The hottest Computing Substack posts right now

And their main takeaways
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Top Technology Topics

The Engine

The Chip Letter • 8299 implied HN points • 05 Jan 25
🕹 Technology AI Computing Literature History Innovation
  1. Jonathan Swift's 'Engine' in Gulliver's Travels resembles a modern language model, using a setup to create phrases like today's AI would. It's an early version of computing that predicts how machines can generate language.
  2. The 'Engine' is set up to show how books can be made easier to create. It suggests that anyone could write on complex topics, even without talent, a concept similar to how AI helps people produce text now.
  3. Swift's work critiques the idea of replacing human creativity with machines. It humorously shows that while technology can produce text, true creativity still involves deeper human thought.

Why I don’t share Sam Altman’s confidence that AGI is basically a solved problem

Marcus on AI • 7786 implied HN points • 06 Jan 25
🕹 Technology Artificial Intelligence Machine Learning Data science Computing Robotics
  1. AGI is still a big challenge, and not everyone agrees it's close to being solved. Some experts highlight many existing problems that have yet to be effectively addressed.
  2. There are significant issues with AI's ability to handle changes in data, which can lead to mistakes in understanding or reasoning. These distribution shifts have been seen in past research.
  3. Many believe that relying solely on large language models may not be enough to improve AI further. New solutions or approaches may be needed instead of just scaling up existing methods.

Back to the Future with Gordon Moore

The Chip Letter • 5897 implied HN points • 28 Jan 25
🕹 Technology Semiconductors AI Innovation Computing Industry Trends
  1. Technology changes rapidly, but some issues, like how to effectively use computing power, seem to stay the same. This means we often find ourselves asking similar questions about the future of tech.
  2. Gordon Moore's insights from years ago still apply today, especially his thoughts on competition and applications for technology. He pointed out the need for practical uses of increased computing power.
  3. Concerns about technology making us 'stupid' remain relevant. However, it's more about using computers without losing understanding of basic principles than about being incapable of learning new skills.

Reinforcement, evolution, and automata

Gradient Ascendant • 7 implied HN points • 26 Feb 25
🕹 Technology AI Machine Learning Computing Algorithms Automation
  1. Reinforcement learning is becoming important again, helping improve AI models by using trial and error. This allows models to make better decisions based on past experiences.
  2. AI improvements are not just for big systems but can also work on smaller models, even those that run on phones. This shows that smarter AI can be more accessible.
  3. Combining reinforcement learning with evolutionary strategies could create more advanced AI systems in the future, leading to exciting developments and solutions.

If You're So Smart, Why Can't You Die?

Desystemize • 3933 implied HN points • 16 Feb 25
🕹 Technology AI Intelligence Philosophy Computing Innovation
  1. AI improvements are not even across the board. While some tasks have become incredibly advanced, other simple tasks still trip them up, showing that not all intelligence is equal.
  2. We should be cautious about assuming that increases in one type of AI ability mean it can do everything we can. Each skill in AI may develop separately, like bagels and croissants in baking.
  3. Understanding what makes intelligence requires looking deeper than just performance. There is a difference between raw capabilities and the contextual, real-life experiences that truly shape how we understand intelligence.
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The Sequence Radar #554 : The New DeepSeek R1-0528 is Very Impressive

TheSequence • 77 implied HN points • 01 Jun 25
🕹 Technology AI Computing Machine Learning Software Engineering
  1. The DeepSeek R1-0528 model is really good at math and reasoning, showing big improvements in understanding complicated problems.
  2. This new model can handle large amounts of data at once, making it perfect for tasks that need lots of information, like technical documents.
  3. DeepSeek is focused on making advanced AI accessible to everyone, not just big companies, which is great for developers and researchers with limited resources.

