The baton of open source AI models has been passed on between several companies over the years since ChatGPT debuted in late 2022, from Meta with its Llama family to Chinese labs like Qwen and z.ai. But lately, Chinese companies have started pivoting back towards proprietary models even as some U.S. labs like Cursor and Nvidia release their own variants of the Chinese models, leaving a question mark about who will originate this branch of technology going forward.
One answer: Arcee, a San Francisco based lab, which this week released AI Trinity-Large-Thinking—a 399-billion parameter text-only reasoning model released under the uncompromisingly open Apache 2.0 license, allowing for full customizability and commercial usage by anyone from indie developers to large enterprises.
The release represents more than just a new set of weights on AI code sharing community Hugging Face; it is a strategic bet that “American Open Weights” can provide a sovereign alternative to the increasingly closed or restricted frontier models of 2025.
This move arrives precisely as enterprises express growing discomfort with relying on Chinese-based architectures for critical infrastructure, creating a demand for a domestic champion that Arcee intends to fill.
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As Clément Delangue, co-founder and CEO of Hugging Face, told VentureBeat in a direct message on X: “The strength of the US has always been its startups so maybe they’re the ones we should count on to lead in open-source AI. Arcee shows that it’s possible!”
Genesis of a 30-person frontier lab
To understand the weight of the Trinity release, one must understand the lab that built it. Based in San Francisco, Arcee AI is a lean team of only 30 people.
While competitors like OpenAI and Google operate with thousands of engineers and multibillion-dollar compute budgets, Arcee has defined itself through what CTO Lucas Atkins calls “engineering through constraint”.
The company first made waves in 2024 after securing a $24 million Series A led by Emergence Capital, bringing its total capital to just under $50 million. In early 2026, the team took a massive risk: they committed $20 million—nearly half their total funding—to a single 33-day training run for Trinity Large.
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Utilizing a cluster of 2048 NVIDIA B300 Blackwell GPUs, which provided twice the speed of the previous Hopper generation, Arcee bet the company’s future on the belief that developers needed a frontier model they could truly own.
This “back the company” bet was a masterclass in capital efficiency, proving that a small, focused team could stand up a full pipeline and stabilize training without endless reserves.
Engineering through extreme architectural constraint
Trinity-Large-Thinking is noteworthy for the extreme sparsity of its attention mechanism. While the model houses 400 billion total parameters, its Mixture-of-Experts architecture means that only 1.56%, or 13 billion parameters, are active for any given token.
This allows the model to possess the deep knowledge of a massive system while maintaining the inference speed and operational efficiency of a much smaller one—performing roughly 2 to 3 times faster than its peers on the same hardware. Training such a sparse model presented significant stability challenges.
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To prevent a few experts from becoming “winners” while others remained untrained “dead weight,” Arcee developed SMEBU, or Soft-clamped Momentum Expert Bias Updates.
This mechanism ensures that experts are specialized and routed evenly across a general web corpus. The architecture also incorporates a hybrid approach, alternating local and global sliding window attention layers in a 3:1 ratio to maintain performance in long-context scenarios.
The data curriculum and synthetic reasoning
Arcee’s partnership with fellow startup DatologyAI provided a curriculum of over 10 trillion curated tokens. However, the training corpus for the full-scale model was expanded to 20 trillion tokens, split evenly between curated web data and high-quality synthetic data.
Unlike typical imitation-based synthetic data where a smaller model simply learns to mimic a larger one, DatologyAI utilized techniques to synthetically rewrite raw web text—such as Wikipedia articles or blogs—to condense the information.
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This process helped the model learn to reason over concepts and information rather than merely memorizing exact token strings.
To ensure regulatory compliance, tremendous effort was invested in excluding copyrighted books and materials with unclear licensing, attracting enterprise customers who are wary of intellectual property risks associated with mainstream LLMs.
This data-first approach allowed the model to scale cleanly while significantly improving performance on complex tasks like mathematics and multi-step agent tool use.
The pivot from yappy chatbots to reasoning agents
The defining feature of this official release is the transition from a standard “instruct” model to a “reasoning” model.
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By implementing a “thinking” phase prior to generating a response—similar to the internal loops found in the earlier Trinity-Mini—Arcee has addressed the primary criticism of its January “Preview” release.
Early users of the Preview model had noted that it sometimes struggled with multi-step instructions in complex environments and could be “underwhelming” for agentic tasks.
The “Thinking” update effectively bridges this gap, enabling what Arcee calls “long-horizon agents” that can maintain coherence across multi-turn tool calls without getting “sloppy”.
This reasoning process enables better context coherence and cleaner instruction following under constraint. This has direct implications for Maestro Reasoning, a 32B-parameter derivative of Trinity already being used in audit-focused industries to provide transparent “thought-to-answer” traces.
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The goal was to move beyond “yappy” or inefficient chatbots toward reliable, cheap, high-quality agents that stay stable across long-running loops.
Geopolitics and the case for American open weights
The significance of Arcee’s Apache 2.0 commitment is amplified by the retreat of its primary competitors from the open-weight frontier.
Throughout 2025, Chinese research labs like Alibaba’s Qwen and z.ai (aka Zhupai) set the pace for high-efficiency MoE architectures.
