In April 2026 — a month before Dutton Ranch made its Paramount+ debut — it was reported that showrunner Chad Feehan had exited the series following alleged “behind-the-scenes friction with series stars Cole Hauser and Kelly Reilly, as well as ‘other key players’ such as Taylor Sheridan.”

Puck News added, “Feehan finished the first season but has been told he won’t return for the second, per three sources. (I think the feeling was mutual and Feehan likely would have bailed anyway.)

Coram AI has raised $35m to turn the security cameras already bolted to walls into something closer to an autonomous detective.

The Series B is co-led by the new investor Ansa Capital and Battery Ventures, with UP Partners, 8VC and Mosaic Ventures joining. It takes the San Francisco company’s total funding to $66m.

Coram’s pitch is that physical security is stuck in the past. When something goes wrong, staff spend hours scrubbing through footage, access logs and alarms to piece together what happened.

Its answer is software it calls ‘Deep Investigation’, an AI agent you query in plain language. It searches months of video, entry records and visitor data across hundreds of cameras and sites, then hands back a report. Work that took hours, the company says, now takes minutes.

Founded four years ago by Ashesh Jain and Peter Ondruska, Coram now runs at more than 1,500 locations, from schools to factories.

Privacy pitch, surveillance reality

Coram leans hard on privacy. Its boxes run AI models on local NVIDIA chips at the edge, it says, so sensitive video never has to leave the building for the cloud. It also works with any existing IP camera, avoiding a costly rip-and-replace.

But the same platform sells facial recognition, licence-plate reading, ‘tailgating’ detection and live gun detection, and it is being pointed at schools, churches and workplaces.

One customer, a Dallas megachurch, watches over 30,000 worshippers across eight campuses. A high school swapped old cameras for real-time weapon detection. The efficiency is real; so is the reach.

That trade-off, safety bought with more monitoring, is not new to AI security. But autonomous agents sharpen it. A system that can investigate on its own, across every camera and door, is also a system that is always watching, and now draws its own conclusions.

The ‘operating system’ land grab

Coram is part of a wave of startups trying to become the ‘operating system’ for a single industry by wrapping AI agents around it. Its bet is that every building will eventually run hundreds of agents in the background.

The money is chasing a real gap. ‘Physical security is one of the largest industries yet to be transformed by modern AI,’ said Allan Jean-Baptiste of Ansa Capital, and the incumbents largely sell cameras and dashboards, not autonomy, even as firms pour record sums into AI elsewhere.

For now, the headline numbers, ’10x more effective’, ‘hundreds of agents per space’, are Coram’s projections, not proof. But with $66m in the bank and 1,500 sites live, it has the runway to test whether the building of the future really does watch itself.

Playing chess can be challenging, fun, and at times frustrating. Garry Kasparov called the game “mental torture.” With virtually limitless possibilities, chess offers unparalleled depth, and you could easily fill a library with books on how to play it. The internet has opened up a wealth of potential competitors, and smart chess boards enable you to play anyone online or off, not to mention dabble in a variety of chess programs.

I’ve been testing smart chess boards for the past month or so, with the help of my chess-mad eldest, and these are my top picks.

The Smart Chess Boards I Recommend Most

For my opening gambit, I’m recommending the Chessnut Pro. With a classic wooden design, the Chessnut Pro feels like a regular board, but there are smarts hidden within. The beechwood pieces are beautifully weighted, an important but often underestimated feature. They feel great in hand, and the set includes a pair of extra Queens. This is a full tournament-size board (55 cm or 21.7 inches), so you’ll need space for it.

The board is very nicely made, with subtle red LEDs hidden in the corner of each square that light up to show moves. I love that it looks like a regular board when you’re not playing online. There are discreet controls on one side with a USB-C port and Bluetooth connectivity to hook it up to your computer, laptop, or smartphone. There’s no need to press down with each move, as every piece has a sensor chip inside that’s automatically detected.

We used the Chessconnect Chrome browser extension to play matches on Chess.com and Lichess.org, and it was quick and easy to get up and running. The official Chessnut app features AI opponents, but they’re a little weak and lack variety. It isn’t great, but you don’t have to use it, and you can link up to different online services with a bit of tinkering (check out Graham’s Programs for some better options). Online play was occasionally a little glitchy. Sometimes there’s a slight lag, and we had to click to reconnect for every game. Battery life is quite good (we got seven to eight hours), though it takes a while to recharge (best to leave it overnight).

If you understandably don’t want to spend that much, the Chessnut Air ($250) is a far more affordable option. It’s also wooden but much smaller (33 cm or 13 inches), with lighter pieces and visible LEDs. The Air+ ($400) is the same size but with superior weighted wooden pieces and subtle LEDs on the board. Functionally, both give you much the same experience as the Pro.

