The hottest Computing Substack posts right now

And their main takeaways
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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.

A Nonconstructive Existence Proof of Aligned Superintelligence

Transhuman Axiology • 99 implied HN points • 12 Sep 24
🕹 Technology AI Superintelligence Computing Robotics Mathematics
  1. Aligned superintelligence is possible, despite some people thinking it isn't. This idea shows proof that it can exist without needing complicated construction.
  2. Desirable outcomes for AI mean producing results that people think are good. We define these outcomes based on what humans can realistically accomplish.
  3. While the concept of aligned superintelligence exists, it faces challenges. It's hard to create, and even if we do, we can't be sure it will work as intended.
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Weekly Dose of Optimism #124

Not Boring by Packy McCormick • 116 implied HN points • 13 Dec 24
🕹 Technology AI Quantum Computing Robotics Innovation
  1. Google's new quantum chip, Willow, makes huge advances, allowing it to perform complex calculations much faster than traditional computers. This could lead to amazing breakthroughs in areas like medicine and materials science.
  2. OpenAI is showcasing its latest technologies during '12 Days of OpenAI,' introducing tools that improve AI's abilities in reasoning, video creation, and more, showing how quickly AI is evolving.
  3. Caltech developed tiny robots that can deliver medicine directly to specific parts of the body, potentially making treatments more effective and reducing side effects. This technology could transform how we treat various diseases.

Elon Musk’s xAI raises Monster $6B Series B for AI Supercomputer and Grok 3

The VC Corner • 379 implied HN points • 28 May 24
🕹 Technology AI Computing Investing Startups Semiconductors
  1. Elon Musk's company xAI just raised $6 billion to build an advanced AI supercomputer and improve their AI model, Grok 3. This new funding makes xAI a key player alongside OpenAI and Anthropic.
  2. The $6 billion Series B funding round is a big deal in the AI world, showing a lot of investor confidence. Musk plans to use this money to get the hardware needed for more powerful AI.
  3. xAI aims to compete with top AI companies by developing a massive number of semiconductors for training their models. This means more competition in the market and potentially exciting innovations in AI technology.

Claude's agentic future and the current state of the frontier models

Democratizing Automation • 277 implied HN points • 23 Oct 24
🕹 Technology AI Models Machine Learning Computing Software Development Tech Trends
  1. Anthropic has released Claude 3.5, which many people find better for complex tasks like coding compared to ChatGPT. However, they still lag in revenue from chatbot subscriptions.
  2. Google's Gemini Flash model is praised for being small, cheap, and effective for automation tasks. It often outshines its competitors, offering fast responses and efficiency.
  3. OpenAI is seen as having strong reasoning capabilities but struggles with user experience. Their o1 model is quite different and needs better deployment strategies.

Human Intelligence, Inc.

Teaching computers how to talk • 136 implied HN points • 10 Dec 24
🕹 Technology AI Computing Data Innovation Big Tech
  1. AI might seem really smart, but it actually just takes a lot of human knowledge and packages it together. It uses data from people who created it, rather than being original itself.
  2. Even though AI can do impressive things, it's not actually intelligent in the way humans are. It often makes mistakes and doesn't understand its own actions.
  3. When we use AI tools, we should remember the hard work of many people behind the scenes who helped create the knowledge that built these technologies.

Turing's Yearning, Part 1

Technohumanism • 99 implied HN points • 01 Aug 24
🕹 Technology Artificial Intelligence Computing History Philosophy Human-Machine Interaction
  1. Alan Turing's foundational paper on artificial intelligence is often overlooked in favor of its famous concepts like the Turing Test. It's filled with strange ideas and a deep human yearning for understanding machines.
  2. The idea behind the Turing Test, where a computer tricks someone into thinking it's human, raises questions about what intelligence really is. Is being able to imitate intelligence the same as actually being intelligent?
  3. Turing's paper includes surprising claims and combines brilliant insights with odd assertions. It reflects his complicated thoughts on machines and intelligence, showing a deeper human story that resonates today.

