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When deep research isn’t enough for your business: Sakana AI launches ‘ultra deep research’ agent for 100+ page reports in 8 hours

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Tokyo-based AI startup Sakana AI has officially launched its first commercial product, Sakana Marlin.

Billed as a “Virtual CSO” (Chief Strategy Officer), Marlin is an autonomous, B2B research agent that deliberately abandons the instantaneous text generation of modern chatbots in favor of deep, long-horizon reasoning.

What sets Marlin apart from the current ecosystem of AI tools is its temporal scale: instead of returning an answer in seconds, it runs continuous, self-governing reasoning loops for up to eight hours at a time to deliver deeply researched, well cited, 100-page strategy reports and executive slides. The company posted sample reports generated by Marlin on its product website here.

Available immediately via the company’s website with pricing starting at a pay-as-you-go tier, the platform is designed strictly for enterprise use—specifically targeting corporations, financial institutions, and think tanks.

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The generative AI hype cycle has largely been defined by speed. For the past two years, the industry standard has been the ability to generate a poem, a line of code, or a surface-level summary in mere milliseconds. But the enterprise frontier is rapidly shifting from shallow, rapid generation to deep, methodical reasoning.

With Marlin, major businesses are no longer asking how fast an AI can answer, but how deeply it can think.

The Product: A Virtual CSO

What exactly is a business getting when they deploy Sakana Marlin? The workflow is fundamentally different from typical large language model (LLM) interactions. Rather than engaging in a tedious back-and-forth prompt engineering session, the user simply provides a core research topic. Following a brief initial exchange to sharpen the scope and direction of the investigation, the human steps away entirely.

For the next several hours, Marlin operates as a self-contained digital strategy team. It formulates its own initial hypotheses, navigates the web to gather data, cross-references sources to verify findings, and maps the causal dynamics within complex business environments. It is effectively searching for the “winning formula” within a sea of noise.

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Think of it less like a search engine and more like a junior strategy consultant locked in a room with a whiteboard and an internet connection. You provide the strategic prompt in the morning, and by the end of the workday, the system delivers a comprehensive, professional-grade portfolio.

In Marlin’s case, the final output is not a generic text blob; it is a structured set of strategic options, complete with executive summary slides, appendices, references, and a deeply researched report.

The company highlighted several real-world use cases to demonstrate Marlin’s capacity for complex synthesis, including generating detailed resolution scenarios for a theoretical blockade of the Strait of Hormuz, mapping out the fragmented global AI regulation patchwork, and analyzing macroeconomic trends like the return of “bond vigilantes”.

Sakana says Marlin relies on multiple AI models, but did not provide specific model names or providers. I’ve reached out on X to find out more and will update when I receive a response.

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VB Transform · July 14–15 · Menlo Park · LLMs, ops & evals

Standard benchmarks fail. Amazon and Waymo explain what they test instead.

The evals track goes deep on the four dimensions of reliability — consistency, robustness, predictability, safety — and how teams at Amazon and Waymo are operationalizing them in production.

See the full agenda →

The Engine of Long-Horizon Reasoning

Under the hood, Marlin is the commercial culmination of Sakana AI’s extensive laboratory breakthroughs over the past two years.

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The product is powered by an exploration engine relying on Sakana’s own prior research breakthrough, Adaptive Branching Monte Carlo Tree Search (AB-MCTS), and leverages frameworks derived from “The AI Scientist,” an earlier Sakana AI research project featured in the journal Nature that successfully automated the scientific discovery process from ideation to peer review.

To understand how this works in practice, consider a real-world analogy: modern chess engines. When a computer plays chess, it doesn’t just look at the board and guess; it plays out thousands of potential future moves, evaluating the strength of each resulting position before committing to an action.

Marlin’s AB-MCTS engine does something similar for research.

Inside the Engine: The Mechanics of AB-MCTS

The chronology of this technology traces back to June 2025, when Sakana AI first introduced the framework to the public alongside the research paper “Wider or Deeper? Scaling LLM Inference-Time Compute with Adaptive Branching Tree Search”.

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At that time, to encourage developer experimentation with collective AI intelligence, the company released the underlying algorithm as an open-source software library called TreeQuest, distributed under the permissive Apache 2.0 license. This open-source milestone laid the technical foundation for what would eventually evolve into the proprietary, enterprise-grade Marlin product a year later.

Traditionally, when developers attempt to extract higher-quality reasoning from large language models, they rely on a brute-force method called “repeated sampling”—essentially running the model dozens of times in parallel and hoping one of the answers is correct. However, repeated sampling operates blindly; it cannot evaluate its own intermediate steps or pivot based on external feedback.

AB-MCTS replaces this paradigm with a principled, multi-turn approach driven by a Bayesian decision framework. As the AI constructs a strategy report, the system treats the research process as a branching tree of possibilities. At each node of the tree, the algorithm dynamically balances two distinct behaviors based on external feedback signals:

  • Going Wider (Exploration): Spawning entirely new, alternative hypotheses or candidate responses when the current path yields diminishing returns or unresolved contradictions.

  • Going Deeper (Exploitation): Methodically refining, auditing, and building upon an existing candidate solution that shows high strategic promise.

What transforms this from a laboratory experiment into a commercial engine is its extension into Multi-LLM AB-MCTS.

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Sakana AI’s architecture introduces a critical third dimension to the search tree: the ability to dynamically choose which model to invoke for a specific sub-task, treating the industry’s leading frontier models as a plug-and-play collective intelligence network.

According to technical documentation published by the company, the engine can coordinate highly heterogeneous models—allowing an orchestration model to delegate initial ideation to one LLM, while utilizing a reasoning-heavy model to audit, verify, and correct intermediate errors generated earlier in the search tree.

By scaling up compute at inference time—leveraging the distinct “personalities” and strengths of multiple foundation models over thousands of automated cycles—AB-MCTS provides the mathematical guardrails Marlin requires. It ensures that the resulting 100-page strategy reports are not merely long-winded AI generations, but the highly vetted product of systemic, automated trial-and-error.

Licensing, Data, and Enterprise Implications

It is crucial to note that Sakana Marlin is distinctly not a general consumer tool; it is a commercial software-as-a-service (SaaS) offering restricted to corporate entities, organizations, and sole proprietors.

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For enterprises, licensing and data handling terms are often the determining factors in software adoption. Unlike many consumer-grade AI tools that silently harvest user inputs and proprietary data to train future foundational models, Sakana Marlin operates under a strict, enterprise-grade data policy.

Neither Sakana AI nor its external AI service providers will use customer data or inputs for model training or fine-tuning unless the client provides explicit opt-in consent.

Even with consent, data is heavily processed to remove personally identifiable information. This closed-loop security is absolutely vital for companies handling sensitive M&A research, unreleased product strategies, or proprietary market analyses.

The commercial licensing is structured into tiered pricing models that reflect its enterprise nature:

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  • Pay-as-you-go: Users can purchase credits on demand, with a single run costing 100 credits, and add-on credits priced at ¥98 ($0.61 USD) each.

  • Pro Plan: At ¥150,000 ($935.68 USD) per month, businesses receive 2,000 credits, bringing down the cost of add-on credits to ¥90 ($0.56 USD).

  • Team Plan: Geared toward larger departments, this ¥400,000 ($2,495.14 USD) per month tier includes 6,000 credits, lowering add-on costs to ¥85 ($0.53 USD) per credit.

  • Enterprise: Fully custom quotes with dedicated support and customized credit allocations.

Why Sakana Is Worth Watching

Sakana AI’s transition into a commercial enterprise powerhouse is rooted in the pedigree of its founders, who famously helped spark the current generative AI boom.

Formed in Tokyo in 2023, the startup was co-founded by Llion Jones—a co-author of Google’s seminal 2017 “Attention Is All You Need” paper who coined the term “transformer”—and David Ha, a former Google Brain researcher and head of research at Stability AI.

The decision to build a new laboratory outside the Silicon Valley bubble was a deliberate rejection of the current AI ecosystem. At a TED AI conference in late 2025, Jones candidly expressed that he was “absolutely sick” of transformers, warning that the intense pressure from investors and the hyper-fixation on scaling single, monolithic models had calcified the industry’s creativity and blinded researchers to the next major breakthrough.

To break free from this “big company-itis,” Jones and Ha structured Sakana AI around principles of biomimicry and evolutionary computing.

