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Google’s neural network takes a step closer to predicting disease using DNA

A protein folding prediction generated by Google DeepMind's AlphaFold AI. | Credit: DeepMind Technologies Limited

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If humans had the ability to predict protein structure solely from DNA information, it would be a medical superpower against disease, and artificial intelligence is our best hope thus far to obtain it. Such a feat is now one step closer with the creation of “AlphaFold”, a neural network designed by Google’s AI company DeepMind, to do that very thing. After entering a biannual protein folding prediction contest called the Critical Assessment of Structure Prediction (CASP), AlphaFold was declared winner out of 98 AI competitors, specifically by most accurately predicting 25 of 43 protein shapes given using genetic sequences alone. The second place winner predicted only three.

In a nutshell (or smaller, really), proteins are key factors in every living thing’s physiological processes. Their structures are encoded in DNA, and they are responsible for contracting muscles, metabolizing food into energy, fighting disease, and transmitting signals, among a great many other things. The function of proteins depends on their unique 3D structure. The way they are shaped is directly related to what they do in the body. For example, antibodies have “hooks” that attach and tag viruses and bacteria, and ligament proteins are cord-shaped, enabling them to transmit tension.

The being said, the ability to predict protein shapes can enable scientists to learn more about how defects specifically affect the body, repair damaged ones with targeted therapies, and design new ones. Their specific structure is key – the 3D shape determines a protein’s function. To further illustrate this importance, misfolding proteins are linked to many health issues such as type 2 diabetes and Parkinson’s disease.

AlphaFold’s predicted folding vs. actual folding. | Credit: DeepMind Technologies Limited

Some medical progress has been made to address protein folding issues such as drug therapies that bind to proteins and alter their function; however, the human body is able to generate around 2 million different types of proteins, and so far we can only identify about 100,000 of them. Out of those proteins, the variety of folded 3D structures possible is calculated to be a googol cubed – 10 to the power of 300. Clearly, this is not really a job for a human. As further described on DeepMind’s website, “[According to] Levinthal’s paradox, it would take longer than the age of the universe to enumerate all the possible configurations of a typical protein before reaching the right 3D structure.”

DeepMind is no stranger to achieving incredible things with its AI software. A program built by the company called “agent” learned to play 49 different retro computer games in 2015, making it the first computer program capable of independently learning a large variety of tasks. Two other programs named “AlphaZero” and “AlphaGo” were able to beat the world’s best human and computer players at chess and the ancient Chinese game “Go”, respectively. AlphaGo was later revised as “AlphaGo Zero” to play the same Go game without any prior human knowledge, i.e., it taught itself to play and subsequently win.

AlphaFold was trained with thousands of known proteins until it could accurately predict those proteins’ 3D shape. This was a significant improvement over other existing technology, not only in levels of accuracy, but in cost-effectiveness. Other protein identification techniques such as cryo-electron microscopy and nuclear magnetic resonance depend on a lot of trial and error, which involves years of work and several thousands of dollars per protein structure to achieve. Considering the complexity involved in this field, the AlphaFold’s achievement in the CASP contest is, to say the least, representative of the expanding possibilities for scientific research and discovery using artificial intelligence.

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

Inside Tesla’s secretive $10 Billion “Project Crystal Sun” filing

Tesla filed for a $10.1 billion Fort Bend solar factory, but the site isn’t confirmed.

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Tesla has filed paperwork in Texas for a second massive manufacturing project in the same week it locked down its chip fabrication site, this time for a $10.1 billion solar cell plant in Fort Bend County. The filing, submitted July 22 under the state’s Jobs, Energy, Technology and Innovation Act and first surfaced by Sawyer Merritt on X, lists an internal project name of “Project Crystal Sun” and targets a site off FM 762 and FM 1994 near Richmond, about 40 minutes outside Houston.

The application, prepared by Kroll Tax Services on Tesla’s behalf, spans five parcels totaling roughly 3,000 acres within the Lamar Consolidated Independent School District. Tesla wants a 10 year property tax limitation in exchange for the investment, split as $1.5 billion in real property and $8.6 billion in equipment and personal property. The company projects 9,712 permanent jobs once the plant reaches full operation, with 1,147 peak construction jobs during a build window running from this year through 2028 and commercial operations targeted for the first quarter of 2029.