The AI Wave Accelerates

Nonzero Newsletter • 485 implied HN points • 24 Jan 25
🕹 Technology AI Computing Innovation Infrastructure Climate
  1. New AI technology like OpenAI's Operator can help with tasks, but it's still not perfect and makes mistakes. This shows that AI is getting better, but we need to manage our expectations.
  2. There's a growing belief among experts that advanced AI could be here sooner than expected. This brings both excitement and concern about what it means for jobs and society.
  3. Recent events highlight the importance of careful thinking and understanding before jumping to conclusions, like in the case of undersea cable damages where initial fears of sabotage were proven wrong.

The Five Stages of AGI Grief

Marcus on AI • 6205 implied HN points • 07 Jan 25
🕹 Technology Artificial Intelligence Machine Learning Computing Innovation Tech Ethics
  1. Many people are changing what they think AGI means, moving away from its original meaning of being as smart as a human in flexible and resourceful ways.
  2. Some companies are now defining AGI based on economic outcomes, like making profits, which isn't really about intelligence at all.
  3. A lot of discussions about AGI don't clearly define what it is, making it hard to know when we actually achieve it.

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.

Qualcomm RISCs, Arm Pulls: The Legal Battle for the Future of Client Computing

The Chip Letter • 8736 implied HN points • 16 Nov 24
🕹 Technology Computing Semiconductors Licensing Legal issues Market Trends
  1. Qualcomm and Arm are in a legal battle over chip design licenses, which could significantly impact the future of smartphone and laptop computing.
  2. Qualcomm recently acquired a company called Nuvia that designed high-performance chips, but Arm claims that this violated their licensing agreement.
  3. The outcome of this legal dispute could decide who dominates the chip market, affecting companies and consumers who rely on these technologies.

DeepSeek: What the Headlines Miss

ChinaTalk • 4121 implied HN points • 26 Jan 25
🕹 Technology AI Geopolitics Export Controls Computing
  1. Export restrictions on AI chips only recently started, so it’s too soon to judge their effectiveness. The new chips might still perform well for AI tasks, keeping development ongoing.
  2. DeepSeek's advancements in efficiency show that machine learning can get cheaper over time. It’s possible for smaller companies to do more with less, but bigger companies benefits from these efficiencies too.
  3. The gap in computing power between the US and China is significant. DeepSeek admits they need much more computing power than US companies to achieve similar results due to export controls.

Satya Nadella and the three stages of scientific truth

Marcus on AI • 8023 implied HN points • 23 Nov 24
🕹 Technology AI Innovation Science Computing Philosophy
  1. New ideas in science often face resistance at first. People may ridicule them before they accept the change.
  2. Scaling laws in deep learning may not last forever. This suggests that other methods may be needed to advance technology.
  3. Many tech leaders are now discussing the limits of scaling laws, showing a shift in thinking towards exploring new approaches.

Breaking news: AGI is not imminent!

Marcus on AI • 4466 implied HN points • 20 Jan 25
🕹 Technology AI Machine Learning Computing Innovation Digital Trends
  1. Many people believe AGI, or artificial general intelligence, is coming soon, but that might not be true. It's important to stay cautious and not believe everything we hear about upcoming technology.
  2. Sam Altman, a well-known figure in AI, suggested we're close to achieving AGI, but he later changed his statement. This shows that predictions in technology can quickly change.
  3. Experts like Gary Marcus are confident that AGI won't arrive as soon as 2025. They think we still have a long way to go before we reach that level of intelligence in machines.

AGI isn’t coming in 2025, and GPT-5 probably isn’t either.

Marcus on AI • 4189 implied HN points • 09 Jan 25
🕹 Technology AI Computing Data science Machine Learning Software
  1. AGI, or artificial general intelligence, is not expected to be developed by 2025. This means that machines won't be as smart as humans anytime soon.
  2. The release of GPT-5, a new AI model, is also uncertain. Even experts aren't sure if it will be out this year.
  3. There is a trend of people making overly optimistic predictions about AI. It's important to be realistic about what technology can achieve right now.

