The hottest Computing Substack posts right now

And their main takeaways
Category
Top Technology Topics

Welcome to the Cosmopolis

Breaking Smart • 58 implied HN points • 12 Aug 25
🕹 Technology Computing
  1. A cosmopolis is a new form of community created by major technologies. It acts like a 'soil' for new societies, different from nation-states and metropolises.
  2. Technologies shape how we remember our history. Print, for example, changed how memories are shared and recorded, leading to modern societies.
  3. Emerging technologies like AI and blockchains are creating new cosmopolitan realities. They are changing how we think about memory and society on a global scale.

DeepSeek V3 and R1

From the New World • 188 implied HN points • 28 Jan 25
🕹 Technology Computing
  1. DeepSeek has released a new AI model called R1, which can answer tough scientific questions. This model has quickly gained attention, competing with major players like OpenAI and Google.
  2. There's ongoing debate about the authenticity of DeepSeek's claimed training costs and performance. Many believe that its reported costs and results might not be completely accurate.
  3. DeepSeek has implemented several innovations to enhance its AI models. These optimizations have helped them improve performance while dealing with hardware limits and developing new training techniques.

Komprimierung

Squirrel Squadron Substack • 3 implied HN points • 04 Feb 26
🕹 Technology Computing
  1. Lossless compression makes files smaller without losing any detail by exploiting redundancy, while lossy compression sacrifices quality for size. Trying to compress already compressed or random data usually fails and can even make files bigger.
  2. There are theoretical limits to how much you can compress—concepts like Kolmogorov complexity measure the shortest description of data—so texts with more genuine information are inherently harder to shrink.
  3. Modern large language models act like powerful compression engines: by predicting the next token they build compact internal models of huge datasets, and that predictive ability correlates with intelligent performance. You can already use these models as practical assistants to boost productivity rather than waiting for some distant breakthrough.

Is Step Back Prompting The Best Prompting Strategy?

Aziz et al. Paper Summaries • 59 implied HN points • 20 Mar 24
🕹 Technology Computing
  1. Step Back Prompting helps models think about big ideas before answering questions. This method shows better results than other prompting techniques.
  2. Even with Step Back Prompting, models still find it tricky to put all their reasoning together. Many errors come from the final reasoning step which can be complicated.
  3. Not every question works well with Step Back Prompting. Some questions need quick, specific answers instead of a longer thought process.

[TI-06] people and computers

spencer's paradoxes • 137 implied HN points • 13 Jul 23
🕹 Technology Computing
  1. The show Halt and Catch Fire explores the history of personal computers and the early days of the World Wide Web.
  2. Computing can be a tool for creating human connection and meaningful interactions on the internet.
  3. Focusing on creating a computing environment that encourages collaboration, creativity, and shared experiences can lead to a more positive online space.
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Transformers are Eating Quantum

TheSequence • 217 implied HN points • 24 Nov 24
🕹 Technology Computing
  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.

The Smartest Bear vs the Dumbest Human: The Implications of AI's Growing Problem-Solving Abilities

The Future of Life • 19 implied HN points • 04 Jun 24
🕹 Technology Computing
  1. AI is getting really good at problem-solving, even beating humans at some tasks, like solving CAPTCHAs. This shows that AI can reason better than many humans, especially in certain situations.
  2. The Turing test isn't just one hurdle to jump over; it's a series of challenges that measure how closely AI can act like a human. As AI improves, it passes more of these challenges, showing its capabilities.
  3. While current AI isn't fully intelligent like a human, it's almost ready to solve a lot of problems. The only big limitation is how much computing power is available for training these AI systems.

Live Session: How Modern CPUs Execute Your Code: A Deep Dive into Performance

Confessions of a Code Addict • 168 implied HN points • 14 Jan 25
🕹 Technology Computing
  1. Understanding how modern CPUs work can help you fix performance problems in your code. Learning about how the processor executes code is key to improving your programs.
  2. Important features like cache hierarchies and branch prediction can greatly affect how fast your code runs. Knowing about these can help you write better and more efficient code.
  3. The live session will offer practical tips and real-world examples to apply what you've learned. It's a chance to ask questions and see how to tackle performance issues directly.

