other platforms’ similar features are the heartland applications of the Florida law. See id. at 744. The Court said that at least on the record then before the Court, “the editorial judgments influencing the content of those feeds” were “protected expressive activity” that Texas could not “interfere with . . . simply because it would prefer a different mix of messages.” Id. (emphasis added). The Court said “influencing,” not “fully determining.” The record now before this court makes clear that editorial judgments of the six platforms addressed in the plaintiffs’ motion at least influence the content of their feeds. The First Amendment applies.

While Justice Barrett made some technically questionable statements in a concurrence about whether AI-driven algorithms might change the equation, Judge Hinkle says that even if she were right, it wouldn’t matter here:

But the defendants are plainly incorrect when they assert, in substance if not explicitly, that the First Amendment does not apply when there is mixed curation—some driven by human editorial discretion and some by algorithms or artificial intelligence. Responding to Justice Barrett’s concurrence, the Court said this case does not deal with “feeds whose algorithms respond solely to how users act online—giving them the content they appear to want, without any regard to independent content standards.” Moody, 603 U.S. at 736 n.5 (emphasis added). The Court continued, “Like them or loathe them, the Community Standards and Community Guidelines make a wealth of user-agnostic judgments about what kinds of speech, including what viewpoints, are not worthy of promotion. And those judgments show up in Facebook’s and YouTube’s main feeds.” Id. Justice Barrett joined that footnote.

And then the key point: the First Amendment protects content moderation. Full stop.

The unmistakable upshot is this: the First Amendment applies to mixed curation. The defendants’ contrary assertion is inconsistent with Moody, the many precedents discussed in Moody, and any coherent view of the First Amendment. This does not mean platforms are not subject to government regulation, but it does mean regulation must pass appropriate First Amendment scrutiny.

The judge also finds that there are constitutional problems with how vague the law is in some areas. And, in others, finds that the law would be impossible to comply with. In discussing the law’s prohibition on “post-prioritization or shadow banning algorithms” for any posts “by or about” a candidate for office, the court finds the provision baffling — saying its plain meaning makes no sense, and that Florida’s defense of it makes even less sense:

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But the provision prohibits a platform from using “post-prioritization or shadow banning algorithms” for content by or about a user known to be a candidate. “‘Post-prioritization’ means action by a social media platform to place, feature, or prioritize certain content or material ahead of, below, or in a more or less prominent position than others in a newsfeed, a feed, a view, or in search results.” Fla. Stat. § 501.2041(1)(e) (emphasis added). Unless a platform shuts down completely, compliance with this provision is literally impossible; posts can only be ahead of or below other posts, and posts can only be in a more or less prominent position than other content.

The defendants say, though, that the provision does not mean what it says— that it requires posts by or about candidates to be placed in chronological order. Perhaps Florida courts will rewrite the provision in this way, but they have not done so to this point. One doubts the Florida legislature really intended to require all candidate posts to go to the top, allowing candidates and their supporters to flood every user’s feed, rendering platforms useless, or nearly so. And if that is not what the provision means, one is at a loss to divine any plausible meaning.

Thus, the court says these provisions are likely unconstitutionally vague.

Still, NetChoice/CCIA don’t win their own summary judgment motion, in part because the court says that their amended complaint is still a “quasi-facial challenge” which runs into the same issues the original challenge faced at the Supreme Court, and because of that the court holds off on granting summary judgment, meaning the case continues to move forward to trial, even as the judge makes it pretty clear this law is a complete constitutional mess.

So this is about as good a ruling as NetChoice and CCIA could realistically hope for, given the procedural mess the Supreme Court handed down in Moody. Hinkle has made it abundantly clear that he thinks Florida’s law is a vague, unconstitutional disaster that can’t survive contact with the First Amendment. And yet, because the Supreme Court decided that “facial vs. as-applied” was the hill to die on, he can’t just say so and end it. Instead, a law that everyone — including the judge — can see is unconstitutional gets to march all the way to trial.

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That’s the real legacy of Moody’s procedural punt: it didn’t save these laws, but it did make killing them slower, costlier, and more painful than it has any right to be.

