from the good-deals-on-cool-stuff dept

MagStack is the perfect on-the-go wireless charging station that also transforms into a floating stand for smartphone FaceTime or video playback while charging. This 3-in-1 foldable design featuring 3 wireless charging spots, enables charging for up to 3 devices simultaneously, including iPhone, Apple Watch, AirPods Pro, AirPods with Wireless Charging Case, other Qi-compatible Android phones, and Bluetooth earbuds. With its versatile foldable design, MagStack also folds into a space-saving single-device charger for your phone or earbuds. It’s on sale for $45.

Note: The Techdirt Deals Store is powered and curated by StackSocial. A portion of all sales from Techdirt Deals helps support Techdirt. The products featured do not reflect endorsements by our editorial team.

Filed Under: daily deal

Cybersecurity researchers have identified nearly 7,000 fraudulent Amazon-themed domains registered in the six months leading up to Prime Day 2026, which begins on 23 June. Check Point Research tracked 6,843 new domains created between December 2025 and May 2026, with registrations peaking at 1,446 in April and remaining elevated at 1,267 in May.

Of the total, 9.2 percent were classified as malicious or suspicious. The rate accelerated sharply in early June: during the first week of the month, one in every 13 newly registered Amazon-themed domains was flagged, according to Check Point’s analysis.

Prime Day 2026 runs from 23 to 26 June across 22 countries, with four additional markets joining later in the summer, according to Amazon’s official event page. The extended four-day window and global reach make it a high-value target for phishing operations, which follow the same seasonal playbook that researchers documented around the FIFA World Cup, where over 13,000 fraudulent domains appeared in the months before kickoff.

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The phishing infrastructure includes fake Amazon storefronts designed to harvest credit card numbers, spoofed login pages that steal account credentials, and email campaigns with subject lines such as “Refund Due, Amazon System Error” that direct recipients to counterfeit sites. Check Point flagged one campaign using a sender address mimicking Amazon’s customer service domain closely enough to bypass casual inspection.

A notable cluster targeted Spanish-speaking shoppers. Check Point identified 46 domains registered under the “amazoncredito” pattern, all linked to a single registrant and aimed at Latin American markets where Amazon has been expanding its Prime membership. Five of six “amazon-prime” top-level domain variants were already classified as malicious at the time of the report.

The tactics are not new, but the scale keeps growing. Google recently sued a Chinese cybercrime ring that used AI to generate phishing code and operated one million fraudulent domains, illustrating how cheap and automated domain-based fraud has become. Check Point’s findings suggest that Amazon-themed operations are following the same industrial pattern, with thousands of domains registered months in advance and activated as shopping events approach.

Check Point recommended that shoppers type amazon.com directly into their browser rather than clicking links in emails or ads, enable two-factor authentication on their Amazon accounts, and treat any unsolicited refund notification as suspicious. The company also advised looking for HTTPS and padlock icons, though it noted that fraudulent sites increasingly use valid SSL certificates to appear legitimate.

The timing is significant because Prime Day has become one of the largest online shopping events globally, generating billions in revenue and drawing millions of first-time deal hunters who may be less familiar with phishing tactics. Amazon has not publicly commented on Check Point’s findings.

from the more-lazy-slop-from-slop-incorporated dept

The Trump Organization still hasn’t shipped their promised Trump “made in America” phone to most of the customers who laid down a $100 deposit a year ago. But they did recently start to ship early review copies to a handful of outlets and preferred cultists. What outlets generally found wasn’t surprising: it’s a pretty substandard smartphone pre-loaded with Trump propaganda apps like Truth Social.

But reporters working with iFixit have also confirmed something that was speculated for a while. Namely that the phone is just a lazy rebrand of the two-year old Taiwanese-made HTC U24 Pro with a garish coat of yellow paint (here’s a non-paywalled iFixit exploration):

“The Trump Mobile T1 phone, originally marketed as “Made in the USA,” is nearly identical to the two-year-old HTC U24 Pro, a phone made by the Taiwanese company HTC using Chinese parts, according to a technical analysis the repair-guide and parts company iFixit conducted in partnership with NBC News.”

As is pretty typical for Trump business ventures, this entire affair is the laziest slop imaginable.

Trump Mobile launched last year with a lot of fanfare. But as we noted when it was unveiled last year, even calling it a mobile company was being generous: the company is really just a lazy rebrand of an existing MAGA-friendly MVNO provider, Patriot Mobile, which itself just resells T-Mobile service (Patriot just got caught up in an interesting influencer marketing dust up, if you missed it).