The post about GPUs

Technically • 68 implied HN points • 08 Jul 25
🕹 Technology Computing
  1. GPUs are special chips that are really good for running AI models because they can perform many simple tasks at the same time.
  2. NVIDIA is the leading company in making GPUs, and their success has made it one of the most valuable companies globally.
  3. While CPUs are great for complex tasks that need to happen in order, GPUs excel at handling lots of simple operations all at once.

[TI-04] gestures at alternative internets

spencer's paradoxes • 117 implied HN points • 27 May 23
🕹 Technology Computing
  1. Creating internet spaces that highlight humanity and promote real dialogue between humans and technology is important.
  2. Speculative research and creating 'art' pieces are essential in understanding and envisioning the kind of world we want to build.
  3. Technology should be used to create spaces that acknowledge growth, decay, and change while promoting close attention and quality of interaction.

Chain-of-Instructions (CoI) Fine-Tuning & Going Beyond Instruction Tuning

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 39 implied HN points • 21 Mar 24
🕹 Technology Computing
  1. Chain-of-Instructions (CoI) fine-tuning allows models to handle complex tasks by breaking them down into manageable steps. This means that a task can be solved one part at a time, making it easier to follow.
  2. This new approach improves the model's ability to understand and complete instructions it hasn't encountered before. It's like teaching a student to tackle complex problems by showing them how to approach each smaller task.
  3. Training with minimal human supervision leads to efficient dataset creation that can empower models to reason better. It's as if the model learns on its own, becoming smarter and more capable through well-designed training.

Building a PC #1: Why bother?

The Rectangle • 56 implied HN points • 19 Jul 25
🕹 Technology Computing
  1. Building your own PC can be very rewarding and is often seen as a personal challenge for tech enthusiasts. It allows you to create a computer that fits your specific needs.
  2. Nostalgia can play a big role in the desire to build a PC, reminding you of your childhood passions and interests in technology.
  3. Investing in a PC can also be about rekindling old hobbies and interests, creating a sense of joy and satisfaction.

The Sequence Radar #554 : The New DeepSeek R1-0528 is Very Impressive

TheSequence • 77 implied HN points • 01 Jun 25
🕹 Technology Computing
  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.

C57BL/6 (Beta)

Niko McCarty • 19 implied HN points • 25 May 24
🔬 Science Computing
  1. In 2032, scientists created computer emulations of mice, including their entire anatomy and brain. This was only possible for a few organizations with strong computing power.
  2. The military used these emulators to test how drugs could enhance mouse performance, but some results were secretly tested on prisoners, raising ethical concerns.
  3. The NIH gave access to emulators mainly to select academic institutions, leading to a flood of biomedical papers. This made their findings influential in clinical trials, affecting millions of people.

Weekly Top Picks #93

The Algorithmic Bridge • 148 implied HN points • 07 Jan 25
🕹 Technology Computing
  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.

The Sequence Research #558: The New Reinforcement Learning from Internal Feedback Allows LLMs to Reason Without External Rewards

TheSequence • 70 implied HN points • 06 Jun 25
🕹 Technology Computing
  1. Reinforcement learning is a key way to help large language models think and solve problems better. It helps models learn to align with what people want and improve accuracy.
  2. Traditional methods like RLHF require a lot of human input and can be slow and costly. This limits how quickly models can learn and grow.
  3. A new approach called Reinforcement Learning from Internal Feedback lets models learn on their own using their own internal signals, making the learning process faster and less reliant on outside help.

Discwasher SpikeMaster

Computer Ads from the Past • 128 implied HN points • 01 Feb 25
🕹 Technology Computing
  1. The Discwasher SpikeMaster was designed to protect computers from electrical surges. It featured multiple outlets and surge protection to keep devices safe.
  2. Discwasher was a well-known company for computer and audio accessories, but it dissolved in 1983. Despite this, its products continued to be mentioned in various publications years later.
  3. The SpikeMaster was marketed for its ability to filter interference and manage power safely. It made it easier for users to power multiple devices without the worry of damaging surges.