Filed Under: 1st amendment, as applied challenge, content moderation, facial challenge, florida, free speech, james uthmeier, moody, moody v. netchoice, robert hinkle, sb 7072, social media

Companies: ccia, netchoice

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I can’t believe how cheap the iPad Air M3 is for Prime Day

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It’s not often we can say an iPad is affordable, but this Prime Day deal makes the iPad Air more appealing to budget-conscious shoppers.

The 13-inch iPad Air M3 is now down to just £650 in this rare price cut. That’s nearly a massive £200 off its usual price, and marks the cheapest the tablet has ever been on Amazon. 

iPad Air M3 on table anglediPad Air M3 on table angled

The iPad Air M3 is seeing nearly £200 off in this rare Prime Day deal

We can’t believe how cheap the iPad Air M3 is for Prime Day. At just £650, not only does it boast a nearly £200 price cut, but this is the cheapest we’ve ever seen the Apple tablet reach on Amazon. Act fast to avoid disappointment.

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Just note that this model isn’t the latest iteration, as it has since been succeeded by the iPad Air M4. Even so, the iPad Air M3 remains a seriously capable tablet and a brilliant alternative for those who don’t necessarily care about owning the latest model.

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Powering the iPad Air is Apple’s desktop-level M3 chip which is able to handle all tasks with ease. In fact, we were even pleasantly surprised at how well the iPad Air performed when gaming, as it supports hardware-accelerated ray tracing in certain titles. In addition, there’s the Apple Intelligence toolkit at your fingertips which, although shouldn’t be the sole reason you opt for the iPad Air, is a nice addition to have.

Otherwise, the iPad Air M3 runs on iPadOS 26 which enables the tablet to work a lot more like a Mac, as you can multitask across apps and move them around much easier than before. If you’re looking for a laptop alternative, then the iPad Air M3 is a hard option to beat.

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Speaking of which, you can turn the iPad Air into a makeshift laptop by purchasing the Magic Keyboard and Apple Pencil separately. How useful either accessory really is will depend on your personal usage and preference.

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Overall, the iPad Air M3 earned a 4.5-star rating with Editor Max Parker concluding the tablet can do “just about everything the Pro series can do” and hailed it as “all the iPad you need”.

Now at its lowest ever price, the iPad Air M3 is finally an appealing choice for those seeking a tablet but not wanting to spend a fortune in the process.

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FOSS dev builds a BASIC compiler using LLVM

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personal tech

Not just any old BASIC, either: OS-9’s BASIC09

Neither LLVM nor GCC directly support the BASIC programming language – but a former Microware boffin proposes fixing that.

An interesting new proposal on the Discourse forum of the LLVM compiler suite has turned into a new standalone BASIC compiler. The original RFC was Adding BASIC09 frontend tool to LLVM. Author Boisy Petre proposed adding a front-end to enable LLVM to compile BASIC source code, and this has now turned into a standalone compiler called basic09c, which uses LLVM as a library. We are irresistibly reminded of the recent addition of ALGOL-68 to GCC

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As we said in 2023, BASIC is anything but dead, and, for BASIC’s 60th birthday, the following year we covered new versions of three modern FOSS dialects. The late Dr Thomas Kurtz would have been proud.

It’s not just any old home-computer BASIC, either. Dr Petre is working on a compiler for Microware BASIC09. This was one of the compilers that Microware offered for its multitasking, multiuser Unix-like OS, called OS-9. Way back in 1999, many users of Apple’s then-new MacOS 9 – often just called “OS 9” – confused it with the already 20-year-old Microware OS-9, and they pestered OS-9 newsgroups and communities with Mac questions and chatter. As The Register reported back then, Microware even sued Apple over the trademark.

BASIC09 is a structured BASIC: it has named procedures with local variables, supports constructs such as IF…THEN…ELSE, user-defined variable types – and no, it did not need line numbers. The best reference we can find to BASIC09 today is its Wikipedia article, but you can also read the manual [PDF].

Tandy Color Computer 2 resting on a table with cables nearby

A Tandy Color Computer 2 sits on a table at a retro computing event in Orlando, Florida, alongside cables and other equipment.

