The hottest Artificial Intelligence Substack posts right now

And their main takeaways
Category
Top Technology Topics

Learning the Language of Life with A.I.

Ground Truths • 6211 implied HN points • 24 Nov 24
🕹 Technology Artificial Intelligence Genetics
  1. AlphaFold2 has greatly advanced science by predicting protein structures. It's one of the most significant achievements in life sciences and has inspired many new AI models.
  2. There's a surge of new AI models focused on life sciences, including predictions of DNA and protein interactions. These advancements are happening quickly and are democratizing scientific research.
  3. The use of AI in biology is just beginning, and it holds exciting potential for future discoveries. It could help us understand complex biological functions better and develop new therapies.

Microsoft's whitepaper for the U.S. Army's 'fighting google' and integration with Maven through Project Convergence

All-Source Intelligence Fusion • 691 implied HN points • 28 Jan 25
🇺🇸 U.S. Politics Military Technology Contracts Artificial Intelligence Government
  1. Microsoft is working with the U.S. Army to integrate augmented reality technology into military operations, focusing on a project called IVAS. This technology aims to give soldiers enhanced situational awareness on the battlefield.
  2. There have been complications with the IVAS technology, including issues like discomfort for users, which led to funding cuts from Congress. The Army is exploring better alternatives for combat effectiveness.
  3. Microsoft is involved in a competitive environment with other tech companies like Anduril and Palantir for military contracts. These partnerships and innovations are crucial for enhancing the capabilities needed in modern warfare.

2.3% chance an asteroid hits Earth, book blurbs finally die, and the new AI Deep Research can't find me a mattress

The Intrinsic Perspective • 10063 implied HN points • 08 Feb 25
🔬 Science Astrophysics Publishing Artificial Intelligence Research Funding
  1. There’s a small but growing chance that an asteroid could hit Earth, currently about 2.3%. This could lead to serious problems if it hits a populated area.
  2. Book publishers like Simon & Schuster are dropping the requirement for authors to get book blurbs, which is a relief for new writers who struggle with this.
  3. The NIH is reducing the indirect costs that universities take from research grants. This means more money will go directly to scientists rather than the universities.

Why the US military should plan for human-level AI

Faster, Please! • 365 implied HN points • 14 Feb 25
🇺🇸 U.S. Politics Military Policy Artificial Intelligence National Security Defense technology Geopolitics
  1. The US military needs to prepare for the future of AI, especially if it reaches human-level intelligence. This preparation is crucial because AI could change how wars are fought.
  2. Unlike nuclear fission, which clearly showed its potential for destructive power, the military uses of AI are still not very clear. It's harder to see what AI can really do for military purposes right now.
  3. There are calls for a major effort, similar to the Manhattan Project, to stay ahead in AI development, particularly to prevent adversaries like China from gaining an advantage. However, the exact military benefits of advanced AI are still uncertain.

AI, Cybernetics, and Complexity: unpacking the 2024 Nobel Prizes

Complexity Thoughts • 319 implied HN points • 14 Oct 24
🔬 Science Complexity Cybernetics Artificial Intelligence Physics Biology
  1. The 2024 Nobel Prizes recognized important advances in AI, but these discoveries are also deeply connected to complex systems. This shows that complexity science is becoming a more accepted area in high-level research.
  2. Understanding complex systems requires looking beyond traditional boundaries of science. The future of breakthroughs may rely on merging different scientific fields and using interdisciplinary approaches.
  3. Success in tackling complex challenges, like climate change and health issues, will need both detailed analysis of parts and a broader view of systems. Researchers must balance reductionist methods with insights from complexity science.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Casey (Newton) Strikes Out

Marcus on AI • 3517 implied HN points • 11 Dec 24
🕹 Technology Artificial Intelligence Skepticism Ethics Industry Trends Innovation
  1. AI skeptics believe that while there were big improvements in AI, those gains seem to be slowing down now. They think the hype isn't matching reality.
  2. Casey Newton's view oversimplifies AI skepticism by dividing it into two groups, but many skeptics have different opinions and concerns about AI's influence.
  3. It's important to recognize the problems with AI and financial issues in the industry, rather than just celebrating advancements without addressing weaknesses.