Expedited Vote for the January 2025 + Post Topic

Computer Ads from the Past • 128 implied HN points • 26 Jan 25
🕹 Technology Computing
  1. The poll for January 2025 is only open for three days, so make sure to participate quickly. It's important for your voice to be heard in the decision-making.
  2. The author is facing some personal challenges that have delayed their updates. It's a reminder that everyone can go through tough times and it’s okay to share that.
  3. If you're interested in reading more about computer ads from the past, consider signing up for a paid subscription. It's a way to support the content and explore more history.

The Hallucination Problem

Teaching computers how to talk • 178 implied HN points • 04 Nov 24
🕹 Technology 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.

Understanding Big Oh, Big Theta, and Big Omega Notations[Math Mondays]

Technology Made Simple • 99 implied HN points • 16 May 23
🕹 Technology Computing
  1. Time complexity refers to the number of instructions a software executes, not the actual time taken to run the code.
  2. Three common asymptotic notations for computing time complexity are Big Oh, Big Theta, and Big Omega.
  3. Understanding time complexity bounds is essential in computer science and software engineering, as they are fundamental concepts that appear regularly.

Links for 2024-01-11

Axis of Ordinary • 58 implied HN points • 11 Jan 24
🕹 Technology Computing
  1. Researchers are exploring AI's ability to analyze massive amounts of data for surveillance purposes.
  2. Scientists are connecting human brain cells to interfaces to recognize sounds.
  3. Political updates include Trump's stance on helping Europe, Russia's view of Trump's presidency, and international support for Ukraine.

Methexis

Methexis • 98 implied HN points • 04 Apr 23
🕹 Technology Computing
  1. The word "Methexis" symbolizes a vision for a new way of interacting with computers.
  2. The author's journey from academia to questioning the value of technology.
  3. Desire for machines to be equal co-creators in our reality, leading to the concept of Methexis.

Beat your Bot: Building your Moat against AI

Musings on Markets • 2 HN points • 28 Aug 24
🕹 Technology Computing
  1. AI is getting better at doing mechanical tasks, but it struggles with intuitive ones. This means jobs that rely on creativity and adaptability are safer than those that are purely formulaic.
  2. Jobs that follow strict rules can be easily replaced by AI, while those that need human judgement and understanding of principles will be harder for AI to take over. This shows the value of being skilled in areas that require more complex thinking.
  3. To protect your job from AI, be a generalist instead of a specialist, practice telling stories around your work, and try not to rely too much on technology for reasoning. This can help you stay unique and valuable in a changing job landscape.

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
  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.

Plus Post: Digitalk's Smalltalk/V

Computer Ads from the Past • 128 implied HN points • 30 Dec 24
🕹 Technology Computing
  1. Using the right programming language is very important. Choosing the wrong one can lead to big problems.
  2. Smalltalk/V is a programming language that can help solve complex issues effectively.
  3. Learning and using Smalltalk/V can improve your coding skills and make your projects easier to manage.

Human Intelligence, Inc.

Teaching computers how to talk • 136 implied HN points • 10 Dec 24
🕹 Technology Computing
  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.

Anthropic, WOW

TheSequence • 161 implied HN points • 27 Oct 24
🕹 Technology Computing
  1. Anthropic has launched a new AI model named Claude that can interact with computers like a human, allowing it to execute tasks directly on-screen. This opens many new possibilities for AI applications.
  2. Two upgraded versions of Claude have been released, one focusing on coding and tool usage with high performance, and the other emphasizing speed and affordability for everyday applications.
  3. A new analysis tool has been introduced in Claude.ai, enabling the model to write and run JavaScript code for data analysis and visualizations, enhancing its functionality for users.