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The company’s name, derived from the Japanese word for fish, reflects its core technical philosophy: leveraging collective intelligence similar to schools of fish, ant colonies, or insect swarms. Rather than attempting to build one massive, do-it-all foundation model, Sakana’s research has consistently focused on deploying networks of smaller, specialized models that collaborate dynamically to adapt to complex environments.

This philosophy posits that by treating individual AI models as members of a “dream team” with complementary strengths, systems can achieve more robust and cost-effective reasoning than relying on sheer scale alone.

This nature-inspired approach quickly yielded dividends in rigorous, competitive testing. Sakana AI has made significant strides in “inference-time scaling”—allocating computational resources during the problem-solving phase to allow models to think, iterate, and refine their own answers over extended periods.

In early 2026, the company’s ALE-Agent took first place in the highly complex AtCoder Heuristic Contest (AHC058), a combinatorial optimization challenge, outperforming over 800 top-tier human programmers by autonomously rebuilding and testing hundreds of solutions over a four-hour window.

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Similarly, Sakana introduced “RL Conductor,” a small 7-billion-parameter model trained via reinforcement learning specifically to orchestrate and delegate tasks among a diverse pool of worker models—ranging from GPT-5 to Claude Sonnet 4—achieving state-of-the-art results on reasoning benchmarks at a fraction of traditional computing costs.

Sakana’s rapid evolution from a disruptive research lab to a commercial software provider has attracted intense attention from global financial heavyweights.

By late 2025, the Tokyo-based startup secured a massive Series B funding round that pushed its post-money valuation past $2.6 billion, cementing its status as one of Japan’s most highly valued private tech companies. The firm boasts a sprawling roster of strategic investors, including early venture backers Khosla Ventures, Lux Capital, and New Enterprise Associates (NEA), alongside industry titans like Nvidia and Google.

As Sakana has expanded its focus toward mission-critical sectors like defense and finance, it has also drawn investments from major global banking institutions like Mitsubishi UFJ Financial Group (MUFG) and Citi, as well as enterprise tech giant Salesforce, positioning the startup to actively reshape corporate AI infrastructure from the ground up.

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Community Reactions and Field Testing

Sakana AI’s shift toward commercial, long-horizon agents did not happen in a vacuum. The company ran a rigorous closed beta test beginning in April 2026, putting the tool in the hands of approximately 300 professionals across financial institutions, consulting firms, and think tanks. The feedback underscores a stark qualitative difference between standard generative chatbots and Marlin’s autonomous, fact-driven approach.

A senior consultant at a major Tokyo consulting firm noted that the tool “exceeded expectations by discovering angles we hadn’t even imagined,” praising its ability to match human comprehensiveness while stripping away human bias. Meanwhile, a cybersecurity division at a major Japanese IT system integrator lauded the system for providing “a highly convincing report driven by high-quality, primary research,” rather than relying on recycled secondary sources.

On social media, the company’s announcement resonated with the broader tech community’s growing appetite for autonomous agents.

As the AI industry matures, the value proposition is clearly shifting. Tools that act as fast, conversational encyclopedias are becoming commoditized. With Sakana Marlin, the focus moves entirely to separating the heavy lifting of thinking from the final act of deciding. By delegating the exhaustive mapping of causal dynamics to an agent capable of sustained reasoning, human executives are free to do what they do best: take action.

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Best Yeedi Prime Day Deals 2026: S14 Plus Drops From $1,200 to $499

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Prime Day is just around the corner, and while AI has tried its best to spoil our beloved tech gadgets and accessories, Yeedi has a few neat deals in store. The robot maker has already kicked off its early Prime Day deals, offering discounts of up to 67% across some of its most popular models. What’s interesting here is that Yeedi is also offering Prime Protection. In simple terms, if you buy an eligible robot vacuum now and its price drops even further during Prime Day, Yeedi says it’ll refund the difference. So you don’t have to play the usual waiting game and wonder if a better deal is coming next week

Yeedi Prime Day Deals

The standout deal is easily the Yeedi S14 Plus. Normally priced at $1,200, it’s currently available for just $499. For the money, you get a robot vacuum with a powerful 18,000Pa suction system and a built-in mop. In expert reviews, the S14 Plus picks up over 80% of sand from hardwood floors and delivers respectable carpet performance as well, making it a solid all-around option for most homes. If you’re looking for a robot vacuum that can handle everything from pet hair to sticky kitchen messes, this is probably the one we’d recommend.

The Yeedi M14 Plus is getting a healthy discount too. The price drops from $600 to $400. It’s a great vacuum for everyday cleaning, performing on par with the S14 Plus on normal surfaces. However, it falls behind the S14 Plus when carpets enter the equation. That’s why we’d still lean toward the S14 Plus if you want a more versatile cleaning companion. On the other hand, if your home is mostly hardwood, tile, or laminate flooring, the M14 Plus becomes a very compelling option at this price.

Starting June 23, shoppers can grab the Yeedi M16 Infinity for $499.99, down from its usual $799.99 price. The company is also discounting the S20 Infinity to $699.99, which is a substantial drop from its original $1,199.99 price. If you’re looking for something more premium, the S20 Infinity Ultra will be available for $849.99 instead of $999.99. Meanwhile, the S16 Plus drops to $499.99 from $699.99. Yeedi is also teasing a deal on an upcoming product called the C14 Pro Plus. Once it launches, the robot vacuum will be available for $279.99, down from its regular price of $349.99.

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Today’s NYT Strands Hints, Answer and Help for June 16 #835- CNET

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Looking for the most recent Strands answer? Click here for our daily Strands hints, as well as our daily answers and hints for The New York Times Mini Crossword, Wordle, Connections and Connections: Sports Edition puzzles.


Today’s NYT Strands puzzle was a bit challenging, but the answers were fun. Some of them are difficult to unscramble, so if you need hints and answers, read on.

I go into depth about the rules for Strands in this story

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If you’re looking for today’s Wordle, Connections and Mini Crossword answers, you can visit CNET’s NYT puzzle hints page.

Read more: NYT Connections Turns 1: These Are the 5 Toughest Puzzles So Far

Hint for today’s Strands puzzle

Today’s Strands theme is: For here or to go?

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If that doesn’t help you, here’s a clue: Time to eat.

Clue words to unlock in-game hints

Your goal is to find hidden words that fit the puzzle’s theme. If you’re stuck, find any words you can. Every time you find three words of four letters or more, Strands will reveal one of the theme words. These are the words I used to get those hints but any words of four or more letters that you find will work:

  • WAND, SAND, HAND, LOAD, SUNG, DRAW, LUNCH, WASH, MUNCH, SPAT, SPATS, PAST, PATS.

Answers for today’s Strands puzzle

These are the answers that tie into the theme. The goal of the puzzle is to find them all, including the spangram, a theme word that reaches from one side of the puzzle to the other. When you have all of them (I originally thought there were always eight but learned that the number can vary), every letter on the board will be used. Here are the nonspangram answers:

  • GYRO, SOUP, WRAP, RAMEN, SANDWICH, SALAD, TACOS

Today’s Strands spangram

completed NYT Strands puzzle for June 16, 2026

The completed NYT Strands puzzle for June 16, 2026.

NYT/Screenshot by CNET

Today’s Strands spangram is WHATSFORLUNCH. To find it, start with the W that’s the first letter on the top row, and wind over and down, kind of forming a numeral 7.

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Toughest Strands puzzles

Here are some of the Strands topics I’ve found to be the toughest.

#1: Dated slang. Maybe you didn’t even use this lingo when it was cool. Toughest word: PHAT.

#2: Thar she blows! I guess marine biologists might ace this one. Toughest word: BALEEN or RIGHT. 

#3: Off the hook. Again, it helps to know a lot about sea creatures. Sorry, Charlie. Toughest word: BIGEYE or SKIPJACK.

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SQL Server may be too lucrative for Microsoft to ditch, but too legacy to love

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While Microsoft sweeps the confetti off the floor of its Build event, it may be a good moment to reflect on what it didn’t say as much as what it did. Taking the spotlight was AI agent Scout, ready to “understand how work gets done” and “take action without needing to be prompted.” The software behemoth’s leading database, SQL Server, barely got a mention.

On its own, it may not be a big deal, but Microsoft watchers also noted that long-time SQL Server champion Rohan Kumar left the company in June, while Arun Ulag, president of Azure Data, currently holds the SQL Server remit. He’s also responsible for the Fabric analytics and AI platform and a portfolio of open source database services.