100 Years of 'IBM'

The Chip Letter • 1965 implied HN points • 15 Feb 24
🕹 Technology Computing Companies History
  1. IBM has had a significant impact on the development of computer systems over 100 years.
  2. IBM's influence extends to technologies like mainframes, personal computers, and databases.
  3. The history of IBM shows both positive contributions to technology and darker aspects like the association with controversial events.

DeepSeek: Does a Small AI Model Invalidate Big Models?

Jakob Nielsen on UX • 27 implied HN points • 30 Jan 25
🕹 Technology AI Models Machine Learning Computing Data Analysis Investments
  1. DeepSeek's AI model is cheaper and uses a lot less computing power than other big models, but it still performs well. This shows smaller models can be very competitive.
  2. Investments in AI are expected to keep growing, even with cheaper models available. Companies will still spend billions to advance AI technology and achieve superintelligence.
  3. As AI gets cheaper, more people will use it and businesses will likely spend more on AI services. The demand for AI will increase as it becomes more accessible.

Star Attention: Efficient LLM Inference over Long Sequences

Gonzo ML • 126 implied HN points • 09 Dec 24
🕹 Technology AI Machine Learning Computing Data processing Software Engineering
  1. Star Attention allows large language models to handle long pieces of text by splitting the context into smaller blocks. This helps the model work faster and keeps things organized without needing too much communication between different parts.
  2. The model uses what's called 'anchor blocks' to improve its focus and reduce mistakes during processing. These blocks are important because they help the model pay attention to the right information, which leads to better results.
  3. Using this new approach, researchers found improvements in speed while preserving quality in the model's performance. This means that making these changes can help LLMs work more efficiently without sacrificing how well they understand or generate text.

Import 355: Local LLMs; scaling laws for inference; free Mickey Mouse

Import AI • 1058 implied HN points • 08 Jan 24
🕹 Technology AI Robotics Programming AI Research Computing
  1. PowerInfer software allows $2k machines to perform at 82% of the performance of $20k machines, making it more economically sensible to sample from LLMs using consumer-grade GPUs.
  2. Surveys show that a significant number of AI researchers worry about extreme scenarios such as human extinction from advanced AI, indicating a greater level of concern and confusion in the AI development community than popular discourse suggests.
  3. Robots are becoming cheaper for research, like Mobile ALOHA that costs $32k, and with effective imitation learning, they can autonomously complete tasks, potentially leading to more robust robots in 2024.

The State of Photonic Computing<2025>

State of the Future • 57 implied HN points • 16 Apr 25
🕹 Technology Computing Networking Innovation Electronics
  1. Light is much faster than electricity and creates less heat, which is great for computers. However, using light instead of electricity in all parts of computers is really hard to do.
  2. One big challenge is that we don't have good ways to store information using only light yet. Current storage methods wear out too quickly, making them less reliable.
  3. Companies are focusing more on using light for connecting computers instead of for thinking tasks. This shift allows them to sell products now while working on more complex uses in the future.

The Hallucination Problem

Teaching computers how to talk • 178 implied HN points • 04 Nov 24
🕹 Technology Artificial Intelligence Machine Learning Data science Software Development Computing
  1. Hallucinations in AI mean the models can give wrong answers and still seem confident. This overconfidence is a big problem, making it hard to trust what they say.
  2. OpenAI's SimpleQA helps check how often AI gets facts right. The results show that many times the AI doesn't know when it’s wrong.
  3. The way AI is built makes it hard for them to understand their own errors. Improvements are needed, but current technology has limitations in recognizing when they're unsure.

Number Theoretic Transform with RVV

Fprox’s Substack • 83 implied HN points • 07 Dec 24
🕹 Technology Algorithms Cryptography Computing Software Development Mathematics
  1. The Number Theoretic Transform (NTT) helps speed up polynomial multiplication, which is important in cryptography. It uses a smart method to do complicated calculations faster than traditional methods.
  2. Using RISC-V Vector (RVV) technology can further improve the speed of NTT operations. This means that by using special hardware instructions, operations can be completed much quicker.
  3. Benchmarks show that a well-optimized NTT using RVV can be substantially faster than basic polynomial multiplication, making it crucial for applications in secure communications.