Debunking 10 Popular Myths About DeepSeek

The Algorithmic Bridge • 976 implied HN points • 28 Jan 25
🕹 Technology Artificial Intelligence Machine Learning Data Privacy Open Source Tech industry
  1. DeepSeek models can be customized and fine-tuned, even if they're designed to follow certain narratives. This flexibility can make them potentially less restricted than some other AI models.
  2. Despite claims that DeepSeek can compete with major players like OpenAI for a fraction of the cost, the actual financial and operational needs to reach that level are much more substantial.
  3. DeepSeek has made significant progress in AI, but it hasn't completely overturned established ideas like scaling laws. It still requires considerable resources to develop and deploy effective models.

AI #98: World Ends With Six Word Story

Don't Worry About the Vase • 1881 implied HN points • 09 Jan 25
🕹 Technology Artificial Intelligence Machine Learning Data science Automation Digital Transformation
  1. AI can offer useful tasks, but many people still don't see its value or know how to use it effectively. It's important to change that mindset.
  2. Companies are realizing that fixed subscription prices for AI services might not be sustainable because usage varies greatly among users.
  3. Many folks are worried about AI despite not fully understanding it. It's crucial to communicate AI's potential benefits and reduce fears around job loss and other concerns.

The new AI scaling law shell game

Marcus on AI • 4663 implied HN points • 24 Nov 24
🕹 Technology Artificial Intelligence Machine Learning Computing Data Analysis
  1. Scaling laws in AI aren't as reliable as people once thought. They're more like general ideas that can change, rather than hard rules.
  2. The new approach to scaling, which focuses on how long you train a model, can be costly and doesn't always work better for all problems.
  3. Instead of just trying to make existing models bigger or longer-lasting, the field needs fresh ideas and innovations to improve AI.

Prophecies of the Flood

One Useful Thing • 1608 implied HN points • 10 Jan 25
🕹 Technology Artificial Intelligence Machine Learning AI Ethics Automation Digital Transformation
  1. AI researchers are predicting that very smart AI systems will soon be available, which they call Artificial General Intelligence (AGI). This could change society a lot, but many think we should be cautious about these claims.
  2. Recent AI models have shown they can solve very tough problems better than humans. For example, one new AI model performed surprisingly well on difficult tests that challenge knowledge and problem-solving skills.
  3. As AI technology improves, we need to start talking about how to use it responsibly. It's important for everyone—from workers to leaders—to think about what a world with powerful AIs will look like and how to adapt to it.

Is Social Media Becoming 'Hollow Media'?

In Bed With Social • 277 implied HN points • 13 Oct 24
🕹 Technology Social media Artificial Intelligence User Experience Digital Culture Community Engagement
  1. Social media is increasingly becoming artificial, with bots and AI taking over real human interactions. These digital companions might seem helpful but they are not real friends.
  2. The rise of AI and superficial connections is causing loneliness, as people miss out on genuine interactions. Meaningful relationships require vulnerability and real dialogue, which AI can't provide.
  3. Some new platforms are showing that authentic connections can still exist. Apps focused on shared hobbies or interests are creating real communities, reminding us that human experiences are vital to social networks.

A Visual Guide to Quantization

Exploring Language Models • 5092 implied HN points • 22 Jul 24
🕹 Technology Artificial Intelligence Machine Learning Computer Science Data processing Software Engineering
  1. Quantization is a technique used to make large language models smaller by reducing the precision of their parameters, which helps with storage and speed. This is important because many models can be really massive and hard to run on normal computers.
  2. There are different ways to quantize models, like post-training quantization and quantization-aware training. Post-training means you quantize after the model is built, while quantization-aware training involves taking quantization into account during the model's training for better accuracy.
  3. Recent advances in quantization methods, like using 1-bit weights, can significantly reduce the size and improve the efficiency of models. This allows them to run faster and use less memory, which is especially beneficial for devices with limited resources.

#98 — Microsoft's casual Quantum Leap, NodeJS to settle on ESM-only, reserve best hours for dreams and more

The Nibble • 4 implied HN points • 22 Feb 25
🕹 Technology Quantum Computing Software Development Artificial Intelligence Web Development
  1. Microsoft has made a big step in quantum computing with their new Majorana chip. This chip could make quantum computing faster and more accurate, which is exciting for the future!
  2. Node.js is moving towards only using ESM (ECMAScript Modules), making it simpler for developers to build applications without worrying about different module systems. This should help streamline coding for many people.
  3. Companies like Apple are releasing new products like the iPhone 16e, while others are making big moves in AI and tech. It’s clear that innovation is happening at a rapid pace across the industry!

