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Engineers develop bio-machine nose that can “sniff” and classify odors

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Engineers from Brown University in Rhode Island have invented a small, low-cost sensor device which is able to classify odors using input from a mimicked “sniffing” action. It’s called TruffleBot, and it’s here to raise the bar on electronic “noses”. It also works with Raspberry Pi, an inexpensive mini-computer popular with electronics hobbyists, students, and others in the “maker” crowd.

Generally, an electronic nose is a device comprising several chemical sensors whose results are fed through a pattern-recognition system to identify odors. In traditional devices, the chemical responses alone are used for classification. The engineers behind this invention, however, decided to incorporate non-chemical data to account for the mechanics of the smell process used in nature for a better result. Their experiment proved successful with an approximate 95-98% rate of accuracy in identification compared to about 80-90% accuracy with the chemical sensors alone.

According to the inventors’ published paper, the guiding knowledge that made TruffleBot so useful in odor detection was this: Different smells have different impacts on the air around them, and measuring the variations enables more accurate identification. Did you know that beer odor decreases air pressure and increases temperature? The changes are slight, but TruffleBot can sense them.

This is where the “sniffing” comes in. The device uses air pumped through four obstructed pathways before sending it through chemical and non-chemical sensors. Odors impact the air surrounding them, and the movement of the air through obstacles (“sniffing”) enables the odors’ impact to be more accurately measured.

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A chart detailing how TruffleBot processes odors. | Credit: Brown University

So, where exactly would one need an electronic nose? Everywhere. Devices with the chemical sensing ability are being used in agriculture, military, and commercial applications to identify all sorts environmental data. Essentially, electronic noses are useful in any industrial application that has odor involved.

Nasal Marketing

Did you know that it’s possible to trademark a smell in the United States? It’s not easy to accomplish given the somewhat difficult requirements to meet, but a few such things exist. The fact that Play-Doh, a product whose smell is probably one of its most distinct features, was granted a trademark for the scent only this year is testament to the difficulty of obtaining such a mark. However, the fact that some companies have found enough incentive to make sure only their company can give your nose a particular chemical experience tells a lot about that sense’s importance from a marketing perspective.

On one hand, utilizing smell in marketing might seem a little manipulative. After all, creating an air freshener that reminds someone of a beloved, deceased relative on purpose might not seem like a particularly ethical way to target their money. On the other hand (or bigger picture), however, the motivation for marketers to use scent as a tool involves a sort of “chicken or the egg” question.

To summarize part of an article in the journal Sensors on the role scent plays in society and commerce, the aroma of products has a direct impact on their appeal to customers and thus, the success of the product. In fact, a change in a product’s formula that impacts its smell can, and often has had, devastating sales results. In other words, it’s not enough for a company to create a good product; it has to be a good smelling product.

Hacking the Human Nose

It’s probably no surprise that the commercial industry has categorized consumer preferences when it comes to smells. As the first sense fully developed after birth, our noses link us to things like memories, emotions, and chemical communication (think pheromones). Is it any wonder, then, why businesses might be interested in the functionality of the organ that is doing the receiving?

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Turns out, there’s an enormous amount of science behind “hacking” a nose. Identifying smells is more than just categorizing chemical mixtures as “floral” or “masculine”. The multitude of chemical combinations available generates such a vast amount of data that scientists have implemented computer neural networks to analyze and classify it. Also, the actual mechanics of smelling something impacts the way the smell is received and processed in the brain. Computers and scientific instruments come in handy there as well. To really get to the core of human response to an aroma, lots of non-human tools are needed, and this is essentially where the TruffleBot fits in the greater realm of “olfactory” science.

I think this is a Sumerian variant for “fruity”. | Credit: AstroJane’s bathroom collection.

More Than Just Your Money

Perhaps one of the most innovative uses found for electronic noses is in disease research. One of the limitations of human smell is its overall weakness. A dog’s sense of smell is around 40 times better than a human’s, and a bear’s is a whopping 2,100 times superior to ours. That said, when researchers learned that certain diseases give off certain odors, the human nose wasn’t exactly the first choice to utilize in sensing them.

An electronic nose makes good use of the simple fact that organic matter releases chemicals into the air. For example, when a plant has been impacted by a fungus, the changes brought on in the plant’s structure release what’s called “volatile organic compounds” (VOCs). These VOCs can be detected by the sensors in an electronic nose and then provide information on the type of disease present without destroying the plants being tested.