However, as we enter 2026, those labs have begun to shift toward proprietary enterprise platforms and specialized subscriptions, signaling a move away from pure community growth.
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The fragmentation of these once-prolific teams, such as the departure of key technical leads from Alibaba’s Qwen lab, has left a void at the high end of the open-weight market. In the United States, the movement has faced its own crisis.
Meta’s Llama division notably retreated from the frontier landscape following the mixed reception of Llama 4 in April 2025, which faced reports of quality issues and benchmark manipulation.
For developers who relied on the Llama 3 era of dominance, the lack of a current 400B+ open model created an urgent need for an alternative that Arcee has risen to fill.
Benchmarks and how Arcee’s Trinity-Large-Thinking stacks up to other U.S. frontier open source AI model offerings
Trinity-Large-Thinking’s performance on agent-specific evaluations establishes it as a legitimate frontier contender. On PinchBench, a critical metric for evaluating model capability on autonomous agentic tasks, Trinity achieved a score of 91.9, placing it just behind the proprietary market leader, Claude Opus 4.6 (93.3).
This competitiveness is mirrored in IFBench, where Trinity’s score of 52.3 sits in a near-dead heat with Opus 4.6’s 53.1, indicating that the reasoning-first “Thinking” update has successfully addressed the instruction-following hurdles that challenged the model’s earlier preview phase.
The model’s broader technical reasoning capabilities also place it at the high end of the current open-source market. It recorded a 96.3 on AIME25, matching the high-tier Kimi-K2.5 and outstripping other major competitors like GLM-5 (93.3) and MiniMax-M2.7 (80.0).
While high-end coding benchmarks like SWE-bench Verified still show a lead for top-tier closed-source models—with Trinity scoring 63.2 against Opus 4.6’s 75.6—the massive delta in cost-per-token positions Trinity as the more viable sovereign infrastructure layer for enterprises looking to deploy these capabilities at production scale.
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When it comes to other U.S. open source frontier model offerings, OpenAI’s gpt-oss tops out at 120 billion parameters, but there’s also Google with Gemma (Gemma 4 was just released this week) and IBM’s Granite family is also worth a mention, despite having lower benchmarks. Nvidia’s Nemotron family is also notable, but is fine-tuned and post-trained Qwen variants.
Benchmark
Arcee Trinity-Large
gpt-oss-120B (High)
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IBM Granite 4.0
Google Gemma 4
GPQA-D
76.3%
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80.1%
74.8%
84.3%
Tau2-Airline
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88.0%
65.8%*
68.3%
76.9%
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PinchBench
91.9%
69.0% (IFBench)
89.1%
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93.3%
AIME25
96.3%
97.9%
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88.5%
89.2%
MMLU-Pro
83.4%
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90.0% (MMLU)
81.2%
85.2%
So how is an enterprise supposed to choose between all these?
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Arcee Trinity-Large-Thinking is the premier choice for organizations building autonomous agents; its sparse 400B architecture excels at “thinking” through multi-step logic, complex math, and long-horizon tool use. By activating only a fraction of its parameters, it provides a high-speed reasoning engine for developers who need GPT-4o-level planning capabilities within a cost-effective, open-source framework.
Conversely, gpt-oss-120B serves as the optimal middle ground for enterprises that require high-reasoning performance but prioritize lower operational costs and deployment flexibility.
Because it activates only 5.1B parameters per forward pass, it is uniquely suited for technical workloads like competitive code generation and advanced mathematical modeling that must run on limited hardware, such as a single H100 GPU.
Its configurable reasoning effort—offering “Low,” “Medium,” and “High” modes—makes it the best fit for production environments where latency and accuracy must be balanced dynamically across different tasks.
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For broader, high-throughput applications, Google Gemma 4 and IBM Granite 4.0 serve as the primary backbones. Gemma 4 offers the highest “intelligence density” for general knowledge and scientific accuracy, making it the most versatile option for R&D and high-speed chat interfaces.
Meanwhile, IBM Granite 4.0 is engineered for the “all-day” enterprise workload, utilizing a hybrid architecture that eliminates context bottlenecks for massive document processing. For businesses concerned with legal compliance and hardware efficiency, Granite remains the most reliable foundation for large-scale RAG and document analysis.
Ownership as a feature for regulated industries
In this climate, Arcee’s choice of the Apache 2.0 license is a deliberate act of differentiation. Unlike the restrictive community licenses used by some competitors, Apache 2.0 allows enterprises to truly own their intelligence stack without the “black box” biases of a general-purpose chat model.
“Developers and Enterprises need models they can inspect, post-train, host, distill, and own,” Lucas Atkins noted in the launch announcement.
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This ownership is critical for the “bitter lesson” of training small models: you usually need to train a massive frontier model first to generate the high-quality synthetic data and logits required to build efficient student models.
Furthermore, Arcee has released Trinity-Large-TrueBase, a raw 10-trillion-token checkpoint. TrueBase offers a rare, “unspoiled” look at foundational intelligence before instruction tuning and reinforcement learning are applied. For researchers in highly regulated industries like finance and defense, TrueBase allows for authentic audits and custom alignments starting from a clean slate.