Data Science Weekly - Issue 550

Data Science Weekly Newsletter • 179 implied HN points • 07 Jun 24
🕹 Technology Data science AI Machine Learning Computing Data Engineering
  1. Curiosity in data science is important. It's essential to critically assess the quality and reliability of the data and models we use, especially when making claims about complex issues like COVID-19.
  2. New fields, like neural systems understanding, are blending different disciplines to explore complex questions. This approach can help unravel how understanding works in both humans and machines.
  3. Understanding AI advancements requires keeping track of evolving resources. It’s helpful to have a well-organized guide to the latest in AI learning resources as the field grows rapidly.

When the Mac was a Munition

Pessimists Archive Newsletter • 648 implied HN points • 24 Jan 24
🕹 Technology Computing History Regulation Innovation AI
  1. The US government classified the Power Mac G4 as a super-computer due to its computing power surpassing 1 GIGAFLOP.
  2. In 1979, a GIGAFLOP was seen as powerful and scary, but now we carry thousands of GIGAFLOPs in our pockets with modern devices.
  3. The marketing genius of Apple used the munition classification of the G4 to promote it as a 'Personal Supercomputer', leveraging the restrictions to market the product.

Review: The Das Keyboard 5QS Mark II

Startup Strategies • 28 implied HN points • 07 Jan 25
🕹 Technology Gadgets Reviews Computing Innovation Hardware
  1. The Das Keyboard 5QS Mark II is well-built and durable, making it a solid choice for keyboard lovers. It has a nice premium feel and doesn’t slide around on the desk.
  2. The keyboard features RGB-lit keys for notifications, which can be customized using special software, but this feature isn't very useful for most people.
  3. At $219, it’s on the expensive side compared to other keyboards with similar features. You might find better value by getting a cheaper keyboard and using a separate monitor for notifications.

ModernBERT, the BERT of 2024

Gonzo ML • 63 implied HN points • 19 Dec 24
🕹 Technology AI Machine Learning Natural Language Processing Computing Data science
  1. ModernBERT is a new version of BERT that improves processing speed and memory efficiency. It can handle longer contexts and makes BERT more practical for today's tasks.
  2. The architecture of ModernBERT has been updated with features that enhance performance, like better attention mechanisms and optimized computations. This means it works faster and can process more data at once.
  3. ModernBERT has shown impressive results in various natural language understanding tasks and can compete well against larger models, making it an exciting tool for developers and researchers.

Inference is FREE and INSTANT

Source Code by Fume • 22 HN points • 26 Aug 24
🕹 Technology AI Software Computing Data Innovation
  1. Many people have different views on the future of AI; some believe it will change a lot soon, while others think it won't become much smarter. It's suggested that rather than getting smarter, AI will just get cheaper and faster.
  2. There's a concern that large language models (LLMs) might not be improving in reasoning skills as expected. They have become more affordable over time, but that doesn't necessarily mean they are getting better at complex tasks.
  3. The Chinese Room Argument highlights that AI can follow instructions without understanding. Even if AI tools become faster, they might still lack the creativity to generate unique ideas, but they can still help with routine tasks.

7 perspectives on machine learning

Mindful Modeler • 279 implied HN points • 09 Apr 24
🕹 Technology Machine Learning Data interpretation Automation Optimization Computing
  1. Machine learning is about building prediction models. It covers a wide range of applications, but may not be perfect for unsupervised learning.
  2. Machine learning is about learning patterns from data. This view is useful for understanding ML projects beyond just prediction.
  3. Machine learning is automated decision-making at scale. It emphasizes the purpose of prediction, which is to facilitate decision-making.

Nvidia and the AI scaling wall, and what founder mode really is

Alex's Personal Blog • 98 implied HN points • 21 Nov 24
🕹 Technology AI Semiconductors Computing Startups Product Development
  1. Nvidia is experiencing strong demand for its new Blackwell GPUs, which are expected to outperform previous models. Major companies are eager to integrate these powerful chips into their systems.
  2. The concept of 'founder mode' is about being deeply involved in the critical details of your business. It's not just about delegating tasks, but collaborating closely with team members to achieve great outcomes.
  3. The AI industry continues to evolve with new ways to improve model performance. Nvidia's focus on scaling in various aspects shows that innovation in AI is still very much alive.