Nobel Prize to the Statistical Physics of artificial neural networks

Complexity Thoughts • 379 implied HN points • 08 Oct 24
🔬 Science Physics Machine Learning Artificial Intelligence Computational Models Statistical Methods
  1. John J. Hopfield and Geoffrey E. Hinton won the Nobel Prize for their work on artificial neural networks. Their research helps us understand how machines can learn from data using ideas from physics.
  2. Hopfield's networks use energy minimization to recall memories, similar to how physical systems find stable states. This shows a connection between physics and how machines learn.
  3. Boltzmann machines, developed by Hinton, introduce randomness to help networks explore different configurations. This randomness allows for better learning from data, making these models more effective.

AI influencer hype gone wild

Marcus on AI • 5572 implied HN points • 31 Oct 24
🕹 Technology Artificial Intelligence Productivity Tools Tech Trends Market Analysis Consumer behavior
  1. Many people are trying AI tools, but not everyone thinks they are effective. This shows there's a mix of interest and skepticism in using new technology.
  2. A recent survey revealed that while 79% of people have tried Microsoft Copilot, only 25% found it worthwhile. This indicates people are testing AI but still unsure about its overall value.
  3. People are not ignoring AI; they are being cautious and waiting to see if it meets their expectations before fully committing. It’s a wait-and-see attitude towards technology.

Fast Speculative Decoding with Llama 3.2 and vLLM

The Kaitchup – AI on a Budget • 219 implied HN points • 14 Oct 24
🕹 Technology Artificial Intelligence Machine Learning Computing Software Development Data processing
  1. Speculative decoding is a method that speeds up language model processes by using a smaller model for suggestions and a larger model for validation.
  2. This approach can save time if the smaller model provides mostly correct suggestions, but it may slow down if corrections are needed often.
  3. The new Llama 3.2 models may work well as draft models to enhance the performance of the larger Llama 3.1 models in this decoding process.

How Meta Plans To Crank the Dopamine Machine With Infinite AI-Generated Content

The Algorithmic Bridge • 1677 implied HN points • 03 Jan 25
🕹 Technology Artificial Intelligence Social media User engagement Digital Content Ethics
  1. Meta is creating AI that generates custom content for users, aiming to keep them engaged on platforms like Facebook and Instagram. This could hook people's attention even more than traditional entertainment.
  2. There's a risk that as AI-generated content becomes more common, people might lose the ability to notice or care about its presence. They could become so used to it that they forget it exists.
  3. The real concern isn't just the entertainment itself but how it distracts people and affects their ability to think and engage with the world around them. It raises questions about what kind of life we actually want to lead.

How I Use ChatGPT

The Ruffian • 663 implied HN points • 25 Jan 25
🕹 Technology Artificial Intelligence Software Consumer Tech Innovation Tools
  1. ChatGPT and Claude are popular AI tools, but users might find Claude to be more useful. Brand recognition plays a big role in which tool people choose.
  2. Many users are just starting to explore how to use LLMs (like ChatGPT and Claude) effectively. There's a lot of potential in these tools that many people are not fully tapping into.
  3. The author lists several ways they have used LLMs for various tasks, from troubleshooting tech issues to summarizing essays. This shows how versatile and helpful these tools can be in everyday life.

Deep Fear!

Aether Pirates of the Matterium! • 18455 implied HN points • 04 Feb 24
🕹 Technology Military Artificial Intelligence Energy Society Innovation
  1. Military analysts are afraid of the future and the rapid advancement of technology.
  2. Tech-minded individuals are seen as a threat by the military due to their knowledge and innovative capabilities.
  3. The release of Zero Point Technology to the public, especially techies, is a major concern for the military as it would shift power dynamics significantly.

OpenAI o3 Model Is a Message From the Future: Update All You Think You Know About AI

The Algorithmic Bridge • 2080 implied HN points • 20 Dec 24
🕹 Technology Artificial Intelligence Software Development Machine Learning Computing Innovation
  1. OpenAI's new o3 model performs exceptionally well in math, coding, and reasoning tasks. Its scores are much higher than previous models, showing it can tackle complex problems better than ever.
  2. The speed at which OpenAI developed and tested the o3 model is impressive. They managed to release this advanced version just weeks after the previous model, indicating rapid progress in AI development.
  3. O3's high performance in challenging benchmarks suggests AI capabilities are advancing faster than many anticipated. This may lead to big changes in how we understand and interact with artificial intelligence.

