The world today is riven by cultural differences, political divisions, and geopolitical disputes — a challenging environment for any investor hunting for startups that can grow large enough to deliver venture-scale returns.

Kompas VC has developed a regionally sensitive strategy to help it navigate, and invest in, this fragmented world. And it’s putting fresh capital towards this approach with a new €160 million fund ($187.5 million), the firm told TechCrunch.

“We see the world really falling into three main spheres of economic activity, of political activity — the U.S., Europe, and China,” Sebastian Peck, partner at Kompas VC, told TechCrunch. “We certainly see today that these three domains follow very, very different trajectories.”

Kompas has staked its reputation on backing startups that tackle core industrial competitiveness challenges, from manufacturing and supply chains to critical infrastructure and sustainability. Those themes haven’t disappeared, but different regions emphasize them to varying degrees.

“There was a lot of enthusiasm around these themes back in 2021,” Peck said. “In 2026, we’re in a very, very different paradigm. It’s all about AI, it’s all about fast growth, very explosive growth. A lot of big topics that we partially play to but also are not really part of what we stand for.”

“Our focus is in the physical world, anything around producing physical goods,” he added, saying that Kompas focuses on startups working on decarbonization, productivity, and risk management. “We’ve found our niche.”

That niche turns out to be pretty broad. Reshoring is en vogue in nearly every market, and depending on the startup, those markets typically have more than enough scale for a firm like Kompas.

Though dwarfed by some venture funds these days, Kompas’s newly raised second fund should give it ample opportunity to lead early stage rounds with checks ranging from €3 million to €5 million. 

As a European fund, Kompas has access to a range of founders and startups in the region. But it must weigh how global fragmentation might limit the potential for some to deliver venture returns. Peck cites prefab housing as an example. The approach is widely used in Scandinavian countries, but it isn’t as common in Germany or the rest of Europe, let alone the United States.

“It feels like such an intuitive solution. It’s a product that is effectively an industrial product. It should be highly scalable,” he said. Ultimately, the reason it doesn’t resonate outside Scandinavia has more to do with “cultural conditioning” than the technology itself, he said. “In that industry, if the U.S. isn’t the market you can go to, you need to look very, very carefully at whether there’s a large enough addressable market.”

The fragmentation extends beyond housing. For example, in Europe, sustainability is still broadly attractive, in contrast to the U.S., where the theme doesn’t have the cachet it did several years ago. 

Still, a lot can change quickly, Peck acknowledges. “We are investing over 10-, 15-year horizons. That’s a few legislative periods to bridge, and sometimes things swing in unexpected directions.”

The shifting landscape poses a challenge, but also an opportunity for a smaller investor like Kompas. “I think there’s a great space for highly focused, highly specialized, smaller funds like ours to be the first check-in and bring sweep up certain themes and certain founders,” Peck said. 

When looking for some powerful wireless headphones, the Skullcandy Crusher Evo, priced at $99.99 (was $210), is a popular choice. Why? Users want something with a lot of bass, but not so much that it drowns out the rest of the track. On the right earcup, there is an adjustable slider that allows you to fine-tune the low end to your liking.

Crank it right down, and the sound remains pleasant and balanced, with nothing getting lost in the mix. Turn it up, though, and you’ll be rewarded with a KNOCK of bass that literally vibrates through the earcups, all the way to the top of your skull. Two full-range drivers handle the rest of the audio, so your voices and guitars stay exactly where they should, even when the bass is turned up to eleven.

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Real-world testing proves that the forty-hour battery is actually rather reasonable, as a single charge will last you several days, including commutes, dog walks, workouts, and so on. When you run low on battery, ten minutes plugged in provides you roughly four hours of music, which is quite useful, especially on those mornings when you’re racing around getting ready and don’t have time to think about your case. The Bluetooth remains rock steady even when you’re bouncing between rooms or working out, and we’ve had reports of zero odd drops from folks who’ve put these through their paces over the course of a few weeks.

The memory foam earcups and padded headband provide a highly comfortable experience, even after hours of use. The over-ear style effectively blocks out background noise, making it ideal for subway commutes or loud offices that do not require active canceling. Some people have reported feeling slightly warm after a few hours, but the fit is really good even if you wear glasses, and it’s rather forgiving on different skull sizes once you’ve worn them in. A handy small control panel on the earcup handles volume, skipping, and calls, eliminating the need to fumble for your phone.

The Skullcandy app adds a personal touch by running a fast hearing test and then adjusting the sound profile to match your individual ears. The Tile tracking embedded into the headphones means that if you ever misplace them, you can simply whip out your phone and it will show you precisely where they are – for example, under the back seat of your car or buried in a gym bag. When you’re going on a trip, packing is a breeze thanks to the easy-to-fold design and supplied case, and there’s even an aux cord in case you run out of juice after a long day.