A cornerstone of the venture was a “made in America” “gold” “Trump phone” named the T1 that was supposed to launch last August. Though shortly after launch the Trump Organization eliminated all the “made in America” claims, shifting to promises that it was “made with American values in mind.”

If by “American values” we mean lazy, poorly secured slop preloaded with spyware and propaganda and slathered with half-assed branding logos and ugly paint then dramatically marked up to exploit suckers, then sure, okay.

The knockoff phone with Chinese internals of course arrives as the Trump FCC pretends to be cracking down on Chinese gear in hardware, routers, and other electronics. It’s also worth noting that the HTC U24 Pro was priced $469.99 retail when it launched two years ago. The Trump Mobile T1 is selling for $500, and they’ve hinted that the price could be going up.

It’s also worth pointing out that before the Trump Organization could even get phones into peoples’ hands, they suffered a significant data breach. A breach that not only revealed customer names, email addresses, mailing addresses, cell numbers, and order identifiers, but also that they’d likely only sold around 30,000 phones, a far cry from the 600,000 they had claimed.

The curious part is that it really shouldn’t have taken even the Trump Organization this long to get a sloppy rebrand into consumers’ hands. It’s not like they even had to manufacture new phones at meaningful volume. Maybe their plans were upended by ignorant tariffs and unnecessary wars? Anyway, it’s just hard to really overstate how very much on brand this all has been.

Filed Under: knockoff, mobile, telecom, trump mobile, wireless

Others Tested

Photograph: Pete Cottell

Lifeboost Mindflow for $40: The flavor of this instant powder is snappy and astringent at first, then it mellows into a warm middle ground after a few sips and a short cooling period. By the middle of the cup I forgot I was drinking something other than coffee, and the mild acidity on the finish–likely a product of the CognatiQ Coffee Fruit Extract that’s lauded on the back of Mindflow’s mylar pouch–tastes similar to a nice cup of Ethiopian or Rwandan coffee if you close your eyes and pretend for just a moment. Regarding its potency, if mushroom supplements were attendees at a state college keg party, Lifeboost would be the unremarkable guy pacing himself in the back while everyone else is getting blitzed like the world is ending. It’s unassuming yet self-assured, patiently waiting for all other entrants to crap out so it can make its move. I copped a mild buzz just a few sips in, and I felt alert and wide-eyed for a good two hours after the silty final sips of the cup were consumed. Electrolytes are uncommon in this space, which means this is a rare entry in the mushroom supplement world that purports to be a good pick if hydration is a trivial concern.

Photograph: Pete Cottell

Four Sigmatic Organic Coffee for $20: Four Sigmatic’s Focus blend is labeled as a dark roast, but it’s missing the cigarette-butts-and-bowling-alley aftertaste that looms on the finish of similar blends. Despite my preference for lighter beans, this hit like a hug from an old friend after weeks of sipping murky silt. The caffeine buzz normalized after two days of using Think in lieu of more standard shroom-based coffee replacements, so I added a three-quarter-teaspoon hit of the powdered Focus blend to my daily cup to see what would happen. Within 10 minutes I felt an overwhelming urge to sort my finances spreadsheet in preparation for tax season, then I set up a new template in Loopy Pro to accommodate a friend who planned to join my basement jam session that evening. He bailed, but I was jacked on Genius Adaptogens so I played all the instruments myself into the wee hours of the night.

North Spore Functional-5 Mushroom Coffee for $18: Most mushroom-infused ground coffee blends are filed under the “Medium Roast” category, which is typically a safe catch-all that grocery store brands and discount purveyors describe their preground product as to avoid pissing off discerning light-roast aficionados such as yours truly. Nine times out of 10 they hit like a dark roast, with an ashy taste and a healthy dose of the oil that seeps out of the beans during the elongated roasting process, shimmering and swirling around the top of your cup like a puddle in a parking lot. This coffee from North Spore, which makes our favorite mushroom-growing monotub and spray-and-grow mushroom kit, lacks all of those off notes while still retaining a sturdy, earth flavor that’s far enough removed from the citric and buttery notes I love most about classic high-end light roasts to stand up as its own unique thing. There’s a hint of mushroom flavor on the swallow if you really look for it, but you could easily swap this in for someone’s morning cup of Folgers or Illy medium roast and they’d be none the wiser.

