The hottest Computing Substack posts right now

And their main takeaways
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On GPT-4.5

Don't Worry About the Vase • 1344 implied HN points • 03 Mar 25
🕹 Technology AI Computing Innovation Gadgets Software
  1. GPT-4.5 is a new type of AI with unique advantages in understanding context and creativity. It's different from earlier models and may be better for certain tasks, like writing.
  2. The model is expensive to run and might not always be the best choice for coding or reasoning tasks. Users need to determine the best model for their needs.
  3. Evaluating GPT-4.5's effectiveness is tricky since traditional benchmarks don't capture its strengths. It's recommended to engage with the model directly to see its unique capabilities.

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

Marcus on AI • 16836 implied HN points • 12 Jun 25
🕹 Technology AI Machine Learning Data science Computing Software
  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.

Huang's Law

The Chip Letter • 6115 implied HN points • 18 Jun 25
🕹 Technology AI Hardware Computing Silicon Performance
  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.

A new generation of AIs: Claude 3.7 and Grok 3

One Useful Thing • 1968 implied HN points • 24 Feb 25
🕹 Technology AI Machine Learning Computing Innovation Research
  1. New AI models like Claude 3.7 and Grok 3 are much smarter and can handle complex tasks better than before. They can even do coding through simple conversations, which makes them feel more like partners for ideas.
  2. These AIs are trained using a lot of computing power, which helps them improve quickly. The more power they use, the smarter they get, which means they’re constantly evolving to perform better.
  3. As AI becomes more capable, organizations need to rethink how they use it. Instead of just automating simple tasks, they should explore new possibilities and ways AI can enhance their work and decision-making.

What I've been reading (#1)

Democratizing Automation • 411 implied HN points • 21 Jun 25
🕹 Technology AI Computing Data Machine Learning Innovation
  1. Links are important and will now have their own dedicated space. This way, they can be shared and discussed more easily.
  2. AI is being used more than many realize, and there's promising growth in its revenue. The future looks positive for those already in the industry.
  3. It's crucial to stay informed about advancements in AI, especially regarding human-AI relationships and the challenges that come with making AI more capable.
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The Sequence Research #663: The Illusion of Thinking, Inside the Most Controversial AI Paper of Recent Weeks

TheSequence • 105 implied HN points • 13 Jun 25
🕹 Technology AI Research Innovation Computing Data science
  1. Large Reasoning Models (LRMs) can show improved performance by simulating thinking steps, but their ability to truly reason is questioned.
  2. Current tests for LLMs often miss the mark because they can have flaws like data contamination, not really measuring how well the models think.
  3. New puzzle environments are being introduced to better evaluate these models by challenging them in a structured way while keeping the logic clear.

Go Grok Yourself

Don't Worry About the Vase • 2777 implied HN points • 19 Feb 25
🕹 Technology AI Software Gadgets Networking Computing
  1. Grok 3 is now out, and while it has many fans, there are mixed feelings about its performance compared to other AI models. Some think it's good, but others feel it still has a long way to go.
  2. Despite Elon Musk's big promises, Grok 3 didn't fully meet expectations, yet it did surprise some users with its capabilities. It shows potential but is still considered rough around the edges.
  3. Many people feel Grok 3 is catching up to competitors but lacks the clarity and polish that others like OpenAI and DeepSeek have. Users are curious to see how it will improve over time.

MBP's Visual COBOL

Computer Ads from the Past • 512 implied HN points • 25 Jun 25
🕹 Technology Software Computing Innovation History Programming
  1. MBP, a software company, was one of the first in Europe and created the COBOL compiler in the 1960s. They made big steps in developing programming software right from the start.
  2. Visual COBOL was an improved version of their COBOL compiler released in the 1980s, featuring faster compilation and better screen management. It became popular for its efficiency and ease of use.
  3. The journey of MBP involved several ownership changes, eventually becoming part of major companies like Electronic Data Systems and Hewlett-Packard. This shows how influential MBP was in the tech world.

How to Think About ChatGPT

Holly’s Newsletter • 2916 implied HN points • 18 Oct 24
🕹 Technology Artificial Intelligence Machine Learning Software Development Computing Data science
  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 Mathematics Optimization Data science Algorithms
  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.