German electronics manufacturer Loewe Technology has acquired French loudspeaker company Cabasse, bringing a 76-year-old acoustic engineering brand into a premium electronics group best known for its high-end television and sound system products.

Cabasse, founded in 1950 in Brittany, has built its reputation around proprietary coaxial driver technology and a catalogue that includes the Sphere Evo loudspeaker. The brand currently has an independent research, development and production operation based in Brest, and this will continue following the transaction.

That operational continuity reflects the terms of the deal, under which Cabasse retains its brand identity and engineering autonomy while gaining access to Loewe’s international sales infrastructure and marketing resources to support wider distribution of its products.

In nearly 10 years since being established, Tribe has supported more than 500 start-ups and scale-ups.

UK start-up IoT Tribe is opening its European operations, with Dublin as its first base. The deep-tech ecosystem builder has already made two hires in the country – a head of venture incubation and acceleration, and an associate of same.

Tribe was formerly known as the ‘Start-up Scale-up’, a European Commission-funded IoT accelerator which operated across four European hubs, Dublin included. The programme ran for one year, from 2015 to 2016, and supported more than 130 start-ups from 47 countries.

Tribe was formally established in 2017. That year, the start-up launched the IoT Tribe North Industrial Tech accelerator in partnership with Innovate UK to help its corporate partners adopt newer innovations.

In nearly 10 years since being established, Tribe has supported more than 500 start-ups and scale-ups, it said. It has also engaged with more than 150 corporations and public agencies, and has built a network of more than 200 experts and mentors, it added.

“Europe’s deep-tech moment is here and it will be won or lost on execution. Dublin is the natural launchpad for Tribe’s European operations – a city we’ve worked with for 10 years, home to world-class engineering talent and a corporate base serious about adopting frontier technology,” said Tribe founder and CEO Tanya Suarez.

“From here, we’ll scale the model that has already proven its value to corporate and government clients across Europe and beyond.”

Tribe Europe, as the new Dublin branch will be called, will work with founders, corporates and public bodies across Ireland to run accelerator programmes and facilitate open innovation challenges, it said.

“Corporates across Europe are done debating whether to engage with deep tech. They want partners who can de-risk adoption at speed. That is the gap Tribe closes in Ireland and across the continent,” said Tribe’s director of operations Marcus Roberts.

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Hundreds of workers in Ireland tasked with refining Meta’s AI models have been told that their jobs are at risk as the company embarks on a sweeping new round of layoffs, according to documents obtained by WIRED.

The affected workers are employed by the Dublin-based firm Covalen, which handles various content moderation and labeling services for Meta.

The workers were informed of the layoffs over a brief video meeting on Monday afternoon and were not allowed to ask questions, according to Nick Bennett, one of the employees on the call. “We had a pretty bad feeling [before the meeting],” he says. “This has happened before.”

In all, more than 700 employees stand to potentially lose their jobs at Covalen, according to an email reviewed by WIRED. Roughly 500 are data annotators. Their job is to check material generated by Meta’s AI models against the company’s rules barring dangerous and illegal content. “It’s essentially training the AI to take over our jobs,” claims another Covalen employee, who asked to remain anonymous for fear of retaliation. “We take actions as the perfect decision for the AI to emulate.”

Sometimes, the work involves cooking up elaborate prompts to try to bypass guardrails meant to prevent models from serving up child sexual abuse material, say, or descriptions of suicide. “It’s quite a grueling job,” claims Bennett. “You spend your whole day pretending to be a pedophile.”

Last week, Meta announced plans to cut one in 10 jobs as part of sweeping layoffs aimed at making the company more efficient. A memo circulated by the company reportedly indicated that layoffs were motivated by a need to increase spending on other aspects of the business. Though the memo did not mention AI, the company recently announced plans to nearly double its spending on the technology. In January, Meta CEO Mark Zuckerberg said, “I think that 2026 is going to be the year that AI starts to dramatically change the way that we work.” In the email reviewed by WIRED, Covalen employees were told only that the layoffs were a result of “reduced demand and operational requirements.”

In a statement, Meta spokesperson Erica Sackin said: “As we shared in March, over the next few years, Meta will be deploying more advanced AI systems to transform our approach to content enforcement and operations across our platforms, so that it delivers the safety and protection people expect. As we do that, we’ll be reducing our reliance on third-party vendors and strengthening our internal systems.”

The latest round of layoffs marks the second time that Covalen has cut staff in recent months. In November, the company announced plans for job cuts (reportedly to number around 400), culminating in a worker strike. Between the two rounds of layoffs, Covalen’s headcount in Dublin is on track to be almost halved, according to the Communications Workers’ Union (CWU), whose members include some Covalen staff.

