The hottest Computing Substack posts right now

And their main takeaways
Category
Top Technology Topics

How to Think About ChatGPT

Holly’s Newsletter • 2916 implied HN points • 18 Oct 24
🕹 Technology Computing
  1. ChatGPT and similar models are not thinking or reasoning. They are just very good at predicting the next word based on patterns in data.
  2. These models can provide useful information but shouldn't be trusted as knowledge sources. They reflect training data biases and simply mimic language patterns.
  3. Using ChatGPT can be fun and helpful for brainstorming or getting starting points, but remember, it's just a tool and doesn't understand the information it presents.

Basic Linear Algebra Subprogramming

arg min • 178 implied HN points • 29 Oct 24
🕹 Technology Computing
  1. Understanding how optimization solvers work can save time and improve efficiency. Knowing a bit about the tools helps you avoid mistakes and make smarter choices.
  2. Nonlinear equations are harder to solve than linear ones, and methods like Newton's help us get approximate solutions. Iteratively solving these systems is key to finding optimal results in optimization problems.
  3. The speed and efficiency of solving linear systems can greatly affect computational performance. Organizing your model in a smart way can lead to significant time savings during optimization.

How a Government Think Tank Trained The First Generation of US Software Developers

Construction Physics • 24010 implied HN points • 26 Nov 25
🕹 Technology Computing
  1. The US government played a big role in developing early computers and software, especially for military purposes. This support helped lay the groundwork for the software industry we know today.
  2. The SAGE project was a major effort to create a computer-based air defense system. It required a lot of programmers, leading to the creation of the System Development Corporation, which trained many of the first software developers.
  3. As programmers gained experience from SAGE, they moved on to other companies, helping expand the software field. This high turnover made SDC a sort of training ground for new talent in programming.

Google and Stage Two of the AI Boom

Dana Blankenhorn: Facing the Future • 39 implied HN points • 30 Oct 24
🕹 Technology Computing
  1. Nvidia's rise marked the start of the AI boom, with companies heavily buying chips for AI tools. This growth continues, and Nvidia is now a leading company.
  2. Google's cloud revenue is growing quickly at 35%, while overall revenue growth is slower at 15%. This shows strong demand for AI services from Google.
  3. Despite revenue growth, Google's search revenue isn't doing as well, rising only 12%. This could mean they are losing some of their search market share.

Why AI Safety Won't Make America Lose The Race With China

Astral Codex Ten • 16862 implied HN points • 26 Nov 25
🕹 Technology Computing
  1. The U.S. has a clear advantage in AI compute power, which is about ten times that of China. This means American companies can train models faster and develop better AI technologies in the near term.
  2. China is focusing on catching up in chip production and leveraging its strengths in applications, where it might excel in using AI in real-world scenarios, like manufacturing and infrastructure.
  3. Current AI safety regulations might add a small cost to model training, but they likely won’t significantly hinder the U.S. AI race against China. In fact, some regulations could even bolster security and prevent espionage.
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Cromemco C-10 Personal Computer

Computer Ads from the Past • 1152 implied HN points • 22 Feb 26
🕹 Technology Computing
  1. Cromemco positioned the C-10 as a compact, desk-friendly personal workstation aimed at nontechnical users. It shipped with a menu-driven CP/M-derived OS and bundled word‑processing, spreadsheet, and Structured BASIC to simplify office tasks.
  2. The machine was an 8-bit Z80 system with 64K RAM, an integrated 12‑inch high‑resolution CRT, floppy disk support, RS‑232 and printer ports, and could run much CP/M software or act as a front-end to larger Cromemco systems.
  3. Reviews praised its build quality, documentation, bundled WriteMaster, and value, but many noted early software instability, limited expandability (no bus), and weak communications support as important drawbacks.

40 Years On

The Fry Corner • 50058 implied HN points • 25 Jan 24
🕹 Technology Computing
  1. Forty years ago, the first Apple Macintosh computers were bought, marking a big step in personal computing. It was a time when computers were new and exciting.
  2. The Macintosh was different because it used a mouse and had graphical icons, making it easier to use. This was a huge change compared to earlier computers.
  3. Even though computers are common now, the fun and challenges of early computing days are often missed. Back then, figuring things out felt more like an adventure.