As OpenAI courts investors and chases enterprise customers, Forrester says today’s AI leader could become tomorrow’s cautionary tale

OpenAI may be headed for Wall Street, but one analyst firm is already warning enterprise customers not to get too attached.

In a note published alongside OpenAI’s confidential IPO filing, Forrester urged companies to keep their AI options open, arguing that today’s market leader could easily become tomorrow’s cautionary tale.

“Don’t lock into long-term contracts; keep your architectures flexible,” the firm advised. “In fact, OpenAI could become AI’s BlackBerry FIFO (First In, First Out). The company that defines a category is often the one most painfully displaced by it.”

The caution comes as OpenAI takes its first formal step toward a public listing. Alongside its confidential SEC filing, the company published a roadmap built around three ambitions: AI systems that can accelerate research, AI that boosts economic growth, and eventually a personal AGI assistant for everyone. Forrester was more interested in a fourth question: what happens if OpenAI doesn’t stay on top?

The firm argues that OpenAI faces what it calls a “trifecta” of challenges: persuade consumers to use its agents instead of rivals’, convince enterprises to build around its technology, and stay ahead in the race toward AGI.

The enterprise battle may prove the most lucrative. “Whoever automates the dull, expensive middle of a company’s operations first becomes the system of record everyone else has to rip out — and almost no one does,” Forrester said. 

In other words, the first company to get AI agents woven into day-to-day business processes stands a decent chance of becoming yet another piece of software that everyone complains about, but nobody can remove.

However, Forrester’s advice is that, rather than standardizing on a single provider, enterprises should “anchor to the capability you need — not the brand that got there first — and keep your switching costs low.”

The warning also comes as OpenAI reportedly weighs cutting prices to fend off growing competition from rivals, including Anthropic. If the AI market is heading for a price war, enterprises may want to think twice before chaining themselves to a single supplier.

Forrester also notes that a public listing could provide customers with something they currently lack: visibility into OpenAI’s finances. Once public, the company would be required to disclose far more information about the cost of training and operating its models, giving enterprise buyers a clearer picture of the economics behind the AI systems they increasingly depend on.

For now, OpenAI remains the company that helped define the generative AI era. Whether it becomes the next Google, the next Microsoft, or AI’s answer to BlackBerry is a question investors will soon be paying very close attention to.  ®

Sega made a splash during this year’s Summer Game Fest opening showcase, revealing that a digitally resurrected Tupac will feature in the forthcoming Stranger Than Heaven. Snoop Dogg even took the stage to talk about working with the rapper’s estate. While my hands-on with the game wasn’t a full dive into the world of Stranger Than Heaven, exploring one of the five cities and eras, it was an extensive demo showcasing the fighting system. It demands that kind of focus, as it’s an entirely new system compared to RGG Studio’s decades-long Yakuza series.

Attack inputs are categorized into left and right sides, RB and RT control your right hand and leg, LB and LT for your left side. During my time with the demo, the trigger buttons led to slower, harder-hitting blows. Each can be held to charge up an attack, while combining LT and RT leads to grapple moves If you time them right. Releasing a charged attack at the ideal moment seemed to be crucial, too.

Several new combat dynamics come from this new system. Each side is blocked separately, meaning you can block (or parry) an attack while readying a counter with the other side. Grab moves feel practically like a street brawl, tackling enemies through furniture or even tumbling down steps, together. Pin them to the floor and you can then rain blows down on your opponent.

Unlike most of the Yakuza titles, weapons appear to be a more core aspect to fights. Protagonist Daigo will be able to eventually upgrade the knives, mallets and other equipment he finds.

Sega has teased that, over a journey spanning 50 years, special weapons could range from “masterworks of old” to brand-new inventions. Well, new in the ’60s. Some weapons will even come with their own special attacks, usually involving a downed enemy.

Sega set up three different demos to feel out the combat system. First, a relatively easy fight against a group of thugs that focused on fighting a group and using your opponent’s weapons against them. This was followed by a more challenging fight against another gang led by a towering heavy that hit much harder.

Sega

Fortunately, you start the fight with a heavy crowbar that was unusually heavy and slow to swing. This fight was where you could really feel a difference to the mostly button-mashing dynamics of Kiryu et al. I’m not sure if I prefer it?

Stranger Than Heaven‘s system seems to demand more from the player (which isn’t necessarily a bad thing) and the final fight was a big example of that. Facing off against a tattooed topless guy chilling in Osaka with his katana demanded some Souls-like levels of timing and dumb luck. I eventually managed to beat him because of the latter.