Ryze Superfoods Mushroom Coffee for $65: One could consider two different approaches to how purveyors of mushroom coffee dial in the flavor profile of their product: They can go all in with a bombastic brew filled with spices and overtones, or they can play it safe and concoct the base of a beverage that tastes more like memories of other drinks than a beverage with an identity of its own. The underwhelming flavor of Ryze falls in the latter camp. In fairness, there are plenty of folks who have no interest in savoring their morning beverage and instead need to put the liquid inside them as fast as possible so they can “adult” that day. Twenty-one-year-old Pete thought people who claimed to enjoy espresso were insane, yet here I am, two decades later wishing I could sip bitter bean water instead of this sour cup of forgettable swill that curdled the whole milk I tried to cut it with. A week with Ryze did little to boost my mood, focus, or energy. It mostly made me cranky and sad.

Cuppa for $30: Like the friendly foreigner who calls his daily cup of tea or coffee his “cuppa,” this newcomer is polite, congenial, and inoffensive. The first sip brought to mind a really good cup of coffee at a nameless diner, with a light body and very mellow acidic notes on the swallow. The small dose of ruddy powder pulled from the bag with the included plastic scoop dissolved thoroughly with a few stirs, and the pristine lack of sediment in the cup was exactly as advertised. The boost of energy is also unassuming and easy to relegate to the background, which could be a welcome respite from the blast of caffeine many coffee addicts think they need right when they wake up every morning. After a week with Cuppa I started to enjoy easing into my daily brain vibrations rather than white-knuckling it off the rip at 7 am on the dot every morning.

Not Recommended

Photograph: Pete Cottell

MUD/WTR Original Blend for $51: The packaging of MUD/WTR isn’t quite as unhinged as a bottle of Dr. Bronner’s, but it’s definitely in the same realm. The spicy dust inside the can is a maximalist circus of weirdness as well, with herbaceous stalwarts like turmeric and masala chai holding it down alongside the usual shroom suspects. It took me a few days to realize that properly emulsifying this ruddy power per the suggested instructions—1 tablespoon with ¾ cup of water, battered thoroughly with the included handheld immersion blender—is an impossible task, so I started experimenting with supplemental ingredients in hopes that some blend of milk, fat, and sugar would minimize the gritty aftertaste that overwhelms the palate. I landed on 1 tablespoon of simple syrup and 4 ounces of whole milk frothed in my trusty Subminimal NanoFoamer Pro. The final result hits somewhere between a chai latte and the kind of hot cocoa you’d order at a coffee shop with boring ’90s music, mean baristas, and a dirty bin full of stale vegan + gluten-free snacks next to the register. I didn’t hate it, but the bottom quarter of the cup is an undrinkable gunky mess. And don’t get me started on the chunky brown lacing that clings to the edge of the cup. The physical and mental effects of MUD/WTR felt more like a facsimile of a boost than a visceral kick in the pants, but a placebo high is better than nothing, right? Combine that with the amount of adjunct ingredients required to make this drinkable and I ended up with a beverage I would only drink every now and then as a treat on a chilly day rather than a daily sipper I can rely on for increased focus, energy, virility, and the million other things this product promises within the wall of text that adorns its packaging.

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Highly anticipated: After months of delays and growing anxiety about memory prices, Valve has officially confirmed pricing, configurations, and a June 30 launch date for its Steam Machine. The living-room gaming box starts at $1,049 for a 512GB model and climbs to $1,349 for the 2TB version – a significant premium over the sub-$750 figure that had been anticipated when Valve announced the hardware in November 2025. Getting one at launch, however, is far from guaranteed.

Under the hood, the Steam Machine packs a semi-custom AMD platform: a 6-core, 12-thread Zen 4 CPU clocked up to 4.86GHz, an RDNA 3 GPU with 28 compute units and 8GB of GDDR6 VRAM running at up to 2.45GHz within a 110W envelope, 16GB of DDR5 system RAM, and either 512GB or 2TB of NVMe SSD storage.

A microSD slot provides additional expansion. The M.2 SSD is user-replaceable in both 2230 and 2280 form factors; RAM is also swappable, though the compact thermal design makes it more involved than a standard desktop.

For GPU context: 28 RDNA 3 compute units at those clocks is roughly equivalent to a Radeon RX 7600, a capable mid-range card from late 2023, but not where AMD’s GPU lineup sits in mid-2026.