Humans have some amazing things to gain from electronic noses, too. Using sensors to process odors from VOCs, things like digestive diseases, kidney diseases, and diabetes, among many others,  are all receiving scientific attention for non-invasive diagnosis by these types of devices. With improvements brought on by inventions like TruffleBot, especially combined with its low-cost and resulting accessibility, a future involving remote diagnoses for any number of illnesses and diseases seems more possible every day.

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Accidental computer geek, fascinated by most history and the multiplanetary future on its way. Quite keen on the democratization of space. | It's pronounced day-sha, but I answer to almost any variation thereof.

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Elon Musk

Tesla just trademarked MEGAPOD: here’s what it is

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tesla showroom
(Credit: Tesla)

Tesla just trademarked ‘MEGAPOD’ with the United States Patent and Trademark Office (USPTO), its latest move in what seems to be a hint that the company is incredibly focused on its AI efforts and storage needs as compute increases.

The application carries serial number 99893717 and lists the applicant as Tesla, Inc., located at 1 Tesla Road, Austin, Texas 78725.

The filing remains in ‘live pending’ status, and it is a new application waiting for assignment to an examining attorney. It has not yet been published or registered.

According to the official goods and services description in the application, Tesla describes ‘MEGAPOD’ as:

“Modular data center hardware systems for artificial intelligence computing, comprised of computer servers, computer hardware for artificial intelligence processing, computer networking hardware, electrical power distribution units, and cooling systems, sold as a unit; self-contained modular computing hardware systems for artificial intelligence workloads; integrated computer hardware platforms for artificial intelligence computing, namely, enclosures containing computer hardware, power distribution hardware, and cooling hardware, sold as a unit; downloadable software for monitoring, managing, optimizing, and regulating modular artificial intelligence computing hardware systems.”

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This description specifies complete, self-contained modular units that integrate servers and specialized AI processing hardware with networking components, power distribution, and cooling systems. It also includes associated downloadable software for oversight and optimization of these systems. The language emphasizes hardware sold “as a unit” and enclosures that combine the necessary elements for AI computing workloads.

Tesla has an established history of developing and commercializing modular hardware systems. Its Megapack product line, for example, consists of utility-scale battery energy storage systems designed as containerized units for grid applications. The MEGAPOD filing follows a similar pattern of protecting a name for modular, integrated hardware platforms, this time focused on artificial intelligence computing infrastructure.

This could be an early move, especially as Tesla did not have trademark rights to the word ‘Cybercab,’ the name of its self-driving, ride-hailing-focused vehicle.

Trademark applications of this type allow companies to secure priority rights to a name for defined categories of goods and services. The USPTO examines applications for compliance with legal requirements, including distinctiveness and absence of conflicts with prior marks. If the application proceeds successfully through examination, publication, and any opposition period, it could result in a federal trademark registration providing nationwide protection. This is what Tesla’s obvious intention is with ‘MEGAPOD.’

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Public reports and analysis suggest MEGAPOD could represent modular, container-style AI computing pods designed for easy deployment. These would bundle servers, AI accelerators, power systems, and cooling into self-contained units suitable for distributed AI workloads. This approach aligns with Tesla’s announced AI compute strategy.

In March 2026, Elon Musk outlined plans for “Digital Optimus” (also referred to as Macrohard), a joint Tesla-xAI project for AI agents capable of handling complex digital tasks. The plans include running these agents on Tesla’s AI4 hardware in parked vehicles as well as dedicated compute units installed at Supercharger stations, which collectively offer substantial unused electrical capacity.

What is Digital Optimus? The new Tesla and xAI project explained

A modular hardware platform like the one described in the ‘MEGAPOD’ filing would support scalable, rapid deployment of such distributed compute resources. It could complement Tesla’s other AI infrastructure efforts, including the Dojo supercomputer used for training models and the development of AI systems for autonomous driving and robotics, by enabling edge or regional AI inference without reliance on traditional centralized data centers.

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Investor's Corner

SpaceX is launching a secret spacecraft that could change how things are made in space

SpaceX’s secret disk-shaped Starfall capsule is targeting a market no reentry vehicle has cracked.