An AI rumor you won’t want to miss

Marcus on AI • 7153 implied HN points • 10 Nov 24
🕹 Technology AI Deep Learning Scaling Data Computing
  1. The belief that more scaling in AI will always lead to better results might be fading. It's thought we might have reached a limit where simply adding more data and computing power is no longer effective.
  2. There are concerns that scaling laws, which have worked before, are just temporary trends, not true laws of nature. They don’t actually solve issues like AI making mistakes or hallucinations.
  3. If rumors are true about a major change in the AI landscape, it could lead to a significant loss of trust in these scaling approaches, similar to a bank run.

End of an Era

Castalia • 1139 implied HN points • 11 Jul 24
🕹 Technology Software AI Computing Innovation Digital Culture
  1. We might be at the end of the 'Software Era' because many tech companies feel stuck and aren't coming up with new ideas. People are noticing that apps and technologies often prioritize ads over user experience.
  2. In past decades, society shifted from valuing collective worker identity to focusing more on individuals. This change brought about personal computing, but it also resulted in fewer job opportunities compared to earlier industrial times.
  3. AI could replace many white-collar jobs, but it clashes with people's desire for individuality. While tech like the Metaverse offers potential growth, it may reshape our identities into something more complex and multiple.

DeepSeek isn't a victory for the AI sceptics

Odds and Ends of History • 2345 implied HN points • 28 Jan 25
🕹 Technology AI Computing Innovation Industry Market Trends
  1. DeepSeek, a new AI model from China, is much more efficient than existing models, meaning it can do more with less resources. This could lead to more widespread use of AI technology.
  2. Even if this new model appears better, it doesn't mean demand for computing power will decrease. Instead, it might increase as more uses for AI are discovered.
  3. The release of DeepSeek highlights the growing competition in AI technology, especially between China and the West. This might push companies to invest more in developing even smarter models.

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.

Physics Professors Are Using AI Models as Physics Tutors

The Algorithmic Bridge • 552 implied HN points • 27 Dec 24
🕹 Technology AI Education Science Computing Physics
  1. AI is being used by physics professors as personal tutors, showing its advanced capabilities in helping experts learn. This might surprise people who believe AI isn't very smart.
  2. Just like in chess, where computers have helped human players improve, AI is now helping physicists revisit old concepts and possibly discover new theories.
  3. The acceptance of AI by top physicists suggests that even in complex fields, machines can enhance human understanding, challenging common beliefs about AI's limitations.

Sam Altman Wants $7 Trillion

Astral Codex Ten • 16656 implied HN points • 13 Feb 24
🕹 Technology AI Machine Learning Artificial Intelligence Big Data Computing
  1. Sam Altman aims for $7 trillion for AI development, highlighting the drastic increase in costs and resources needed for each new generation of AI models.
  2. The cost of AI models like GPT-6 could potentially be a hindrance to their creation, but the promise of significant innovation and industry revolution may justify the investments.
  3. The approach to funding and scaling AI development can impact the pace of progress and the safety considerations surrounding the advancement of artificial intelligence.

Diffusion large language model, GPT‑4.5, 3.7 Sonnet, Wan2.1 open-source video model, Phi-4-multimodal, Proxy Lite, Omni-capable text and voice engine, Poe Apps and App Creator, FastRTC,Scribe and more

AI Brews • 5 implied HN points • 28 Feb 25
🕹 Technology AI Software Innovation Computing Digital
  1. GPT-4.5 has been released, improving pattern recognition and creative insights. This is a big step for AI technology and helps make better connections.
  2. New models like Claude 3.7 Sonnet and Mercury are making advancements in coding and video processing. These models are faster and more efficient than previous ones.
  3. Companies are launching tools that help with various tasks, like AI task management and seamless communication. These tools aim to reduce stress and improve productivity.