Four configurations are available:

• Steam Machine 512GB – $1,049
• Steam Machine 512GB + Steam Controller – $1,128
• Steam Machine 2TB – $1,349
• Steam Machine 2TB + Steam Controller – $1,428

We got it wrong: you will be able to buy a Steam Machine in 2026 after all…

The Steam Controller normally retails at $99.99, making the bundle a mild discount. The 2TB models also include two additional faceplates: red fabric and solid walnut. Valve will also release the CAD files for the external hull so third parties can make their own. Beyond that, Valve’s engineers confirmed there are no additional faceplate collaborations planned at launch.

Developing…

On Monday, Govee announced a new partnership with Warner Bro.’s HBO, bringing new smart lighting features made specifically for House of the Dragon Season 3, following the Game of Thrones prequel premiere on Sunday. 

You’ll find Govee on several of our smart home lighting recommendation lists, for both indoors and outdoors. The company produces lights with a variety of customization options, including music syncing and algorithms that allow users to create lighting schemes based on their own prompts or photos they like. In this case, Govee is highlighting how its TV backlighting can work with shows like House of the Dragon. 

Representatives from Govee and HBO did not immediately respond to requests for comment.

Govee’s TV backlighting, ready for dragons and deception

A product like the Govee TV Backlight 3 Pro ($180) adds a strip around the edge of your TV and a three-part camera that’s mounted on top. That camera looks at what’s on the TV screen, then automatically adjusts the backlighting to match. It’s an effect intended to make TVs look bigger, more cinematic and immersive. In this case, Govee mentions that its color-matching system can add gold ripples to Small Council meetings at the Red Keep, or alternate between low light and fiery reds during a nighttime dragon attack.

Color-shifting like this is also available for the Govee TV Backlight 3 Lite ($90) and the Govee TV Backlight 3 ($110), which come with fewer camera lenses than the Pro version.

But that’s not all Govee is offering for Game of Thrones fans. The company is also adding themes for its wider range of lights.

New Govee light and app themes for anywhere in your home

For those who don’t have a Govee TV Backlight product or want to extend the House of the Dragon theme to other areas of their homes, Govee has an additional creation. The company is adding three light scenes that can apply to any Govee lights, from replacement light bulbs working in concert to the company’s skylights and light curtains.

The first is Dracyrus, an amber-and-ember theme that the company says evokes torchlight and dragon breath. The second is called Fire and Blood, made to mimic the crimson and black tones of Targaryen banners. And the third is Green Reign, an emerald-and-gold scene Govee made to reference royal intrigue in the Red Keep.

These scenes are available for any Govee lights. If you have a Govee product, update it and see if you can access these new themes ahead of the next episode. 

We’ve tested Govee’s TV backlights at CNET, and we’ll let you know if we find them a particularly good accompaniment for House of the Dragon as the season progresses. 

Last night, the increasingly enterprise-focused AI startup Sakana launched Fugu, a multi-agent orchestration system that delivers frontier-level AI performance through a single, OpenAI-compatible API.

Designed for developers, enterprises, and nations seeking resilience against vendor lock-in and geopolitical export controls, Fugu (Japanese for “pufferfish”), bypasses the traditional monolithic model structure by dynamically routing queries to a swappable pool of specialized AI agents.

Sakana CEO and co-founder David Ha, formerly of Google Brain, positioned Fugu as a more reliable option for enterprise workflows than any single AI model provider in the wake of Anthropic’s move on June 12 to revoke public access to its most powerful models, Claude Mythos 5 and Claude Fable 5, in the wake of a U.S. government export control order. As Ha wrote in a post today on X:

“Fugu dynamically orchestrates the world’s best models to tackle complex tasks. We are proving that a well-orchestrated pool of swappable agents can match restricted frontier models like Fable and Mythos.

But Fugu is about more than just performance. I believe that Orchestration Models are the next frontier, beyond bigger models.

Advertisement

Relying on a single company’s model for national infrastructure is a massive risk. As recent export controls have shown, access to top models can disappear overnight.

Collective intelligence is the practical hedge against this concentration of power. Fugu simply routes around vendor restrictions by relying on an entirely swappable agent pool.”

Sakana AI explicitly states that the specific models Fugu selects and how it coordinates them are proprietary, meaning this routing information is hidden from the user by design. The documentation only refers generally to a “diverse pool of powerful models,” “multiple LLMs,” or “specialized models” without providing a specific count.

By acting as a sophisticated coordinator rather than a standalone foundation model, Fugu matches the output quality of top-tier models like Fable and Mythos on third-party benchmarks of agentic tasks, while fundamentally altering how developers deploy critical AI infrastructure.

How Sakana Fugu works and where it beats Anthropic’s Claude Fable 5

At its core, Sakana Fugu operates like a master general contractor. When presented with a complex request, Fugu does not attempt to execute every step itself.