For affected Covalen workers, the search for new work will be hampered by a six-month “cooldown period,” during which they are unable to apply to a competing Meta vendor, claims the CWU. “It’s undignified, you know,” says the Covalen employee who asked to remain anonymous. “It’s rude.”

Covalen did not immediately respond to a request for comment.

Unions representing the affected employees are pushing for Covalen to enter negotiations over severance terms. They also hope to meet with the Irish government to discuss how AI is impacting workers in the country. “Tech companies are treating the workers whose labor and data helped build AI as disposable,” says Christy Hoffman, general secretary of UNI Global Union. “To fight back, it’s absolutely critical that workers organize and demand notice about the introduction of AI, training linked to employment, and a plan for their futures. Workers should also have the right to refuse to train their AI replacements.”

But some of those caught up in the layoffs are doubtful of their chances of securing stable employment in a labor market being rehewn in real time by AI and the deep-pocketed companies leading its development. “It’s a universal battle between downtrodden white-collar workers and big capital, really,” claims Bennett. “That normally only goes one way.”

Update 4/28/25 3:30pm ET: This story has been updated to include comment from Meta.

Elon Musk and Sam Altman appeared in a federal courtroom together for the first time on Tuesday as they fight over OpenAI’s decade-long evolution and what it means for the company’s future.

The trial in Musk’s lawsuit against Altman could result in financial damages and, more significantly, governance changes at OpenAI that may complicate its plans for an initial public offering as soon as this year.

As the first witness on the stand, Musk immediately sought to frame his case as more than just about OpenAI. Siding with Altman “will give license to looting every charity in America” and shake the “entire foundation of charitable giving,” Musk told a panel of nine jurors advising US District Judge Yvonne Gonzalez Rogers on how to rule.

Musk has been concerned about computers becoming smarter than people “since he was a young man in college,” his attorney Steven Molo told jurors. Molo explained that Musk lobbied governments to pass regulations addressing the prospect of so-called artificial general intelligence, including meeting with then-president Barack Obama in 2015. “But the government was not stepping up,” Molo said. “Elon felt he had to do something.”

Around the same time, Musk met with Altman, a then-30-year-old investor “whom he didn’t know very well,” Molo said. They soon launched OpenAI together as a nonprofit. Google’s unchecked progress on AI development had sparked concerns for both OpenAI cofounders, and they wanted to create a competing lab with a greater focus on safety. “My perspective is [OpenAI] exists because Larry Page called me a speciesist for being pro-humanity,” Musk said, referring to the Google cofounder. “What would be the opposite of Google? An open-source nonprofit.”

While Musk believes AI could cure diseases and generate prosperity for humanity, he also told the court that he thinks the technology could veer off into catastrophic scenarios straight out of science fiction. “It could also kill all of us … the Terminator outcome. I think we want to be in a movie … like Star Trek, not a James Cameron movie,” Musk said. (While Musk has long raised alarms about AI safety, his current firm, xAI, has been criticized by researchers at other AI labs for its “reckless” safety culture.)

As OpenAI began notching some of its own successes, Musk and Altman agreed that a for-profit arm with fixed returns for investors was necessary to raise extraordinary sums of money needed to fund hiring and computing, according to Molo. He compared it to a nonprofit museum that receives some proceeds from a for-profit store. “I was not opposed to there being a small for-profit as long as the tail didn’t wag the dog,” Musk said on the stand.

Musk felt that the approach had gone too far when Microsoft, another defendant in the trial, agreed to invest $10 billion in 2023, and OpenAI increasingly moved intellectual property and staff to the for-profit company. “The museum store sold the Picassos so they were locked up where no one could see them,” Molo said.

OpenAI’s Rebuttal

William Savitt, an attorney for OpenAI, told jurors that OpenAI never promised Musk that it would remain a nonprofit and publish all its code. “The evidence here will show what Musk says happened did not happen,” Savitt said.

He added that Musk knew about plans to raise corporate investment exceeding $10 billion as far back as 2018. Musk even raised concerns about Microsoft’s involvement in a 2020 tweet. But he didn’t file a lawsuit until he founded a competitor, xAI, in 2023.

If you’ve finished Stranger Things: Tales From ’85 and immediately wanted more, Netflix heard you. The animated series has officially been renewed for season 2, and it is returning to Netflix this fall.

For context, Tales From ’85 is a spin-off of the original Stranger Things franchise, set in the winter of 1985 in Hawkins, Indiana. It follows the Hawkins Investigators Club as they face paranormal threats in an animated format separate from the live-action series.

What happened at the end of Tales From ’85 season 1?

[Spoiler warning: please skip this section if you have not finished season 1 yet.]