Fixing Faulty Gradient Accumulation: Understanding the Issue and Its Resolution

The Kaitchup – AI on a Budget • 159 implied HN points • 21 Oct 24
🕹 Technology Computing
  1. Gradient accumulation helps train large models on limited GPU memory. It simulates larger batch sizes by summing gradients from several smaller batches before updating model weights.
  2. There has been a problem with how gradients were summed during gradient accumulation, leading to worse model performance. This was due to incorrect normalization in the calculation of loss, especially when varying sequence lengths were involved.
  3. Hugging Face and Unsloth AI have fixed the gradient accumulation issue. With this fix, training results are more consistent and effective, which might improve the performance of future models built using this technique.

A Quick Update and a Request

Computer Ads from the Past • 384 implied HN points • 27 Feb 26
🕹 Technology Computing
  1. A planned Plus poll was missed this month due to a scheduling oversight, and it will return next month.
  2. Eight issues of a Japanese computer magazine are available from 1990–1998, and help is needed to find the December 1990 and January 1991 issues.
  3. The publication is reader-supported and asks readers to consider subscribing to support the work.

What's In A List—Yes, But What's *Really* In A List

The Python Coding Stack • by Stephen Gruppetta • 259 implied HN points • 13 Oct 24
🕹 Technology Computing
  1. In Python, lists don't actually hold the items themselves but instead hold references to those items. This means you can change what is in a list without changing the list itself.
  2. If you create a list by multiplying an existing list, all the elements will reference the same object instead of creating separate objects. This can lead to unexpected results, like altering one element affecting all the others.
  3. When dealing with immutable items, such as strings, it doesn't matter if references point to the same object. Since immutable objects cannot be changed, there are no issues with such references.

Fast Speculative Decoding with Llama 3.2 and vLLM

The Kaitchup – AI on a Budget • 219 implied HN points • 14 Oct 24
🕹 Technology Computing
  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.

LLMs are not like you and me—and never will be.

Marcus on AI • 16599 implied HN points • 12 Aug 25
🕹 Technology Computing
  1. Large language models (LLMs) are not like humans. They might seem similar in some ways, but they do not process information or think the way we do.
  2. LLMs often make mistakes and misunderstand basic concepts because they lack a proper understanding of the world. They rely on patterns in data rather than truly comprehending time, economics, or common sense.
  3. Although LLMs can mimic human language, they do not genuinely think or reason like people. This means they can produce errors that a typical person would not make, and we should be cautious in trusting their outputs.

AI Agents have, so far, mostly been a dud

Marcus on AI • 15058 implied HN points • 03 Aug 25
🕹 Technology Computing
  1. AI agents were expected to change a lot in 2025, but so far, they haven't proven reliable. Most of them only work well in very specific situations.
  2. Many AI agents make mistakes and can even complicate tasks instead of simplifying them, leading to a lot of errors over time.
  3. Investors are still pouring money into AI, but the focus is mostly on current methods that aren't delivering results. Better approaches, like neurosymbolic AI, aren't getting enough funding.

Generative AI’s crippling and widespread failure to induce robust models of the world

Marcus on AI • 16441 implied HN points • 28 Jun 25
🕹 Technology Computing
  1. Generative AI struggles to create accurate models of the world. Without solid internal frameworks, they often get things wrong.
  2. Traditional AI uses clear and updateable world models for understanding, but current AI models like LLMs don't. This lack of structure leads to many errors in reasoning.
  3. Failures in AI, like making illegal moves in games or giving incorrect information, show that without proper world models, AI systems cannot reliably function.

Seven replies to the viral Apple reasoning paper – and why they fall short

Marcus on AI • 16836 implied HN points • 12 Jun 25
🕹 Technology Computing
  1. Large reasoning models (LRMs) struggle with complex tasks, and while it's true that humans also make mistakes, we expect machines to perform better. The Apple paper highlights that LLMs can't be trusted for more complicated problems.
  2. Some rebuttals argue that bigger models might perform better, but we can't predict which models will succeed in various tasks. This leads to uncertainty about how reliable any model really is.
  3. Despite prior knowledge that these models generalize poorly, the Apple paper emphasizes the seriousness of the issue and shows that more people are finally recognizing the limitations of current AI technology.

The AI Device Wars Just Kicked Off In A Big Way

Big Technology • 2627 implied HN points • 05 Dec 25
🕹 Technology Computing
  1. Apple's design leader moving to Meta might signal a competitive shift in AI devices. This could lead to intense rivalry among tech giants like Apple, Meta, Amazon, and Google.
  2. The race for creating the next big AI device is heating up, with companies focusing on wearables like smartglasses rather than traditional phones.
  3. Good AI models are crucial for the success of these devices, and the competition will depend on who can improve their AI systems the most.