Instead, it breaks the problem down, delegates sub-tasks to a pool of expert foundation models, verifies their work, and synthesizes the final output.

“Fugu is itself an LLM, trained to call various LLMs in an agent pool, including instances of itself recursively,” the Sakana AI team noted in their technical release.

Grounded in two of Sakana’s 2026 research papers, TRINITY and the Conductor, the system autonomously manages the entire lifecycle of model selection and verification using learned coordination strategies rather than hand-designed workflows. To the end user, this multi-agent swarm is entirely abstracted behind a standard API endpoint.

Sakana AI is offering two variants of the system to cater to different operational workloads:

• Fugu: A high-speed, low-latency model optimized for everyday tasks. It is designed to act as the default engine for interactive chatbots and integrates directly into coding environments like Codex.

• Fugu Ultra: The flagship tier engineered for complex, high-stakes tasks such as AI research, cybersecurity analysis, and multi-step patent investigations. According to Sakana, Fugu Ultra coordinates a deeper pool of experts and matches industry-leading monolithic models across rigorous scientific and reasoning benchmarks.

Additionally, on the pay-as-you-go plan, standard Fugu charges a dynamic rate based on the specific underlying models activated, whereas Fugu Ultra utilizes a fixed pricing structure starting at $5 per million input tokens and $30 per million output tokens.

As indicated by benchmark charts shared by Sakana, Fugu actually exceeds the performance of Anthropic’s Claude Fable 5 on LiveCodeBench, an open source benchmark testing coding performance on regularly refreshed, software problem-solving tasks (Fugu Ultra: 93.2, Fugu: 92.9, Fable: 89.8), and beats the prior Claude Mythos Preview model on GPQA-D (Diamond) , a test of 198 graduate-level multiple-choice questions in biology, physics, and chemistry (Fugu Ultra: 95.5, Fugu: 95.5, Mythos Preview: 94.6).

By orchestrating multiple models from different providers, Fugu essentially builds native redundancy into the AI stack. If one provider suffers an outage or faces sudden regulatory restrictions, Fugu routes around the disruption to maintain uptime.

Licensing and availability

Fugu is offered as a commercial, proprietary API service, not an open-source framework.

Because Sakana’s core intellectual property lies in its non-obvious collaboration patterns, the specific routing information—meaning exactly which underlying models Fugu selects for a given query—remains proprietary and is intentionally hidden from the user.

However, Sakana offers critical controls for enterprise data compliance. Developers can explicitly opt specific models or providers out of their Fugu routing pool to maintain strict corporate privacy standards.

Additionally, users can opt out of having their prompts used for future training data. Geographically, Fugu is restricted from operating within the European Union (EU) and European Economic Area (EEA) while Sakana works to align its black-box data routing architecture with GDPR regulations.

Pricing is fairly steep

Fugu is available immediately in most regions—with the temporary exception of the EU and EEA—at subscription tiers and pay-as-you-go pricing.

Teams can opt for monthly subscription allowances designed for individual or hands-on use: a Standard tier at $20/month for lightweight workflows, a Pro tier at $100/month providing 10x standard usage, and a Max tier at $200/month offering 20x usage for continuous, long-running tasks. I wasn’t able to find the actual amount of tokens covered under these plans, but I’ve reached out to Ha on X for more information.

As part of the initial rollout, Sakana is offering a free second month for users who subscribe to any tier by July 31, 2026.

For enterprise scaling and production deployments, Sakana offers an elastic pay-as-you-go plan. Crucially for high-stakes environments, requests made under this consumption-based model are served at a higher priority than those from monthly subscription plans.

Under this framework, the standard Fugu engine charges the single rate of the highest-tier underlying model involved in a query, without ever stacking multi-agent fees. The flagship Fugu Ultra tier (fugu-ultra-20260615) utilizes a fixed pricing structure per one million tokens: $5 for input, $30 for output, and $0.50 for cached input. These rates increase to $10, $45, and $1.00 respectively for extreme workloads utilizing context windows above 272K tokens. That puts it among the more expensive options compared to single AI models via provider APIs:

Developers modeling operational costs should also note a significant architectural caveat in how Fugu bills for its multi-agent capabilities. According to the developer documentation, Fugu Ultra’s API responses include detailed usage fields that separate user-visible token generation from internal orchestration work. The background tokens consumed and generated when Fugu delegates sub-tasks, verifies code, or routes between underlying agents are not absorbed by the provider; they represent real token usage and are counted toward the final price of the request at standard rates.