Microware is still around and still supports OS-9, which is marketed as an RTOS these days. Home microcomputer users, though, might have known OS-9: it was an optional OS for the British Dragon 32 micro and was on the list for the first British laptop too. For Stateside readers, the Dragon 32 was a relative of the American Tandy Color Computer, which had the same Motorola 6809 CPU and could also run OS-9.

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Aside from its technical merits, there are other good reasons he chose this particular BASIC: Although Boisy Gene Petre became Dr Petre last year, he has been in the industry for quite a while. He worked at Microware early in his career, and even quarter of a century ago he was writing articles about the Tandy CoCo.

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This is not the first time that the Reg FOSS desk has been bamboozled by boffin Boisy’s brilliance. That was way back in 2012, when he created one of our favorite ever retrocomputing projects: the astonishing Liber809. This performed a total brain transplant on the original Atari 8-bit machines, installing a 6809 CPU and supporting firmware.

As he explained in a post called The Beginning, the goal was to marry the most advanced eight-bit CPU with the most capable eight-bit computer of its time. Retrocomputing blog The Byte Cellar described it well. This would of course render the machine incompatible with all existing Atari software, but the plan was to make it able to run NitrOS-9 – a community distribution of the original 6809 version of the Microware OS. ®

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Ford Rehires 350 Engineers After AI Fails To Preserve Expertise or Train Juniors

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After Ford’s automated quality-control systems and AI tools fell short, the automaker hired 350 veteran engineers over the past three years to mentor younger staff and reprogram the underperforming technology. “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 on a call Wednesday. “Over prior years, we didn’t pay as much attention as we should have to the experience of our most knowledgeable engineers that have been with us through many product cycles.” Bloomberg reports: Those engineers were “at the heart” of Ford’s efforts to turn around quality problems, said Kumar Galhotra, chief operating officer. They now run mandatory meetings that rigorously troubleshoot quality problems and they have reprogrammed AI tools to head off glitches before they happen. “We had been relying more and more on automated quality systems” and not getting the desired results, Galhotra said. “We brought back technical specialists” and “they hunt for failure points before a part ever reaches the plant floor.”

The return of the veteran engineers at Ford cuts against the prevailing wisdom — and fear — that AI will replace all kinds of knowledge workers. But Ford found the machines couldn’t replace experience. “Mistakenly we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that that would produce a high-quality product,” Poon said. But “we recognized that for us to enhance some of our automation and machine learning and artificial intelligence tools we needed to ensure that they were trained by the most experienced individuals.”

As a result of the efforts of the old hands, Ford vaulted above quality stalwarts such as Toyota and Honda on JD Power’s bellwether survey that measures the quality of a car during the first three months of ownership. Only luxury brands Porsche and Genesis topped Ford this year.

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Increasing Local GPS Accuracy For A Small Robot

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Even though GPS makes it possible for us to easily navigate around the planet in almost any vehicle we’d like, whether that’s a passenger vehicle, airplane, or cargo ship, it’s not really suitable for applications that require sub-meter accuracy. For that, some specialized hardware is needed, and [GreatScott!] shows us how to do it using a small robot as a platform.

The key to extremely accurate GPS signals in this case is using a receiver that supports real-time kinematic positioning (RTK). This type of system relies on a base station with a known position communicating with local mobile receivers to increase the precision of those mobile receivers by comparing the phase angle of the received signals. Of course these modules are much more expensive than the average standard GPS receiver, but for this kind of accuracy there is always a cost.

After getting a baseline accuracy of around two meters with a standard GPS receiver, [GreatScott!] installs the RTK GPS mobile receiver on a tracked robotic platform and a base station on a fence post. With the RTK system running, the limiting factor in accuracy became the robot’s steering system, as its turning radius and steering algorithms weren’t up to the task of hitting centimeter-sized targets out of the box.

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But, as a proof-of-concept, it goes to show how accurate GPS can be as long as the right hardware is used, and for practical applications is good enough to mow a lawn with a robot or even do some amateur land surveying.