Tesla is not fully committed to Fort Bend County yet. The filing states the company is weighing the site against an unnamed out of state alternative, and frames the tax abatement as what would make Texas competitive against that option. If the district and county decline the incentive, Tesla says it may build elsewhere.

Tesla Megapack Megafactory in Texas advances with major property sale

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The plant would handle the full solar cell production chain in one facility, according to the filing, covering wafer and ingot manufacturing, coating, metallization and printing lines, cell testing, automated material handling and cleanroom infrastructure. That scope points to Tesla vertically integrating a part of its supply chain it currently sources largely from overseas partners, mirroring the approach behind its expanding Megapack production in Brookshire, Texas, where Tesla has already built out two buildings for its grid battery business.

The timing lines up closely with Tesla and SpaceX’s other big Texas commitment this month. SpaceX confirmed its Terafab chip factory would land in Grimes County days before this filing surfaced, with construction on that $16.8 billion project starting almost immediately after local officials met with residents. Terafab is meant to produce the AI chips running Tesla’s Optimus robots and Full Self-Driving software, while a solar cell plant would feed a different part of the business, the panels and storage systems Tesla sells to homeowners, businesses and utilities, and increasingly needs to power its own data centers.

Musk has talked about building domestic solar manufacturing capacity before, tying it to the amount of power Tesla’s AI ambitions will require.

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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:

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“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.”

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.’

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.

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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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Zuckerberg’s Meta taps Musk’s Tesla for massive clean energy project

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Credit: Tesla

In a notable intersection of Big Tech powerhouses, Meta, led by Mark Zuckerberg, has partnered with Canadian energy infrastructure giant Enbridge on a significant renewable energy initiative that will rely on battery technology from Elon Musk’s Tesla.

The project, which was announced this week, marks another step in Meta’s aggressive push to power its expanding data center operations with clean energy, dispelling many of the complaints people have about them.

This new development is located near Cheyenne, Wyoming, and will feature a 365-megawatt (MW) solar farm paired with a 200 MW/1,600 megawatt-hour (MWh) battery energy storage system, also known as BESS. Tesla is providing the batteries for the project, valued at roughly $200 million.

The story was originally reported by Utility Dive.

This Wyoming project represents the first phase of Enbridge and Meta’s joint “Cowboy Project.” Once operational, it will deliver power to Meta’s regional data centers through Cheyenne Light, Fuel, and Power under Wyoming’s Large Power Contract Service tariff.

This tariff, originally developed in collaboration with Microsoft and Black Hills Energy, is designed specifically for large loads like data centers. It ensures that the renewable supply serves hyperscale customers without impacting retail electricity rates for other users.

The battery system will operate under a long-term tolling agreement, providing dispatchable capacity that enhances grid reliability. During periods of high demand, the utility can access the backup generation, addressing one of the key challenges of integrating large-scale renewables with the explosive growth of data center electricity demand driven by artificial intelligence.

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This latest collaboration builds on prior joint efforts between Enbridge and Meta in Texas, including the 600 MW Clear Fork Solar, 152 MW Easter Wind, and 300 MW Cone Wind projects. Together with the Wyoming initiative, the companies have now partnered on roughly 1.6 gigawatts (GW) of combined solar, wind, and storage capacity.

The deal highlights the intensifying demand for reliable, low-carbon power from technology giants. Meta has committed to supporting its data center growth with renewable energy, joining peers like Microsoft and Google in seeking large-scale solutions. Enbridge’s Allen Capps described the project as “one of the larger utility-scale battery installations supporting U.S. data center operations and growth.”

The involvement of Tesla’s battery technology adds an intriguing layer, linking two of the world’s most prominent tech leaders—Zuckerberg and Musk—in the clean energy transition.

As data centers continue to drive unprecedented electricity load growth across the United States, projects like this one illustrate how hyperscalers are turning to strategic partnerships with traditional energy players and innovative storage solutions to meet both sustainability goals and reliability needs.

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