Update re: Microsoft and training data

Marcus on AI • 2766 implied HN points • 26 Nov 24
🕹 Technology Artificial Intelligence Data Privacy Machine Learning Software Development Corporate Communication
  1. Microsoft claims they don't use customer data from their applications to train AI, but it's not very clear how that works.
  2. There is confusion around the Connected Services feature, which says it analyzes data but doesn't explain how that affects AI training.
  3. People want more clear answers from Microsoft about data usage, but there hasn't been a detailed response from the company yet.

What just happened

One Useful Thing • 1936 implied HN points • 19 Dec 24
🕹 Technology Artificial Intelligence Machine Learning Tech advancements Computer Science Innovation
  1. There are now many smart AI models available for everyone to use, and some of them are even free. It's easier for companies with tech talent to create powerful AIs, not just big names like OpenAI.
  2. New AI models are getting smarter and can think before answering questions, helping them solve complex problems, even spotting mistakes in research papers. These advancements could change how we use AI in science and other fields.
  3. AI is rapidly improving in understanding video and voice, making it feel more interactive and personal. This creates new possibilities for how we engage with AI in our daily lives.

AI #90: The Wall

Don't Worry About the Vase • 3494 implied HN points • 14 Nov 24
🕹 Technology Artificial Intelligence Regulations Machine Learning Data Privacy Ethics
  1. AI is improving quickly, but some methods of deep learning are starting to face limits. Companies are adapting and finding new ways to enhance AI performance.
  2. There's an ongoing debate about how AI impacts various fields like medicine, especially with regulations that could limit its integration. Discussions about ethical considerations and utility are very important.
  3. Advancements in AI, especially in image generation and reasoning, continue to demonstrate its growing capabilities, but we need to be cautious about potential risks and ensure proper regulations are in place.

DeepSeek-V3: Technical Details

Gonzo ML • 252 implied HN points • 06 Feb 25
🕹 Technology Artificial Intelligence Machine Learning Computer Science Data Analysis Software Development
  1. DeepSeek-V3 uses a new technique called Multi-head Latent Attention, which helps to save memory and speed up processing by compressing data more efficiently. This means it can handle larger datasets faster.
  2. The model incorporates an innovative approach called Multi-Token Prediction, allowing it to predict multiple tokens at once. This can improve its understanding of context and boost overall performance.
  3. DeepSeek-V3 is trained using advanced hardware and new training techniques, including utilizing FP8 precision. This helps in reducing costs and increasing efficiency while still maintaining model quality.

Your brain is a washing machine, AGI's job prospects, Americans are alone, the Earth passes "peak child"

The Intrinsic Perspective • 5983 implied HN points • 14 Jan 25
🔬 Science Neuroscience Psychology Artificial Intelligence Sociology Child development
  1. Our brains clean themselves while we sleep, which is super important for our health. If we use strong sleep aids, like Ambien, it might mess with this cleaning process.
  2. The world is seeing fewer children being born, which means we might be reaching a point where there are not as many kids in the future. This can affect society in various ways.
  3. There's a common fear that artificial general intelligence (AGI) could take away all jobs. However, it's likely that human jobs will still have value even as technology improves.

AI & Python #27: Books I Read to Learn Data Science and Machine Learning

Artificial Corner • 119 implied HN points • 16 Oct 24
🕹 Technology Data science Machine Learning Artificial Intelligence Education
  1. Reading is essential for understanding data science and machine learning. Books can help you learn these subjects from scratch or deepen your existing knowledge.
  2. One recommended book is 'Data Science from Scratch' by Joel Grus. It covers important math and statistics concepts that are crucial for data science.
  3. For beginners in Python, it's important to learn Python basics before diving into data science books. Supplement your reading with beginner-friendly Python books.