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SpaceX is targeting Tuesday, June 23 for the first flight of Starfall, a reentry capsule the company has developed almost entirely in private. The Falcon 9 launch window opens at 6:43 a.m. ET from Space Launch Complex 40 at Cape Canaveral Space Force Station, with a backup window available the same time on June 24. SpaceX has made no public announcement about the vehicle, only providing launch details. Everything known about it has come through FAA and FCC regulatory filings.

What makes Starfall different starts with its shape. Rather than the traditional cone used by Dragon and every other cargo return capsule in operation, Starfall is a flat disk that measures roughly  10.2 feet (3.1 meters) wide and just 2.5 feet (0.75 meters) tall, and weighing 4,630 pounds (2,100 kg) and capable of returning up to 2,200 pounds (1,000 kilograms) of payload from orbit. The disk geometry maximizes structural efficiency and payload volume relative to mass, and the heat shield mechanically jettisons just before splashdown, allowing recovery teams to retrieve both the capsule and the shield separately from the Pacific Ocean.

The difference with Starfall from existing competitors, such as Varda Space Industries, which has largely built the orbital manufacturing market and returns heavy payloads per flight is that Starfall’s specification is roughly 30 times more per mission, and is designed to be mass-produced and launched on either Falcon 9 or Starship. That combination of volume and launch access is something no standalone startup can replicate, and it puts SpaceX in direct competition with the companies that currently pay it to reach orbit.

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The intended market is orbital manufacturing: pharmaceuticals, protein crystals, semiconductors, and advanced optical fiber that physically cannot be produced in the presence of gravity. FAA documents describe Starfall’s long-term purpose as building a “self-sustaining commercial in-space manufacturing market” and as a potential successor to the industrial capabilities of the International Space Station, which is set to retire in the late 2020s. Military rapid global cargo delivery is a parallel application under active discussion with the Pentagon.

The reason some industries seek manufacturing in space comes down to gravity. On Earth, gravity causes materials to settle, separate, and deform during production. In microgravity, those constraints disappear.

SpaceX’s already controls launch access, which means it currently functions as the landlord for every competitor in the orbital manufacturing return space. Starfall converts that landlord position into vertical ownership, and it would no longer just carry other companies’ capsules to orbit, but rather operate the capsule, own the return logistics, and capture the service revenue directly. Viewed alongside Starlink, Colossus, and the xAI merger, Starfall fits a consistent pattern: SpaceX identifying infrastructure layers that others depend on and moving to own them outright. Orbital manufacturing return is the next layer on that list.

If Tuesday’s reentry, parachute sequence, and recovery demonstration goes as planned, the second FAA-approved test flight follows. A successful pair of demos would position SpaceX to begin offering Starfall as a commercial service, likely first to pharmaceutical and materials science customers before scaling toward the military and broader manufacturing segments.

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Tesla Semi spotted with ground truth validation equipment as launch looms

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Credit: Tesla

The Tesla Semi was spotted mounted with ground truth validation equipment as the company nears its looming launch. The Semi is Tesla’s Class 8 all-electric truck, and has been utilized in its earlier stages by many companies like PepsiCo. and Frito-Lay, who have been using it in a pilot program.

The Semi was spotted in Sunnyvale, California, and sports a typical ground truth validation unit that Tesla routinely uses on its vehicles. Ground truth validation is essentially the process of training supervised algorithms to ensure they can perform reliably. Tesla typically performs this on vehicles that are being released soon:

The Semi being spotted with this type of validation rig is important because it means the company is working on solidifying a Full Self-Driving model for its commercial vehicle offering. This would be a massive development for not only Tesla but also the logistics industry as a whole.

There are strict regulations on driving hours for commercial truck drivers, and autonomy is a way to potentially combat these issues. FSD is already a widely effective way that owners of typical passenger vehicles take stress out of travel. Even launching a semi-autonomous platform for truck drivers to use to increase safety, reduce fatigue, and increase productivity would be a huge development.

Tesla Semi gets strange-but-understandable comparison from Jay Leno

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The Semi has already proven to be an ideal solution for companies that use commercial logistics. It has increased efficiency and reduced operating costs for many companies that have been able to use it in pilot programs.

There are expected to be some bumps along the way. Tesla saw some challenges with FSD on the Cybertruck, as it had never had a vehicle with cameras at that height, so some of the features with FSD were not immediately available. Just a week ago, Tesla launched Actually Smart Summon (ASS) for Cybertruck, nearly three years after the vehicle was first delivered to customers.

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