Community verdict and the future of distillation
The response from the developer community has been largely positive, reflecting the desire for more open weights, U.S.-made mdoels.
On X, researchers highlighted the disruption, noting that the “insanely cheap” prices for a model of this size would be a boon for the agentic community.
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On open AI model inference website OpenRouter, Trinity-Large-Preview established itself as the #1 most used open model in the U.S., serving over 80.6 billion tokens on peak days like March 1, 2026.
The proximity of Trinity-Large-Thinking to Claude Opus 4.6 on PinchBench—at 91.9 versus 93.3—is particularly striking when compared to the cost. At $0.90 per million output tokens, Trinity is approximately 96% cheaper than Opus 4.6, which costs $25 per million output tokens.
Arcee’s strategy is now focused on bringing these pretraining and post-training lessons back down the stack. Much of the work that went into Trinity Large will now flow into the Mini and Nano models, refreshing the company’s compact line with the distillation of frontier-level reasoning.
As global labs pivot toward proprietary lock-in, Arcee has positioned Trinity as a sovereign infrastructure layer that developers can finally control and adapt for long-horizon agentic workflows.
Revel is heading into AXPONA 2026 (April 10-12) with a clear focus: the debut of its new Performa4 speaker series, expected to fall between $2,000 and $7,000 per pair. Under the HARMAN Luxury Audio Group umbrella, which also includes Arcam, JBL Synthesis, Lexicon, and Mark Levinson, Revel isn’t going after statement pricing here. Performa4 is aimed at the part of the market where most serious systems are actually built, and where the competition is crowded, well established, and not particularly forgiving.
Revel Performa4 Speaker Series
Revel’s Performa4 Series is a new loudspeaker line built on the company’s established approach to acoustic design and measurement. It reflects three decades of engineering focused on controlled performance and consistent results in real-world listening environments.
Revel’s Performa4 Series consists of two floorstanding models (F346 and F345), two bookshelf speakers (M146 and M145), a center channel (C245), and a powered subwoofer (B140). With multiple configurations available, the Performa4 lineup can be used in both two-channel music systems and multichannel home theater setups.
“At Revel, science is at the heart of everything we do. The Performa4 series represents the culmination of thousands of hours of research, development, and real-world testing. With our new 7th-generation Acoustic Lens waveguide and advanced DCC and MCC transducers, we’ve raised the bar for what’s possible in this class,” said Jim Garrett, Senior Director of Product Strategy and Planning at HARMAN Luxury Audio.
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Acoustic Lens Waveguide, DCC and MCC Transducers Explained
The Performa4 series uses Revel’s Deep Ceramic Composite (DCC) and Micro Ceramic Composite (MCC) drivers, developed to improve stiffness while keeping mass low and reducing unwanted coloration.
Woofer and midrange drivers are built on cast aluminum frames designed with Finite Element Analysis to optimize airflow, control resonance, and maintain structural stability. Each driver also uses an inverted surround and integrated trim ring, which simplifies the front baffle and keeps the layout clean.
Revel’s 7th-generation Acoustic Lens waveguide is paired with a 1-inch DCC dome tweeter to improve integration with the midrange driver. The waveguide is designed to control dispersion more consistently across the listening area, while also supporting higher efficiency and lower distortion, including at off-axis positions.
Revel Performa4 Industrial Design
The Performa4 series adopts a clean, modern design that builds on Revel’s established cabinet approach without adding unnecessary complexity.
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All models use magnetically attached grilles for a flush, hardware-free front panel, along with black accent detailing that keeps the visual profile consistent across the range. Cabinets are internally cross-braced to improve rigidity and reduce unwanted vibration.
Curved side panels are finished in real wood veneers, available in Natural Walnut and Black Walnut, offering a straightforward aesthetic that fits into both traditional and contemporary spaces.
Revel Performa4 Models
F346
The F346 is a 3-way floorstanding loudspeaker and the top model in the Performa4 series.
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It uses three 6.5-inch (165mm) MCC woofers, a 6.5-inch (165mm) DCC midrange driver, and a 1-inch (25mm) DCC dome tweeter paired with Revel’s 7th-generation Acoustic Lens waveguide.
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Dual rear-firing ports provide low-frequency extension, while dual 5-way binding posts support bi-amping or bi-wiring. The cabinet is fitted with solid aluminum feet and includes optional floor spikes for added stability.
The F346 is rated for a frequency response of 30Hz to 40kHz (±6dB), with 88dB sensitivity and a nominal impedance of 6 ohms. Recommended amplifier power ranges from 20 to 250 watts.
F345
The F345 is a 3-way floorstanding loudspeaker that shares the same core design as the F346, using smaller drivers in a more compact cabinet.
It features three 5.25-inch (130mm) MCC woofers, a 5.25-inch (130mm) DCC midrange driver, and a 1-inch (25mm) DCC dome tweeter paired with Revel’s 7th-generation Acoustic Lens waveguide.
Dual rear-firing ports support low-frequency output, while dual 5-way binding posts allow for bi-amping or bi-wiring. The cabinet includes solid aluminum feet with optional spikes for placement flexibility.
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The F345 is rated for a frequency response of 36Hz to 40kHz (±6dB), with 87dB sensitivity and a nominal impedance of 6 ohms. Recommended amplifier power ranges from 30 to 225 watts.