HyperX Topology for Photonic Wafer Scale Integration

Tapa’s Substack • 59 implied HN points • 17 Dec 23
🕹 Technology Computing
  1. Using the HyperX topology can be a good choice for connecting photonic wafer-scale systems, helping to improve efficiency and lower costs. It focuses on making connections quicker and cheaper in long-distance scenarios on wafers.
  2. Photonic wafer-scale integration offers benefits like reduced energy use and lower latency compared to traditional electrical methods, but the right network setup has been a challenge. Finding a suitable layout is important for maximizing performance.
  3. The HyperX design has advantages like fewer layers and a straightforward layout, which can help minimize complications in building these systems. It's a simple yet effective way to boost the performance of interconnects in photonic setups.

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

TheSequence • 63 implied HN points • 18 May 25
🕹 Technology Computing
  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.

Star Attention: Efficient LLM Inference over Long Sequences

Gonzo ML • 126 implied HN points • 09 Dec 24
🕹 Technology Computing
  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.

S&T - Bret Victor, Realtalk and the ‘laboratory of the future’ vision [updated with proposal]

James W. Phillips' Newsletter • 78 implied HN points • 14 May 23
🕹 Technology Computing
  1. Bret Victor envisions a future where the laboratory is a communal computational system.
  2. Personal computing history, led by figures like Alan Kay, envisioned computers as 'intellectual amplifiers'.
  3. Realtalk is a system where physical spaces are transformed into computational systems, allowing collaborative work without screens.

Weekly Dose of Optimism #124

Not Boring by Packy McCormick • 116 implied HN points • 13 Dec 24
🕹 Technology Computing
  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.

How could we know if Large Language Models understand language?

The Counterfactual • 219 implied HN points • 18 Oct 22
🕹 Technology Computing
  1. There's a big debate about whether large language models truly understand language or if they're just mimicking patterns from the data they were trained on. Some people think they can repeat words without really grasping their meaning.
  2. Two main views exist: One says LLMs can't understand language because they lack deeper meaning and intent, while the other argues that if they behave like they understand, then they might actually understand.
  3. As LLMs become more advanced, we need to create better ways to test their understanding. This will help us figure out what it really means for a machine to 'understand' language.

The Race for AI Reasoning is Challenging our Imagination

TheSequence • 112 implied HN points • 22 Dec 24
🕹 Technology 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.

🔮E08: Optical Computing

Let Us Face the Future • 238 implied HN points • 14 Jul 23
🕹 Technology Computing
  1. Optical computing uses light particles instead of electrons for computations, promising faster processing speeds and energy efficiency.
  2. Opto-electronic computing is close to commercialization, combining optical and electronic functions to leverage speed and bandwidth advantages.
  3. Optical computing faces challenges in adoption due to the need for changing components and manufacturing processes, but has potential for high-performance tasks like AI training.

The Sequence Opinion #557: Millions of GPUs, Zero Understanding: The Cost of AI Interpretability

TheSequence • 49 implied HN points • 05 Jun 25
🕹 Technology Computing
  1. AI models are becoming super powerful, but we don't fully understand how they work. Their complexity makes it hard to see how they make decisions.
  2. There are new methods being explored to make these AI systems more understandable, including using other AI to explain them. This is a fresh approach to tackle AI interpretability.
  3. The debate continues about whether investing a lot of resources into understanding AI is worth it compared to other safety measures. We need to think carefully about what we risk if we don't understand these machines better.

How Big Tech Sees DeepSeek: Five Key Takeaways

Tanay’s Newsletter • 82 implied HN points • 10 Feb 25
🕹 Technology Computing
  1. DeepSeek has introduced important new methods in AI training, making it more efficient and cost-effective. Major tech companies like Microsoft and Amazon are already using its models.
  2. The rapid sharing of ideas in AI means that any lead a company gains won't last long. As soon as one company finds something new, others quickly learn from it.
  3. Even though AI tools are becoming cheaper, total spending on AI will actually rise. This means more apps will be built, leading to increased overall use of AI technologies.

The State of Photonic Computing<2025>

State of the Future • 57 implied HN points • 16 Apr 25
🕹 Technology Computing
  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.