Mechanisms too simple for humans to design

Telescopic Turnip • 274 implied HN points • 22 Jan 25
🔬 Science Biology Evolution Genetics Technology Artificial Intelligence
  1. Living organisms, like butterflies and bacteria, are incredibly complex, yet humans struggle to replicate them fully because they are surprisingly simple in construction. It's like trying to build a working insect but only using a few basic parts.
  2. The information contained in the genomes of living beings is often much less than what we assume. For example, the human genome contains less useful information than what fits on a CD, showcasing how nature efficiently packs information.
  3. Natural evolution leads to a balance where simpler designs can survive better, while human-made technologies often have complex specifications and high error rates. This means some amazing designs in nature might be too bizarre for humans to create intentionally.

Weekly Top Picks #96

The Algorithmic Bridge • 276 implied HN points • 03 Feb 25
🕹 Technology Artificial Intelligence Machine Learning Automation Geopolitics Research
  1. OpenAI has launched two new AI agents, Operator and Deep Research, which focus on web tasks and detailed reports. Deep Research is particularly useful right now.
  2. OpenAI's o3-mini model is now free and demonstrates strong reasoning capabilities. This shows that powerful AI tools can be accessible to everyone.
  3. AI technology is evolving rapidly, and companies can benefit collectively from its advancements. Telling an AI to think longer can actually improve its performance.

Fit For Purpose

Doomberg • 6134 implied HN points • 26 Dec 24
🕹 Technology Cybernetics Artificial Intelligence Machine Learning Energy
  1. Cybernetics studies how information is used in complex systems, which helps in fields like AI and managing big teams. Understanding this can make complex situations easier to handle.
  2. The principle of POSIWID means that the real purpose of a system is shown by what it actually does, not just what it says it aims for. This can help us see the truth behind many actions and motives.
  3. Current hype around fusion energy suggests it might soon be commercially viable, but we should question if the excitement aligns with real progress or hidden agendas in energy politics.

Last Week in AI #291 - Nobel Prizes, Adobe's Video Model, Tesla's Robots

Last Week in AI • 99 implied HN points • 16 Oct 24
🕹 Technology Artificial Intelligence Software Development Research Innovation Automation
  1. Two scientists won a Nobel Prize in Physics for their important work on artificial intelligence and neural networks, showing how AI is changing technology and society.
  2. Adobe has released a new AI video model that helps users create and edit videos easily, bringing exciting tools to programs like Premiere Pro.
  3. Tesla showcased new robots and vehicles at an event, but some people felt the demonstrations weren't as impressive as expected, leading to a decline in Tesla's stock.

AI: A Means to an End or a Means to Our End?

The Fry Corner • 186 HN points • 15 Sep 24
🕹 Technology Artificial Intelligence Computing Innovation Data Ethics
  1. AI can change our world significantly, but we must handle it carefully to avoid negative outcomes. It's crucial to put rules in place for how AI is developed and used.
  2. Humans and AI have different strengths; machines can process data faster, but humans have emotions and creativity that machines can't replicate. We shouldn't be too quick to believe AI can think like us.
  3. The growth of AI might disrupt many industries and change how we live. We need to be aware of these changes and adapt, ensuring that technology serves humanity rather than harms it.

Deepseek: The Quiet Giant Leading China’s AI Race

ChinaTalk • 1615 implied HN points • 27 Nov 24
🕹 Technology Artificial Intelligence Startups Innovation Research Open Source
  1. Deepseek is a rising Chinese AI startup that has surpassed major competitors like OpenAI in some technical benchmarks. They are focused on foundational research and open-sourcing their models.
  2. The company has started a price war in the Chinese AI market by offering their technology at much lower rates than the competition, making AI more accessible.
  3. Deepseek's approach prioritizes innovation over immediate profit, aiming to contribute to the global technological landscape rather than just following existing trends.

Complexity Thoughts: Issue #45

Complexity Thoughts • 139 implied HN points • 11 Oct 24
🔬 Science Evolution Artificial Intelligence Neuroscience Complex Systems
  1. New ideas in network science can help understand complex systems better. This approach looks at how systems behave over time, rather than just focusing on stable points.
  2. The evolution of multicellular organisms has led to many new species and ecosystems. Key innovations in multicellularity help organisms adapt and thrive in different environments.
  3. Research shows that convolutional neural networks (CNNs) face limits in recognizing patterns. This limitation is linked to the complexity of the data they're trained on, raising questions about their reliability.