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China’s LineShine Supercomputer Takes the Crown as World’s Fastest Using Only CPUs

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China LineShine Supercomputer World's Fastest
LineShine reached the top of the latest Top500 ranking this week. The machine, installed at the National Supercomputing Center in Shenzhen, delivered sustained performance of 2.198 exaflops on the standard High Performance Linpack benchmark.



That equates to more than two quintillion calculations per second in double-precision arithmetic. It is the first time a system on the public list has exceeded two exaflops while using only regular CPUs. Engineers designed the system with 13,789,440 cores spread among customized LX2 processors. Each CPU contains 304 cores running at 1.55 GHz, two compute dies, and integrated high-bandwidth memory. The cores are designed on the ARMv9 architecture and include units for high-precision vector and matrix computations.

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More than 22,000 nodes are contained in 90 cabinets, which are linked by a high-speed network created in house. The overall facility consumes around 42.2 megawatts while in operation and achieves approximately 52 gigaflops per watt on the primary benchmark. Previous leader El Capitan is now in second place. The AMD-based system at the Lawrence Livermore National Laboratory in California achieved 1.809 exaflops. LineShine is now more than 20% ahead. El Capitan uses both regular processors and graphics accelerators, but LineShine does not.

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The design choice is noteworthy because most existing exascale computers rely heavily on graphics processors for raw performance on certain tasks. Instead, LineShine builds specialized acceleration circuitry right into its main chips. These circuits address the dense linear algebra at the heart of the benchmark test without the need for any additional accelerator hardware. China has regained the top rank in the Top500 for the first time since Sunway TaihuLight in 2017. For many years, the country concealed some of its most powerful weapons from the public eye. Submitting LineShine shows a willingness to benchmark freely again.


The supercomputer also leads the HPCG rating, which focuses on more realistic scientific workloads, with 22 petaflops. It performed worse on a mixed-precision test often used for AI-related tasks, demonstrating a preference for full 64-bit accuracy over the reduced-precision shortcuts employed in training large models. Scientists use machines of this size for climate modeling, advanced physics simulations, medical research, and nuclear stockpile stewardship work that demands high numerical accuracy. LineShine focuses on traditional high-performance computing applications rather than low-precision matrix computations, which are prevalent in many current AI programs.

Power consumption is higher than El Capitan’s (about 30-megawatt range), and efficiency falls short of the US system’s 60-plus gigaflops per watt. However, obtaining record double-precision performance on CPUs requires a distinct engineering path impacted by available components and design considerations. The processors run the Kylin operating system and interact across a proprietary network capable of 1.6 terabits per second per node. Everything is contained under a single, tightly integrated platform built on a single CPU family.

LineShine’s inclusion raises the total number of publicly validated exascale systems to five, which are spread across Asia, North America and Europe. Its introduction indicates that varied architecture options may still achieve high-quality results on the benchmarks that have defined supercomputing leadership for decades. Further tuning or additional nodes may increase performance, but the current numbers set a new benchmark for CPU-only architectures of this scale. The success is the result of synchronized improvements in CPU architecture, interconnect technology, and system integration, rather than a single breakthrough component.
[Source]

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How Shopify built an AI stack that doesn’t care which models survive

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Shopify built an LLM proxy that gives every engineer access to multiple AI providers — with automatic failover when any one of them goes down, changes, or disappears. When Claude Fable 5 shut down, Shopify’s engineers didn’t go into panic mode. The proxy shifted them to Claude Opus or GPT 5.5 automatically, without interrupting their workflows.

“Fable looks amazing; we used it of course,” Farhan Thawar, Shopify’s head of engineering, says in a new VentureBeat Beyond the Pilot podcast. “When a model comes and then it goes, or it could be as innocuous as an update, the proxy allows us to spray across the different providers,” Thawar says.

Shopify buys tokens in bulk and all users connect to models through its proxy, Thawar says. This gives his team access to reporting and failover; when there’s an availability issue with one provider, users can be “automatically, seamlessly” transferred to another.

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Enterprises can learn from this example and consider how a disruption might affect their business, Thawar says. At the very least, they should establish a solid backup plan. It’s important to have a system that allows for movement across models so enterprises are not “super tied” to a specific provider.