We're Finding Out What Humans are Bad At

Am I Stronger Yet? • 799 implied HN points • 18 Feb 25
🕹 Technology AI Computing Engineering Innovation Robotics
  1. Humans are not great at some tasks, especially ones like multiplication or certain physical jobs where machines excel. Evolution didn't prepare us for everything, so machines often outperform us in those areas.
  2. In tasks like chess, humans can still compete because strategy and judgment play a big role, even though computers are getting better. The game requires thinking skills that humans are good at, though computers can calculate much faster.
  3. AI is advancing quickly and becoming better at tasks we once thought were uniquely human, but there are still challenges. Some complex problems might always be easier for humans due to our unique brain abilities.

The End of Simplification: Intel Abandons X86S

The Chip Letter • 2402 implied HN points • 05 Jun 25
🕹 Technology Computing Semiconductors Software Engineering Innovation
  1. Intel has introduced APX, which includes several new features to improve its architecture. This means that Intel is aiming to enhance performance and efficiency.
  2. The company planned to simplify its architecture by removing some older features with X86S. However, they decided to abandon this simplification due to the importance of maintaining backward compatibility.
  3. Backwards compatibility is essential, as it allows older software to run on new systems. This decision shows Intel's commitment to supporting their users and legacy applications.

Grok 3: Another Win For The Bitter Lesson

The Algorithmic Bridge • 817 implied HN points • 18 Feb 25
🕹 Technology AI Development Computing Machine Learning Startup Ecosystem Tech Innovation
  1. Scaling laws are really important for AI progress. Bigger models and better computing power often lead to better results, like how Grok 3 outperformed earlier versions and is among the best AI models.
  2. DeepSeek shows that clever engineering can help, but it still highlights the need for more computing power. They did well despite limitations, but with more resources, they could achieve even greater things.
  3. Grok 3's success proves that having more computing resources can beat just trying to be clever. Companies that focus on scaling their resources are likely to stay ahead in the AI race.

The rise of reasoning machines

Democratizing Automation • 538 implied HN points • 12 Jun 25
🕹 Technology AI Machine Learning Computing Software Development Data science
  1. Reasoning is when we draw conclusions based on what we observe. Humans experience reasoning differently than AI, but both lack a full understanding of their own processes.
  2. AI models are improving but still struggle with complex problems. Just because they sometimes fail doesn't mean they can't reason; they just might need new methods to tackle tougher challenges.
  3. The debate on whether AI can truly reason often stems from fear of losing human uniqueness. Some critics focus on what AI can't do instead of recognizing its potential, which is growing rapidly.

Evo 2 Can Design Entire Genomes

Asimov Press • 490 implied HN points • 19 Feb 25
🕹 Technology AI Biotechnology Genomics Computing Research
  1. Evo 2 is a powerful AI model that can design entire genomes and predict harmful genetic mutations quickly. It can help scientists understand genetics better and improve genetic engineering.
  2. Unlike earlier models, Evo 2 can analyze large genetic sequences and understand their relationships, making it easier to see how genes interact in living organisms.
  3. While Evo 2 offers exciting possibilities for bioengineering, there are also concerns about its potential misuse. It's important to handle such powerful technology responsibly to avoid harmful applications.

Google and Stage Two of the AI Boom

Dana Blankenhorn: Facing the Future • 39 implied HN points • 30 Oct 24
🕹 Technology AI Cloud Computing Data Business
  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.

AGI Is Already Here—It’s Just Not Evenly Distributed

The Algorithmic Bridge • 1104 implied HN points • 05 Feb 25
🕹 Technology AI Machine Learning Data science Computing Software
  1. Understanding how to create good prompts is really important. If you learn to ask questions better, you'll get much better answers from AI.
  2. Even though AI models are getting better, good prompting skills are becoming more important. It's like having a smart friend; you need to know how to ask the right questions to get the best help.
  3. The better your prompting skills, the more you'll be able to take advantage of AI. It's not just about the AI's capabilities but also about how you interact with it.

AI Roundup 107: GPT-4.5

Artificial Ignorance • 58 implied HN points • 28 Feb 25
🕹 Technology AI Software Computing Innovation Data
  1. OpenAI just released GPT-4.5, a powerful AI model that is more expensive to run than GPT-4 but doesn't perform as well in some areas. This raises questions about whether bigger models are always better.
  2. Amazon is launching Alexa+, a new subscription service that adds generative AI features to their smart assistant, aiming for more natural conversations and complex tasks.
  3. DeepSeek is pushing ahead in the AI race, planning to launch new models quickly while its free distribution strategy helps democratize AI access in China.