Taken together with the news that Microsoft’s own terms and conditions allow customers to take SQL Server licenses to AWS’s RDS database service without paying twice – thanks to a feature that lets them provide their own SQL Server installation media – the vibe around SQL Server has changed.

“I don’t think it is a priority,” said Andrew Snodgrass, research vice president of analyst company Directions on Microsoft. “With Kumar leaving, that’s become very evident. I think the world of Ulag, but [SQL Server] is not where his focus is for the future. I’m afraid Microsoft are going to leave it languishing.”

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He said his concerns for Microsoft’s flagship DBMS began when the 2022 version was released with a “bunch of Azure integration capabilities that no one was really asking for.” It ended up being “more of a marketing release than something that was truly engineered to meet customer needs,” Snodgrass said.

While the introduction of vector search in the 2025 edition was welcomed by users, PostgreSQL, MongoDB, and Oracle users had been benefiting from the feature for years.

“At Build, Arun Ulag stood up there and talked about all the new stuff: highlights of the database news there was HorizonDB, a PostgreSQL database service with a new form of scale-out capability,” Snodgrass said.

“There was no news about SQL Server, which was stunning, because SQL Server 2025 just came out at the end of last year, and in that they put in AI vector search, which I think is one of the greatest additions to SQL Server I’ve seen in ten years.”

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But it seems Microsoft is as interested in its PostgreSQL and other open source database services as it is in its own SQL Server offering. So long as it drives workloads in Azure, it is all good for Microsoft, Snodgrass said.

“It’s the kind of thing Dad might say: it’s not that I’m angry at Microsoft for what they’ve done to SQL Server, I’m just disappointed,” he said.

A Microsoft spokesperson said: “Customers have real choice in how they run SQL Server, and we’ve designed our licensing to be clear and flexible across environments. We’re fully committed to SQL Server and continuing to invest in its innovation, security, and long-term support so customers can confidently run their most critical workloads and build what’s next.”

Microsoft first released SQL Server in 1989 as a 16-bit version for the OS/2 operating system, which was a joint project with IBM. Despite challenges from Oracle, open source systems like PostgreSQL and MySQL, as well as a string of NoSQL databases such as MongoDB, it remains highly popular with users and developers. It is third behind Oracle and MySQL – ahead of PostgreSQL – on the DB-Engines ranking, which measures citations, Google data, and job searches. In the Stack Overflow survey of professional developers, it ranks fourth behind PostgreSQL, MySQL, and SQLite, but well ahead of Oracle, which lies in tenth.

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Adam Ronthal, vice president analyst at Gartner, said Microsoft’s approach to SQL Server can be explained by looking at two different priorities.

First, despite the hype around the cloud and AI, Microsoft made around $15 billion in revenue from the on-prem DBMS market, largely from SQL Server. It’s second in terms of market share (33 percent) only to Oracle, which holds nearly 40 percent of the on-prem DBMS market.

“If you look at Microsoft’s growth in the on-prem business in 2025, they were growing around 8 percent, so Microsoft continues to have a business in the on-prem that is growing in high single digits,” he said.

There is no way that Microsoft will walk away from that kind of revenue, Ronthal told The Register.

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Meanwhile, SQL Server customers represent a good opportunity for Microsoft to convert users to Azure SQL, and the SQL database in Fabric, its data analytics environment, as they are built on a consistent database engine. Microsoft wants people to see that Azure provides a seamless path to build and scale AI applications with deeply integrated data services, security, and governance.

However, Ronthal added that specific compatibility would depend on the implementation of T-SQL in the application users want to move.

“As we go full into managed services, I don’t have full control over the underlying operating system, and I might not have the same level of control over the configuration of the database itself.”

For commercial, off-the-shelf software, the ease of migration would depend on the vendor certification, he said.

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As well as wanting to defend its on-prem SQL Server revenue, Microsoft also sees that AI and cloud are driving the market.

In the cloud, the market is dominated by a family of databases based on PostgreSQL or closely related to the open source database.

“The de facto API for relational databases has emerged to be Postgres right now, and so we see many vendors implement wire from compatible Postgres APIs, which provides end users a hedge against lock-in,” Ronthal said.

A string of startups have tried to grab this market, including Cockroach Labs, Yugabyte, and pgEdge, all of which offer distributed capabilities and varying compatibility with PostgreSQL. Microsoft cannot ignore this development, hence its investment in HorizonDB, its own distributed PostgreSQL. Microsoft also has the DBaaS offering, Azure Database for PostgreSQL.

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As well as defending the growing on-prem database market, Microsoft is trying to capture the higher growth in cloud databases and catch up with AWS.

As such, it is incorporating operational databases under the Fabric umbrella, including NoSQL database Cosmos, Azure SQL, and Postgres capabilities. “If we look at the drivers of the market right now, which are cloud and AI – Fabric is a core component of AI – then the growth for Microsoft is largely going to be driven by Fabric adoption, where they’re putting a tremendous amount of focus and effort,” Ronthal said.

Nonetheless, Microsoft has deep enough pockets in terms of engineering budget to afford to battle it out on both fronts. In that sense, SQL Server workloads that end up on AWS still make sense.

“Microsoft has some rationalization to do in the portfolio, because there are multiple ways to run SQL Server,” Ronthal said. “You’ve got Azure SQL, managed instances, SQL Server in VMs. These provide slightly different levels of compatibility with what you might be doing in the on-prem world, and right now, the fact that there are multiple options actually makes it difficult for end users to figure out what to do. I would love to see Microsoft make it more unified and easier for people to consume.”

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In the cloud DBMS market, AWS has the upper hand by a considerable margin. In 2025, AWS made about $37 billion in cloud DBMS revenue, according to Gartner, while Microsoft made about $18.3 billion.

If a SQL Server customer can leverage an existing investment in Microsoft and bring it to AWS, Microsoft loses that business for Azure, “but on the plus side, they don’t lose a SQL Server customer, and that’s probably more important,” Ronthal said.

Of the leading vendors – Oracle, IBM, Microsoft, and SAP – only Microsoft has grown their market share in the last 15 years, Ronthal pointed out. Microsoft has proved capable of riding out changes in the market with both its cloud services and SQL Server strategy. Whether that’s also good for SQL Server customers might be up for debate, but since support for the 2025 version ends in 2036, they have plenty of time to plan. ®

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Trump’s ‘Made In the USA’ Phone Is Just a Reskinned HTC U24 Pro

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Longtime Slashdot reader necro81 writes: The heavily promoted, $499 T1 “Trump Phone” was originally said to be “Made in the USA” and ship in September 2025. Later, that was downgraded to “Assembled in the USA.” Given the Trump Organization’s lack of engineering or supply chain expertise, many assumed the “T1” would just be a private-label phone made by someone else. After a number of delays, the first phones are finally shipping.

iFixit has performed a teardown and concluded that the T1 is a just gold-painted 2024 HTC U24 Pro — a device from a Taiwanese company, probably using mainland China design and supply chains. In collaboration with NBC News, the iFixit team examined both phones using CT scans, side-by-side teardowns, and even reassembled a working T1 using a U24 Pro main board. As for “assembled in the USA,” that may be true, in the same sense that your phone’s repairman can “assemble” a phone from a handful of subassemblies sourced from someone else. Or it may have been assembled in Guangdong, China like the other U24 Pros.

iFixit sums it up: “What you have is not an ‘American-Proud Design,’ but a phone designed in China, made in China, with the vast majority of parts sourced from China. I’m failing to find any stirring of American pride within me. I’ve certainly felt it before, so I can confirm that it is absent at this time.” Quinn Nelson of Snazzy Labs on YouTube also published a comprehensive video of his experience ordering, unboxing, and tearing down the phone. “From pre-order emails landing in Gmail spam thanks to botched DMARC records, to paying for the $47.45 Trump Mobile 47 Plan over the phone, the entire buying experience was a disaster worthy of its own review,” writes Nelson.

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How Lorong AI is shaping Singapore’s AI ecosystem

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[This is a sponsored article with the Singapore Government Partnerships Office.]

Singapore’s push in artificial intelligence (AI) has accelerated in recent years, with adoption expected to contribute billions to the economy.

To unlock this potential, the government is creating spaces that bring together industry, researchers, and public sector players to collaborate, innovate and share knowledge. 

One such initiative is Lorong AI, launched in Jan 2025 by the Ministry of Digital Development and Information (MDDI). The hub provides a collaborative space for practitioners across sectors to connect, exchange ideas, and partner on AI projects.