M146
The M146 is a 2-way bookshelf or standmount speaker positioned in the middle of the Performa4 lineup.
It uses a 6.5-inch (165mm) MCC woofer paired with a 1-inch (25mm) DCC dome tweeter and Revel’s 7th-generation Acoustic Lens waveguide.
The crossover network incorporates air-core inductors, and dual 5-way binding posts support bi-amping or bi-wiring. Optional MFS4 floor stands are available for proper placement and listening height.
The M146 is rated for a frequency response of 43Hz to 40kHz (±6dB), with 86dB sensitivity and a nominal impedance of 6 ohms. Recommended amplifier power ranges from 15 to 200 watts.
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M145
The M145 is a compact 2-way bookshelf speaker and the smaller option in the Performa4 lineup.
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It features a 5.25-inch (130mm) MCC woofer paired with a 1-inch (25mm) DCC dome tweeter and Revel’s 7th-generation Acoustic Lens waveguide.
Like the M146, it includes 5-way binding posts for flexible connectivity and is compatible with the optional MFS4 floor stands for proper positioning.
The M145 is rated for a frequency response of 54Hz to 40kHz (±6dB), with 85dB sensitivity and a nominal impedance of 6 ohms. Recommended amplifier power ranges from 15 to 150 watts.
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C245
The C245 is a dedicated center channel speaker designed for use in multichannel systems with other Performa4 models.
It features dual 5.25-inch (130mm) MCC woofers flanking a 1-inch (25mm) DCC dome tweeter, paired with Revel’s 7th-generation Acoustic Lens waveguide for consistent dispersion across the front soundstage.
The C245 is rated for a frequency response of 55Hz to 40kHz (±6dB), with 86dB sensitivity and a nominal impedance of 6 ohms. Recommended amplifier power ranges from 15 to 200 watts.
B140
The B140 is a powered subwoofer designed to integrate with the Performa4 series in both two-channel and home theater systems.
It uses a 10-inch (250mm) fiber-composite woofer driven by a 750-watt RMS Class D amplifier, with up to 1,500 watts peak output. The design targets low-frequency extension down to 26Hz.
Rear-panel controls include a variable low-pass filter (50–150Hz), LFE input, phase adjustment, volume control, and auto on/off functionality. A rear-ported enclosure using Revel’s Constant Pressure Gradient design is intended to reduce turbulence and maintain cleaner low-frequency output.
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MFS4 Floorstands
Revel also offers the optional MFS4 floor stands for the M146 and M145 bookshelf speakers.
Constructed from extruded aluminum and steel, the MFS4 stands are designed to position each speaker at an appropriate listening height. They include built-in cable management and optional spikes for use on carpeted surfaces. The stands are sold in pairs.
Revel Performa 4 Comparisons
Revel Model
F346
F345
M146
M145
C245
Speaker Type
Floorstanding
Floorstanding
Bookshelf
Bookshelf
Center
Price
$6,999/pair
$4,999/pair
$2,999/pair
$1,999/pair
$1,499/each
Speaker Configuration
3-way
3-way
2-way
2-way
2-way
Tweeter
1-inch (25mm) DCC Dome Tweeter with Acoustic Lens and 7th-Generation Waveguide
1-inch (25mm) DCC Dome Tweeter with Acoustic Lens and Waveguide
1-inch (25mm) DCC Dome Tweeter with Acoustic Lens and Waveguide
1-inch (25mm) DCC Dome Tweeter with Acoustic Lens and Waveguide
1-inch (25mm) DCC Dome Tweeter with Acoustic Lens and Waveguide
Midrange
1 x 6.5 in (165 mm) Deep Ceramic Composite (DCC) Cone Driver
1 x 5.25 in (135 mm) Deep Ceramic Composite (DCC) Cone Driver
N/A
N/A
N/A
Woofer
3 x 6.5 in (165 mm) Micro Ceramic Composite (MCC) Cone Woofers
3 x 5.25 in (135 mm) Micro Ceramic Composite (MCC) Cone Woofers
250mm (10-inch) Coated Fibre Composite Cone Driver in a cast-Aluminum frame
Amplifier Type
Class D amplifier
Power Output
750W RMS (1500W peak)
Enclosure Tuning
Bass-Reflex via Rear-Mounted Port
Controls
Auto Power, Crossover, Level, Phase
Inputs
RCA LFE/Line Level, 3.5mm, 12V Trigger
Frequency Response +/-6 dB
26Hz – 150Hz
Crossover Frequency (Variable)
50Hz – 150Hz
Dimensions
14.8 x 16.9 x 17.2 inches (376.2 x 429.28 x 436.37 mm)
Weight
61.3 lbs / 27.8 kg
The Bottom Line
Revel’s Performa4 series is a calculated move into one of the most competitive segments in loudspeakers. The combination of DCC and MCC driver materials, the 7th-generation Acoustic Lens waveguide, and consistent cabinet engineering across the range points to a focus on controlled dispersion, tonal consistency, and predictable in-room performance; areas where Revel has historically been very disciplined.