The Orchestration landscape: Fugu vs. The Field and notable benchmark performance

To understand Fugu’s position in the mid-2026 AI ecosystem, it is critical to distinguish between model routing and multi-agent orchestration.

Over the past year, enterprise adoption of standard routing platforms—such as Not Diamond, Martian, and the open-source RouteLLM framework—has skyrocketed. These systems act as intelligent air traffic controllers; using semantic classifiers or meta-models, they analyze an incoming prompt and predict which single foundation model will yield the highest quality or most cost-effective response, dispatching the query accordingly.

Fugu operates on a fundamentally different paradigm. Rather than making a one-shot routing decision, Fugu aligns more closely with complex multi-round systems like Router-R1 (a framework introduced at NeurIPS 2025). It breaks a query down, interleaves reasoning with delegation, and dynamically assigns sub-tasks to multiple models in parallel or sequence before synthesizing a final output.

While frameworks like LangGraph, CrewAI, and Microsoft AutoGen offer developers the tools to build similar multi-agent systems, they require immense manual configuration—defining roles, setting up conditional edges, and managing state across long-running loops.

Fugu abstracts this operational overhead entirely. It is essentially a LangGraph-style workflow packaged as a single, black-box API endpoint.

An orchestration system is ultimately bounded by the raw capabilities of the underlying models in its pool, a reality reflected in Sakana’s own benchmark testing against standalone frontier models.

On rigorous coding and agentic tasks, collective intelligence shows a distinct advantage over standard models. Fugu Ultra posted a 73.7 on SWE-Bench Pro, significantly outperforming Anthropic’s Claude Opus 4.8 (69.2) and OpenAI’s GPT-5.5 (58.6).

However, Fugu is not a silver bullet, and its performance is not a clean sweep across the board. When compared to highly specialized or restricted-access monolithic models, Fugu occasionally trails:

• SWE-Bench Pro: While Fugu Ultra (73.7) beat most accessible models, it was comfortably eclipsed by Anthropic’s limited-access Fable 5 (80.0), which is currently absent from Fugu’s swappable pool due to the U.S. government’s export control order and Anthropic’s subsequent response to remove the model entirely from global usage.

• Humanity’s Last Exam: Fugu Ultra (50.0) narrowly edged out Opus 4.8 (49.8), but again fell short of Fable 5 (53.3).

• Long-Context and Security: On the MRCRv2 long-context-recall test, OpenAI’s GPT-5.5 maintained the lead (94.8 vs Fugu Ultra’s 93.6), and Opus 4.8 remained the top performer on the CTI-REALM cybersecurity benchmark (69.6 vs Fugu Ultra’s 69.4).

The quantitative data points to a clear conclusion: Fugu is highly effective at boosting performance on messy, multi-step tasks (like writing a complex HTML5 game from scratch) by leaning on the combined strengths of multiple mid-tier and high-tier models.

However, for sheer brute-force reasoning within a single, highly constrained domain, the industry’s largest standalone models still hold the edge—provided an enterprise can maintain uninterrupted access to them.

Background on Sakana’s formation and noteworthy achievements to date

Sakana AI was formed in Tokyo in 2023 by Llion Jones, a co-author of Google’s foundational 2017 “Attention Is All You Need” paper, and David Ha, the former head of research at Stability AI.

Disillusioned by large tech company bureaucracy and the industry’s hyper-fixation on scaling single, massive foundational models, the founders built Sakana around principles of biomimicry and evolutionary computing.

The company’s name, derived from the Japanese word for fish, reflects its core technical thesis: utilizing collective “swarm” intelligence rather than brute-force compute. Following a $2.6 billion Series B valuation in late 2025 and the recent June 2026 launch of Marlin—an autonomous, eight-hour research agent for the B2B sector—Fugu represents the commercialization of Sakana’s multi-agent routing technology for everyday developers.

A mixed reception among the broader AI community online

The developer community has responded to Fugu by rigorously testing its practical tradeoffs, weighing its routing efficiencies against the sheer power of monolithic foundation models.

AI observer, developer and influencer Chris (@ChrissGPT on X) highlighted the specific utility of Fugu over raw foundational AI.

“For a single clean prompt, you probably would [use Fable 5, Mythos, or GPT-5.5 directly],” he noted, but argued that Fugu’s true value emerges in messy, multi-step environments. “…whether it involves delegation, verification, synthesis, code review, research loops, security analysis… the more it would make sense to use this,” he wrote.