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Lorong AI has been operating as a pilot initiative at a WeWork co-working space at Cross Street over the past year. There is an activity held almost every day of the week, and in Feb 2026, Lorong AI expanded its operations to a larger space at Vidacity in one-north. 

This expansion comes at a pivotal time, as Deputy Prime Minister Gan Kim Yong recently announced on Mar 2, 2026 that Kampong AI—Singapore’s first AI park combining work and living spaces—will be developed at one-north by 2028.

Vulcan Post went down to the new Lorong AI site at one-north to find out more about how Lorong AI is trying to bring people in the industry, as well as other sectors, together. 

Lorong AI was inspired by large tech hubs overseas

Speaking to Vulcan Post, Edmund Zhou, director-in-charge of Lorong AI, shared that the idea for the hub came about following a visit by MDDI officers to large tech centres like Silicon Valley and Dubai’s Area 2071, a few years ago. 

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What they observed at these hubs was not just the presence of capital or talent, but people running into each other, conversations spilling beyond scheduled meetings, and communities forming around shared problems. Natural partnerships emerged from purposeful and incidental interactions.

Neural Networking at Lorong AI./ Image Credit: Lorong AI

This gave inspiration to open a place in Singapore where ideas and collaborations on AI can flow as freely as “conversations down an alley.” The name “Lorong AI” was chosen to reflect this vision—a local flavour for a space where people could gather to find out “what the word on the street is” about AI.

“If you are a practitioner, you can easily relate to some of the interesting things happening here,” Zhou said. “And if you are someone looking to explore, adopt, or just learn more about AI, this can also be a place for you.” 

Lorong AI’s events can attract hundreds

AI Wednesdays on Agents and Governance./ Image Credit: Lorong AI

Lorong AI started with trialling weekly ‘AI Wednesdays’ in 2024, before opening it up to the general public. 

AI Wednesdays was created for public officers, and led by scientists, engineers, and researchers, covering various topics from technical deep dives on model development to discussions on emerging industry trends. 

As the scope of engagement grew, programmes like ‘Co-working Mondays,’ ‘AI ToolsDays,’ ‘ThursTalks’ and ‘Fri-DIYs’—were added to the programming for the week, with the latest addition of ‘Out of Lorong Experiences’ and ‘Gotong Royong.’ 

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“No two days are the same, as with every week,” said Assistant Director of MDDI, Muhammad Zahari bin Abu Talib, who is also one of the MDDI officers who oversees Lorong AI’s daily operations. 

Of course, participant demographics expanded as well. Lorong AI now sees practitioners, industry players, and curious newcomers all coming together to learn from each other and explore potential collaborations. 

The Co-Creation Playbook: Prompt-to-Partnership with Figma./ Image Credit: Lorong AI

Although Lorong AI’s initial WeWork space at Cross Street could only seat 70 people, talks by industry leaders from companies like OpenAI, Manus and Figma frequently attract over 100 attendees, far exceeding its capacity.

While the team invites companies to share their insights with the community, they draw firm lines to ensure the collaborative space isn’t used as a channel for product promotion. This keeps the focus on genuine knowledge-sharing and partnership-building.

The importance of face-to-face interactions

At Lorong AI’s newest one-north space, the team foresees more exciting events, workshops, and hackathons to come with its larger 140-person seating space.

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It is intentionally designed as an open-concept space, which encourages informal conversations in which anyone can chime in, catalysing future collaborations. 

OpenAI Codex Hackathon./ Image Credit: Lorong AI

Small, tangible details—like member lanyards or a board showing who’s around—make it easier to spot each other and start talking. With so much AI talent in one place, those casual interactions can quietly grow into new ideas and unexpected partnerships.

While it may seem ironic for a tech-driven initiative to emphasise physical space, the team believes that face-to-face interaction is essential for building the trust that drives partnerships. “If it’s purely virtual, a lot of things seem a little bit more transactional, and then the communication somehow lacks a little bit of the human touch,” Zhou noted. 

In addition, if an individual ever needs help in any area, the Lorong AI team would suggest companies or individuals with expertise and link them up.

From conversations to concrete outcomes

In 2025 alone, Lorong AI saw 4,000 attendees across its events. It now has over 260 paying members, about two-thirds of whom are AI practitioners, with the remainder comprising researchers and curious newcomers working on products or programmes.

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Besides locals, there have been transient visitors from overseas, who, on their pit stops in Singapore, give talks at Lorong AI’s programmes. 

Prof Torsten Hoefler’s sharing on AI for Climate Sciences./ Image Credit: Lorong AI

Impressively, more than 14 collaborations have sprouted from discussions at Lorong AI. The team’s goal is simple: “We are trying to increase the number of collisions through both engineered interactions via events and incidental interactions through co-working.”

For example, Zhou shared that a group of members who were comparing AI models ended up collaborating and publishing a research paper together. The paper, co-authored by responsible AI and cybersecurity experts, focused on detecting and disrupting hostile AI agents. 

Another instance saw an individual with a limited AI background come into Lorong AI, thinking of ways to launch a business. Through involvement in the community, they acquired the skills and knowledge needed to develop an AI coach for physical fitness.

Lorong AI as a foundational piece of the bigger AI puzzle

ThursTalks sharing on Personalised Computing with Hui Chien./ Image Credit: Lorong AI

Loy Hui Chien, one of the first distinguished contributors, shared that having a space like Lorong AI is vital to the AI scene in Singapore. 

As someone who doesn’t work in the technology space, he has found the community at Lorong AI to be pivotal to his AI journey, from self-learning fundamentals to building a research agent that can curate sources and produce reports in minutes. 

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Especially for a small country like Singapore, we can do more with (more) synergy fostered amongst the AI community in spaces like Lorong AI.

Loy Hui Chien

Zhou, who has been closely involved in the initiative, sees it as a reinforcing loop: the more value participants derive from the space, the more like-minded individuals it attracts.

As Singapore positions itself in the global AI race, Lorong AI aims to play a role in building the collaborative networks that will drive the nation’s AI future.

Lorong AI will play a contributing role [to support Singapore’s AI landscape]. What we hear from people who come is that they appreciate having a community they can turn to.

Edmund Zhou

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This vision of creating collaborative spaces extends beyond Lorong AI to initiatives like the upcoming Kampong AI, further cementing Singapore’s commitment to partnership-driven AI development.

In a domain defined by algorithms and automation, Lorong AI is betting that the real differentiator may still be something fundamentally human: the power of conversation.

You can find out more about Lorong AI here, and sign up for programmes through this link

Inspired by Lorong AI’s community-driven approach? Beyond project guidance, the Singapore Government Partnerships Office has launched a new SG Partnerships Fund to support citizen-led initiatives at different stages of development.

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Featured Image Credit: Lorong AI

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How to run Minecraft: Bedrock Edition on Mac

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Playing Minecraft is better with Bedrock, but it’s not directly available for macOS. Here’s how to get around the limitation and improve your building experience.

Minecraft is one of the longest-running online games that is still actively being played by a lot of people. Originally playable since 2009 and officially released in 2011, it has stood the test of time.

However, while it has been improved over the years, Mac gamers have missed out on one important update: Bedrock Edition. It’s a version that is available on many other platforms, but never made its way to macOS.

Though you can continue using the original Java version on Mac, it is possible to get Bedrock working. It takes a little work, a GitHub project, and buying the game from the Google Play Store.

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What is Minecraft Bedrock?

The original version of Minecraft was made in Java, which enabled it to be easily ported to multiple platforms without much trouble. It’s also a version that is very easily modded by the community, without necessarily requiring permission from Microsoft beforehand.

However, the Java edition has a key issue, in that it’s not natively built for any specific platforms. It wasn’t really intended to build a game as complex as Minecraft at all.

As an interpreted language, Java has to be compiled into an intermediate “bytecode” format before being run by a Java Virtual Machine. This just-in-time interpretation means that processing performance is impacted directly compared to a compiled native version.

There is also the issue of the earliest iterations of Minecraft being developed by Markus “Notch” Persson, and later by his studio, Mojang. Eventually, Microsoft got involved with its purchase of the studio in 2014.

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Man in glasses onstage beside large screen showing Minecraft city scene with tall blocky buildings, trees, villagers, and cloudy sky, suggesting a presentation about the game or technology

Tim Cook introduced Minecraft on Apple TV in 2016. It survived until 2018.

Since it was made by Notch alone at first, it meant that there were elements of code that he would be able to manage, but a team of developers would struggle with. After years of development, there was enough technical debt to prompt a rethink by those managing the game.