Season 1 threw a lot at the Hawkins Investigators Club as they came face to face with a snow shark and a group of sinister pumpkin monsters called the Gourd Horde.

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The real threat, however, was the Horde Queen, a creature that evolved from an Upside Down vine after Hawkins Food Mart clerk Daniel Fischer conducted secret experiments using Mrs. Baxter’s stolen research.

His green serum combined years of botanical science with extracted Upside Down vine DNA, creating something far beyond what he bargained for. The gang managed to stop the Queen from opening a new gate to the Upside Down, with Eleven sealing it shut using the creature’s own body.

But the finale left one ominous image burned into viewers’ minds. In the Upside Down, a stem burst through the Queen’s corpse and unfurled into a glowing flower with the maw of a Demogorgon.

What to expect from Stranger Things: Tales From ’85 season 2?

Season 1 ended with the Hawkins Investigators Club uncovering a genuinely terrifying mystery, and season 2 is picking up that thread with a brand new paranormal threat.

Showrunner Eric Robles confirmed that the glowing flower is the beginning of a whole new mystery. The threat in season 2 apparently emerges from Hawkins’ abandoned silver mines. The mysterious blue flower spotted blooming in the Upside Down at the end of season 1 is also set to play a significant role going forward.

Robles has also been clear that the seasons of the Stranger Things spinoff series are not standalone stories. They are directly connected, which means every detail from season 1 will matter heading into the next chapter. Eleven, Mike, Will, Dustin, Lucas, Max, and Nikki will all be returning.

Why did Netflix renew Stranger Things: Tales From ’85 despite mixed reviews?

Season 1 just dropped a few days ago, and Netflix did not waste any time with the renewal announcement. However, the spinoff received a divisive reception, to put it mildly. It currently holds the lowest Rotten Tomatoes scores of any entry in the Stranger Things franchise, sitting at 63% from critics and 54% from audiences. Meanwhile, on IMDB, the show received only 5.7/10.

Common complaints pointed to unlikable characters, particularly newcomer Nikki, and uneven plotting. Despite all of that, Netflix moved ahead with the renewal anyway, and did so just four days after season 1 debuted. The deciding factor appears to have been viewership.

The series pulled in 2.8 million views in its opening weekend and landed at number 7 on Netflix’s global Top 10, also securing a spot in the platform’s top 15 animated series debuts of all time. That kind of traction, regardless of critical reception, was enough to greenlight a second season.

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There seems to be no ceiling for mechanical keyboards and the Keychron Q1 Ultra 8K is proof. It features an all metal build, wireless options, ZMK, 8KHz polling, and layer after layer of foam.

Keychron continues to pump out new keyboards at a breakneck pace. The Q1 Ultra 8K is the latest model to sit at the top of Keychron’s lineup, replacing the Max.

I’ve reviewed a lot of Keychron’s keyboards, and with the exception of the unique Q3 Pro SE, I believe the Q1 series is my favorite layout. I love that it has all the keys I need while remaining compact yet dense.

Keychron has proven to be remarkably consistent in its products. Each release isn’t overly iterative and justifies the price, which makes them incredibly difficult to give a review score.

You’ll note this and others have received 4 or more stars. As with any review on AppleInsider, it is best to read through the text and understand the granular points rather than try to boil the entire thing to a numerical score.

Enthusiasts will happily pay the high price to get these features and premium build, so let’s dive into why the Q1 Ultra 8K is yet another winner from Keychron.

Keychron Q1 Ultra 8K review: design

Look at the Q1 Ultra 8K straight on and you’ll struggle to find any differentiation from the other Q1 models. There are tiny signs those with a discerning eye will catch, like the different knob, but the biggest tell is in the back.

Keychron Q1 Ultra 8K review: a familiar layout

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Keychron has included a fancy “aesthetic PC” back plate. It is a gold colored plate with celestial designs that really pop out.

Of course, you’ll never see this plate during use, but I suppose it is cool that it’s there. As I said, it certainly helps in distinguishing it from other models.

I stuck with my usual preference of dark keycaps with Keychron’s splash of gray and blue. The RGB backlight continues to shine through bottom-facing LEDs with 22+ combinations.

Keychron Q1 Ultra 8K review: a fancy backplate

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The Keychron launcher software lets you take things even further. Create your own RGB patterns on a per-key basis.

That means you can color-code select keys or just go all out and make every key a solid color. I prefer a randomized rainbow pattern, but it is cool that you can go to that level of detail.

Like with all Keychron keyboards, you can fully customize the keyboard top to bottom. Take it apart down to the frame and you’ve got a pile of foam, stabilizers, switches, and keycaps.

I emphasized this with the Q1 Max review, and the point still stands, this thing is packed to the gills with foam. Remove what you like to get the right sound and feel out of every keypress.