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The lineup is clearly built for flexibility. It can anchor a straightforward two-channel system or scale into a full home theater without mixing and matching across different voicings. That matters for buyers who want system coherence without overthinking every component swap.
What’s less clear is how much separation there is between models beyond size and output, and whether the subwoofer’s pricing will make sense for buyers building out a full system. There’s also no indication of built-in room correction or system-level integration features, which are becoming more common even in passive speaker ecosystems when paired with modern electronics.
This is for listeners who want a complete, measurement-driven speaker system in the $2,000 to $7,000 range without stepping into five-figure territory. Not entry-level, not cost-no-object—right in the middle where most serious systems live.
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At AXPONA 2026, the F346 will be demonstrated with an Arcam SA45 integrated streaming amplifier and CD5 CD player, which should give a clear sense of how the top model performs with both streaming and physical sources.
The technology is designed to reduce strike zone disputes, long the source of baseball’s most heated arguments. Under the new system, each team receives two challenges per game and only loses a challenge if it is incorrect. In practice, this incentive has quickly reshaped game-day strategy – and last Saturday’s… Read Entire Article Source link
French fries are delicious, but notoriously unhealthy. A research team at the University of Illinois, however, has developed a deceptively straightforward method to keep the satisfying taste and crunch without requiring as much oil.
The cooking method combines traditional frying and microwave heating. Adding that microwave step could reduce the amount of oil used in the process, meaning you would absorb less fat with each bite. All the secrets to being able to cook fries in this way have been laid out in two studies published in Current Research in Food Science and The Journal of Food Science.
French Fries and Health
Although popular, fried foods contain high levels of fat, which is linked to several health problems, including obesity and hypertension. “Consumers want healthy foods, but at the time of purchase, cravings often prevail,” says Pawan Singh Takhar, author of one of the two studies. “The high oil content adds flavor, but it also contains a lot of energy and calories.”
It’s precisely with the goal of helping consumers make better food choices without feeling deprived that researchers have been trying to figure out how they can cook healthier french fries, achieving lower fat content without altering their taste and texture.
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One of the main difficulties in frying, as the studies explain, is preventing the oil from penetrating the food. In the early stages of the french fry process, in fact, the pores of the potato are filled with water, leaving no room for the oil.
As cooking continues, however, the water evaporates, creating empty spaces that allow the oil to be drawn in by negative pressure. Much of the frying process takes place under that negative pressure, which essentially increases the tendency of the oil to be sucked into the fries
A New Wavelength
In the new study, therefore, the researchers tried to figure out how to extend the time in positive pressure and reduce the period under negative pressure. “When we heat something in a traditional oven, the heat transfers from the outside to the inside, but a microwave oven heats from the inside to the outside because the microwaves penetrate everywhere in the material,” Takhar says.
Specifically, microwaves cause water molecules to oscillate, resulting in increased vapor formation and thus shifting the pressure profile toward positive values that prevent the oil from being easily absorbed.
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Microwave frying alone, however, would not produce the desired texture. “If only microwaving is used, the food turns out mushy,” says Takhar. In order to achieve crispness, frying and microwaving should be combined.
To achieve the right balance, the researchers carried out an experiment in which they specially designed a microwave fryer, monitoring temperature, pressure, volume, texture, moisture, and oil content of the chips. “We propose to combine the two methods in the same device. Traditional heating maintains crispness, while microwave heating reduces oil consumption,” the study concludes.
Mustafa Suleyman, CEO of Microsoft AI. (GeekWire File Photo / Kevin Lisota)
Microsoft is expanding its roster of in-house AI models, releasing a new speech-to-text system and making two existing models broadly available to developers for the first time.
The moves by Microsoft AI (MAI) are part of a broader effort by the company to expand its proprietary AI capabilities beyond its partnership with OpenAI, giving Microsoft more control over its own destiny in the competition against Google, Amazon, and others.
Microsoft announced MAI-Transcribe-1 on Thursday, a speech-to-text model that it says is the most accurate currently available. The company also released its existing voice and image generation models, known as MAI-Voice-1 and MAI-Image-2, for broad commercial use.
It’s Microsoft’s first major model release since a March reorganization, announced by CEO Satya Nadella, in which Microsoft AI CEO Mustafa Suleyman shifted away from day-to-day Copilot oversight to focus on frontier model development and superintelligence.
Suleyman told The Verge that the transcription model runs at “half the GPU cost of the other state-of-the-art models.” He told VentureBeat that the model was built by a team of just 10 people, and that Microsoft plans to eventually build a frontier large language model to be “completely independent” if needed.
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Microsoft also recently hired former Allen Institute for CEO Ali Farhadi and other top AI researchers from the Seattle-based institute to further bolster Suleyman’s team, as GeekWire reported last week.
MAI-Transcribe-1 is designed to handle noisy real-world conditions such as call centers and conference rooms, and Microsoft says it is testing integrations with Copilot and Teams. Microsoft says it offers the best price-performance of any large cloud provider, competing directly with OpenAI’s Whisper and Google’s Gemini on the FLEURS benchmark.
In a blog post, Suleyman called the model “not just the most accurate but also lightning fast.”
MAI-Voice-1 generates natural-sounding speech and now lets developers create custom voices from short snippets of sample audio. MAI-Image-2 ranks in the top three on the Arena.ai image generation leaderboard and is rolling out in Bing and PowerPoint.