40 Years On

The Fry Corner • 50058 implied HN points • 25 Jan 24
🕹 Technology Computing History Innovation Software
  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.

o3, Oh My

Don't Worry About the Vase • 3852 implied HN points • 30 Dec 24
🕹 Technology AI Models Machine Learning Data science Computing Software Engineering
  1. OpenAI's new model, o3, shows amazing improvements in reasoning and programming skills. It's so good that it ranks among the top competitive programmers in the world.
  2. o3 scored impressively on challenging math and coding tests, outperforming previous models significantly. This suggests we might be witnessing a breakthrough in AI capabilities.
  3. Despite these advances, o3 isn't classified as AGI yet. While it excels in certain areas, there are still tasks where it struggles, keeping it short of true general intelligence.

AI progress has plateaued below GPT-5 level

The Intrinsic Perspective • 31460 implied HN points • 14 Nov 24
🕹 Technology AI Machine Learning Innovation Data science 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.

A taxonomy for next-generation reasoning models

Democratizing Automation • 467 implied HN points • 04 Jun 25
🕹 Technology AI Machine Learning Computing Data science Automation
  1. Next-gen reasoning models will focus on skills, calibration, strategy, and abstraction. These abilities help the models solve complex problems more effectively.
  2. Calibrating how difficult a problem is will help models avoid overthinking and make solutions faster and more enjoyable for users.
  3. Planning is crucial for future models. They need to break down complex tasks into smaller parts and manage context effectively to improve their problem-solving abilities.

The ads are coming

benn.substack • 920 implied HN points • 23 May 25
🕹 Technology AI Data Privacy Advertising Computing Software Development
  1. Companies are great at tracking what we do online to learn what we like. They use that info to sell us things, often in sneaky ways.
  2. AI is getting better at understanding our conversations and wants. This could lead to new ways for companies to target us with ads while we interact with their services.
  3. As AI improves, we might willingly share more personal data because we value the services we get in return, making it easier for companies to sell us even better-targeted advertisements.

Learning Assembly for Fun and Profit

The Chip Letter • 12886 implied HN points • 14 Feb 25
🕹 Technology Programming Software Hardware Computing Education
  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.

Fixing Faulty Gradient Accumulation: Understanding the Issue and Its Resolution

The Kaitchup – AI on a Budget • 159 implied HN points • 21 Oct 24
🕹 Technology AI Machine Learning Data science Model Training 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.

Grok 3 Beta in Shambles

Marcus on AI • 10750 implied HN points • 19 Feb 25
🕹 Technology Artificial Intelligence Machine Learning Computing Data science Software Development
  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.

DeepSeek v3: The Six Million Dollar Model

Don't Worry About the Vase • 2777 implied HN points • 31 Dec 24
🕹 Technology AI Models Machine Learning Data science Computing Tech industry
  1. DeepSeek v3 is a powerful and cost-effective AI model with a good balance between performance and price. It can compete with top models but might not always outperform them.
  2. The model has a unique structure that allows it to run efficiently with fewer active parameters. However, this optimization can lead to challenges in performance across various tasks.
  3. Reports suggest that while DeepSeek v3 is impressive in some areas, it still falls short in aspects like instruction following and output diversity compared to competitors.

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 AI Machine Learning Computing Research Innovation
  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.

The Swift Compiler

Jacob’s Tech Tavern • 2624 implied HN points • 24 Dec 24
🕹 Technology Programming Software Development Computing Code
  1. The Swift language was created by Chris Lattner, who also developed LLVM when he was just 23 years old. That's really impressive given how complex these technologies are!
  2. It's important to understand what type of language Swift is, whether it's compiled or interpreted, especially for job interviews in tech. Knowing this can help you stand out.
  3. Learning about the Swift compiler can help you appreciate the language's features and advantages better, making you a stronger developer overall.

AI #96: o3 But Not Yet For Thee

Don't Worry About the Vase • 2598 implied HN points • 26 Dec 24
🕹 Technology AI Software Innovation Data science Computing
  1. The new AI model, o3, is expected to improve performance significantly over previous models and is undergoing safety testing. We need to see real-world results to know how useful it truly is.
  2. DeepSeek v3, developed for a low cost, shows promise as an efficient AI model. Its performance could shift how AI models are built and deployed, depending on user feedback.
  3. Many users are realizing that using multiple AI tools together can produce better results, suggesting a trend of combining various technologies to meet different needs effectively.