Cue the development of a C++ version, which started off with a demo of Pocket Edition in 2011. Over time, the codebase was expanded and improved upon, until it was rebranded as Bedrock Edition in 2022.

With that change, it became a more widely available version, including a release for Windows. The change also made it possible to create versions of Minecraft for other platforms, and for the games to more easily communicate with each other between different platforms.

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The change also meant Microsoft could incorporate an in-game store, monetizing their expensive acquisition, as well as other elements.

Working around the limits

While there’s Minecraft: Bedrock Edition for Windows, Xbox, PlayStation, Nintendo Switch, Android, and iOS, there is not a specific macOS version. You also can’t use the workaround of buying the iOS version and trying the iPadOS game in macOS, as that has been disabled.

There’s nothing wrong with sticking to the Java edition of Minecraft on your Mac, but there are ways to use the Bedrock edition. Just not by officially buying a macOS app.

A legitimate way of doing it is through using Windows on your Mac. Software like Parallels will let you run the Windows version of Minecraft Bedrock, but you again get that dreaded performance penalty.

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Three Minecraft launcher windows on a brown abstract background, showing installation instructions, a Microsoft account sign in screen, and an error message with green and red buttons

Phases of installing the Minecraft Launcher

There’s also the possibility of sideloading an iOS or iPadOS version, but we’d rather not anger Apple with that method.

Another way is to use the Linux Minecraft Launcher. There’s a build available for macOS, which works using the Android version of the game.

If you happen to have a Google account with Minecraft Bedrock already on it, you can use that. If not, you will have to pay for it from the Google Play Store.

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This can be a bit tricky if you don’t have an Android device on the account. By running the launcher and trying to download the game without the purchased version on your Google account, it will come up as a device under the Google Play Store.

How to run Minecraft Bedrock Edition on a Mac using Linux Minecraft Launcher

  • Download the macOS launcher from GitHub.
  • Open the DMG. Drag the Minecraft Bedrock Launcher to the Applications folder shortcut. After the transfer, you can close the installer and unmount the DMG.
  • Open Minecraft Bedrock Launcher. If you’re blocked from opening, head to System Settings then Privacy & Security, then next to the blocked app warning, click Open Anyway.
  • On the Linux Minecraft Launcher changelog, click Continue.
  • Log into the Google account associated with the Android game’s purchase. You will be asked to create a password to save the credentials, then click Save & Complete Login.
  • Click Download And Play.

Once completed, the game will run in a window, which you can make larger from the edges. There are also video settings available, both in a menu at the top and in the game’s settings.

Minecraft game window open on a computer desktop, showing the title screen with a grassy field, flowers, villagers, and a copper golem-like figure in the center with Start Game button

You should see this if installing the Minecraft launcher goes correctly.

Feel free to push things like the draw distance and frame rate up, as well as the resolution. It’s arguably one of the best features of Bedrock edition over Java, and you can use it to the fullest on your Mac desktop.

Now, go mine some redstone.

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Nuvei confirm plans to acquire Payoneer in deal valued at $2.75bn

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The deal will give Nuvei greater market access across an increasingly complex, cross-border landscape.

Payment platform Nuvei has confirmed plans to acquire Payoneer Global in a deal valued at roughly $2.75bn. The organisation previously confirmed that it had entered into “advanced talks” with Payoneer and that a future deal was likely. 

As part of the deal, which is now a definitive agreement, Nuvei will acquire all of the issued and outstanding shares of common stock of Payoneer Global Inc. for $7.40 per share in cash, representing a total transaction equity value of approximately $2.75 bn.

The acquisition combines Nuvei’s payment processing business with Payoneer’s cross-border payments solution, which according to Nuvei is needed in increasingly complex local and cross-border markets. In combining Nuvei’s payment capabilities with Payoneer’s cross-border payouts, the organisations aim to build a unified financial structure.

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A key element of the deal is Payoneer’s established regulatory footprint which creates access to several global jurisdictions, particularly as the company holds multiple licenses and authorisations, including licensing for online payment services in mainland China and authorisation in principle as a cross-border payment aggregator in India.

Commenting on the announcement, Phil Fayer, the chair and CEO of Nuvei said, “The acquisition of Payoneer marks a defining step in Nuvei’s evolution into a global financial infrastructure leader. By combining complementary capabilities, we can offer businesses a more complete platform to accept payments, send funds, issue cards, manage treasury and FX needs and access embedded financial services at scale.”

John Caplan, the CEO of Payoneer, added, “For two decades, Payoneer has earned the trust of millions of businesses in markets where trust takes years to build. We have transformed our business with extraordinary results and our combination with Nuvei will extend what we can offer customers. Together, we will reach more businesses, in more markets, with a more complete platform.”

The transaction has already been approved by the boards of directors at both Nuvei and Payoneer and the deal is expected to close in mid-2027, subject to customary closing conditions.

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How William Heronemus Kickstarted Wind Energy

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A half century ago, a scrappy crew at the University of Massachusetts Amherst erected a wind turbine on Orchard Hill, the highest point on campus. It was a frugal production, cobbled together from the rear axle of a Ford truck, a donated generator and microcontroller, a steam pipe, and various handcrafted steel and fiberglass parts, including its 4.5-meter blades.

The team of UMass engineering grad students, faculty advisors, and one precocious undergrad built it to prove that wind energy could keep rural homes toasty in New England’s frigid winters, as a way of trimming U.S. oil dependence—a national imperative in the aftermath of the 1973–1974 energy crisis. To illustrate the point, they also assembled a modular home there on Orchard Hill, and outfitted it with heaters that would be powered by the turbine.

Nine men standing and sitting on scaffolding that holds up the rotor and blades of a wind turbine In 1975 and 1976, a crew from the University of Massachusetts Amherst designed and constructed the 25-kilowatt wind turbine that kick-started the U.S. wind industry. Sandy Butterfield

It worked—too well. “We had to open up the doors in the dead of winter. It was just too damn hot,” recalls Michael Edds, who designed the turbine’s electrical system and served as the project’s first resident engineer. Fittingly, they dubbed the turbine the “Wind Furnace.”

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The turbine maxed out at 25 kilowatts—puny compared to modern machines that generate up to 26 megawatts, but more than most energy experts expected from wind technology in November 1976. Back then, wind power still conjured up images of quaint Dutch mills and creaky prairie water pumpers. Crafty engineers would soon show that wind power could be so much more. And it all began with the brilliant, commanding, and often polarizing UMass professor leading the Wind Furnace project: William Heronemus.

A retired U.S. Navy captain, Heronemus had joined the UMass faculty in 1967. He’d earned Bronze Stars for valor in World War II, designed and built nuclear submarines, and liaised with the British Royal Navy on the Polaris missile. UMass had recruited Heronemus to do ocean engineering, but the energy crisis and his growing misgivings about nuclear power shifted his attention to renewable energy.

A man in a suit jacket leaning over a map that\u2019s rolled out on a table Heronemus, photographed circa 1973, publicly advocated for the buildout of wind turbines, both onshore and off, at immense scale. Robert S. Cox Special Collections and University Archives Research Center/UMass Amherst Libraries

By 1972, Heronemus was advancing detailed designs to deploy wind turbines at immense scale. That year, at the Marine Technology Society’s annual gathering in Washington, D.C., he presented schemes for building thousands of them across the Great Plains as well as a vast grid of massive floating turbines transecting New England’s continental shelf. Wind power, he contended, could generate nearly a fifth of U.S. electricity needs by the year 2000. Never mind that the technology for such an enormous buildout had yet to be commercialized. Espousing grand schemes made Heronemus a quixotic figure.

He also vigorously attacked the commercialization of nuclear power, creating enemies within electric utilities and U.S. government agencies that saw nuclear technology as the future. They didn’t appreciate his claims that a cleaner energy future via wind was ready to be tapped, and that the push for nuclear power and its radiological risks was unnecessary. As author and energy analyst Peter Asmus put it in his 2000 book, Reaping the Wind: “William Heronemus was a dangerous man suggesting an audacious departure from the status quo.”

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Modular home and wind turbine on a grassy hill on a sunny day The UMass Amherst wind turbine generated most of the energy to heat a modular home through the cold, windy winters on Orchard Hill. Solar thermal panels provided some heat during windless periods. Robert S. Cox Special Collections and University Archives Research Center/UMass Amherst Libraries

What happened on Orchard Hill in 1976 marked Heronemus’s turn from provocateur to changemaker. The success of the experimental turbine set off waves of technological and industrial developments that forever changed the energy landscape. Within a few years, the students he trained and the entrepreneurs he inspired were building the world’s first modern wind farms and leading the Great California Wind Rush—the market that turned wind craft into an industry that’s still growing fast half a century later.