Distillation is another important strategy.

With distillation, a student model learns from a teacher model and typically becomes specialized in a narrower task. These small language models (SLMs) can be more beneficial than generalized, off-the-shelf models in some circumstances. For instance, Shopify’s flagship AI assistant, Sidekick, which performs numerous specialized subtasks for merchants so they can “remove toil” from their day-to-day.

Using smaller distilled models can be faster and cheaper than more generalized models, Thawar says. In some cases they have proven to be 2x cheaper and faster; in more extreme cases 30x cheaper and faster, he says.

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But “it isn’t just about cost and latency, which are big; it’s about accuracy,” Thawar says.

Engineers feed the UDP their teacher model, training data, evals, and a target model — say, Opus 4.8 distilling down to Qwen 3.5. The pipeline runs for about a day, then returns an evaluation showing what the fine-tuned model actually achieved on speed, cost, and accuracy for that subtask. If the tradeoff looks good, the engineer deploys it — no approval process required. Shopify’s internal platform, Tangle, lets anyone visualize the pipeline as it runs.

Thawar says his “dream” is to eventually not give the distillation pipeline a target model at all. Instead, users could provide the teacher model with data and evals and the directive: ‘Based on your learnings over time, I want you to look at a different class of model, different sizes, different types, and you tell me what the right distillation target is.’

“Maybe we’ll get surprised. Maybe it’ll be such a small model it could run on a phone,” Thawar says. “Other times, maybe it comes back and says, ‘There isn’t a way to distill this down to anything better than what we have at the frontier.’”

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Moving away from “AI reflexivity” to “AI leverage”

Shopify users can apply whatever harness they want: Claude Code, Codex, Cursor, GitHub Copilot for VS Code. “We expose everyone to the different harnesses so they can get a feel for what may or may not work in their workflow.”

But the company also implemented a usage dashboard; this allows Thawar’s team to ask interesting questions around not just token spend, but: Who’s using the most expensive tokens? Who’s spending more time on reasoning? What types of models are being used, and what disciplines and levels?

Regarding the “tokenmaxxing” question, Shopify does have “circuit breakers” in place. If a user has a model running for a long time (say, 10 hours) and it’s consuming a lot of tokens, they will get pinged, “Did you mean to spend this?”

As Thawar explains, sometimes the reply is “Oh, absolutely.” Other times it’s: ‘Whoa, I didn’t know that was running in the background. I totally forgot about it. I’d rather stop it now.’

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The ultimate goal, as Thawar describes it, is to move from “AI reflexivity” to “AI leverage,” and get people to really think deeply about where they can benefit most from AI in their workflows.

Listen to the full podcast to hear more about:

  • Shopify’s philosophy of building infrastructure before features. As Thawar puts it: “We’ve always built more infra. We will continue to always build more infra.”

  • How Shopify’s internal AI agent, River, creates a “substrate of information” across the company.

  • How Thawar’s OpenClaw agent figured out he was traveling from his calendar — and what that moment told him about where agents are actually headed.

You can also listen and subscribe to Beyond the Pilot on Spotify, Apple or wherever you get your podcasts.

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Inside the Computer That Powered Microsoft’s $10,000 Surface Table from 2008

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Inside Microsoft Surface Table 2008
Photo credit: Bot Junkie
Michael MJD recently tracked down something most people never knew existed. Back in 2008 Microsoft released a giant touch table called Surface. It weighed close to 200 pounds, cost around $10,000, and turned an ordinary tabletop into a shared computer screen. The public saw the glowing surface and the wild multi-touch demos. What stayed hidden was the actual computer that made the whole thing work.



MJD purchased only the computer portion, which was marketed as new old stock on eBay. He brought the metal box home, opened it, and showed everyone what hardware Microsoft chose for one of its most ambitious products. He began by removing a row of screws around the enclosure. The first thing to come out was a molded plastic component. It was designed to divert airflow out of the power supply and keep everything cool inside the cramped enclosure. With the plastic removed, a metal cover was lifted, revealing the components underneath.