The layers go like this, starting from the top:

• Keycaps
• Top Case
• Switches
• Plate
• Sound absorbing foam for a cleaner typing sound
• IXPE Foam to reduce keystroke vibration

PET Film for protection from dust and insulates from shorts

• PCB

Latex Bottom Pad that cushions typing while reducing vibration and noise

• Bottom Case Acoustic Foam also cushions typing and reduces vibration and noise
• Bottom Case PET Film adds another layer of protection from dust and insulates from shorts
• Bottom Case

It’s quite the pile of materials and fittings. I personally like how the keyboard feels and sounds with the Red Silk POM Switches that were included.

The weight and material of Keychron keyboards are a big part of their premium look and feel. I’ve joked about it before, but these keyboards feel like they could double as a home defense tool.

Keychron Q1 Ultra 8K review: features

Keychron has packed the Q1 Ultra 8K with specs and features that will benefit every user. Whether you’re a mechanical keyboard enthusiast, or just looking for a premium keyboard, this one covers all of the bases.

Keychron Q1 Ultra 8K review: a new knob

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The feature that gives it its name is an 8K polling rate. It means that the keyboard is sampling every keypress at 8x the speed of other high-end keyboards.

That 8,000 Hz polling rate isn’t on by default. All of Keychron’s 8K keyboards ship with the default polling rate set to 1,000 Hz to ensure wider compatibility.

If you’re working on newer Macs and PCs, go ahead and switch over to 8K. This is done via the Keychron launcher tool that’s accessible via the web on a Chrome, Edge, or Opera browser.

Luckily, I keep Chrome installed on my Mac mini for podcasting purposes. A quick switch flip to the 2.4GHz channel and I was in business.

Keychron Q1 Ultra 8K review: Keychron Launcher lets you edit every aspect

I used to spend more time on the keyboard modification software in these reviews, but they’ve become so standard that I don’t feel the need to. Like most mechanical keyboards, and all Keychron keyboards, you can customize it top to bottom using different switches, keycaps, and program each key individually.

If you want to get even more advanced, program multiple typing layers so a button press puts you into a completely different layout. Careful with this, as the physical keyboard doesn’t look any different even if the “G” key is suddenly performing a different action.

Other specs include three pairing methods. Pick from three Bluetooth 5.3 channels, a 2.4 GHz USB dongle, or wired.

Keychron Q1 Ultra 8K review: swap between multiple connection options

Mix and match every connection option to easily switch between five devices at once. Those concerned with latency will use the wired option, but the 2.4 GHz connection is just as solid and both offer the 8K polling rate.

Really, it comes down to your individual setup and needs.

The Q1 Ultra 8K has some incredible battery life thanks to optimizations made via the ZMK firmware. Users that enable the 8K polling feature and connect wirelessly without a backlight can squeeze out 660 hours of battery life.

That backlight is the battery drain, so every level of brightness you enable cuts that battery life down quick. That said, I’ve had to charge the internal battery one time in a month even with the backlight enabled. YMMV.

Using the Keychron Q1 Ultra 8K

I spend a lot of time typing on a keyboard each day, so it is needless to say that I’ve spent a lot of hours with the Q1 Ultra 8K. It’s an interesting evolution of a keyboard that I’ve used across multiple generations.

Keychron Q1 Ultra 8K review: still a typing dream

From a general use perspective, beyond the move to a new switch, the typing experience is nearly identical. The Q1 Max has the same internal layers of foam and the case appears to be identical.

I’ve found that I prefer whatever red-equivalent switch is my go-to. It isn’t too loud and is just right in terms of travel.

There really isn’t much else I can add here that I haven’t said time and time again about Keychron keyboards. This is a highly customizable mechanical keyboard that isn’t too flashy out of the box, but lets you get there if you like.

Keychron Q1 Ultra 8K review: peak customization

I’ll keep noting as long as I can remember to that I wish someone could replicate the signal sent to Apple Vision Pro from a Magic Keyboard. I love using third-party keyboards, but it is never not annoying that I can’t see the keyboard when fully immersed — only Apple gets that privilege.

The Q1 series might be my favorite layout. It has all of the keys that I’ll need, including a well-labeled function row.

There’s even three “bonus keys” on the right side. I’m sure someone on Earth uses them as designed, but the pgup, pgdn, and home keys are totally unnecessary for my workflows. So, they make great reprogrammable keys.

Advancing the spec

The Keychron Q1 Ultra 8K is an iterative upgrade over the Q1 Max. Though that isn’t to say the upgrades aren’t impactful.

Keychron Q1 Ultra 8K review: an iterative but welcome upgrade

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What’s actually new here is the Keychron MCU chip with 1MP flash memory. It enables the 8K polling rate.