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All three are available on the Microsoft Foundry developer AI platform and MAI Playground.
It’s been more than 50 years since NASA astronaut Harrison Schmitt took the famous Big Blue Marble photograph, showing a breathtaking vision of Earth taken aboard the Apollo 17 spacecraft on its way to the moon. Now, as the four-astronaut crew of the Artemis II mission heads toward the moon, more spectacular images are being released.
This stunning photo is perhaps the most reminiscent of the Big Blue Marble, showing Earth in all its fragile, lovely glory.
“That’s us!” NASA wrote in a post. The post also quoted astronaut Christina Koch as saying of Earth, “You guys look great.”
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In a reply to questions on the post, NASA wrote, “Two auroras (top right and bottom left) are visible in this image. Zodiacal light (bottom right), is also visible, as well as airglow from Earth’s atmosphere.”
Another neat photo from the Artemis mission shows the planet neatly bisected, with one side lit up by the sun and the other in darkness.
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This image of the Earth was taken by one of the Artemis II crew out the Orion’s window.
Reid Wiseman/NASA
“You look amazing, you look beautiful,” Victor Glover, Artemis II pilot, said of the views of Earth in a video call with ABC News.
A view of the Earth from NASA’s Orion spacecraft as it orbits above the planet during the Artemis II test flight.
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NASA
Another intriguing image shows part of the spacecraft itself. USA Today noted that “the image appears to show the bottom of Orion’s service module where its main engine and auxiliary thrusters are housed.”
The total workforce at Tesla’s factory outside Austin, Texas shrunk dramatically last year as the company suffered its second straight year of declining sales, according to a compliance report spotted by Austin American-Statesman.
Tesla went from employing 21,191 people at the factory in 2024 to 16,506 workers in 2025, a drop of 22%. That’s despite the company’s global workforce growing from 125,665 employees in 2024 to 134,785 employees in 2025, according to filings with the U.S. Securities and Exchange Commission.
It’s not clear which teams were most affected by Tesla scaling back its workforce at the plant. But the company has become one of the largest employers in the Austin area since it opened the factory in 2022. CEO Elon Musk also relocated Tesla’s headquarters to the factory in 2021 before it opened. The company has invested more than $6.3 billion in the facility to date, according to the new report.
Though the AirPods Max 2 offer new features, a teardown of the headphones shows they’re still plagued by the same flaws of the original 2020 model.
Apple’s AirPods Max 2 gained the H2 chip, but not much else.
Apple’s AirPods Max 2 debuted on March 16, with their core feature being the H2 chip. With it, Apple’s high-end headphones gained capabilities like Adaptive Audio, Conversation Awareness, and gesture controls, among others. Active Noise Cancellation was improved as well. However, as explained in our review, the AirPods Max 2 are an iterative upgrade, that ultimately leaves something to be desired. New features and ANC enhancements aside, Apple effectively delivered more of the same with its AirPods Max 2. Continue Reading on AppleInsider | Discuss on our Forums
Meta has paused all its work with the data contracting firm Mercor while it investigates a major security breach that impacted the startup, two sources confirmed to WIRED. The pause is indefinite, the sources said. Other major AI labs are also reevaluating their work with Mercor as they assess the scope of the incident, according to people familiar with the matter.
Mercor is one of a few firms that OpenAI, Anthropic, and other AI labs rely on to generate training data for their models. The company hires massive networks of human contractors to generate bespoke, proprietary datasets for these labs, which are typically kept highly secret as they’re a core ingredient in the recipe to generate valuable AI models that power products like ChatGPT and Claude Code. AI labs are sensitive about this data because it can reveal to competitors—including other AI labs in the US and China—key details about the ways they train AI models. It’s unclear at this time whether the data exposed in Mercor’s breach would meaningfully help a competitor.
While OpenAI has not stopped its current projects with Mercor, it is investigating the startup’s security incident to see how its proprietary training data may have been exposed, a spokesperson for the company confirmed to WIRED. The spokesperson says that the incident in no way affects OpenAI user data, however. Anthropic did not immediately respond to WIRED’s request for comment.
Mercor confirmed the attack in an email to staff on March 31. “There was a recent security incident that affected our systems along with thousands of other organizations worldwide,” the company wrote.
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A Mercor employee echoed these points in a message to contractors on Thursday, WIRED has learned. Contractors who were staffed on Meta projects cannot log hours until—and if—the project resumes, meaning they could functionally be out of work, a source familiar claims. The company is working to find additional projects for those impacted, according to internal conversations viewed by WIRED.
Mercor contractors were not told exactly why their Meta projects were being paused. In a Slack channel related to the Chordus initiative—a Meta-specific project to teach AI models to use multiple internet sources to verify their responses to user queries—a project lead told staff that Mercor was “currently reassessing the project scope.”
An attacker known as TeamPCP appears to have recently compromised two versions of the AI API tool LiteLLM. The breach exposed companies and services that incorporate LiteLLM and installed the tainted updates. There could be thousands of victims, including other major AI companies, but the breach at Mercor illustrates the sensitivity of the compromised data.