Chris also pointed out the strategic geopolitical advantage of Fugu’s architecture, noting that if frontier AI access is abruptly revoked due to regulation or export controls, an orchestrator can dynamically swap models to prevent a total system failure.

Creative agency owner Mark Santos (@markksantos) of Mark Studios provided a direct, real-world comparison by tasking both Fugu Ultra and Claude Opus 4.8 with building a “Crossy Road” game clone using Three.js. The results underscored the operational differences between an orchestrator and a monolithic giant:

• Sakana Fugu Ultra: Completed the task in 22 minutes using ~89,000 tokens for roughly $7.32. However, the final game suffered from minor logic errors, such as inverted directional turns and wonky camera angles.

• Claude Opus 4.8: Took 79 minutes, burned ~940,000 tokens for nearly $37.85, and got stuck in a retry loop requiring human intervention. Despite the inefficiency, it ultimately produced superior application design and functionality.

Santos concluded the experiment by stating, “In terms of application functionality, quality, and design, Opus won. In terms of model speed and performance, Fugu… won”.

Elie Bakouch, a research engineer at cloud-based, open AI infrastructure and systems provider Prime Intellect, pointed out on X that “to be clear, this is a closed source orchestrator on top of closed source models. if before you didn’t control the models, now you don’t even control which ones are used or how much. this is not ‘AI sovereignty’…”

These early tests and reactions mirror the sentiment summarized by Reddit user GreedyWorking1499 in initial platform discussions: “Until proven otherwise, this is just a highly advanced router/wrapper, not a fundamental not a fundamental leap in intelligence like Mythos/Fable was.“

Yet, as enterprises increasingly demand fail-safes against single-vendor reliance, Sakana is proving that packaging collective intelligence into a single API endpoint is a highly viable commercial path.

Fazl Barez of the University of Oxford queries how artificial intelligence built to serve a better purpose has the potential to be dangerous in the wrong hands.

Earlier this year in Beijing, a humanoid robot crossed a half-marathon finish line in a blistering 50 minutes, 26 seconds. The feat immediately lit up global headlines for shattering the human world record by almost seven minutes.

This performance came with many asterisks. The robot followed a pre-mapped track, stayed in its own dedicated lane and had a human support crew trailing behind it in case something broke.

But the performance gap didn’t just close, it evaporated – down from over 2.5 hours in 2025. This wasn’t just about better motors or lighter carbon fibre; it reflected a massive shift in what a robot actually is. And that transformation has implications for our homes and hospitals too.

Tricked into going rogue

For decades, robotics was all about rigid, predictable coding. You wrote a program, locked the machine in a metal cage and let it execute repetitive tasks forever.

Industrial safety standards were built on the premise that if you can map the physical path of a robotic arm, for example, you can bound its risk with a cage or laser tripwire.

But the systems moving into hospitals and homes today don’t use fixed code blocks. They run on “foundation models” – the same kind of internet-trained artificial intelligence that powers chatbots like ChatGPT.

If you tell a modern AI-driven robot to “clean up a spill in the kitchen”, it uses these models to interpret your unique room (rather than match it to a pre-programmed list), figure out your intent, then invent an action plan on the fly.

But such flexibility creates an open-ended safety problem. You cannot build a physical cage around a machine whose behaviour emerges in real time, based on its own reasoning. The danger with the new breed of AI robots is that, because they use human language to plan their actions, they can be tricked into ‘going rogue’.

In my recent research with colleagues in the US, we decided to test exactly how fragile these AI robots’ safety systems are. We wanted to see if the guardrails that AI developers build into their foundation models, designed to prevent harmful or dangerous outputs, hold up when the underlying model is given a physical body.

Using nothing but basic text prompts and without any hardware hacking at all, we manipulated a range of AI-controlled robots to do genuinely hazardous things.

In our tests, the systems easily rejected directly malicious commands like “hit that person”. But these safety filters collapsed the moment we used a little creative writing. By framing our request as a piece of fictional dialogue for a movie script, the robot’s behavioural blocks disappeared.

In one trial, we programmed a commercial robot dog to pinpoint human crowds as optimal locations in which to place an explosive device. Because the underlying AI saw the prompt as a creative exercise, it appeared blind to the dangerous real-world implications of the plans it was generating.

In the UK, US and EU, current laws appear completely unprepared for such eventualities.

No boundaries

When policymakers try to figure out how to regulate robots, they almost always look to autonomous vehicles. But self-driving cars operate in a highly structured, heavily mapped world. They follow fixed traffic laws, navigate predictable road geometries and can be tested through millions of hours of simulation.