Inverse frontiers

arg min • 158 implied HN points • 07 Oct 24
🕹 Technology Optimization Machine Learning Algorithms Data science Artificial Intelligence
  1. Convex optimization has benefits, like collecting various modeling tools and always finding a reliable solution. However, not every problem fits neatly into a convex framework.
  2. Some complex problems, like dictionary learning and nonlinear models, often require nonconvex optimization, which can be tricky to handle but might be necessary for accurate results.
  3. Using machine learning methods can help solve inverse problems because they can learn the mapping from measurements to states, making it easier to compute solutions later, though training the model initially can take a lot of time.

DeepSeek-V3: Training

Gonzo ML • 126 implied HN points • 08 Feb 25
🕹 Technology Machine Learning Artificial Intelligence Data science Software Development Computer Science
  1. DeepSeek-V3 uses a lot of training data, with 14.8 trillion tokens, which helps it learn better and understand more languages. It's been improved with more math and programming examples for better performance.
  2. The training process has two main parts: pre-training and post-training. After learning the basics, it gets fine-tuned to enhance its ability to follow instructions and improve its reasoning skills.
  3. DeepSeek-V3 has shown impressive results in benchmarks, often performing better than other models despite having fewer parameters, making it a strong competitor in the AI field.

✨ While tech CEOs talk up AGI imminence, real-world AI use cases emerge

Faster, Please! • 639 implied HN points • 06 Jan 25
🕹 Technology Artificial Intelligence Cybersecurity Software Development Data science Emerging technologies
  1. In a few years, we might see AI agents start working alongside humans, which could really change how companies function.
  2. Tech leaders believe that powerful AI could lead to huge advances in science and medicine, speeding up progress significantly.
  3. While there is excitement about AI's potential, it's also important to manage the risks to make sure it benefits everyone.

Last Week in AI #290 - OpenAI's massive VC round and DevDay, Flux 1.1, NotebookLM

Last Week in AI • 139 implied HN points • 08 Oct 24
🕹 Technology Artificial Intelligence Software Development Venture Capital Tech news Innovation
  1. OpenAI raised a massive $6.6 billion in funding, making it one of the most valuable tech companies. This will help them expand their research and computing power.
  2. At OpenAI's DevDay, they introduced a new Realtime API for developers, allowing nearly instant AI-generated voice responses for apps. Developers are excited about the new possibilities they can create.
  3. Black Forest Labs released a faster and improved version of their image generation model, Flux 1.1 Pro. This could change the game for how quickly and effectively images are created using AI.

Global Existential Risks

The Honest Broker Newsletter • 1648 implied HN points • 13 Nov 24
🔬 Science Global Risks Climate change Artificial Intelligence Pandemics Nuclear Threats
  1. The U.S. government identified six major risks that could threaten humanity, including artificial intelligence and nuclear war. These risks could lead to catastrophic events affecting civilization.
  2. Climate change was found to be significant but not classified as an existential risk, meaning it won't likely cause human extinction. It's seen as a serious issue but not at the same level as other threats.
  3. Experts warn that focusing too much on familiar risks may blind us to emerging threats, like pandemics or asteroid impacts, which could have severe consequences. We need to pay attention to a broader range of potential dangers.

✨ Life in an AI utopia: A Quick Q&A with futurist and philosopher Nick Bostrom

Faster, Please! • 731 implied HN points • 27 Dec 24
🕹 Technology Artificial Intelligence Futurism Philosophy Ethics Innovation
  1. It's often easier for people to imagine a bad future, like in movies, than a good one. This can affect how cultures think about their future.
  2. When thinking about a perfect world, many people share similar ideas, like having peace and cleanliness. But if everything goes perfectly, we might miss out on challenges that give our lives meaning.
  3. The future of artificial intelligence could be really bright or really dark. We need to prepare for both possibilities because we are entering a new era with big changes ahead.

Weekly Dose of Optimism #128

Not Boring by Packy McCormick • 125 implied HN points • 24 Jan 25
🕹 Technology Artificial Intelligence Blockchain Energy Synthetic Biology Digital innovation
  1. OpenAI's new Stargate Project plans to invest $500 billion over four years in AI infrastructure in the U.S. This could lead to many new jobs and significant economic benefits.
  2. The recent Executive Orders focus on boosting American energy production and supporting the growth of digital assets like cryptocurrency. It's a big move to reinforce economic growth and secure energy independence.
  3. Scientists have created the first fully synthetic eukaryotic genome using yeast. This achievement could lead to making better medicines and biofuels in the future.