That Silk POM Switch is a nice addition too. Keychron specifically designed it with high performance in mind for gamers and those demanding precision.

Lay the Max and the Ultra side-by-side and the only notable difference besides potential keycap selection is the knob. If you’re looking for a wholly new experience or design, this isn’t it.

The battery life is about 6x longer in the Q1 Ultra 8K versus the Max. You’re also interfacing through ZMK, which is a newer standard built with wireless keyboards in mind.

Those looking for a premium mechanical keyboard experience with a little extra cash should get the Q1 Ultra 8K. While the Q1 Max is still available, you’re not saving much on that model, so go for the best.

Keychron Q1 Ultra 8K review – Pros

• Sturdy design, clacky keys, interesting backplate
• Incredible battery life
• 8K polling if you want it
• Incredible range of customization options

Keychron Q1 Ultra 8K review – Cons

• Iterative upgrade design wise
• 8K polling not necessary for everyone

Rating 4 out of 5

As usual, these numeric scores are not representative of the product, but Google demands them. I love the Q1 Ultra 8K and it is a good upgrade over the Q1 Max, but owners of the older model don’t need to rush out for the new one either.

I expect the next iteration will tackle more in the design department, so it’s getting the smallest knock for the lack of design change. Otherwise, this is an expensive, premium keyboard that has amazing specs and is worth buying if you’re in the market for one.

Hey Wes, here are two affiliate links. Let’s use Amazon for the blue buy button.

Where to buy the Keychron Q1 Ultra 8K

The Keychron Q1 Ultra 8K wireless mechanical keyboard can be purchased from Keychron directly for $229.99. Amazon also carries the keyboard for the same price.

The AI race lately has felt a bit like a game of tennis: first, Anthropic releases a new, pricey state-of-the-art proprietary model for general users (Claude Opus 4.7), then, a week or so later, its rival OpenAI volleys back with one of its own (GPT-5.5). And all the while, Chinese companies like DeepSeek and even Xiaomi are seeking to appeal to users by playing a different game: nearing the frontier, but with open licensing and far lower costs.

So it’s a big surprise when a new, affordable, highly performant open source contender from the U.S. emerges. Today, we got one from the smaller, lesser-known U.S. AI startup, Poolside, founded in San Francisco in 2023.

The company launched its two new Laguna large language models, both of which offer affordable intelligence optimized for agentic workflows (AI that does more than just chat or generate content, but can, in this case, write code, use third-party tools, and take actions autonomously), as well as a new coding agent harness called (fittingly) “pool” and a new web-based, mobile optimized agentic coding development and interactive preview environment, “shimmer,” which lets you write code with the Laguna models on the go.

The new AI models that Poolside released today include:

• Laguna M.1: a proprietary 225-billion parameter Mixture of Experts (MoE) model with 23 billion active parameters. This flagship model is optimized for high-consequence enterprise and government environments, designed to solve complex, long-horizon software engineering problems that require maximum reasoning and planning capabilities.

• Laguna XS.2: an Apache 2.0 open licensed 33-billion parameter MoE with 3 billion active. Engineered for efficiency and community innovation, this model is designed for local agentic coding tasks and provides a versatile foundation for developers looking to fine-tune, quantize, or serve powerful agents on a single GPU. In other words, developers can download and run Laguna XS.2 on their desktop or even laptop computers without an internet connection — completely private and secured.

Notably, as mentioned above, only the smaller of the two models, XS.2, is available now under an open source Apache 2.0 license (on Hugging Face) — yet Poolside is offering even the larger M.1 for free temporarily through its API and third-party distribution partners, OpenRouter, Ollama, and Baseten, making it a great use case for developers who wish to test it out.

Also noteworthy: the two new Lagunas were trained from scratch — not fine-tuned/post-trained base models from Chinese giant Alibaba’s Qwen series like some other U.S. labs have pursued lately (*cough cough* Cursor *cough).

As Poolside wrote in a blog post today, it’s spent the last few years “focused on serving our government and public sector clients with capable models deployable into the highest-security environments,” yet is now going open source “to support builders and the wider research community.”

When I asked on X why government agencies would seek to use Poolside instead of leading proprietary U.S. labs like Anthropic, OpenAI and Google, Poolside post-training engineer George Grigorev told me in a reply that: “we think that we can be faster to deploy our models to enterprise customers, and we can literally ship weights in fully isolated environments on-prem, so it can work offline. which might be critical for gov/public sectors 🙂 but ofc anthropic enterprise is hard to beat”

How Poolside’s Laguna M.1 and Laguna XS.2 were trained

Poolside constructs its AI models within a specialized digital environment called the “Model Factory“.

At the heart of this process is Titan, the company’s powerful internal software that serves as the “furnace” for training. To help the AI learn as efficiently as possible, Poolside uses a unique tool called the Muon optimizer.