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The foundation was an Asus P5LD2-VM motherboard with Intel’s LGA 775 socket. This was a typical board from the mid-2000s, chosen because it was dependable and well-known to manufacturers. Everything else in the box is connected to it. The socket housed an Intel Core 2 Duo E6400 clocked at 2.13 GHz. MJD left the heatsink in place during the initial assessment, but later validated that the processor precisely matched the specifications Microsoft gave for the original tables. It came with two 1 GB memory sticks, giving the machine a total of 2 GB of DDR2 RAM. Storage came from a 250 GB hard drive, however official records for the first-generation Surface frequently indicate 160 GB, so the larger drive raised a minor concern. It could have been swapped at some time during the unit’s life, or the published numbers merely differed between early manufacturing runs. In any case, the drive was installed in the same location as a typical desktop PC.

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A Sapphire Radeon X1650 Pro card with 256 MB of internal memory handled graphics responsibilities. This was a reliable mid-range card from 2006 that handled the graphics for the table’s rear-projection display. The motherboard included minimal integrated graphics as a backup, but the separate card handled the actual job here. A second expansion card placed alongside the graphics card. This one carried Microsoft’s branding and was dubbed the Milan DSP board. It handled specialized digital signal processing tasks designed for the Surface platform. In the full table, this card most likely managed data from the infrared cameras or enabled the system’s unique networking characteristics for communicating with things on the surface.

Inside Microsoft Surface Table 2008
Power came from a supply housed within the same box. MJD didn’t have the original table’s custom wiring harness, so he connected a simple power button salvaged from an old eMachines PC to the motherboard’s two-pin socket. That simple hack was sufficient to bring the system to life. When turned on, the machine booted into a modified version of Windows Vista designed specifically for these tables. The desktop featured large, finger-friendly icons and a suite of programs based on the Surface vision. MJD navigated through a music app, a few games, and other programs created for the platform years ago. Everything functioned as it would have when the entire table and its large screen were still attached.

Inside Microsoft Surface Table 2008
He did a few quick checks to confirm the hardware. Task Manager and basic system utilities showed that the Core 2 Duo processor and Radeon card were working as expected for hardware at the time. There were no surprises, simply a clean, working example of the identical configuration that Microsoft delivered with the original Surface tables. The entire tables are extremely rare today. Complete working devices are practically never available for sale, and when they do, they attract high collector prices. Owning the computer part alone still provides a direct look at the practical decisions behind a device that appeared futuristic in 2008.

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I’m a robot vacuum tester, and the robovac I use every night to stop myself drowning in dog hair just crashed to a new lowest-ever price

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Don’t get me wrong, I love my dog. He’s a really, really good boy. Maybe the best boy? I don’t know, I haven’t met all the dogs. Yet. But I what I don’t love so much is his hair, which seems to shed at such a rate that it’s astounding to me that he has any left at all.

It’s been an absolute blessing, then, to have spent two years testing vacuums for TechRadar. I’ve tested all kinds of vacuums, at all kinds of price points, but the one I’ve clung on to is the Roborock Saros 10. It arrived in my home in spring 2025, and since then it has been a vital tool in my anti-hair arsenal. It gets sent out cleaning every night — and it hasn’t let me down yet.

View the full UK Amazon Prime Day sale / US Prime Day sale

Despite being one of my most-used gadgets, I found it a little hard to recommend at list price (a cool £1.5K at launch). Since then it’s attracted a number of deals, but none so good as the current Prime Day discount: the Roborock Saros 10 is now £699.99 at Amazon (down from an adjusted list price of £1,199.99). At that price, I would absolutely recommend it, as it’s one of the very best robot vacuums I’ve ever used — and, like I say, I’ve used plenty.

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Shoppers in the US get a slightly different deal: the Saros 10R is now $884.99 at Amazon (was $1,599.99). That’s another all-time-low price on this sister model, which comes equally well reviewed (by another dog owner). These are amongst the very best Amazon Prime Day deals I’ve seen.

If you’re suspicious of such a hefty discount, don’t be. The robot vacuum market rattles along at a rate of knots, with multiple new models released each year (the two bots showcased here have officially been replaced by the Saros 20 and Saros 20 Sonic), That means models like this might be considered old news, even though they’ve only been around a couple of years.