Globally, annual wind generation more than tripled between 2015 and 2025, according to data from Ember Energy, a think tank based in London. It will best nuclear’s global output by the end of this year, Ember predicts. And it all started with Heronemus, says Robert Thresher, longtime former director of wind research at the National Renewable Energy Laboratory (NREL) in Golden, Colo. (a U.S. Department of Energy lab rebranded late last year as the National Laboratory of the Rockies). “In my mind he was the father of the people that went out and really made the industry what it is today,” he says.

William Heronemus and the History of Wind Power

I got to know Captain Heronemus posthumously, interviewing his contemporaries and sifting through boxes delivered to the UMass Amherst archival research center’s 25th-floor reading room. During three visits there since 2023, I have discovered clues to his life, thinking, and research process amid the writings where he pitched his big ideas to the world. His papers include proposals to governments, utilities, and deep-pocketed philanthropists and investors, including Jane Fonda and Goldman-Sachs. Papers reveal the internationalism and commitment to service that took Heronemus on renewable-energy consulting trips to Pakistan, Cuba, Côte d’Ivoire, and beyond. Records show meetings with corporate powerhouses like Boeing and Grumman Aerospace and calls on politicians, including the senator and presidential hopeful Ted Kennedy. Postcards from former students exude gratitude.

Man sits in a chair at his desk, leaning back and holding his eye glasses Heronemus sits with a mock-up of a multirotor turbine in his cramped office in Marston Hall, UMass Amherst’s main engineering building. Robert S. Cox Special Collections and University Archives Research Center/UMass Amherst Libraries

I learned that Heronemus turned his attention from ocean engineering to energy a few years after arriving at UMass, when he saw the growing string of nuclear power plants going up along the Connecticut River, which flows past Amherst en route to Long Island Sound. The U.S. government had picked nuclear power as an antidote to the 1970s oil crises, and Northeast utilities had jumped in big. But Heronemus and other UMass engineers worried that the riverside reactors’ waste heat would threaten the river’s ecosystem and bounty.

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The advent of cooling towers to blow off heat into the air addressed the thermal pollution concern but created another: water depletion. (Nuclear plants consume about 60 million gallons of water per day, per reactor, on average.) And Heronemus perceived other nuclear power liabilities, stemming from his experience with nuclear propulsion on Navy ships. As a design engineer and head of construction and repair for a shipyard, he valued the military’s zero-accident standard for reactors but also knew the high cost of adhering to it. He argued that building expanded versions of the Navy’s pressurized water reactors to power cities and factories couldn’t be both safe and economical.

Hand-drawn sketch of three wind turbine rotors mounted on a single freestanding pole In 1971, Heronemus designed an offshore turbine with three rotors, but the first big multirotor prototype wouldn’t be built for another four decades. Robert S. Cox Special Collections and University Archives Research Center/UMass Amherst Libraries

He predicted—accurately, as it turned out—that costs would rise sharply as the nuclear industry addressed safety and environmental concerns. “Each plant costs more than its predecessor. The shipyards involved with nuclear reactors came to that conclusion years ago,” he wrote in a 1973 research proposal. He also argued that the risks inherent in nuclear reactors and their radioactive waste were unnecessary given Earth’s abundant solar and wind energy resources. He broadcast those views wherever and whenever he could: before congressional committees, at U.S. Atomic Energy Commission hearings, at academic conferences, in media interviews, and even at Rotary Club luncheons.

At a 1973 licensing hearing for the proposed 820-MW Shoreham Nuclear Power Plant on Long Island, N.Y., for example, Heronemus called affordable nuclear energy a “myth.” He detailed, in its stead, a floating wind power system that could be moored off Long Island and sized to deliver more than four times as much electricity as the Shoreham plant. Each of the 640 floating platforms would carry six rotors and crank out up to 12 MW, some of which would power electrolyzers to generate hydrogen. The hydrogen would be fed to power plants or fuel cells to produce electricity when the wind wasn’t blowing. This seemingly futuristic idea drew on his Navy experience with water-splitting electrolyzers, which supplied the oxygen that enabled subs to remain submerged for months at a time, and NASA’s use of hydrogen fuel cells to power the Apollo missions.

More than five decades later, his vision for offshore wind power is big business. Floating platforms are now widely accepted as the future of offshore wind, as necessity pushes the industry to build in deeper waters. Testing began on the first floating electrolysis platforms in 2023, and multirotor turbine prototypes are in development in China, Norway and Scotland.

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The UMass Amherst Wind Turbine Legacy

Photos in the UMass archives invariably capture Heronemus in jacket and tie, usually standing bolt straight. That commanding affect, plus his World War II veteran pedigree, Cold War engineering credentials, and his informed, pugnacious attacks made him a hard target for his adversaries in the nuclear establishment. He certainly wasn’t your typical antinuclear activist.

A man in a suit standing very straight outsider a modular home Wielding his Cold War engineering credentials and often dressed in a suit and tie, Heronemus fought hard against nuclear energy, arguing that wind was a far safer and cost-competitive resource.Robert S. Cox Special Collections and University Archives Research Center/UMass Amherst Libraries

But brutal candor in public settings probably won him as many enemies as friends. Consider his presentation at the IEEE Power and Energy Society’s 1974 winter meeting, where Heronemus suggested scrapping the utilities’ then nuclear-focused research arm, the Electric Power Research Institute. That stance no doubt created discomfort for the engineers in attendance who were involved in EPRI projects, or who aspired to be.

It’s hard to say whether Heronemus’s campaign slowed nuclear development. The industry was already struggling with cost overruns when, in 1979, a reactor at Three Mile Island in Pennsylvania partially melted down and slammed the brakes on further expansion.

What is certain is that Heronemus spurred investment in wind power. When he started talking up wind in the early ’70s, even fellow travelers in the fledgling renewable energy movement were writing it off. As future White House science advisor John Holdren opined in a 1971 Sierra Club book: “There are few places in the world where the wind is strong enough and steady enough to make harnessing it for the large-scale production of power at all interesting.”

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Hand-drawn sketch of a bridge-like structure across a highway containing five wind turbines that resemble giant fans Heronemus dreamed up networks of wind turbines over and along highways after driving down the Garden State Parkway to a conference in Cape May, New Jersey. Ellen Heronemus

Heronemus countered the naysayers by quickly forging expert consensus around wind power’s immense potential, playing a key role as the sole wind expert on a 1972 federal panel on renewable energy. That joint National Science Foundation–NASA panel concluded that, in fact, wind could meet up to 19 percent of projected U.S. power demand by the year 2000.

Congress listened, sort of. After most Persian Gulf states restricted oil shipments to the United States in 1973, congressional appropriators dedicated US $1.8 million to wind-power research and development for 1974—up from zero—and by 1976 it had bumped that to $22 million. (For comparison, Congress gave nuclear power $714 million in 1976.)

Hand-drawn sketch of a massive structure built over the length of a highway holding wind turbines that resemble giant fans Heronemus’s vision for a massive highway wind-power scheme was inspired in part by the wind-power advocate Percy Thomas, who in the 1940s and 1950s “talked a lot about how fresh New Jersey winds are,” he told the New York Times in 1974. “I got to thinking about what Thomas had said and how wind energy could be captured there.” Ellen Heronemus

The bulk of the funding for wind power flowed to big aerospace firms and to NASA, financing an ultimately fruitless attempt to leap straight to megawatt-scale wind turbines. UMass struggled to grab a slice of the leftovers to pursue Heronemus’s offshore wind system. Professors and students who worked with Heronemus told me they felt they’d been blackballed as payback for his activism and antagonism.

UMass finally caught a funding break when Heronemus dialed back his ambitions and proposed the 25-kW unit for Orchard Hill. A $130,000 federal grant landed in early 1975, and $150,000 more the following year. It was a “trivial” sum, according to team member Sandy Butterfield, who would later become chief engineer for wind-turbine testing at NREL. “They gave us just enough to fail,” says Butterfield.

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A crane in the midst of vertically erecting a wind turbine on a single pole    A crane erects the “Wind Furnace” in November 1976. Sandy Butterfield

But the project triumphed, resulting in Wind Furnace 1, or WF-1 (pronounced “woof one”). The young engineers behind it credit their success to the confidence, sense of mission, and structure that Heronemus gave them. The self-described “hippies” called Heronemus “the Captain” out of both affection and respect.