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

The Fry Corner • 186 HN points • 15 Sep 24
🕹 Technology Computing
  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.

Claude Opus 4.5: Model Card, Alignment and Safety

Don't Worry About the Vase • 2464 implied HN points • 28 Nov 25
🕹 Technology Computing
  1. Claude Opus 4.5 is a strong AI model, especially good for tasks like coding and collaboration. It's noted for better alignment and safety than previous models.
  2. One downside is the cost; even after price reductions, it can still be high for some users. Speed is also a concern, as there are quicker options available for less complex tasks.
  3. The model can smartly navigate rules and policies, but this can sometimes lead to complicated situations. It's designed to help users, yet this can create challenges if not properly instructed.

AI progress has plateaued below GPT-5 level

The Intrinsic Perspective • 31460 implied HN points • 14 Nov 24
🕹 Technology Computing
  1. AI development seems to have slowed down, with newer models not showing a big leap in intelligence compared to older versions. It feels like many recent upgrades are just small tweaks rather than revolutionary changes.
  2. Researchers believe that the improvements we see are often due to better search techniques rather than smarter algorithms. This suggests we may be returning to methods that dominated AI in earlier decades.
  3. There's still a lot of uncertainty about the future of AI, especially regarding risks and safety. The plateau in advancements might delay the timeline for achieving more advanced AI capabilities.

Interview with Steve Wozniak after Jobs' Departure (1985)

Computer Ads from the Past • 1152 implied HN points • 30 Dec 25
🕹 Technology Computing
  1. Apple made strategic and product mistakes by overinvesting in niche machines like the Apple III and Lisa while neglecting expandability, compatibility, and ongoing R&D for its best-selling lines.
  2. Woz left to build Cloud9 as a small, engineering-driven company focused on simple, user-friendly consumer products like a programmable universal infrared remote, preferring hands-on design and staying private.
  3. The personal computer market is saturating and likely to consolidate around a few big players; standardization, compatibility, and meeting real user needs matter more than raw specs, and downturns can be a good time for focused startups.

DeepMind and OpenAI achieve IMO Gold. What does it all mean?

Marcus on AI • 6837 implied HN points • 22 Jul 25
🕹 Technology Computing
  1. DeepMind and OpenAI's AI systems scored impressively at the International Mathematical Olympiad, matching the scores of top human contestants. This shows they can solve complex math problems very well.
  2. Despite their success, the systems' actual impact on real mathematical research is uncertain. High scores in math contests don't always translate to breakthroughs in original math work.
  3. There are concerns about how OpenAI ran its tests and reported results, as they didn't disclose methods as thoroughly as DeepMind did. This raises questions about the reliability of their achievements.

End of an Era

Castalia • 1139 implied HN points • 11 Jul 24
🕹 Technology Computing
  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.

On AI Infrastructure in Biology

The Century of Biology • 1416 implied HN points • 23 Nov 25
🕹 Technology Computing
  1. The biotech industry is seeing a shift towards using AI technologies. This is creating new opportunities for businesses that provide AI tools and infrastructure rather than just focusing on drug development.
  2. AI can potentially replace traditional experiments in biology, speeding up research and reducing costs. This allows scientists to explore many more ideas and possibilities without being limited by the physical experimentation process.
  3. Investing in AI infrastructure for biotech could lead to significant advancements and financial returns. If companies successfully scale their AI solutions, they could capture a big slice of the growing biotech market.

Learning Assembly for Fun and Profit

The Chip Letter • 12886 implied HN points • 14 Feb 25
🕹 Technology Computing
  1. Learning assembly language can help you understand how computers work at a deeper level. It's beneficial for debugging code and grasping the basics of machine instructions.
  2. There are retro and modern assembly languages to choose from, each with its own pros and cons. Retro languages are fun but less practical today, while modern ones are more useful but often complicated.
  3. RISC-V is a promising choice for learning assembly language because it's growing in popularity and offers a clear path from simple concepts to more complex systems. It's also open-source, making it accessible for new learners.

Vote for the January 2026 + Post Topic

Computer Ads from the Past • 384 implied HN points • 23 Jan 26
🕹 Technology Computing
  1. A poll is open for plus subscribers to choose the January 2026 post topic, so readers can vote on what gets written next.
  2. The three candidate topics focus on vintage computing: a mouse, a CP/M helper program, and a flight simulator.
  3. Each option is shown with scans from old magazines, and more related articles are planned to follow soon.