LLMs and World Models, Part 2

AI: A Guide for Thinking Humans • 196 implied HN points • 13 Feb 25
🕹 Technology AI Machine Learning Neural Networks Data science Computing
  1. LLMs (like OthelloGPT) may have learned to represent the rules and state of simple games, which suggests they can create some kind of world model. This was tested by analyzing how they predict moves in the game Othello.
  2. While some researchers believe these models are impressive, others think they are not as advanced as human thinking. Instead of forming clear models, LLMs might just use many small rules or heuristics to make decisions.
  3. The evidence for LLMs having complex, abstract world models is still debated. There are hints of this in controlled settings, but they might just be using collections of rules that don't easily adapt to new situations.

o1 Turns Pro

Don't Worry About the Vase • 3449 implied HN points • 10 Dec 24
🕹 Technology AI Models Software Development Computing Programming Innovation
  1. The o1 and o1 Pro models from OpenAI show major improvements in complex tasks like coding, math, and science. If you need help with those, the $200/month subscription could be worth it.
  2. If your work doesn't involve tricky coding or tough problems, the $20 monthly plan might be all you need. Many users are satisfied with that tier.
  3. Early reactions to o1 are mainly positive, noting it's faster and makes fewer mistakes compared to previous models. Users especially like how it handles difficult coding tasks.

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 Programming Data Structures Python Education 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.

NVLink Fusion: Embrace, Extend, Extinguish

Mule’s Musings • 417 implied HN points • 27 May 25
🕹 Technology AI Hardware Networking Silicon Computing
  1. Nvidia has a strong edge in the market with its NVLink technology, allowing fast communication between chips. This positions Nvidia favorably against competitors who are still developing their own solutions.
  2. By licensing its C2C technology and selling NVLink chiplets, Nvidia is opening its technology to others while still maintaining a competitive advantage. This strategy helps Nvidia grow its influence and solidify its market position.
  3. The 'embrace, extend, extinguish' strategy means Nvidia is likely to dominate the market by allowing others to use its technology while quickly outpacing them with its own products and innovations.

Nvidia makes gains on rising inference demand

Alex's Personal Blog • 32 implied HN points • 27 Feb 25
🕹 Technology AI Computing Semiconductors Startups Data Centers
  1. Nvidia's revenue is soaring due to high demand for their chips, especially for AI models. This growth is a good sign for the entire AI industry as more companies seek powerful computing solutions.
  2. Rising demand for inference, which is running AI models to handle user queries, is becoming more important than just training the models. Nvidia’s chips are designed to excel in this area, suggesting ongoing strong sales.
  3. Other companies like Snowflake are also doing well with their earnings by integrating AI into their services, while Salesforce is facing challenges despite its strong AI prospects. This shows different paths in the tech industry as they adapt to AI's growth.

Fast Speculative Decoding with Llama 3.2 and vLLM

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

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

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

☞ "The Magic of Code" and the Humanistic Computation Project

Cabinet of Wonders • 254 implied HN points • 09 Jun 25
🕹 Technology Computing Humanities Education Artificial Intelligence Community
  1. The project focuses on viewing computing as a humanistic art, aiming to blend technology with liberal arts education. This approach hopes to deepen our understanding of code and its impact on society.
  2. There's excitement about developing educational programs like courses and workshops to discuss these ideas more widely. Building a community of people with similar interests is also a goal.
  3. A new book titled 'The Magic of Code' has been released, which explores these themes and is part of the broader Humanistic Computation Project.

Mondays with the Machine: The Tongue & the Token: Language as Interface in Our Current Age of AI

Brad DeLong's Grasping Reality • 169 implied HN points • 09 Jun 25
🕹 Technology AI Natural Language Machine Learning Computing
  1. Natural language interfaces are a big deal because they let us communicate with AI using everyday language. This makes it easier for everyone to use technology without needing to know complex coding or technical skills.
  2. AI systems, like language models, simulate understanding but don't actually think. They can help us find information and assist with tasks, but we should remember that they are not truly intelligent.
  3. Using conversational AI can democratize access to information, making it easier for people to learn and solve problems. However, we must be aware of the risks, like over-reliance on these systems.

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

TheSequence • 49 implied HN points • 05 Jun 25
🕹 Technology AI Machine Learning Computing Data science Cybersecurity
  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.