As team member Edds puts it: “What showed in his demeanor and his actions was discipline, and it sort of rubbed off on us. We didn’t always dress like the Captain, but we knew we had to be disciplined, to be prepared, and just do the job.”

From Helicopter Rotor to Wind Turbine

Team WF-1 got a quick start, thanks to earlier, privately financed work by a couple of doctoral students, including Forrest “Woody” Stoddard. Stoddard had been designing helicopter rotors for the U.S. Air Force when Heronemus invited him to come work on wind power in 1972. Stoddard set about adapting helicopter-rotor theory to the closely related wind rotors, and his aerodynamics modeling proved essential to the engineering of the entire machine.

Six men squat around a turbine blade that\u2019s wrapped in plastic Woody Stoddard [far right, in hat] designed the fiberglass blades with Ted Van Dusen. The team assembled the blades in a campus shop, and when it was time to squeegee epoxy from the blades, it was all hands on deck. Robert S. Cox Special Collections and University Archives Research Center/UMass Amherst Libraries

As WF-1’s de facto chief designer, Stoddard likely supported the team’s early choice to mimic a helicopter’s ability to “pitch” its blades. To fly forward, a helicopter continuously adjusts the lift created by each blade, turning the airfoil on its long axis to reduce lift as it swings past the front of the aircraft. Doing so tilts the nose down and moves the vehicle forward. In WF-1’s case, blades pitched to regulate torque, helping get the rotor spinning in low winds and then easing off to protect the machine in dangerously high winds.

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Repurposing a truck axle to mechanically couple WF-1’s rotor and generator was one of several design elements borrowed from engineers at McGill University in Montreal. Production of WF-1’s fiberglass blades got started at UMass in 1974 under the direction of doctoral student Ted Van Dusen. A competitive rower, he had a side hustle making ultralight composite boats—a trade that had stalled his doctoral work at MIT but was an accelerant for WF-1.

The federal funds in 1975 allowed Heronemus to really spin up the project and recruit a squad of students to engineer the balance of WF-1’s components. They made good use of the UMass engineering machine shop and received guidance from faculty, including mechanical engineering professors Duane Cromack and Jon McGowan. But it was the dozen or so students who really cranked out the parts.

Most were master’s students, like Butterfield, who designed the blade-pitching mechanics. Edds, the team’s only electrical engineer, had come to UMass to learn ocean engineering, only to be diverted into handling WF-1’s generator. Louis Manfredi, another ocean engineering student, teamed up with master’s student Jim Sexton on the nacelle housing the generator and drivetrain. Fred Antoon adapted the truck axle. Brian Kuhn did drawings.

Chains and moving parts inside the rotor of a wind turbine WF-1 contained a mechanism that pitched its blades to regulate torque in response to wind speed, a feature that became an industry standard.Sandy Butterfield

An 18-year-old freshman, Dan Handman, came aboard and soon made himself indispensable. When he approached Heronemus to introduce himself, Heronemus handed him three months’ worth of anemometer readings punched into recording paper, and told him to turn it into 15-minute averages. Figuring there had to be a more efficient method for analyzing wind speeds, Handman asked around and found a wind-averaging machine from an earlier student project. A month or so later, he’d installed it in a cabinet near Heronemus’s office and wired it to an anemometer on Orchard Hill.

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Handman’s primary role on WF-1 was setting up its computerized control system, which tracked wind speed and sent commands to Butterfield’s pitch mechanism. The controls also tracked the generator’s speed and adjusted the current to its rotor windings, in accordance with calculations by Edds. Tweaking the current ensured that power demand from the electric heaters installed in the home below didn’t stop the rotor in weak winds.

A man in a harness standing at the top of a wind turbine on a single pole, high in the air Sandy Butterfield, part of the 1970s “UMass Mafia” team that built WF-1, became a wind-power entrepreneur and a top engineer at the National Renewable Energy Laboratory in Golden, Colo. Sandy Butterfield

The finished WF-1 really cranked up the heat, some of which was stored by heating water in tanks in the modular house’s basement, to be circulated through baseboards in windless periods. It turned out WF-1 was unusually efficient at capturing wind energy because its rotor could change speed with the wind, keeping the blades close to an aerodynamic optimum.

This varying rotor speed meant that the frequency of the electric power WF-1 produced also varied. Turbines linked to power lines must strive for the opposite—a steady output that synchronizes with the grid’s frequency—primarily 50 or 60 hertz. But it suited the home’s low-tech heating scheme just fine. (Electronic converters let today’s turbines have it all by ingesting a variable wave and outputting a new wave that’s synced to the grid.)

The Great California Wind Rush

In 1977, with WF-1’s success in hand, Heronemus projected that 3 million homes like the one on Orchard Hill could soon slash U.S. heating oil demand by 90 million barrels a year. That never happened, but an industry was born, starting with a Burlington, Mass. startup called US Windpower—the first “credible” U.S. turbine manufacturer, according to Thresher, who is now an emeritus researcher at the National Laboratory of the Rockies.

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Five wind turbines mounted on freestanding poles on farmland Belgian-made WindMaster turbines erected at Altamont Pass signaled the internationalism of the California wind rush. UMass team member Woody Stoddard conducted engineering analyses of many early designs deployed there.Bettman/Getty Images

Boston-area entrepreneurs Russell Wolfe and Stanley Charren launched US Windpower with Stoddard and Van Dusen after visiting Heronemus in 1974 and liking what they heard. They adapted WF-1’s design to make it suitable for grid-connected operation, building and breaking prototypes before erecting the world’s first grid-connected wind farm in 1980—20 turbines on a mountain in New Hampshire. California’s water authority placed an order for 100 MW of wind power, and in 1981 US Windpower began installing hundreds of turbines in Altamont Pass, east of San Francisco.

As more firms jumped to California, drawn by state government incentives, WF-1’s creators and the next cohort of UMass grads assumed important roles in the nascent market. Seven joined Energy Sciences, a startup cofounded by Butterfield. More joined U.S. Windpower. Stoddard left that company to start a consulting firm and ended up advising some of Denmark’s modern wind pioneers, which rapidly expanded thanks to the California market. Those early Danish firms made relatively simple, sturdy machines that subsequently scaled up and dominated globally for several decades — until China embraced wind power.

The California wind power boom peaked in 1986, after which energy prices collapsed and incentives faded. Most manufacturers were bankrupted by equipment failures and financial challenges, making the 1990s a tough time for wind power’s pioneers. Many UMass wind engineers, like Butterfield, joined Thresher’s operation at NREL, culling everything they could from the California experience.

“An entire generation of U.S. wind engineers got their graduate training, at least in part, using the Wind Furnace.”—Harold Wallace

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There, Heronemus’s protégés became known as the “UMass Mafia.” Thresher says it attests to the crew’s impact: “There were others. But that UMass Mafia were really leaders in the field. I think that’s the heritage we got from Bill Heronemus. Those people were so impactful and the education they got [with Heronemus] was the key.” What Heronemus began at the university became the UMass Wind Energy Center, which has awarded over 300 graduate degrees.

WF-1 now rests in the Smithsonian Institution’s collections in Washington, D.C. It earned its place there, as Smithsonian’s only modern wind turbine, because it represents wind energy’s revival, according to Harold Wallace, Smithsonian’s curator for electricity collections. “An entire generation of U.S. wind engineers got their graduate training, at least in part, using the Wind Furnace,” he says.

Heronemus didn’t get to witness the production of the massive offshore machines that he foresaw. He lost his long fight with cancer in November 2002, at the age of 82, even as former students and family members were racing to patent his multirotor and floating turbine designs.

Had he lived longer, the Captain would almost certainly have railed against current U.S. energy policy. The U.S. government has never backed wind power as generously as he’d hoped. Wind supplied 10 percent of U.S. generation last year—that’s half the share in Europe—with offshore turbines providing only a tiny sliver. Federal support for wind power has been in a stop-go cycle since Ronald Reagan’s administration, and it’s hit a low again under President Donald Trump, who has vowed to stop wind power cold. As Trump boasted to oil executives in January: “We have not approved one windmill since I’ve been in office, and we’re going to keep it that way.”

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Under Trump, stop-work orders have disrupted offshore projects from Massachusetts to Virginia, contributing to a nearly $600 million loss in 2025 for GE Vernova’s wind business. GE Vernova is the only major wind turbine manufacturer remaining in the United States, and it too can be traced back to Heronemus via a US Windpower patent.