A brief history of AI, visually explained 🎢

Year 2049 • 15 implied HN points • 22 Jan 25
🕹 Technology AI History Computing Learning Innovation
  1. AI has a long history, with many ups and downs, before becoming popular recently. It didn't just happen overnight with tools like ChatGPT.
  2. Understanding AI involves knowing its different types, how it learns, and how it can be biased. Each of these topics has a lot of depth.
  3. Creating engaging content about AI takes effort and a balance between being informative and accessible. Feedback is welcome to improve future topics.

The Sequence Opinion #489: CRAZY: How DeepSeek R1 Bypassed CUDA with Lower-Level GPU Optimization Techniques

TheSequence • 112 implied HN points • 13 Feb 25
🕹 Technology Computing Programming GPU Optimization Innovation
  1. DeepSeek R1 has found new ways to optimize GPU performance without using NVIDIA's CUDA. This is impressive because CUDA is widely used for GPU programming.
  2. The team utilized PTX programming and NCCL to improve communication efficiency. These lower-level techniques help in overcoming GPU limitations.
  3. These innovations show that there are still creative ways to enhance technology, even against established systems like CUDA. It's exciting to see where this might lead in the future.

#96 — OAI on "wrong side of history", Superglue brings React to Rails, Waiter at Denny's talks about GPUs and future

The Nibble • 4 implied HN points • 04 Feb 25
🕹 Technology AI Software Development Computing Innovation
  1. OpenAI has released a new model called o3-mini, which is faster and cheaper than previous versions. This model is meant to improve reasoning tasks and is available for various subscription plans.
  2. Superglue is a new library that helps combine React and Rails for building web applications. It makes development easier and more efficient by enhancing server-side rendering and dynamic interactions.
  3. The Doomsday clock is now only 89 seconds to midnight, raising concerns about global threats like AI and nuclear weapons. This reflects how urgent these issues have become in today's world.

Some Speculation on Future Computational Physics

Bzogramming • 61 implied HN points • 27 Nov 24
🕹 Technology Computing Physics AI Algorithms
  1. There are two main ways to tackle physics problems: symbolic methods that involve working with symbols directly, and numerical methods that use simpler calculations. Both have their pros and cons.
  2. Quantum mechanical problems can be very tough to solve and require immense computational power, often beyond what we currently have. Even with advancements, some problems could remain very hard for a long time.
  3. As computing develops, we should explore combining the best parts of symbolic and numerical physics. We might discover new tools and methods that make it easier to solve complex problems in the future.

AGI won't use computations

John Ball inside AI • 79 implied HN points • 23 Jun 24
🕹 Technology AI Computing Natural Language
  1. Artificial General Intelligence (AGI) might be achieved by focusing on pattern matching rather than traditional computations. This means understanding and recognizing complex patterns, just like how our brains work.
  2. Current AI systems struggle with tasks like driving or conversing naturally because they don't operate like human brains. Instead of tightly-coupled algorithms, more flexible and efficient pattern-based systems might be the key.
  3. Patom theory suggests that brains store and match patterns in a unique way, which allows for better learning and error correction. By applying these ideas, we could improve AI systems to be more human-like in understanding and interaction.

Transformers are Eating Quantum

TheSequence • 217 implied HN points • 24 Nov 24
🕹 Technology AI Quantum Computing Machine Learning Innovation
  1. Quantum computing faces challenges due to noise affecting performance. AI, specifically AlphaQubit, helps improve error correction in quantum systems.
  2. AlphaQubit uses a neural network design from language models to better decode quantum errors. It shows greater accuracy and adapts to various data types effectively.
  3. While AlphaQubit is a major step forward, there are still issues to tackle, mainly concerning its speed and ability to scale for larger quantum systems.