Wafer Scale - Trilogy Systems: Part 2

The Chip Letter • 4149 implied HN points • 27 Oct 24
🕹 Technology Computing Startups Innovation Integration Capital
  1. Trilogy Systems, founded by Gene Amdahl in 1979, aimed to revolutionize the mainframe market with a new technology called Wafer Scale Integration, which promised to be faster and cheaper than existing solutions. However, the company struggled with technical challenges and internal issues.
  2. As delays mounted and financial troubles grew, Trilogy abandoned its mainframe plans and, ultimately, its Wafer Scale technology. Distractions like personal tragedies and a lack of cohesive vision contributed to the company's downfall.
  3. After losing credibility and facing mounting losses, Trilogy merged with Elxsi, but that too did not lead to success. Amdahl felt a deep personal responsibility for the failure, which haunted him even after the company's collapse.

Street Smart

polymathematics • 159 implied HN points • 30 Aug 24
🕹 Technology AI Computing Innovation Community Applications
  1. Communal computing can connect people in a neighborhood by using technology in shared spaces. Imagine an app that helps you explore local history or find nearby restaurants right from your phone.
  2. AI could work for more than just individuals; it can help whole communities. For example, schools could have their own AI tutors to assist students together.
  3. There are cool projects like interactive tiles in neighborhoods that let people share information and connect with each other in real life, making technology feel more personal and community-focused.

Why reasoning models will generalize

Democratizing Automation • 1535 implied HN points • 28 Jan 25
🕹 Technology AI Machine Learning Data science Natural Language Processing Computing
  1. Reasoning models are designed to break down complex problems into smaller steps, helping them solve tasks more accurately, especially in coding and math. This approach makes it easier for the models to manage difficult questions.
  2. As reasoning models develop, they show promise in various areas beyond their initial focus, including creative tasks and safety-related situations. This flexibility allows them to perform better in a wider range of applications.
  3. Future reasoning models will likely not be perfect for every task but will improve over time. Users may pay more for models that deliver better performance, making them more valuable in many sectors.

The AI Nobels

Dana Blankenhorn: Facing the Future • 59 implied HN points • 09 Oct 24
🕹 Technology AI Machine Learning Research Innovation Computing
  1. Two major Nobel prizes were awarded to individuals working in AI, highlighting its importance and growth in science. Geoffrey Hinton won a physics prize for his work in machine learning.
  2. Current AI technology is still in the early stages and relies on brute force data processing instead of true creativity. The systems we have are not yet capable of real thinking like humans do.
  3. Exciting future developments in AI could come from modeling simpler brains, like that of a fruit fly. This may lead to more efficient AI software without requiring as much power.

8 Insights to Make Sense of OpenAI o3

The Algorithmic Bridge • 424 implied HN points • 23 Dec 24
🕹 Technology AI Machine Learning Computing Innovation Data science
  1. OpenAI's new model, o3, has demonstrated impressive abilities in math, coding, and science, surpassing even specialists. This is a rare and significant leap in AI capability.
  2. There are many questions about the implications of o3, including its impact on jobs and AI accessibility. Understanding these questions is crucial for navigating the future of AI.
  3. The landscape of AI is shifting, with some competitors likely to catch up, while many will struggle. It's important to stay informed to see where things are headed.

The Bitter Religion: AI’s Holy War Over Scaling Laws

The Generalist • 920 implied HN points • 14 Nov 24
🕹 Technology Artificial Intelligence Computing Innovation Research Engineering
  1. The AI community is divided over whether achieving higher levels of computation will lead to better artificial intelligence or if there are limits to this approach. Some think more resources will keep helping AI grow, while others fear we might hit a ceiling.
  2. There’s a growing debate about the importance of scaling laws and whether they should continue to guide AI development. People are starting to question if sticking to these beliefs is the best path forward.
  3. If doubt begins to spread about scaling laws, it could impact investment and innovation in AI and related fields, causing changes in how companies approach building new technologies.