The enemy would heal himself if left alone and would occasionally kneel down, goading the player to approach him before unleashing a swift slice. Perfectly timed parries (or dodges) were crucial, enabling powerful counterattacks, as were follow-up attacks when he was downed. During this fight, my character was equipped with a short knife and could use both weapon attacks with his left hand and punch and kick with his right hand. It seemed that each weapon creates a different range of attacks.

I’ll admit, I missed the ability to ram a mafia underling into a microwave or other ridiculous contextual moves. Hopefully, some showpiece moves will appear in the full game — Sega has teased fights on moving vehicles, which is at least a start.

This was a demo focused on combat, so I’m intrigued to see how the rest of the game shapes up. Hopefully, STH holds on to some of the ridiculous humor of Like a Dragon and Yakuza. It was a welcome shift in tone from all the melodrama and violence. 

Stranger Than Heaven is scheduled to launch on January 15, 2027 on PS5, Steam and Xbox Series S/X.

Wear OS 7 might be closer than we thought, and Verizon may have just given that away a little early.

Updated support pages spotted for the Pixel Watch 2, Pixel Watch 3 and Pixel Watch 4 on Verizon’s website now reference the upcoming Wear OS 7 update. They also mention a June 2026 security patch and a build number (CP2A.260603.001). On paper, that sounds like a routine software note. However, the timing makes it a lot more interesting.

The pages also mention a June 9 release date. Although that looks more like a placeholder than anything concrete. The update hasn’t started rolling out yet. Google hasn’t made any official announcement, which suggests things are still in the final stages behind the scenes.

“Space computing, the final frontier, has arrived,” Nvidia CEO Jensen Huang declared at the Nvidia GTC conference in March.

Indeed, the idea of data centers in orbit has gone from science fiction to a serious spending category. Elon Musk’s SpaceX has acquired xAI (also Musk’s) and is planning a constellation of space-based data centers. Google, not to be outdone, announced Project Suncatcher in partnership with Planet, planning to launch two satellites equipped with Google Tensor Processing Unit (TPU) AI chips by early 2027. Startup Starcloud has already filed a proposal with the Federal Communications Commission for an 88,000-satellite constellation for orbital data centers. As Starcloud’s filing suggests, these companies are all proposing fleets of satellites numbering in the thousands, each housing a rack or multiple racks of AI-grade GPUs, interconnected with each other through free-space optical links and communicating back to Earth via microwave links, either directly or through other satellites.

Proponents tout the many wonders of computing in space: abundant solar energy, free cooling, and freedom from Earth-based disturbances like earthquakes, floods, and protesters. But a sober look at the physics of space-based computing paints a much more nuanced picture.

Free cooling is perhaps the biggest misconception. Space is cold, but it also has no atmosphere. That means the best heat-removal mechanisms, conduction and convection, are off the table. The only option is radiation. To prevent a chip from overheating in space, a large, costly surface area is required to dissipate the energy and then radiate it.

Solar energy is abundant, but collecting it with functional solar panels that maintain perfect alignment toward the sun is a complex task requiring extensive attitude control systems. On top of that, ionizing radiation in space from cosmic rays and other sources poses a unique challenge, degrading the solar panels, the radiative coolers, and the chips themselves. Because regular maintenance in space is difficult, redundancy has to be built in at launch, and cost estimates have to account for efficiency degradation over time.

At ABI Research, where I work as an aerospace analyst, we did a rough total-cost-of-ownership comparison between a data center on Earth and one in space. It showed that the cost to launch and run a GPU in space for a year is at least an order of magnitude higher than the same feat in a terrestrial data center. Our model was simple, assuming an Nvidia H100 server rack launched with the requisite-size solar panel and radiator on a spacecraft akin to Starcloud’s pilot launch. We assumed SpaceX’s Starship was used at a highly optimistic launch cost per kilogram of US $44, and a terrestrial energy cost of $0.20 per kilowatt hour. This is a simple back-of-the-envelope calculation, but it does signal something real.

From our perspective, the cost of delivery and space hardening of the payload makes general-purpose space-based data centers difficult to justify economically today, despite the fact that data-center builders in many regions are scrambling for electric power. However, there are niche applications where the much higher costs of computing in space could be justified. Examples include preprocessing data from Earth-observation satellites, real-time detection and tracking of hypersonic missiles, and active collision avoidance in the increasingly crowded low Earth orbit. Even for these, though, contending with fundamental physics will still be a demanding challenge. And a technologically compelling one, too.

The Cooling Challenge in Space

Cooling is where physics separates the science from the fiction. The governing equation for radiative cooling, the only type of cooling available in space, is known as the Stefan-Boltzmann Law. It states that the amount of power you can radiate is proportional to the area of the radiator times its temperature to the fourth power. For a space systems architect, the implications of this law are brutal. In orbit, the only variable we can control is area. This restriction creates a geometric penalty, or a “physics tax,” for cooling in space: The more power you need to reject, the bigger the area of the radiator you need to bring along from Earth.