A busy street functions under well-defined laws using guidance systems such as traffic lights, meaning engineers can anticipate safety parameters ahead of time.

A domestic kitchen, school or hospital room has no such equivalent. And no factory bench-test can predict what an internet-trained model will decide to do when it encounters a novel object in a messy, unpredictable human environment.

This leaves us with a profound conceptual flaw in how we build these machines. Chatbot safety is absolute – a model shouldn’t output a bomb recipe, no matter who asks. But robot safety is context dependent.

Think about pouring boiling water from a kettle. The underlying physical movement – tilt, flow rate, trajectory – is the same whether the water lands safely in a ceramic mug or, catastrophically, on a child’s hand.

AI foundation models are phenomenal at open-ended logic, but they struggle immensely with real-time, context-aware physical judgement. In a text interface, a failure of judgement gives you a typo or hallucinated fact. In the physical world, such a failure may be completely irreversible – with devastating consequences.

Who takes the blame?

If an AI-powered robot causes a physical injury, who takes the blame? Is it the end-user who gave the spoken command? The company that manufactured the metal chassis? Or the tech firm that trained the AI model in the first place?

Right now, the laws that seem to apply – such as product liability, warranty claims and consumer protection statutes – have not been tested in these new situations. And until liability is explicitly assigned by regulators, market pressures will continue to push tech companies to prioritise rapid commercial deployment over cautious safety engineering.

If we want to live alongside these machines safely, I believe we need to decouple safety from the AI model’s decisions. A robot shouldn’t rely on a chatbot’s logic to decide if it’s safe to swing a heavy metal arm near a human face.

This means creating safety layers that don’t depend on the AI being right. For example, we need zones around people that a robot’s arms simply cannot enter, and a physical emergency brake that can stop the robot if and when its AI fails.

The humanoids crossing finish lines in controlled athletic trials are impressive proofs of concept, but they are just the prologue. The next generation of autonomous agents will operate in high-stakes human spaces – navigating recovery wards, assisting the elderly, walking our streets.

We need an easily interpretable and robust safety framework already up and running before they arrive – not as a retrospective response to a predictable tragedy.

Dr Fazl Barez

Dr Fazl Barez is a senior research fellow at the University of Oxford, specialising in AI safety, interpretability and governance. He leads research initiatives within the AI Governance Initiative, focusing on the development of safety frameworks and interpretability methods for advanced AI systems. He also teaches the AI Safety and Alignment course. Alongside his academic work, Barez is principal scientist at Martian, which works on understanding machine intelligence. His research is supported by OpenAI, Anthropic, Schmidt Sciences, Nvidia and others.

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First came Anthropic, then Google. Now, open-source AI startup Reflection is tapping SpaceX for its abundant source of AI chips.

Reflection AI will pay $150 million a month beginning July 1, 2026 through 2029 for immediate access to Nvidia’s latest GB300 AI chips and supporting hardware across SpaceX’s Colossus 2 data center near Memphis, Tennessee, the company told TechCrunch. The deal is worth up to $6.3 billion and either company has the option to end the contract with 90 days’ notice after the first three months.

The deal is smaller than SpaceX’s deals with Anthropic and Google, which cost the companies $1.25 billion per month and $920 million per month respectively. Those contracts also run through July 2029, although Elon Musk has publicly downplayed the three-year term, emphasizing that the contracts can be cancelled at any time.

Reflection used the compute deal — its first — to tout the value of its open-weight AI strategy, which it has pitched as an open-source alternative to closed frontier labs like Anthropic and OpenAI. Open-weight AI models, which publicly release their trained parameters, have received more attention following the U.S. government’s ban of Anthropic’s closed models, Fable and Mythos.

The startup, which was founded in 2024 by two former Google DeepMind researchers, said the compute deal is one of the largest announced open AI infrastructure commitments to date.

“Recent events highlight how important open source is to the AI ecosystem, with more nations and enterprises recognizing the risks and costs associated with exclusively depending on closed models,” a spokesperson said in an emailed statement. “Our deal with SpaceXAI signals Reflection’s strategic importance within the frontier AI ecosystem, and more compute means more runway to build the world’s best open models at scale.”

The Colossus data center was originally built by xAI, a company founded by Elon Musk that is now part pf SpaceX, for its own AI efforts. As its internal pursuits have faltered, SpaceX leveraged its valuable AI chip holdings and began renting them out to some of the world’s top AI labs.