Think of Muon as a high-speed tutor; it helps the model master new information approximately 15% faster than standard industry methods, a critical gain when training at the 30-trillion-token scale.

It achieves this by ensuring that every update to the model’s “brain” is mathematically balanced and pointing in the right direction, which prevents the AI from getting confused or stuck during its intensive training sessions.

The information used to train these models—a staggering 30 trillion “tokens” or pieces of data—is carefully selected using a system called AutoMixer.

Rather than just feeding the AI everything it finds on the internet, AutoMixer leverages a a “swarm” of sixty proxy models on different data mixes to scientifically determine which combination of code, math, and general web data produces the best reasoning capabilities.

In this way, it acts like a master chef, scientifically testing thousands of different “recipes” to find the perfect balance of computer code, mathematics, and general knowledge.

While much of this data comes from the public web, about 13% of it is “synthetic data”. This is high-quality, custom-made practice material created by other AIs to teach the models specific skills that are difficult to find in the real world.

Once the model has finished its basic “schooling,” it enters a virtual gym for Reinforcement Learning. In this stage, the AI practices solving real software engineering problems in a safe, isolated digital playground. It learns through trial and error, receiving a “reward” or positive signal every time it successfully fixes a bug or writes a working piece of code. This constant cycle of practice and feedback is what transforms the AI from a simple text generator into a capable “agent” that can plan and execute complex, multi-step projects just like a human software engineer.

While M.1 represents the peak of Poolside’s current research, the smaller Laguna XS.2 may be the more disruptive entry.

At just 33 billion total parameters (3 billion activated), XS.2 is a “second-generation” MoE model that incorporates everything the team learned from training M.1.

Benchmarks show Poolside’s Laguna models punch far above their weight class

Langua M.1’s performance on the SWE-bench Pro—a benchmark designed to test an AI’s ability to solve real-world software issues—reached 46.9% on SWE-bench Pro, nearing the performance of the far-larger Qwen-3.5 and DeepSeek V4-Flash.

Despite being a fraction of the size, Laguna XS.2 achieves a 44.5% score on SWE-bench Pro, nearly matching its larger sibling.

On the SWE-bench Verified track, M.1 scored 72.5%, outperforming the dense Devstral 2 (72.2%) but trailing Claude Sonnet 4.6, which leads the category at 79.6%.

These results highlight M.1’s specialization in long-horizon software tasks, particularly those involving complex planning across interconnected files.

The smaller Laguna XS.2 exhibits remarkable efficiency, nearly matching the performance of its much larger sibling on high-consequence tasks. Despite having only 3B active parameters, XS.2 surpasses Claude Haiku 4.5 (39.5%) and the significantly larger Gemma 4 31B dense model (35.7%) on SWE-bench Pro.

In terminal-based reasoning, XS.2’s 30.1% on Terminal-Bench 2.0 also edges out Haiku 4.5’s 29.8%, although it remains behind specialized “nano” models such as GPT-5.4 Nano, which reached 46.3% on the same benchmark.

Collectively, these benchmarks suggest that Poolside’s focus on agentic RL and synthetic data curation has allowed its smaller models to “punch up” into weight classes typically reserved for far denser architectures.

While top-tier proprietary models like Claude Sonnet 4.6 maintain a lead in overall success rates, the Laguna family—particularly the open-weight XS.2—offers a competitive alternative for developers who prioritize local execution and customizable agent workflows.

All benchmarking was conducted using the Harbor Framework with sandboxed execution, ensuring that the results reflect the models’ ability to function in realistic, resource-constrained environments.

Running Laguna XS.2 locally

To run the Laguna XS.2 (33B) model locally, your hardware must accommodate its 33 billion total parameters. On Apple Silicon, the baseline requirement is 36 GB of unified memory.

For PC and Linux users, while the standard weights would typically require over 60 GB of VRAM, the model’s support for 4-bit quantization (Q4) allows it to run on consumer-grade GPUs with at least 24 GB to 32 GB of VRAM, such as the newly released RTX 5090.

Storage is also a factor; you should reserve at least 70 GB for the full model or roughly 20–35 GB for a compressed version suitable for local “agent” tasks.

For the most seamless experience, Poolside recommends utilizing Ollama or their own terminal-based agent, pool, which are designed to manage the model’s native reasoning and tool-calling capabilities on consumer hardware.

You can find the full technical requirements, including specific quantization configurations and code execution sandboxing details, on the official Hugging Face model page and the Poolside release blog. Some sample suggested hardware is listed below:

Mac

• MacBook Pro (14-inch or 16-inch): You should look for models equipped with the M5 Max chip, which specifically supports a starting configuration of 36 GB of unified memory. While the M5 Pro is available, you would need to custom-configure it to exceed its base memory to meet the 36 GB threshold.

