I try to test every individual product critically and neutrally, but sometimes a brand comes along that seemingly cannot miss. We have liked everything we’ve tried from Silk & Snow, and right now the company is hosting a Cyber Week Sale that runs through December 7. You can save on some of our favorite bed frames and mattresses that we’ve tested long-term in our own homes, and if you prefer to browse on your own, the sale offers up to 35 percent off site-wide. I’ve rounded up the highlights on WIRED-tested gear below.

WIRED Featured Deals:

Silk & Snow Bed Frame With Drawer Storage for $560 ($140 off)

In our Bed Frame Buying Guide, I say that Silk & Snow could charge more for this bed frame—but right now, it’s 20 percent off. Assembly is a little bit annoying. But the fabric-covered headboard is gorgeous, and the frame is sturdy. My favorite part is the drawer cleverly concealed at the foot of the frame, with its two partitions and its magnetic closure. It does feel a little jarring if you open and close the drawer while your partner is in bed, but I like having extra storage in my room, especially since this frame is too low to the ground to allow for stashing things under it.

Silk & Snow S&S Organic Mattress for $765 ($235 off)

Courtesy of Silk & Snow

Silk & Snow

S&S Organic Mattress

The S&S Organic Mattress is the best organic latex mattress. It’s very soft and offers a lot of pressure relief and support, but the responsive material means it’s better suited to solo sleepers than it is to couples. It feels like a soft, dense marshmallow, per our sleep reviewer Julia Forbes, who advises using lightweight blankets and cooling sheets if you’re a hot sleeper and also raved about the lengthy year-long sleep trial. As part of the Cyber Week sale, you can choose between a free sleep bundle (with a sheet set, a duvet, two microfiber pillows, and a mattress protector) or you can opt out of the bundle to get an extra 10 percent off your mattress.

Silk & Snow Wooden Bed Frame for $1,040 ($260 off)

This bed frame from Silk & Snow made our list of the best Thuma dupes. The Japanese joinery platform frame comes in your choice of natural rubber wood or cortado acacia wood, each in two different finishes. Assembly is pretty easy and doesn’t require any special tools, and you’ll get a robust five-year warranty. The resulting frame is sturdy and gorgeous, though we recommend having a helper around if you need to disassemble the frame to protect the finish on the wood.

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Julie Bornstein thought it would be a cinch to implement her idea for an AI startup. Her résumé in digital commerce is impeccable: VP of ecommerce at Nordstrom, COO of the startup Stitch Fix, and founder of a personalized shopping platform acquired by Pinterest. Fashion has been her obsession since she was a Syracuse high schooler inhaling spreads in Seventeen and hanging out in local malls. So she felt well-positioned to create a company for customers to discover the perfect garments using AI.

The reality was much harder than she expected. I had breakfast recently with Bornstein and her CTO, Maria Belousova, to learn about her startup, Daydream, funded with $50 million from VCs like Google Ventures. The conversation took an unexpected turn as the women schooled me on the surprising difficulty of translating the magic of AI systems into something people actually find useful.T

Her story helps explain something. My first newsletter of 2025 announced that it would be The Year of the AI App. Though there are indeed many such apps, they haven’t transformed the world as I anticipated. Ever since ChatGPT launched in late 2022, people have been blown away by the tricks performed by AI, but study after study has shown that the technology has not yet delivered a significant boost in productivity. (One exception: coding.) A study published in August found that 19 out of 20 AI enterprise pilot projects delivered no measurable value. I do think that productivity boost is on the horizon, but it’s taking longer than people expected. Listening to the stories of startups like Daydream that are pushing to break through gives some hope that persistence and patience might indeed make those breakthroughs happen.

Fashionista Fail

Bornstein’s original pitch to VCs seemed obvious: Use AI to solve tricky fashion problems by matching customers with the perfect garments, which they’d be delighted to pay for. (Daydream would take a cut.) You’d think the setup would be simple—just connect to an API for a model like ChatGPT and you’re good to go, right? Um, no. Signing up over 265 partners, with access to more than 2 million products from boutique shops to retail giants, was the easy part. It turns out that fulfilling even a simple request like “I need a dress for a wedding in Paris” is incredibly complex. Are you the bride, the mother-in-law, or a guest? What season is it? How formal a wedding? What statement do you want to make? Even when those questions are resolved, different AI models have different views on such things. “What we found was, because of the lack of consistency and reliability of the model—and the hallucinations—sometimes the model would drop one or two elements of the queries,” says Bornstein. A user in Daydream’s long-extended beta test would say something like, “I’m a rectangle, but I need a dress to make me look like an hourglass.” The model would respond by showing dresses with geometric patterns.

Ultimately, Bornstein understood that she had to do two things: postpone the app’s planned fall 2024 launch (though it’s now available, Daydream is still technically in beta until sometime in 2026) and upgrade her technical team. In December 2024 she hired Belousova, the former CTO of Grubhub, who in turn brought in a team of top engineers. Daydream’s secret weapon in the fierce talent war is the chance to work on a fascinating problem. “Fashion is such a juicy space because it has taste and personalization and visual data,” says Belousova. “It’s an interesting problem that hasn’t been solved.”