Mercor and its competitors—such as Surge, Handshake, Turing, Labelbox, and Scale AI—have developed a reputation for being incredibly secretive about the services they offer to major AI labs. It’s rare to see the CEOs of these firms speaking publicly about the specific work they offer, and they internally use codenames to describe their projects.
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Adding to the confusion around the hack, a group going by the well-known name Lapsus$ claimed this week that it had breached Mercor. In a Telegram account and on a BreachForums clone, the actor offered to sell an array of alleged Mercor data, including a 200-plus GB database, nearly 1 TB of source code, and 3 TBs of video and other information. But researchers say that many cybercriminal groups now periodically take up the Lapsus$ name and that Mercor’s confirmation of the LiteLLM connection means that the attacker is likely TeamPCP or an actor connected to the group.
TeamPCP appears to have compromised the two LiteLLM updates as part of an even larger supply chain hacking spree in recent months that has been gaining momentum, catapulting TeamPCP to prominence. And while launching data extortion attacks and working with ransomware groups, such as the group known as Vect, TeamPCP has also strayed into political territory, spreading a data wiping worm known as “CanisterWorm” through vulnerable cloud instances with Farsi as their default language or clocks set to Iran’s time zone.
“TeamPCP is definitely financially motivated,” says Allan Liska, an analyst for the security firm Recorded Future who specializes in ransomware. “There might be some geopolitical stuff as well, but it’s hard to determine what’s real and what’s bluster, especially with a group this new.”
Looking at the dark-web posts of the alleged Mercor data, Liska adds, “There is absolutely nothing that connects this to the original Lapsus$.”
Intel Core Ultra 270K Plus improves Adobe Premiere workflows by 15% over 9700X
Rendering in Cinebench and Blender achieves up to 23% faster results
250K Plus outperforms previous-generation AMD CPUs by roughly 35%
Intel’s latest Core Ultra 200S Plus series has drawn attention for delivering performance that is difficult to ignore, especially compared to older Intel models and some similarly priced AMDprocessors.
In testing by Puget Systems, the 270K Plus and 250K Plus both increase E-core counts, boost clocks, and raise maximum memory speeds, creating a tangible improvement over prior generations.
While AMD’s Ryzen 9 X3D chips remain strong in certain workloads, the new Intel chips close gaps in many professional applications.
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Performance in rendering and content creation
In CPU-based rendering in applications like Cinebench, V-Ray, and Blender, the Core Ultra 7 270K Plus demonstrates impressive results, performing up to 9% of the higher-priced 9950X3D, while frequently outpacing other CPUs in the same price bracket by up to 23%.
The 250K Plus also shows substantial gains, often matching or beating older high-end AMD chips, with improvements of about 35% over the 245K.
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These performance improvements tie not just to additional cores but also to enhancements in memory latency and bandwidth.
In Adobe Premiere, the 270K Plus performs as well as or slightly better than previous high-end Intel models, offering a 15% advantage over the 9700X.
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This trend continues across intraframe codecs (13% faster than 245K), RAW processing (30% faster than 9700X), and QuickSync-accelerated workflows.
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After Effects shows a slightly mixed picture: while the 270K Plus handles 2D tasks efficiently, 3D and tracking workloads favor AMD’s Ryzen chips.
DaVinci Resolve shows a similar balance, with the 270K Plus leading marginally in several CPU-bound tasks while GPU-bound processes show little difference between models.
In Unreal Engine shader compilation and Visual Studio builds, AMD’s 3D V-Cache processors maintain some lead, but the 270K Plus outperforms older Intel models by up to 100% in some cases.
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Compilation times in particular show major gains over the 9700X, with improvements ranging from 15% to nearly 100% depending on the test scenario.
The 250K Plus also shows strong relative performance, often outpacing CPUs that were previously considered superior at the same price point.
Tests using Llama and MLPerf benchmarks reveal modest CPU-level improvements – and while the integrated NPU could not be directly assessed, the 270K Plus consistently handles small-model inference faster than earlier Intel offerings.
This trend is consistent across content creation and professional workloads, where the new chips deliver strong performance gains without commanding a premium price.
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Considering its $299 price and the improvements in memory and E-core architecture, the 270K Plus makes the 9700X, which retails at around $340, look underwhelming.
The pitch is seductive in its simplicity: AI needs more power than terrestrial grids can supply, so move the data centres into orbit, where the sun never sets and the electricity is free. SpaceX, Blue Origin, and a growing constellation of startups are now racing to make that vision real. The problem, according to the scientists and engineers who would have to make the physics work, is that the vision skips several chapters of thermodynamics, economics, and orbital mechanics that have not yet been written.
SpaceX filed with the Federal Communications Commission on 30 January for permission to launch up to one million satellites into low Earth orbit, each carrying computing hardware that would collectively form what the company described as a constellation with “unprecedented computing capacity to power advanced artificial intelligence models.” The satellites would operate at altitudes between 500 and 2,000 kilometres, in orbits designed to maximise time in sunlight, and route traffic through SpaceX’s existing Starlink network. SpaceX requested a waiver of the FCC’s standard deployment milestones, which typically require half a constellation to be operational within six years.