The Unreasonable Impact of Gradient Checkpointing for Fine-tuning LLMs

The Kaitchup – AI on a Budget • 79 implied HN points • 03 Oct 24
🕹 Technology AI Machine Learning Data science Programming Computing
  1. Gradient checkpointing helps to reduce memory usage during fine-tuning of large language models by up to 70%. This is really important because managing large amounts of memory can be tough with big models.
  2. Activations, which are crucial for training models, can take up over 90% of the memory needed. Keeping track of these is essential for successfully updating the model's weights.
  3. Even though gradient checkpointing helps save memory, it might slow down training a bit since some activations need to be recalculated. It's a trade-off to consider when choosing methods for model training.

Did OpenAI Just Solve Abstract Reasoning?

AI: A Guide for Thinking Humans • 344 implied HN points • 23 Dec 24
🕹 Technology AI Machine Learning Computing Data science Research
  1. OpenAI's new model, o3, showed impressive results on tough reasoning tasks, achieving accuracy levels that could compete with human performance. This signals significant advancements in AI's ability to reason and adapt.
  2. The ARC benchmark tests how well machines can recognize and apply abstract rules, but recent results suggest some solutions may rely more on extensive compute than true understanding. This raises questions about whether AI is genuinely learning abstract reasoning.
  3. As AI continues to improve, the ARC benchmark may need updates to push its limits further. New features could include more complex tasks and better ways to measure how well AI can generalize its learning to new situations.

The Sequence Radar #544: The Amazing DeepMind's AlphaEvolve

TheSequence • 63 implied HN points • 18 May 25
🕹 Technology AI Machine Learning Computing Research Innovation
  1. AlphaEvolve is a new AI model from DeepMind that helps discover new algorithms by combining language models with evolutionary techniques. This allows it to create and improve entire codebases instead of just single functions.
  2. One of its big achievements is finding a faster way to multiply certain types of matrices, which has been a problem for over 50 years. It shows how AI can not only generate code but also make important mathematical discoveries.
  3. AlphaEvolve is also useful in real-world applications, like optimizing Google's systems, proving it's not just good in theory but has practical benefits that improve efficiency and performance.

GPTs Are Maxed Out

The Algorithmic Bridge • 647 implied HN points • 11 Nov 24
🕹 Technology AI Computing Machine Learning Data science Software Development
  1. AI companies are hitting limits with current models. Simply making AI bigger isn't creating better results like it used to.
  2. The upcoming models, like Orion, may not meet the high expectations set by previous versions. Users want more dramatic improvements and are getting frustrated.
  3. A new approach in AI may focus on real-time thinking, allowing models to give better answers by taking a bit more time, though this could test users' patience.

Deep Research, information vs. insight, and the nature of science

Democratizing Automation • 775 implied HN points • 12 Feb 25
🕹 Technology AI Science Research Innovation Computing
  1. AI will change how scientists work by speeding up research and helping with complex math and coding. This means scientists will need to ask the right questions to get the most out of these tools.
  2. While AI can process a lot of information quickly, it can't create real insights or make new discoveries on its own. It works best when used to make existing scientific progress faster.
  3. The rise of AI in science may change traditional practices and institutions. We need to rethink how research is done, especially how quickly new knowledge is produced compared to how long it takes to review that knowledge.

Demystifying GPU Compute Software

The Chip Letter • 6989 implied HN points • 10 Mar 24
🕹 Technology Computing Programming Hardware Frameworks Development
  1. GPU software ecosystems are crucial and as important as the GPU hardware itself.
  2. Programming GPUs requires specific tools like CUDA, ROCm, OpenCL, SYCL, and oneAPI, as they are different from CPUs and need special support from hardware vendors.
  3. The effectiveness of GPU programming tools is highly dependent on support from hardware vendors due to the complexity and rapid changes in GPU architectures.