In stark contrast, European and Asian countries have been going big on offshore wind and are now developing floating wind farms to push into deeper waters. China might be the one to finally conjure up Heronemus’s favored wind design: floating platforms bearing massive multirotor machines. In 2024, Zhongshan-based turbine maker Ming Yang Smart Energy Group deployed a two-rotor offshore prototype. The company says its next iteration will generate a whopping 50 MW—a twin-headed beast that would be the world’s most powerful wind machine.

That will be a bittersweet moment for the U.S. wind industry and Captain William Heronemus’s UMass Mafia, for whom such massive machines are a dream come true. Joanne Carroll, a retired member of the UMass Mafia, says she remembers the very moment, her freshman year, when Heronemus’s dream became hers. While he was lecturing in Introduction to Engineering about the hidden costs of coal-fired power, Heronemus walked to the window and said: “‘But out there there’s wind, and you can harvest that energy,’” Carroll recalled. “And I remember thinking: That’s what I want to do with my life.”

The author would like to give special thanks to UMass professor emeritus James Manwell for his assistance with this story.

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Qobuz Is the Anti-Spotify Music Streamer You’ve Been Waiting For

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When Dan Mackta, Qobuz’s New York–based managing director, was looking for musicians to endorse the music streaming service after its US launch in 2019, he tapped up a friend—the manager of the Flaming Lips. It was mid-pandemic levels of tricky.

“I flew to Oklahoma to shoot with Wayne Coyne,” Mackta says. “He shows up wearing one of those helmets, with the ventilation system to protect you, a metallic puffer jacket and big silver moon boots.” They couldn’t hear a word Coyne said in the helmet, so the frontman went home and shot the promo video himself: “How to pronounce this weird word ‘ko-buzz.’”

The Qobuz questions after “How do you say it?” are likely “Can I transfer my music library across?” and “Does it have everything?” The answers: yes and almost. Case in point: I recently switched to Qobuz, after nearly 20 years with Spotify. (Emotional.) I used a third-party service called Soundizz to transfer my songs; it took half an afternoon to port, with a more than 90 percent hit rate for my playlists.

One Million Club

I’m not alone, according to Mackta, who landed at Qobuz after years at major and indie record labels—2025 was a banner year for the 19-year-old company. Twelve months ago, Qobuz had around 500,000 subscribers. The French streamer had grown steadily since 2007, targeting “people who already knew what hi-res music was” with its 100 million–plus catalog of lossless CD-quality and 24-bit music.

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The first winds of change arrived with Liz Pelly’s January 2025 book Mood Machine, which criticized Spotify’s business practices, featuring interviews with former employees and artists calling for fairer industry economics. As Mackta puts it, “This is not a music company; music was just a means to an end.” It renewed the scuttlebutt amongst artists about low payouts, and Qobuz’s daily US trial numbers started to pick up.

In mid-October, free-tier users started posting the ICE recruitment ads they saw on Spotify, which went viral on TikTok and Instagram Reels. “The day that story broke was our biggest day ever in the US,” Mackta says. Qobuz saw another spike in numbers, plateauing until Spotify’s own marketing convinced more people to switch in early December. “The second best day was Spotify Wrapped,” he says. Qobuz hoovered up everyone from audiophiles and “conscious consumers” responding to boycotts like Death to Spotify and Indivisible, to K-pop superfans searching for high-quality downloads.

Qobuz now has 1.2 million active monthly users, and its streaming revenue shot up 45.7 percent in 2025, compared to 8.8 percent growth in overall paid music streaming. Around a third of its revenue now comes from the US, its biggest market. Those are still teeny numbers next to Spotify (293 million paid subscribers) and Apple Music (more than 100 million). “For us to say we’re gonna compete with Apple or Amazon,” Mackta says, “we might as well say we’re trying to launch a rocket.” Qobuz’s goal is to reach 1 percent of the paid streaming market; under its French CEO Denis Thébaud, it expects to reach profitability by March 2027.

Higher Payouts

For years, Qobuz had popped up in posts by artists bemoaning being paid “a quarter of a cent per stream” on big platforms versus “a much higher number” on Qobuz. Wading into digital payment structures to labels and rights holders can get murky, with low transparency, vague payout ranges and, same as it ever was, conflicts between labels and artists. But in multiple evaluations and artist anecdotes, Qobuz has the highest pay-per-stream, edging out rival hi-res music service Tidal and, in some cases, paying out five to six times as much as Spotify.

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An average per-stream rate is an artificial metric, which doesn’t reflect how everyone gets paid. But in March 2025, the company released that all-important number, verified by an independent auditor: Qobuz pays an average of $0.01873 per stream, or $18.73 per 1,000 streams. “We knew we had the best number so we thought we’ll just lay it down,” Mackta says. “Anyone else want to tell us what theirs is? They don’t.” Spotify’s average per-stream range is around $0.003 to $0.005 per stream, or $3 to $5 per 1,000 streams.

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Ultra Compact M5StickC Plus Delivers a Fake Windows XP Boot on Hardware You Can Hold in One Hand

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M5StickC Plus Windows XP Mini Computer
Small enough to disappear into a pocket or clip onto a keychain, the M5StickC Plus from M5Stack feels more like a finished gadget than a bare development board. Its bright orange plastic shell measures just 48 by 24 by 13.5 millimeters and weighs under 17 grams with the internal battery installed. A 1.14-inch color TFT screen sits on the front, surrounded by two programmable buttons and an M5 logo on the side. Flip it over and the back carries clear pin labels plus a Grove expansion port that accepts a wide range of cheap add-on modules.



A 120mah lithium battery powers the device, and it can be charged using the USB-C connector. Real-world battery life varies based on screen brightness and wireless usage, but most users can get a few hours out of it before needing to charge it again. The device wakes up like a flash, literally, only a couple of seconds after you press the reset button, and shuts down just as rapidly once you hold it down for six seconds, so unintentional shutdowns are uncommon.

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At the heart of things is an ESP32-PICO-D4 CPU, which can run at up to 240mhz and includes WiFi and Bluetooth. You have 4MB of flash memory and half a megabyte of RAM to deal with, so whether you’re scripting basic tasks or working on anything more ambitious, you’ll have enough of power to get the job done. There are also lots of extras built in, including a six-axis motion sensor, a microphone, a real-time clock, a red LED, an infrared transmitter, and a passive buzzer that can produce brief little songs.

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M5StickC Plus Windows XP Mini Computer
One of the cool things about this board is that the programming won’t be too difficult for newbies to the game, as you can simply drag and drop blocks around with the free UiFlow tools, which is a quick way to start. More experienced developers can use the Arduino IDE, PlatformIO, or Espressif’s native ESP-IDF environment to create regular C or MicroPython code as usual. The upload happens via the same USB-C cable used to charge the battery, and no extra drivers are required; simply install the normal FTDI virtual COM port package.

M5StickC Plus Windows XP Mini Computer
We can’t forget about the self-contained sketch that takes the small screen through the entire Windows XP launch routine. The iconic startup logo appears first, followed by a short loading animation and the famous startup tones played by the buzzer. Then the desktop appears, replete with a start bar and a My computer icon, and pressing a button causes a blue screen error, after which the device “reboots” back to the desktop and plays the iconic Windows XP sound. All of the graphics and animations were reduced to small little data arrays that fit into the board’s limited memory, and the little color screen is realistic enough to make you double take.

M5StickC Plus Windows XP Mini Computer
This is still a visual and aural display, rather than a comprehensive operating system, because the ESP32 just cycles through a collection of recorded images and tones in response to button presses or timers. What’s truly telling is how much attention to detail the inventor has put in; it demonstrates what you can cram into even the most basic of hardware when you set your mind to it. While it’s nice to see nostalgia demos like this, the M5Stack is capable of much more. Some people use it as a portable network tester, scanning WiFi networks and pretending to be another device. Others will connect it to a watch strap and write some code to create a custom wearable that displays information such as step counts or heart rate from add-on sensors.

M5StickC Plus Windows XP Mini Computer
The reason it’s so simple to add to is that the Grove connectors in the entire M5Stack world follow the same standard, so all you need is a single wire to connect the stick to a module that provides more display power, motors, a camera, or a radio transmitter. The best aspect is that no soldering is required, so prototypes may be assembled in minutes rather than hours. You can get one for roughly $30, or a little less if you’re purchasing on a larger marketplace.

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