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Omar Yaghi was a quiet child, diligent, unlikely to roughhouse with his nine siblings. So when he was old enough, his parents tasked him with one of the family’s most vital chores: fetching water. Like most homes in his Palestinian neighborhood in Amman, Jordan, the Yaghis’ had no electricity or running water. At least once every two weeks, the city switched on local taps for a few hours so residents could fill their tanks. Young Omar helped top up the family supply. Decades later, he says he can’t remember once showing up late. The fear of leaving his parents, seven brothers, and two sisters parched kept him punctual. Yaghi proved so dependable that his father put him in charge of monitoring how much the cattle destined for the family butcher shop ate and drank. The best-­quality cuts came from well-fed, hydrated animals—a challenge given that they were raised in arid desert. Specially designed materials called metal-organic frameworks can pull water from the air like a sponge—and then give it back. But at 10 years old, Yaghi learned of a different occupation. Hoping to avoid a rambunctious crowd at recess, he found the library doors in his school unbolted and sneaked in. Thumbing through a chemistry textbook, he saw an image he didn’t understand: little balls connected by sticks in fascinating shapes. Molecules. The building blocks of everything. “I didn’t know what they were, but it captivated my attention,” Yaghi says. “I kept trying to figure out what they might be.” That’s how he discovered chemistry—or maybe how chemistry discovered him. After coming to the United States and, eventually, a postdoctoral program at Harvard University, Yaghi devoted his career to finding ways to make entirely new and fascinating shapes for those little sticks and balls. In October 2025, he was one of three scientists who won a Nobel Prize in chemistry for identifying metal-­organic frameworks, or MOFs—metal ions tethered to organic molecules that form repeating structural landscapes. Today that work is the basis for a new project that sounds like science fiction, or a miracle: conjuring water out of thin air. When he first started working with MOFs, Yaghi thought they might be able to absorb climate-damaging carbon dioxide—or maybe hold hydrogen molecules, solving the thorny problem of storing that climate-friendly but hard-to-contain fuel. But then, in 2014, Yaghi’s team of researchers at UC Berkeley had an epiphany. The tiny pores in MOFs could be designed so the material would pull water molecules from the air around them, like a sponge—and then, with just a little heat, give back that water as if squeezed dry. Just one gram of a water-absorbing MOF has an internal surface area of roughly 7,000 square meters. Yaghi wasn’t the first to try to pull potable water from the atmosphere. But his method could do it at lower levels of humidity than rivals—potentially shaking up a tiny, nascent industry that could be critical to humanity in the thirsty decades to come. Now the company he founded, called Atoco, is racing to demonstrate a pair of machines that Yaghi believes could produce clean, fresh, drinkable water virtually anywhere on Earth, without even hooking up to an energy supply. That’s the goal Yaghi has been working toward for more than a decade now, with the rigid determination that he learned while doing chores in his father’s butcher shop. “It was in that shop where I learned how to perfect things, how to have a work ethic,” he says. “I learned that a job is not done until it is well done. Don’t start a job unless you can finish it.” Most of Earth is covered in water, but just 3% of it is fresh, with no salt—the kind of water all terrestrial living things need. Today, desalination plants that take the salt out of seawater provide the bulk of potable water in technologically advanced desert nations like Israel and the United Arab Emirates, but at a high cost. Desalination facilities either heat water to distill out the drinkable stuff or filter it with membranes the salt doesn’t pass through; both methods require a lot of energy and leave behind concentrated brine. Typically desal pumps send that brine back into the ocean, with devastating ecological effects. Heiner Linke, chair of the Nobel Committee for Chemistry, uses a model to explain how metalorganic frameworks (MOFs) can trap smaller molecules inside. In October 2025, Yaghi and two other scientists won the Nobel Prize in chemistry for identifying MOFs.JONATHAN NACKSTRAND/GETTY IMAGES I was talking to Atoco executives about carbon dioxide capture earlier this year when they mentioned the possibility of harvesting water from the atmosphere. Of course my mind immediately jumped to Star Wars, and Luke Skywalker working on his family’s moisture farm, using “vaporators” to pull water from the atmosphere of the arid planet Tatooine. (Other sci-fi fans’ minds might go to Dune, and the water-gathering technology of the Fremen.) Could this possibly be real? It turns out people have been doing it for millennia. Archaeological evidence of water harvesting from fog dates back as far as 5000 BCE. The ancient Greeks harvested dew, and 500 years ago so did the Inca, using mesh nets and buckets under trees. Today, harvesting water from the air is a business already worth billions of dollars, say industry analysts—and it’s on track to be worth billions more in the next five years. In part that’s because typical sources of fresh water are in crisis. Less snowfall in mountains during hotter winters means less meltwater in the spring, which means less water downstream. Droughts regularly break records. Rising seas seep into underground aquifers, already drained by farming and sprawling cities. Aging septic tanks leach bacteria into water, and cancer-causing “forever chemicals” are creating what the US Government Accountability Office last year said “may be the biggest water problem since lead.” That doesn’t even get to the emerging catastrophe from microplastics. So lots of places are turning to atmospheric water harvesting. Watergen, an Israel-based company