What’s more, Daydream has to solve this problem twice—first by interpreting what the customer says and then by matching their sometimes quirky criteria with the wares on the catalog side. With inputs like I need a revenge dress for a bat mitzvah where my ex is attending with his new wife, that understanding is critical. “We have this notion at Daydream of shopper vocabulary and a merchant vocabulary, right?” says Bornstein. “Merchants speak in categories and attributes, and shoppers say things like, ‘I’m going to this event, it’s going to be on the rooftop, and I’m going to be with my boyfriend.’ How do you actually merge these two vocabularies into something at run time? And sometimes it takes several iterations in a conversation.” Daydream learned that language isn’t enough. “We’re using visual models, so we actually understand the products in a much more nuanced way,” she says. A customer might share a specific color or show a necklace that they’ll be wearing.

Bornstein says Daydream’s subsequent rehaul has produced better results. (Though when I tried it out, a request for black tuxedo pants showed me beige athletic-fit trousers in addition to what I asked for. Hey, it’s a beta.) “We ended up deciding to move from a single call to an ensemble of many models,” says Bornstein. “Each one makes a specialized call. We have one for color, one for fabric, one for season, one for location.” For instance, Daydream has found that for its purposes, OpenAI models are really good at understanding the world from the clothing point of view. Google’s Gemini is less so, but it is fast and precise.

Generative AI and robotics are moving us ever closer to the day when we can ask for an object and have it created within a few minutes. In fact, MIT researchers have developed a speech-to-reality system, an AI-driven workflow that allows them to provide input to a robotic arm and “speak objects into existence,” creating things like furniture in as little as five minutes.  

With the speech-to-reality system, a robotic arm mounted on a table is able to receive spoken input from a human, such as “I want a simple stool,” and then construct the objects out of modular components. To date, the researchers have used the system to create stools, shelves, chairs, a small table, and even decorative items such as a dog statue.

“We’re connecting natural language processing, 3D generative AI, and robotic assembly,” says Alexander Htet Kyaw, an MIT graduate student and Morningside Academy for Design (MAD) fellow. “These are rapidly advancing areas of research that haven’t been brought together before in a way that you can actually make physical objects just from a simple speech prompt.”  

The idea started when Kyaw — a graduate student in the departments of Architecture and Electrical Engineering and Computer Science — took Professor Neil Gershenfeld’s course, “How to Make Almost Anything.” In that class, he built the speech-to-reality system. He continued working on the project at the MIT Center for Bits and Atoms (CBA), directed by Gershenfeld, collaborating with graduate students Se Hwan Jeon of the Department of Mechanical Engineering and Miana Smith of CBA.

The speech-to-reality system begins with speech recognition that processes the user’s request using a large language model, followed by 3D generative AI that creates a digital mesh representation of the object, and a voxelization algorithm that breaks down the 3D mesh into assembly components.

After that, geometric processing modifies the AI-generated assembly to account for fabrication and physical constraints associated with the real world, such as the number of components, overhangs, and connectivity of the geometry. This is followed by creation of a feasible assembly sequence and automated path planning for the robotic arm to assemble physical objects from user prompts.

By leveraging natural language, the system makes design and manufacturing more accessible to people without expertise in 3D modeling or robotic programming. And, unlike 3D printing, which can take hours or days, this system builds within minutes.

“This project is an interface between humans, AI, and robots to co-create the world around us,” Kyaw says. “Imagine a scenario where you say ‘I want a chair,’ and within five minutes a physical chair materializes in front of you.”

The team has immediate plans to improve the weight-bearing capability of the furniture by changing the means of connecting the cubes from magnets to more robust connections. 

“We’ve also developed pipelines for converting voxel structures into feasible assembly sequences for small, distributed mobile robots, which could help translate this work to structures at any size scale,” Smith says.

The purpose of using modular components is to eliminate the waste that goes into making physical objects by disassembling and then reassembling them into something different, for instance turning a sofa into a bed when you no longer need the sofa.

Because Kyaw also has experience using gesture recognition and augmented reality to interact with robots in the fabrication process, he is currently working on incorporating both speech and gestural control into the speech-to-reality system.

Leaning into his memories of the replicator in the “Star Trek” franchise and the robots in the animated film “Big Hero 6,” Kyaw explains his vision.

“I want to increase access for people to make physical objects in a fast, accessible, and sustainable manner,” he says. “I’m working toward a future where the very essence of matter is truly in your control. One where reality can be generated on demand.”

The team presented their paper “Speech to Reality: On-Demand Production using Natural Language, 3D Generative AI, and Discrete Robotic Assembly” at the Association for Computing Machinery (ACM) Symposium on Computational Fabrication (SCF ’25) to be held at MIT on Nov. 21.