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Red Dead Redemption is coming to PC this month

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Red Dead Redemption is coming to PC this month

Rockstar Games has officially announced the release date for Red Dead Redemption on PC, and it’s arriving later this month for anyone who wants to play it in this revived form. Originally launching on consoles back in May of 2010 for the PS3 and Xbox 360, the game was later ported to PS4 some years later. It also recently received a port for Nintendo Switch, coming alongside the PS4 port back in 2023. The game can also be played on PS5 at an updated 60 frames per second with a patch that was issued last October.

What many fans have been waiting on, however, was the ability to play the game on PC. Perhaps with updated graphics and additional features. After Red Dead Redemption 2 received rave reviews, fans of the original have been wanting to head back and play the first game. The PC port will also be an opportunity for newcomers who never got to experience the first game to see where it all started for the franchise. Rockstar has now confirmed the date and it’s quite close.

Red Dead Redemption arrives on PC on October 29

If you’re eager to play the first game on PC you won’t have to wait too long. It’ll arrive on PC on October 29, which means you only have a couple more weeks. To make things even sweeter the game comes with a bunch of PC enhancements to play the game in its best form. This includes but isn’t limited to support for new Ultrawide and Super Ultrawide aspect ratios. Specifically 21:9 and 32:9.

It’s also going to have support for HDR10 as well as full keyboard and mouse functionality. So you won’t be required to play with a controller if you prefer the KBM setup. Rockstar also confirms that the PC version of the game will support 144Hz monitors and with native 4K resolution, so long as you have the hardware to handle it.

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If your PC is a little lacking, you can still boost things a bit if you have an NVIDIA RTX 40 series GPU. The PC port will also support NVIDIA DLSS 3.7 and NVIDIA Frame Generation, as well as AMD FSR3.0 to help upscale the game’s graphics and kick the frame rates up a bit if needed.

The PC version comes with the Undead Nightmare DLC

Rockstar hasn’t mentioned a cost for the game just yet, but it does mention that those who buy the game on PC will get the Undead Nightmare DLC along with the base game. If we had to guess, players are probably looking at somewhere around $50 – $60.

In addition to the game’s PC release date announcement, Rockstar has released a new trailer for the PC version that you can watch below.

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Is There Any Use For This $45 Server I Bought From College? (Dell PowerEdge 2950 in 2020)

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Is There Any Use For This $45 Server I Bought From College? (Dell PowerEdge 2950 in 2020)



I found a website where schools, police stations, and other businesses auction off their old stuff and found an old decommissioned server and couldn’t pass it up. I was able to buy this Dell PowerEdge 2950 with 2 Xeon 5450’s for only $45 and it works perfectly…The only thing missing is an Operating System so let’s take a look at the hardware and get it up and running!

Wisconsinsurplus.com is the website where I participate in these auctions online, I stumbled upon this website as I was searching for a cheap way to find Optiplex computers to flip. It may be worth it for you to take a look or ask your local colleges around you if they sell any of their old stuff online or even just straight from the school. I don’t know exactly how much this is worth but for $45 I feel like that’s a pretty good deal on a PowerEdge 2950 even just to learn about Server operating systems.

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Apple, Sony, Anker and more

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Apple, Sony, Anker and more

October Prime Day has come and gone but, as to be expected, there are still a few deals floating around on the internet. This year’s fall Prime Day brought a slew of deals across all categories, including tech, and some of those deals haven’t expired just yet. There’s a chance we see some of these sale prices return in a month when Black Friday comes around, but if you’re keen on getting some of your holiday shopping done now, you still have a bit more time. Here are the best Prime Day deals you can still get today.

AirPods Pro

Billy Steele / Engadget

Apple deals can be hit or miss during Amazon Prime Day, but we saw a number of good ones this time and many of them are still available. Whether you’ve been on the hunt for a new Apple device for yourself or you know you want to pick one up as a gift, you can save a bit of cash if you do so now.

Anker Prime Power Bank 200W

Photo by Amy Skorheim / Engadget

One can never have too many charging accessories, be they cables, wireless charging pads or power banks. Anker makes some of our favorites across all those categories, and you’ll find deep discounts on Anker gear even now after Prime Day has ended.

A number of Lego sets are still on sale post October Prime Day. Some of our favorites come from the Star Wars, Super Mario and Harry Potter lineups, and you’ll find savings up to 41 percent on those.