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Meta has released SAM Audio, a prompt driven audio separation model that targets a common editing bottleneck, isolating one sound from a real world mix without building a custom model per sound class. Meta released 3 main sizes, sam-audio-small, sam-audio-base, and sam-audio-large. The model is available to download and to try in the Segment Anything Playground. Architecture SAM Audio uses separate encoders for each conditioning signal, an audio encoder for the mixture, a text encoder for the natural language description, a span encoder for time anchors, and a visual encoder that consumes a visual prompt derived from video plus an object mask. The encoded streams are concatenated into time aligned features, then processed by a diffusion transformer that applies self attention over the time aligned representation and cross attention to the textual feature, then a DACVAE decoder reconstructs waveforms and emits 2 outputs, target audio and residual audio. https://ai.meta.com/blog/sam-audio/ What SAM Audio does, and what ‘segment’ means here? SAM Audio takes an input recording that contains multiple overlapping sources, for example speech plus traffic plus music, and separates out a target source based on a prompt. In the public inference API, the model produces 2 outputs, result.target and result.residual. The research team describes target as the isolated sound, and residual as everything else. That target plus residual interface maps directly to editor operations. If you want to remove a dog bark across a podcast track, you can treat the bark as the target, then subtract it by keeping only residual. If you want to extract a guitar part from a concert clip, you keep the target waveform instead. Meta uses these exact kinds of examples to explain what the model is meant to enable. The 3 prompt types Meta is shipping Meta positions SAM Audio as a single unified model that supports 3 prompt types, and it says these prompts can be used alone or combined. Text prompting: You describe the sound in natural language, for example “dog barking” or “singing voice”, and the model separates that sound from the mixture. Meta lists text prompts as one of the core interaction modes, and the open source repo includes an end to end example using SAMAudioProcessor and model.separate. Visual prompting: You click the person or object in a video and ask the model to isolate the audio associated with that visual object. Meta team describes visual prompting as selecting the sounding object in the video. In the released code path, visual prompting is implemented by passing video frames plus masks into the processor via masked_videos. Span prompting: Meta team calls span prompting an industry first. You mark time segments where the target sound occurs, then the model uses those spans to guide separation. This matters for ambiguous cases, for example when the same instrument appears in multiple passages, or when a sound is present only briefly and you want to prevent the model from over separating. https://ai.meta.com/blog/sam-audio/ Results Meta team positions SAM Audio as achieving cutting edge performance across diverse, real world scenarios, and frames it as a unified alternative to single purpose audio tools. The team publishes a subjective evaluation table across categories, General, SFX, Speech, Speaker, Music, Instr(wild), Instr(pro), with General scores of 3.62 for sam audio small, 3.28 for sam audio base, and 3.50 for sam audio large, and Instr(pro) scores reaching 4.49 for sam audio large. Key Takeaways SAM Audio is a unified audio separation model, it segments sound from complex mixtures using text prompts, visual prompts, and time span prompts. The core API produces two waveforms per request, target for the isolated sound and residual for everything else, which maps cleanly to common edit operations like remove noise, extract stem, or keep ambience. Meta released multiple checkpoints and variants, including sam-audio-small, sam-audio-base, sam-audio-large, plus tv variants that the repo says perform better for visual prompting, the repo also publishes a subjective evaluation table by category. The release includes tooling beyond inference, Meta provides a sam-audio-judge model that scores separation results against a text description with overall quality, recall, precision, and faithfulness. Check out the Technical details and GitHub Page. Feel free to check out our GitHub Page for Tutorials, Codes and Notebooks. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well. The post Meta AI Releases SAM Audio: A State-of-the-Art Unified Model that Uses Intuitive and Multimodal Prompts for Audio Separation appeared first on MarkTechPost.