The rate of appreciable improvement has slowed, though, so while the specs in the newest models might be technically better, you’ll likely struggle to see much — if any — difference in the performance. That’s true in the case of the Saros 20, which I also tested.

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Roborock Saros 10 robot vacuum in mopping mode

(Image credit: Future)

What’s so good about the Roborock Saros 10?

Why do I love the Roborock Saros 10 so much? It’s an ultra-capable all-rounder that requires minimal effort and maintenance from me. The vacuuming is efficient and effective, and the navigation and object avoidance is reliable — even when faced with the complicated corner of my kitchen that includes a thin rug, a table and multiple dining chairs, and also acts as the dog’s preferred toy discard area. The mopping is also effective and thorough, clearing pawprints and smudges with ease.

The dock is also a standout. It auto-empties the dust cup, and has enough oomph that I’ve never experienced a hair blockage during this process (not true of all self-empty robovacs I’ve used). It’ll also top up the onboard water tank from its larger dock reservoir — adding the right amount of cleaning fluid as it does so — and clean and dry the mop pads when it’s done cleaning. Opting for a slightly older flagship model is my #1 tip for saving money when shopping for a robot vacuum.

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What’s the difference between the Saros 10 and 10R?

The Roborock Saros 10 and 10R are very similar in terms of specs and performance, but there are a couple of design differences. The first is that the 10R uses a different navigation system; one which doesn’t require a raised LiDAR puck. So instead of a puck that can pop up and down, there’s no puck at all. On test, we found this robovac navigated just as accurately as the 10. Both bots are the same height, and both are equally good at cleaning under the sofa.

The second difference is the mop pad style. While the 10 has a vibrating, D-shaped pad, the 10R boasts two, spinning pads. If I had to choose, I’d say I slightly prefer the dual mop-pad setup, but there’s barely anything in it. In terms of wet cleaning, neither of these robot vacuums will let you down.

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Prime Day Live: We Picked Out the 164+ Best Deals Worth Buying on Day 3

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The NotePin S AI wearable, seen here on the wrist of CNET’s Katie Collins, could be really useful for my job. And it’s on sale for Prime Day.

Andrew Lanxon/CNET

I took over the role of CNET’s editorial leader earlier this year, and while I’ve participated in Prime Day sales as a TV reviewer and general deals editor here for (literally) decades, this is my first Prime Day as EIC. In case you’re wondering what purchases a person like me is considering this time around, here’s a sampling.

iPad 11-inch A16 ($300): My artistic daughter has been asking for an iPad and if my wife approves, I’ll likely get her this basic version, our top pick for most people. I’d also get her the Apple Pencil (on sale for $60). We’d save both of these for Christmas presents.

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Belkin Portable Charger Bank ($38): My family and I always need portable chargers. Half our devices call for Lightning and the other half for USB-C. This does both and I like the built-in cables.

Plaud NotePin S AI Notetaker ($152): In my new role I take more meetings than ever, and I also have plenty of valuable face-to-face conversations in the office and beyond. I currently depend on the Otter app on my phone and Gemini+Google Meet recordings at work to take notes (with appropriate permission, of course). This AI wearable could be my “secret weapon” to consolidate everything in one place.

JBL Go 4 Bluetooth Speaker ($38): I actually bought this one a few days ago when it was $40 – still a great deal, but now even better. It’s no longer one of our best Bluetooth speakers but it’s good enough for my (other) daughter, who wants one for the beach. At this price, I won’t be too annoyed if (when?) it gets destroyed by sand and surf. And yes, I got her the pink one which I know she’ll love. We’re saving this for her birthday.

Anker Solix F2000 portable power station ($749): I own a travel trailer and upgraded to solar with an inverter, but at a recent (shady) campsite, I still had to break out my loud, annoying propane generator. Sure, I could just add more standard 12V LiPo4 batteries, but this portable power station is so much more versatile. It includes a 30A RV outlet, and the wheels make it worth the extra $50 over the Bluetti AC200L. No way my wife approves this one, but it stays on the list anyway because I’m camping tech obsessed.

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