Transformer^2: Self-adaptive LLMs

Gonzo ML • 63 implied HN points • 27 Jan 25
🕹 Technology Artificial Intelligence Machine Learning Data science Computing Software Development
  1. Transformer^2 uses a new method for adapting language models that makes it simpler and more efficient than fine-tuning. Instead of retraining the whole model, it adjusts specific parts, which saves time and resources.
  2. The approach breaks down weight matrices through a process called Singular Value Decomposition (SVD), allowing the model to identify and enhance its existing strengths for various tasks.
  3. At test time, Transformer^2 can adapt to new tasks in two passes, first assessing the situation and then applying the best adjustments. This method shows improvements over existing techniques like LoRA in both performance and parameter efficiency.

Weekly Top Picks #93

The Algorithmic Bridge • 148 implied HN points • 07 Jan 25
🕹 Technology AI Computing Data Robotics Video
  1. ChatGPT Pro is losing money despite its high subscription cost. This shows that even popular AI tools can face financial troubles.
  2. Nvidia has introduced an expensive new AI supercomputer for individuals. This highlights the growing demand for advanced AI technology in personal computing.
  3. More artists are embracing AI-generated art, sparking discussions about creativity and technology. This signals a shift in how art is produced and appreciated.

AI Companies Have Lost the Mandate of Heaven

The Algorithmic Bridge • 339 implied HN points • 04 Dec 24
🕹 Technology AI Software Data Computing Innovation
  1. AI companies are realizing that simply making models bigger isn't enough to improve performance. They need to innovate and find better algorithms rather than rely on just scaling up.
  2. Techniques to make AI models smaller, like quantization, are proving to have their own problems. These smaller models can lose accuracy, making them less reliable.
  3. Researchers have discovered limits to both increasing and decreasing the size of AI models. They now need to find new methods that work better while balancing cost and performance.

What Did You Think Getting Closer to AGI Would Be Like?

The Algorithmic Bridge • 318 implied HN points • 07 Dec 24
🕹 Technology Artificial Intelligence Machine Learning Software Development Computing Data science
  1. OpenAI's new model, o1, is not AGI; it's just another step in AI development that might not lead us closer to true general intelligence.
  2. AGI should have consistent intelligence across tasks, unlike current AI, which can sometimes perform poorly on simple tasks and excel on complex ones.
  3. As we approach AGI, we might feel smaller or less significant, reflecting how humans will react to advanced AI like o1, even if it isn’t AGI itself.

"Please wait while we access the functions on this card"

Disaffected Newsletter • 1938 implied HN points • 06 Feb 24
🕹 Technology Software Computing User Experience Hardware Automation
  1. Many everyday machines now have annoying delays when performing simple tasks that used to be instant, like using ATMs or accessing files. It's frustrating because these are basic functions.
  2. Modern devices often prioritize a fancy user experience over speed and efficiency, making us wait longer for actions that used to happen quickly. This creates a feeling of disconnect between users and their machines.
  3. The trend seems to be moving towards making everything software-controlled, even when it seems unnecessary. This can make basic interactions tedious and less intuitive for users.

What's Quantum Computing

Technically • 59 implied HN points • 28 Jan 25
🕹 Technology Computing Quantum Innovation Research Engineering
  1. Quantum computing uses qubits instead of bits. While bits can be either 0 or 1, qubits can be both at the same time, allowing for much faster problem-solving.
  2. Qubits can work together in a unique way, using superposition and interference to find answers much faster than traditional computers. This makes them great for complex problems like drug discovery.
  3. Quantum computers are still in the experimental stage and have challenges like needing very cold temperatures and handling errors, but they hold great promise for the future.

LLMs Know More Than What They Say

LLMs for Engineers • 120 HN points • 15 Aug 24
🕹 Technology AI Machine Learning Data science Software Development Computing
  1. Using latent space techniques can improve the accuracy of evaluations for AI applications without requiring a lot of human feedback. This approach saves time and resources.
  2. Latent space readout (LSR) helps in detecting issues like hallucinations in AI outputs by allowing users to adjust the sensitivity of detection. This means it can catch more errors if needed, even if that results in some false alarms.
  3. Creating customized evaluation rubrics for AI applications is essential. By gathering targeted feedback from users, developers can create more effective evaluation systems that align with specific needs.