Enterprises can now harness the power of a large language model that’s near that of the state-of-the-art Google’s Gemini 3 Pro, but at a fraction of the cost and with increased speed, thanks to the newly released Gemini 3 Flash. The model joins the flagship Gemini 3 Pro, Gemini 3 Deep Think, and Gemini Agent, all of which were announced and released last month. Gemini 3 Flash, now available on Gemini Enterprise, Google Antigravity, Gemini CLI, AI Studio, and on preview in Vertex AI, processes information in near real-time and helps build quick, responsive agentic applications.  The company said in a blog post that Gemini 3 Flash “builds on the model series that developers and enterprises already love, optimized for high-frequency workflows that demand speed, without sacrificing quality. The model is also the default for AI Mode on Google Search and the Gemini application.  Tulsee Doshi, senior director, product management on the Gemini team, said in a separate blog post that the model “demonstrates that speed and scale don’t have to come at the cost of intelligence.” “Gemini 3 Flash is made for iterative development, offering Gemini 3’s Pro-grade coding performance with low latency — it’s able to reason and solve tasks quickly in high-frequency workflows,” Doshi said. “It strikes an ideal balance for agentic coding, production-ready systems and responsive interactive applications.” Early adoption by specialized firms proves the model’s reliability in high-stakes fields. Harvey, an AI platform for law firms, reported a 7% jump in reasoning on their internal ‘BigLaw Bench,’ while Resemble AI discovered that Gemini 3 Flash could process complex forensic data for deepfake detection 4x faster than Gemini 2.5 Pro. These aren’t just speed gains; they are enabling ‘near real-time’ workflows that were previously impossible. More efficient at a lower cost Enterprise AI builders have become more aware of the cost of running AI models, especially as they try to convince stakeholders to put more budget into agentic workflows that run on expensive models. Organizations have turned to smaller or distilled models, focusing on open models or other research and prompting techniques to help manage bloated AI costs. For enterprises, the biggest value proposition for Gemini 3 Flash is that it offers the same level of advanced multimodal capabilities, such as complex video analysis and data extraction, as its larger Gemini counterparts, but is far faster and cheaper.  While Google’s internal materials highlight a 3x speed increase over the 2.5 Pro series, data from independent benchmarking firm Artificial Analysis adds a layer of crucial nuance. In the latter organization’s pre-release testing, Gemini 3 Flash Preview recorded a raw throughput of 218 output tokens per second. This makes it 22% slower than the previous ‘non-reasoning’ Gemini 2.5 Flash, but it is still significantly faster than rivals including OpenAI’s GPT-5.1 high (125 t/s) and DeepSeek V3.2 reasoning (30 t/s). Most notably, Artificial Analysis crowned Gemini 3 Flash as the new leader in their AA-Omniscience knowledge benchmark, where it achieved the highest knowledge accuracy of any model tested to date. However, this intelligence comes with a ‘reasoning tax’: the model more than doubles its token usage compared to the 2.5 Flash series when tackling complex indexes. This high token density is offset by Google’s aggressive pricing: when accessing through the Gemini API, Gemini 3 Flash costs $0.50 per 1 million input tokens, compared to $1.25/1M input tokens for Gemini 2.5 Pro, and $3/1M output tokens, compared to $ 10/1 M output tokens for Gemini 2.5 Pro. This allows Gemini 3 Flash to claim the title of the most cost-efficient model for its intelligence tier, despite being one of the most ‘talkative’ models in terms of raw token volume. Here’s how it stacks up to rival LLM offerings: Model Input (/1M) Output (/1M) Total Cost Source Qwen 3 Turbo $0.05 $0.20 $0.25 Alibaba Cloud Grok 4.1 Fast (reasoning) $0.20 $0.50 $0.70 xAI Grok 4.1 Fast (non-reasoning) $0.20 $0.50 $0.70 xAI deepseek-chat (V3.2-Exp) $0.28 $0.42 $0.70 DeepSeek deepseek-reasoner (V3.2-Exp) $0.28 $0.42 $0.70 DeepSeek Qwen 3 Plus $0.40 $1.20 $1.60 Alibaba Cloud ERNIE 5.0 $0.85 $3.40 $4.25 Qianfan Gemini 3 Flash Preview $0.50 $3.00 $3.50 Google Claude Haiku 4.5 $1.00 $5.00 $6.00 Anthropic Qwen-Max $1.60 $6.40 $8.00 Alibaba Cloud Gemini 3 Pro (≤200K) $2.00 $12.00 $14.00 Google GPT-5.2 $1.75 $14.00 $15.75 OpenAI Claude Sonnet 4.5 $3.00 $15.00 $18.00 Anthropic Gemini 3 Pro (>200K) $4.00 $18.00 $22.00 Google Claude Opus 4.5 $5.00 $25.00 $30.00 Anthropic GPT-5.2 Pro $21.00 $168.00 $189.00 OpenAI More ways to save But enterprise developers and users can cut costs further by eliminating the lag most larger models often have, which racks up token usage. Google said the model “is able to modulate how much it thinks,” so that it uses more thinking and therefore more tokens for more complex tasks than for quick prompts. The company noted Gemini 3 Flash uses 30% fewer tokens than Gemini 2.5 Pro.  To balance this new reasoning power with strict corporate latency requirements, Google has introduced a ‘Thinking Level’ parameter. Developers can toggle between ‘Low’—to minimize cost and latency for simple chat tasks—and ‘High’—to maximize reasoning depth for complex data extraction. This granular control allows teams to build ‘variable-speed’ applications that only consume expensive ‘thinking tokens’ when a problem actually demands PhD-level lo The economic story extends beyond simple token prices. With the standard inclusion of Context Caching, enterprises processing massive, static datasets—such as entire legal libraries or codebase repositories—can see a 90% reduction in costs for repeated queries. When combined with the Batch API’s 50% discount, the total cost of ownership for a Gemini-powered agent drops significantly below the threshold of competing frontier models “Gemini 3 Flash delivers exceptional performance on coding and agentic tasks combined with a lower price point, allowing teams to deploy sophisticated reasoning costs across high-volume processes without hitting barriers,” Google said.  By offering a model that delivers strong multimodal performance at a more affordable price, Google is making the case that enterprises concerned with controlling their AI spend should choose its models, especially Gemini 3 Flash.