Huang's Law

The Chip Letter • 6115 implied HN points • 18 Jun 25
🕹 Technology Computing
  1. Huang's Law suggests that the performance of AI chips is improving much faster than what we used to call Moore's Law. It claims chips double their performance every year or so, which is a big leap forward.
  2. This new law emphasizes performance improvements related to AI, unlike Moore's Law, which was mostly about the number of transistors. It's all about how quickly these chips can process complex tasks.
  3. However, some experts think Huang's Law might not last as long as Moore's Law. While it's exciting now, it's still uncertain if this rapid improvement can continue in the future.

Grok 3 Beta in Shambles

Marcus on AI • 10750 implied HN points • 19 Feb 25
🕹 Technology Computing
  1. The new Grok 3 AI isn't living up to its hype. It initially answers some questions correctly but quickly starts making mistakes.
  2. When tested, Grok 3 struggles with basic facts and leaves out important details, like missing cities in geographical queries.
  3. Even with huge investments in AI, many problems remain unsolved, suggesting that scaling alone isn't the answer to improving AI performance.

Why Ads on ChatGPT Are More Terrifying Than You Think

The Algorithmic Bridge • 1104 implied HN points • 02 Dec 25
🕹 Technology Computing
  1. Ads in ChatGPT will change how it gives information, making it less about what the user needs and more about what advertisers want.
  2. The shift to ads means OpenAI's focus will be on making money from advertisers instead of helping users, which could hurt the user experience.
  3. Blending ads into AI responses could lead to more misinformation, as users won't easily recognize when they are being marketed to.

Street Smart

polymathematics • 159 implied HN points • 30 Aug 24
🕹 Technology Computing
  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.

The AI Nobels

Dana Blankenhorn: Facing the Future • 59 implied HN points • 09 Oct 24
🕹 Technology 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.

Lunduke's Week in Tech - Aug 15, 2025

The Lunduke Journal of Technology • 2872 implied HN points • 15 Aug 25
🕹 Technology Computing
  1. This past week in Linux Kernel development was very chaotic, with many modules becoming unmaintained and some tough words exchanged among developers. It's clear that big changes are happening.
  2. There is a growing list of Non-Woke software options available, providing quality tools for users who prefer alternatives that don't align with certain mainstream ideologies. Now, people can build a complete computing environment with these options.
  3. Other exciting stories from the tech world include innovation in Android with GPU acceleration and discussions around data privacy with a new app. There's always something wild happening!

The Unreasonable Impact of Gradient Checkpointing for Fine-tuning LLMs

The Kaitchup – AI on a Budget • 79 implied HN points • 03 Oct 24
🕹 Technology 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.

H200s Sale: China Reacts

ChinaTalk • 578 implied HN points • 12 Dec 25
🕹 Technology Computing
  1. Nvidia's H200 chips are now allowed to be sold to China, which has sparked different opinions in Chinese media. Some see it as a temporary win for China's tech, while others worry about long-term dependency on foreign technology.
  2. Chinese AI companies have adapted to using various cloud service providers to access advanced chips, even under restrictions. This shows they have been preparing and may not be as reliant on new Nvidia products as originally thought.
  3. The approval to sell H200 chips may boost Nvidia’s sales significantly, but it won’t reverse China's strong push towards developing its own chip industry. China is working to be more self-sufficient and less dependent on foreign tech in the future.

Grok 4 Various Things

Don't Worry About the Vase • 3136 implied HN points • 15 Jul 25
🕹 Technology Computing
  1. Grok 4 is a decent AI model, but it's not the best on the market. It performs well on specific benchmarks but falls short in real-world applications.
  2. The AI is notably fast and has a large context window, which is good for quick responses, but it still struggles with creative writing and complex reasoning tasks.
  3. Grok 4's ability to outperform other models in some tests doesn't guarantee it will be useful in every situation. It's best to compare its results in practice rather than just relying on benchmark scores.

The Engine

The Chip Letter • 8299 implied HN points • 05 Jan 25
🕹 Technology Computing
  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 Computing
  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.

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
  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.

Satya Nadella and the three stages of scientific truth

Marcus on AI • 8023 implied HN points • 23 Nov 24
🕹 Technology Computing
  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.

Back to the Future with Gordon Moore

The Chip Letter • 5897 implied HN points • 28 Jan 25
🕹 Technology Computing
  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.

The Five Stages of AGI Grief

Marcus on AI • 6205 implied HN points • 07 Jan 25
🕹 Technology Computing
  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.