Roku Streaming Stick 4K

Will Lipman Photography for Engadget

Yes, Black Friday is right around the corner, but it would be unwise to sleep on these Prime Day tech deals that you can still get today. We’re seeing steep discounts on headphones, TVs, streaming devices, gaming gear and more, making it a good time to pick up something for yourself or cross a few items off your holiday shopping list early.

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Follow @EngadgetDeals on Twitter for the latest tech deals and buying advice, and stay tuned to Engadget.com for all of the best tech deals coming out of October Prime Day 2024.

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Google AI wins another Nobel Prize, this time in Chemistry

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Google AI wins another Nobel Prize, this time in Chemistry

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A trio of scientists consisting of Demis Hassabis, co-founder and CEO of Google’s AI division DeepMind, as well as John Jumper, Senior Research Scientist at Google DeepMind and David Baker of the University of Washington have been awarded the 2024 Nobel Prize in Chemistry for their groundbreaking work in predicting and developing new proteins.

The DeepMinders won for AlphaFold 2, an AI system capable of predicting the 3D structure of proteins from their amino acid sequences. Meanwhile, Baker won for leading a laboratory where the 20 amino acids that form proteins were used to design new ones, including proteins for “pharmaceuticals, vaccines, nanomaterials and tiny sensors,” according to the Nobel committee’s announcement.

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The award highlights how artificial intelligence is revolutionizing biological science — and comes just one day after what I believe to be the first Nobel Prize awarded to an AI technology, that one for Physics to fellow Google DeepMinder Geoffrey Hinton and Princeton professor John J. Hopfield, for their work in artificial neural networks.

The Royal Swedish Academy of Sciences announced the prize as it did with the Physics one, valued at 11 million Swedish kronor (around $1 million USD), split among the laureates — half will go to Baker and the other half divided again in fourths of the total to Hassabis and Jumper.

The committee emphasized the unprecedented impact of AlphaFold, describing it as a breakthrough that solved a 50-year-old problem in biology: protein structure prediction, or how to predict the three-dimensional structure of a protein from its amino acid sequence.

For decades, scientists knew that a protein’s function is determined by its 3D shape, but predicting how the string of amino acids folds into that shape was incredibly complex. Researchers had attempted to solve this since the 1970s, but due to the vast number of possible folding configurations (known as Levinthal’s paradox), accurate predictions remained elusive.

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AlphaFold, developed by Google DeepMind, made a breakthrough by using AI to predict the 3D structures of proteins with near-experimental accuracy, meaning that the predictions made by AlphaFold for a protein’s 3D structure are so close to the results obtained from traditional experimental methods—like X-ray crystallography, cryo-electron microscopy, or nuclear magnetic resonance (NMR) spectroscopy—that they are almost indistinguishable.

When AlphaFold achieved “near-experimental accuracy,” it was able to predict protein structures with a level of precision that rivaled these methods, typically within an error margin of around 1 Ångström (0.1 nanometers) for most proteins. This means the model’s predictions closely matched the actual structures determined by experimental means, making it a transformative tool for biologists.

Hassabis and Jumper’s work, developed at DeepMind’s London laboratory, has transformed the fields of structural biology and drug discovery, offering a powerful tool to scientists worldwide.

“AlphaFold has already been used by more than two million researchers to advance critical work, from enzyme design to drug discovery,” Hassabis said in a statement. “I hope we’ll look back on AlphaFold as the first proof point of AI’s incredible potential to accelerate scientific discovery.”

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AlphaFold’s Global Impact

AlphaFold’s predictions are freely accessible via the AlphaFold Protein Structure Database, making it one of the most significant open-access scientific tools available. Over two million researchers from 190 countries have used the tool, democratizing access to cutting-edge AI and enabling breakthroughs in fields as varied as molecular biology, drug development, and even climate science.

By predicting the 3D structure of proteins in minutes—tasks that previously took years—AlphaFold is accelerating scientific progress. The system has been used to tackle antibiotic resistance, design enzymes that degrade plastic, and aid in vaccine development, marking its utility in both healthcare and sustainability.

John Jumper, co-lead of AlphaFold’s development, reflected on its significance, stating, “We are honored to be recognized for delivering on the long promise of computational biology to help us understand the protein world and to inform the incredible work of experimental biologists.” He emphasized that AlphaFold is a tool for discovery, helping scientists understand diseases and develop new therapeutics at an unprecedented pace.

The Origins of AlphaFold

The roots of AlphaFold can be traced back to DeepMind’s broader exploration of AI.