The only cooling method available in space is radiation, and the radiator area required is derived using the Stephan-Boltzmann law. For a single chip drawing 700 watts, like Nvidia’s popular H100 GPU, the area required to keep it at 20 °C is just under 3 square meters, and it goes down to 1 square meter for an operating temperature of 85 °C. However, as the radiator surface is exposed to ionizing radiation, its emissivity decreases, and after 5 years in space the required area increases by about 40 percent.

SpaceX’s Starlink system currently has over 10,000 satellites in low Earth orbit, each depicted here as a colored dot.

Satellitemap.space

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As low Earth orbit gets increasingly populated, collision avoidance will have to break the traditional ground-loop model. In the megaconstellation era of space, the OODA (observe, orient, decide, act) loop must happen onboard, thereby reducing the analysis turnaround from minutes to milliseconds.

The problem is that the flight computers standard on satellites are not built for this level of processing. The complex probability models required for maneuvering cannot currently be implemented by onboard computers in conjunction with their navigation systems. Clearly, more powerful computers are needed.

This is the true economic justification for moving compute to space: to move insight generation there. By placing high-performance computing adjacent to the sensors, we can process terabytes of data in orbit and downlink only the relevant data in real time, and we can do the computations necessary to avoid satellite collisions in real time.

The Future of Computing in Space

So, assuming that some form of computing is inevitable in low Earth orbit in the foreseeable future, how will the heat be handled? The industry is currently experimenting with two main classes of solutions to cope with the Stefan-Boltzmann law.

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One creative option is to use origami-inspired radiators, the kind used for the James Webb telescope. Companies are developing flexible, high-conductivity composite radiators that fold into a tight cube for launch and unfurl into enormous yet lightweight thermal wings in orbit.

Another possibility is to use liquid-droplet radiators. This concept proposes removing the rigid radiator structure completely and instead spraying a stream of coolant oil directly into the vacuum of space. The fluid travels through an open loop, exposed to the near-absolute zero of the void, maximizing radiative surface area before being caught by a collector and pumped back into the ship. It sounds like science fiction, but as the heat loads climb into the megawatts, liquid-droplet cooling may be the only way to cheat the mass limits of this exponential reality.

Our rough total-cost-of-ownership model uses optimistic versions of current numbers, such as launch cost, chip cost, and power use. A critic might point out that future technology will improve, both in efficiency, purpose-built designs, and costs.

Sure, the technology is bound to improve. But the critical factor isn’t just launch cost; it’s the computing power per unit mass and electric-power economics. Radiators and solar arrays can consume 65 to 70 percent of total satellite mass, and space-grade photovoltaics run orders of magnitude more expensive than terrestrial equivalents.

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Chris Philpot

Even as launch costs fall, the mass and cost burden of power generation and thermal management will remain a fundamental problem.

Current space-grade solar panels rely on germanium substrates, whose supply is concentrated in China. It will be extremely difficult to scale up availability of these substrates. A transition to radiation-tolerant perovskite solar panels or a similar alternative could change the economics significantly, but that possibility is five years away or more. The technology will get cheaper, but the bottlenecks of power and thermal architecture will remain.

Recognizing the thermal reality of cooling in space forces us to shift how we view satellite operations. We are moving away from the “launch and forget” era toward an era of “autonomous logistics.” As our thermal model demonstrated, the harsh environment of space steadily attacks the hardware. UV radiation degrades thermal coatings; cosmic rays degrade silicon. In a traditional satellite model, when the radiator degrades or the memory fails, the satellite becomes space junk. For a multimillion-dollar data center, that disposal model is potentially ruinous.

To make the economics of orbital computation work, the infrastructure must be serviceable and the rockets to launch them reusable. The orbital domain will require automated servicing vehicles capable of swapping out degraded radiator panels and upgrading fried servers. In these ways, the future of the orbital data centers is dependent on the innovations of an emergent in-space economy.

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There’s a good argument to be made that the need for space-based computation is less of a hype cycle and more of an enabler for the new space economy. Look no further than SpaceX’s recent regulatory filings proposing a constellation of up to a million satellites in low Earth orbit. At such a scale, routing all raw data back to Earth is physically impossible; the network itself must become the data center.

However, the winners in this sector will be determined by the systems architects who most cleverly accommodate the thermodynamics and the companies with sufficient vertical integration to take on the massive costs of operating data centers in orbit. Ultimately, the physics tax is universal. Whether managing heat rejection in the vacuum of low Earth orbit or managing power density in a hyperscale facility in Northern Virginia, the constraint is never the silicon. It’s the thermodynamics.

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