The hottest Computing Substack posts right now

And their main takeaways
Category
Top Technology Topics

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.

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

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

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.

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.

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.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

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.

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.

Back to the Future with Gordon Moore

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

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.

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

Democratizing Automation • 760 implied HN points • 12 Feb 25
🕹 Technology AI Science Research Innovation Computing
  1. AI will change how scientists work by speeding up research and helping with complex math and coding. This means scientists will need to ask the right questions to get the most out of these tools.
  2. While AI can process a lot of information quickly, it can't create real insights or make new discoveries on its own. It works best when used to make existing scientific progress faster.
  3. The rise of AI in science may change traditional practices and institutions. We need to rethink how research is done, especially how quickly new knowledge is produced compared to how long it takes to review that knowledge.

DeepSeek: What the Headlines Miss

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

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.

Breaking news: AGI is not imminent!

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

The Engine

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

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

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

The Five Stages of AGI Grief

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

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.

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.

Why reasoning models will generalize

Democratizing Automation • 1504 implied HN points • 28 Jan 25
🕹 Technology AI Machine Learning Data science Natural Language Processing Computing
  1. Reasoning models are designed to break down complex problems into smaller steps, helping them solve tasks more accurately, especially in coding and math. This approach makes it easier for the models to manage difficult questions.
  2. As reasoning models develop, they show promise in various areas beyond their initial focus, including creative tasks and safety-related situations. This flexibility allows them to perform better in a wider range of applications.
  3. Future reasoning models will likely not be perfect for every task but will improve over time. Users may pay more for models that deliver better performance, making them more valuable in many sectors.

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

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

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.

Satya Nadella and the three stages of scientific truth

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

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.

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

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

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.

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.

An AI rumor you won’t want to miss

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

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 new AI scaling law shell game

Marcus on AI • 4663 implied HN points • 24 Nov 24
🕹 Technology Artificial Intelligence Machine Learning Computing Data Analysis
  1. Scaling laws in AI aren't as reliable as people once thought. They're more like general ideas that can change, rather than hard rules.
  2. The new approach to scaling, which focuses on how long you train a model, can be costly and doesn't always work better for all problems.
  3. Instead of just trying to make existing models bigger or longer-lasting, the field needs fresh ideas and innovations to improve AI.

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.

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.