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Hassabis, a chess prodigy, began his career in 1994 at the age of 17, co-developing the hit video game Theme Park, which was released on June 15 that year.

After studying computer science at Cambridge University and completing a PhD in cognitive neuroscience, he co-founded DeepMind in 2010, using his understanding of chess to raise funding from famed contrarian venture capitalist Peter Thiel. The company, which specializes in artificial intelligence, was acquired by Google in 2014 for around $500 million USD.

As CEO of Google DeepMind, Hassabis has led breakthroughs in AI, including creating systems that excel at games like Go and chess.

By 2016, DeepMind had achieved global recognition for developing AI systems that could master the ancient game of Go, beating world champions. It was this expertise in AI that DeepMind began applying to science, aiming to solve more meaningful challenges, including protein folding.

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The AlphaFold project formally launched in 2018, entering the Critical Assessment of protein Structure Prediction (CASP) competition—a biannual global challenge to predict protein structures. That year, AlphaFold won the competition, outperforming other teams and heralding a new era in structural biology. But the real breakthrough came in 2020, when AlphaFold2 was unveiled, solving many of the most difficult protein folding problems with an accuracy previously thought unattainable.

AlphaFold 2’s success marked the culmination of years of research into neural networks and machine learning, areas in which DeepMind has become a global leader.

The system is trained on vast datasets of known protein structures and amino acid sequences, allowing it to generalize predictions for proteins it has never encountered—a feat that was previously unimaginable.

Earlier this year, Google DeepMind and Isomorphic Labs unveiled AlphaFold 3, the third generation of the model, which the creators say uses an improved version of the Evoformer module, a deep learning architecture that was key to AlphaFold 2’s remarkable performance.

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The new model also incorporates a diffusion network, similar to those used in AI image generators, which iteratively refines the predicted molecular structures from a cloud of atoms to a highly accurate final configuration.

David Baker’s Contribution to Protein Design

While Hassabis and Jumper solved the prediction problem, David Baker’s work in de novo protein design offers an equally transformative approach: the creation of entirely new proteins that do not exist in nature.

Based at the University of Washington’s Institute for Protein Design, Baker’s lab developed Rosetta, a computational tool used to design synthetic proteins.

Baker’s work has led to the development of proteins that could be used to create novel therapeutics, including custom-designed enzymes and virus-like particles that may serve as vaccines. His group has even designed proteins to detect fentanyl, an opioid at the center of a global health crisis.

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By designing new proteins from scratch, Baker’s research expands the boundaries of what proteins can do, complementing the predictive power of AlphaFold by enabling the creation of molecules tailored to specific functions.

The Future of AI in Science

The Nobel Prize recognition of AlphaFold and Baker’s work underscores a broader trend: AI is rapidly becoming an indispensable tool in scientific research. AlphaFold’s success has sparked new interest in the potential of AI to solve complex problems across various fields, including climate change, agriculture, and materials science.

The Nobel Committee highlighted the transformative potential of these discoveries, emphasizing that they “open up vast possibilities” for the future of biology and chemistry. Hassabis has long been vocal about AI’s potential to drive innovation, but he is also clear-eyed about the risks. “AI has the potential to accelerate scientific discovery at a rate we’ve never seen before, but it’s crucial that we use it responsibly,” he said in a recent interview.

As AI systems like AlphaFold continue to evolve, their ability to simulate biological processes and predict outcomes could revolutionize healthcare, sustainability efforts, and beyond. Jumper and Hassabis’ Nobel Prize is a recognition of their work’s enormous impact, but it also signals the dawn of a new era in science—one where AI plays a central role in unlocking the mysteries of life.

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What’s next?

The 2024 Nobel Prize in Chemistry recognizes the profound contributions of Demis Hassabis, John Jumper, and David Baker, whose pioneering work has reshaped the landscape of protein science. AlphaFold, now a cornerstone tool for researchers worldwide, has accelerated discovery in ways previously unimaginable.

David Baker’s work in computational protein design further expands the possibilities for biological innovation, offering new solutions to global challenges.

Together, these advancements mark the beginning of a new era for artificial intelligence in science—one where the possibilities are just beginning to unfold (pun intended).

While he remains optimistic about AI’s positive impact, Hassabis warns that the risks, including the potential for societal-scale disasters, must be taken as seriously as the climate crisis.

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SYSRACKS Soundproof Server Rack

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SYSRACKS Soundproof Server Rack



A soundproof server rack cabinet is now more critical than ever. It’s specifically designed to eliminate IT & Networking equipment noise that emanates from servers and switches.