GPU peak calling for epigenetics

LatchBio • 11 implied HN points • 21 Jan 25
🕹 Technology Bioinformatics Data Analysis Algorithms Computing Genetics
  1. Peak calling is crucial for analyzing epigenetic data like ATAC-seq and ChIP-seq. It helps scientists identify important regions in the genome related to gene expression and diseases.
  2. The MACS3 algorithm is a common tool used for peak calling but struggles with handling large data volumes efficiently. Improving its implementation with GPUs can speed up analyses significantly.
  3. By using GPUs, researchers have achieved about 15 times faster processing speeds for peak calling, which is vital as more genetic data is generated in the field.

The Sequence Research #490: A Practical Deep Dive Inside DeepSeek-R1

TheSequence • 70 implied HN points • 14 Feb 25
🕹 Technology AI Machine Learning Software Computing Innovation
  1. DeepSeek-R1 is a new AI model that performs well without needing to be very big. It uses smart training methods to achieve great results at a lower cost.
  2. The model successfully matches the performance of a larger, more expensive model called GPT-o1. This shows that size isn't the only thing that matters for good performance.
  3. DeepSeek-R1 challenges the idea that you always need large models for reasoning, suggesting that clever techniques can also lead to impressive results.

Import AI 340: Drone VS human (Drone wins); Adept's small but good AI model; "AI Succession"

Import AI • 499 implied HN points • 18 Sep 23
🕹 Technology AI Computing Policy
  1. Adept has released an impressive small AI model that performs exceptionally well and is optimized for accessibility on various devices.
  2. AI pioneer Richard Sutton suggests the idea of 'AI Succession', where machines could surpass humans in driving progress forward, emphasizing the need for careful navigation of AI development.
  3. A drone controlled by an autonomous AI system defeated human pilots in a challenging race, showcasing advancements in real-world reinforcement learning capabilities.

OpenAI's o-1 and inference-time scaling laws

Tanay’s Newsletter • 63 implied HN points • 28 Oct 24
🕹 Technology AI Models Machine Learning Computing Data science Tech Trends
  1. OpenAI's o-1 model shows that giving AI more time to think can really improve its reasoning skills. This means that performance can go up just by allowing the model to process information longer during use.
  2. The focus in AI development is shifting from just making models bigger to optimizing how they think at the time of use. This could save costs and make it easier to use AI in real-life situations.
  3. With better reasoning abilities, AI can tackle more complex problems. This gives it a chance to solve tasks that were previously too difficult, which might open up many new opportunities.

Teaching Small Language Models to Reason

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 39 implied HN points • 10 Jul 24
🕹 Technology Artificial Intelligence Machine Learning Data science Natural Language Computing
  1. Using Chain-Of-Thought prompting helps large language models think through problems step by step, which makes them more accurate in their answers.
  2. Smaller language models struggle with Chain-Of-Thought prompting and often get confused because they don't have enough knowledge and understanding like the bigger models.
  3. Google Research has a method to teach smaller models by learning from larger ones. This involves using the bigger models to create helpful examples that the smaller models can then learn from.

The Race for AI Reasoning is Challenging our Imagination

TheSequence • 112 implied HN points • 22 Dec 24
🕹 Technology AI Machine Learning Research Innovation Computing
  1. OpenAI and Google are in a fierce competition to improve AI reasoning capabilities. Their advancements could lead to machines that think and solve problems more like humans.
  2. Better reasoning in AI could transform many fields, such as healthcare and law. Imagine AI helping doctors diagnose diseases with high accuracy or assisting lawyers in complex cases.
  3. As AI models become smarter at reasoning, they will change the way we live and work. This could open up many new opportunities and challenges for society.

A Note on the Changing Faces of (D)ARPA

FreakTakes • 13 implied HN points • 31 Dec 24
🕹 Technology Military Tech R&D Computing Innovation History
  1. DARPA has gone through many changes over the years due to political and regulatory shifts, which have affected how it operates. Understanding the political climate is essential for grasping DARPA's past successes.
  2. The level of freedom for project managers (PMs) varies depending on whether project ideas come from office directors or the PMs themselves. This affects how projects are pursued and the creative input allowed.
  3. The expected timelines for projects and their military focus play a significant role in what gets funded. Sometimes projects are pushed for quick results, while other times there’s room for more exploratory research.