Seven weeks later, Blue Origin filed its own application. Project Sunrise proposes 51,600 satellites in sun-synchronous orbits between 500 and 1,800 kilometres, complemented by the previously announced TeraWave constellation of 5,408 satellites providing ultra-high-speed optical backhaul. Where SpaceX’s filing emphasised raw scale, Blue Origin’s emphasised architecture: the system would perform computation in orbit and relay results to the ground through TeraWave’s mesh network.
The startup ecosystem is moving even faster.Starcloud, formerly Lumen Orbit, raised $170 million at a $1.1 billion valuationin March, becoming the fastest unicorn in Y Combinator history just 17 months after completing the programme. The company launched its first satellite carrying an Nvidia H100 GPU in November 2025 and filed with the FCC in February for a constellation of up to 88,000 satellites. Aethero, a defence-focused startup building space-grade computers with Nvidia Orin NX chips wrapped in radiation shielding, raised $8.4 million and is testing hardware on orbit this year.
The commercial logic rests on a genuine problem.Global data centre electricity consumptionreached roughly 415 terawatt-hours in 2024 and the International Energy Agency projects it could exceed 1,000 TWh by 2026, with accelerated AI servers driving 30 per cent annual growth. In Virginia alone, data centres consume 26 per cent of total electricity supply. Ireland’s share could reach 32 per cent by year’s end. The grid constraints are real, the permitting delays are real, and the political resistance to building more terrestrial capacity is real.
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What is also real, scientists argue, is the physics that makes orbital computing spectacularly difficult at any meaningful scale. The most fundamental challenge is heat. In space, there is no air to carry heat away from processors, only radiative cooling, which requires vast surface areas. Dissipating just one megawatt of thermal energy while keeping electronics at a stable 20 degrees Celsius demands approximately 1,200 square metres of radiator, roughly four tennis courts. A several-hundred-megawatt data centre, the minimum threshold for commercial relevance, would require radiators thousands of times larger than anything ever deployed on the International Space Station.
Radiation presents the second structural problem. Low Earth orbit exposes unshielded chips to cosmic rays and trapped particles that induce bit flips and permanent circuit damage. Radiation hardening adds 30 to 50 per cent to hardware costs and reduces performance by 20 to 30 per cent. The alternative, triple modular redundancy, means launching three copies of every chip, three times the cooling, three times the electricity, and three times the mass. Starcloud’s approach of flying commercial GPUs with external shielding is an interesting experiment, but no one has demonstrated that it works at scale or over hardware lifetimes measured in years rather than months.
Latency is the third constraint. A million satellites spread across orbital shells from 500 to 2,000 kilometres cannot achieve the tight coupling required for frontier model training, where inter-node communication latencies must remain in the microsecond range. Low Earth orbit introduces minimum latencies of several milliseconds for inter-satellite links and 60 to 190 milliseconds for ground-to-orbit round trips, compared to 10 to 50 milliseconds for terrestrial content delivery networks. That makes orbital infrastructure potentially viable for inference workloads, not for training, which is where the overwhelming majority of AI compute demand currently sits.
Then there is cost. IEEE Spectrum estimated that a one-gigawatt orbital data centre would cost upwards of $50 billion, roughly three times the cost of an equivalent terrestrial facility including five years of operation. Google has said that launch costs must fall to under $200 per kilogram before space-based computing begins to make economic sense. SpaceX’s current Starlink economics operate at roughly $1,000 to $2,000 per kilogram. Some analysts argue the true threshold for competing with terrestrial refresh economics is $20 to $30 per kilogram, a figure no credible projection places within the next two decades. The economics look even less favourable when set against thedeep-tech funding landscape on the ground, where terrestrial infrastructure projects can draw on established supply chains and proven unit economics.
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Even OpenAI’s Sam Altman, who explored a multibillion-dollar investment in rocket maker Stoke Space as a potential SpaceX competitor for orbital data centres, has publicly called the concept “ridiculous” for the current decade. Altman told journalists that the rough maths of launch costs relative to terrestrial power costs simply does not work yet, and he pointedly asked how anyone plans to fix a broken GPU in space.
The astronomical community adds a separate objection entirely. The vast majority of the roughly 1,000 public comments on SpaceX’s FCC filing urged the commission not to proceed. If approved, the constellation would place more satellites than visible stars in the sky for large portions of the night throughout the year,further militarising and commercialising an orbital environmentthat is already straining under the weight of existing megaconstellations.
None of this means orbital data centres will never exist. SpaceX’s Starship, if it achieves its cost targets, could fundamentally change the mass-to-orbit economics that currently make the concept unworkable. Starcloud’s incremental approach of flying small payloads and iterating on radiation performance is the kind of engineering pathway that occasionally produces breakthroughs. And the terrestrial grid constraints driving the interest are not going away.
But the gap between filing an FCC application for a million satellites and actually making orbital computation economically competitive with a warehouse full of GPUs in Iowa is not measured in years. It is measured in physics problems thatthe current pace of AI infrastructure investmentcannot shortcut, no matter how many billionaires are willing to try. The question scientists are asking is not whether space data centres are theoretically possible. It is why, given the magnitude of the unsolved engineering, anyone is treating them as a near-term solution to a problem that requires near-term answers. The sky, it turns out, is not the limit. The radiator is.
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