SYSRACKS soundproof server rack models are ideal for home or small office, recording, or music studios. Its two-layer structure with a sound-absorbing unique foam screen lining its interior creates an effective sound barrier that helps reduce the noise level from inside the cabinet by up to 36% dB(A).
Sysracks acoustic server cabinets are available in 12U, 15U, 18U models with 35” depth.
These are floor-standing units that can also be used as a wall mounted with the mounting hardware.

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Health insurtech startup Qantev raises €30 million to outperform LLMs with small AI models

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Health insurtech startup Qantev raises €30 million to outperform LLMs with small AI models

Health and life insurance providers face a rising volume of claims due to chronic disease and aging populations, but their processes lack scalability. AI automation can help, but it takes more tailoring than for other insurance types.

Automating property and casualty insurance, for instance, gave rise to public company Guidewire decades ago, but health insurance is a tougher vertical. “You can’t automate in a simplistic way, like you could for a broken car windshield,” Paris-based entrepreneur Tarik Dadi told TechCrunch. 

His startup, Qantev, is hoping to solve this. It provides clients like AXA and Generali with software that helps them manage claims via AI models that go through the same checks as in-house medical staff currently do: “Is the care medically necessary? Is the price right? Is the bill fraudulent?” But it does it much faster, which helps reduce costs and customer churn.

Dadi saw that need while working as a senior data scientist at AXA, while now-CTO Hadrien de March, a doctorate and former quant, had the math chops to address it. The two of them joined forces at Entrepreneur First in late 2018. “EF’s concept is ‘pre-idea, pre-team,’ but I cheated and brought the idea,” Dadi said.

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Armed with an idea and a small team, Qantev went on to raise a €1.7 million seed round led by Elaia in 2020, followed by a €10 million Series A round led by Omnes and Raise Ventures in 2022. These three VC firms are now participating in Qantev’s €30 million Series B round, which happened sooner than planned, Dadi said.

“Our topics are quite hot at the moment, and we saw that YC, at the beginning of the year, included at least three of our topics in their wishlist,” he said, referring to Y Combinator’s Request for Startups and to what he calls the “LLM craze.” “We started to see lots of small startups popping up in the U.S. and just throwing an LLM at the problem. […] We know that it’s a hard problem and that we have an asset.”

One conviction Qantev developed over the last five years is that one large model is not enough; its software relies on a collection of AI models trained on historical data from its clients and aiming for accuracy. “You can’t have hallucinations or anything like that. It’s human health; you can’t refuse care for someone’s cancer. That’s why we are still a big AI shop. We have many PhD and ML experts in our team because we have to create small AI models that are highly specialized in our topics,” Dadi said.

Qantev is aware it could still get leapfrogged by newcomers, and the company plans to use its new funding to recruit the AI and engineering talent it needs to maintain a technical advantage. Its goal is to double its headcount by the end of the year. 

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Led by Blossom Capital, the Series B round will also support Qantev’s international expansion; it plans to grow its Asia-focused Hong Kong office and make a strong push in North America.

While it has competitors there, such as Alaffia Health and Anomaly, other Blossom portfolio companies have made strong headways in the U.S. in recent years, and Qantev has an advantage of its own: Its customers are large and global, generating organic expansion when a new subsidiary adopts its software.

The downside of targeting such large clients is that sales cycles are long and complicated. “But the upside is that they’re big-ticket items,” Dadi said. He liked Blossom’s understanding of enterprise software as a category, and of Qantev’s ambition to become an operating system for health insurance. “We like saying it is a platform, because it’s going to be multiple products.”

What these products might be remains to be confirmed, although underwriting seems to be a strong candidate. For now, Qantev prioritizes claims management, but it is easy to see how it could leverage the legitimacy and data access it is gaining from its early customers to help them streamline other operations, as it is already doing with fraud detection. Looking at the big picture, this would tie back to the trend of AI as a way to fight rising healthcare costs.

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Watercooled, rack mounted SERVER Build!

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Watercooled, rack mounted SERVER Build!



I squeezed this server in a rack mounted case, then water cooled it….

Watch me take the server and build it into this rack mounted case step by step.

Asus Z10PE D16 Motherboard
two xeon E5-2683 V3 28c 56T
Kingston 16GB 2133MHz ECC Buffered DDR4
Custom Watercooling setup

To see me show of the server hardware and test the cpu watch here:

To watch the explanation video on the live stream click here:

Check out the live stream here:

Thanks for watching

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