• Mac Studio / Mac Mini: A Mac Mini (M4 or M5 Pro) configured with at least 48 GB or 64 GB of RAM is an excellent desktop alternative.

• NO “MacBook Neo”: this model is not suitable for running Laguna XS.2. Released in early 2026 as a budget-friendly option, the MacBook Neo is capped at 8 GB of non-upgradable memory, which is insufficient for a 33B parameter model.

PC

• Single-GPU Setup: The NVIDIA GeForce RTX 5090 is the premier choice for 2026, offering 32 GB of GDDR7 VRAM, which can handle the Laguna XS.2 at high speeds (approximately 45 tokens/sec) using Q4 quantization.

• Pro-Grade Setup: For professional developers running complex, long-horizon agents, the RTX PRO 6000 Blackwell (96 GB VRAM) or a dual RTX 5090 configuration allows the model to run without any compression loss.

• Minimum PC Spec: An RTX 4090 (24 GB) can run the model with heavier quantization, though performance may be slower during complex reasoning tasks.

pool (agent) and shimmer (IDE)

Models are only as useful as the environments they inhabit, and Poolside has released two “preview” products to house the Laguna series: pool and shimmer.

pool is a terminal-based coding agent designed for the developer’s local environment. It acts as an Agent Client Protocol (ACP) server, the same harness the team uses internally for reinforcement learning (RL) training.

By bringing the researchers’ own tools to the general public, Poolside is effectively inviting the developer community to participate in the “real-world gym” that trains their future models.

Shimmer represents a vision for the cloud-native future of development. It is an instant-on Virtual Machine (VM) sandbox where developers can iterate on web apps, APIs, and CLIs in seconds.

Unlike traditional integrated developer environments (IDEs) such as Microsoft Visual Studio, shimmer integrates the Poolside Agent directly into the workspace, allowing it to push changes to GitHub or import existing repositories with ease.

Perhaps the most surprising feature of shimmer is its portability. Poolside Founding Designer Alasdair Monk shared a demonstration showing shimmer running entirely on a smartphone.

In the demo, a split-screen interface shows the Poolside Agent generating a “Happy New Year 2026!” animation while a dev environment runs below.

As Monk noted, it offers an instant-on VM with Poolside Agent in split screen and a full dev environment on a mobile device.

This suggests a future where high-consequence engineering isn’t tethered to a desktop, but can happen wherever an engineer has a screen.

Why release Laguna XS.2 as Apache 2.0 open weights?

The most significant strategic move in this release is the licensing of Laguna XS.2. Poolside has released the weights of XS.2 under the Apache 2.0 license.

This is a highly permissive license that allows users to use, distribute, and modify the software for any purpose, including commercial use, without royalties. This is a stark contrast to the “closed” models of many competitors or even the more restrictive “open-ish” licenses used by some other labs.

Poolside’s leadership is explicit about why they chose this path. Poolside’s blog post states its conviction that “the West needs strong open-weight models” and that releasing the weights is the fastest way for the team to improve their work through community evaluation and fine-tuning.

By putting the weights of a highly capable, 33B-parameter agentic model in the hands of researchers and startups, Poolside is positioning itself as a cornerstone of the open-AI ecosystem.

While Laguna M.1 remains primarily behind an API, the open release of XS.2 ensures that Poolside’s technology will be baked into the next generation of third-party tools.

Poolside’s philosophy and approach

The core thesis behind Poolside’s work is that software development serves as the ultimate proxy for general intelligence.

Creating software requires long-horizon planning, complex reasoning, and the ability to manipulate abstract systems—all traits central to human cognition. While most current AI “agents” are restricted to tool-calling via pre-defined interfaces, Poolside’s agents are designed to write and execute their own code to solve problems.

This shift from using tools to building systems marks a fundamental evolution in how AI interacts with the digital world.

The team of roughly 60 people in the Applied Research organization spent three years and conducted tens of thousands of experiments to reach this point. Their vision of AGI is not just about intelligence, but about “abundance for humanity”.

By focusing on software engineering—a domain with verifiable rewards like test passes and compilation results—they have created a self-improving feedback loop. As the team puts it, they are building a “fusion reactor” for data: extracting every last drop of intelligence from existing human knowledge while using RL to harvest the “wind energy” of new, fresh experiences.

Poolside’s journey is just beginning, but the Laguna release sets a high bar for what “agentic” AI should look like in 2026. By combining frontier-level performance with a commitment to open weights and novel developer surfaces, they are charting a path to AGI that is as much about the way we build as it is about the what we build.

For the enterprise and the individual developer alike, the message is clear: the future of work is agentic, and the language of that future is code.