The hottest Computing Substack posts right now

And their main takeaways
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Top Technology Topics

Sam Altman Wants $7 Trillion

Astral Codex Ten • 16656 implied HN points • 13 Feb 24
🕹 Technology Computing
  1. Sam Altman aims for $7 trillion for AI development, highlighting the drastic increase in costs and resources needed for each new generation of AI models.
  2. The cost of AI models like GPT-6 could potentially be a hindrance to their creation, but the promise of significant innovation and industry revolution may justify the investments.
  3. The approach to funding and scaling AI development can impact the pace of progress and the safety considerations surrounding the advancement of artificial intelligence.

An AI rumor you won’t want to miss

Marcus on AI • 7153 implied HN points • 10 Nov 24
🕹 Technology 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.

"Please wait while we access the functions on this card"

Disaffected Newsletter • 1938 implied HN points • 06 Feb 24
🕹 Technology Computing
  1. Many everyday machines now have annoying delays when performing simple tasks that used to be instant, like using ATMs or accessing files. It's frustrating because these are basic functions.
  2. Modern devices often prioritize a fancy user experience over speed and efficiency, making us wait longer for actions that used to happen quickly. This creates a feeling of disconnect between users and their machines.
  3. The trend seems to be moving towards making everything software-controlled, even when it seems unnecessary. This can make basic interactions tedious and less intuitive for users.

Top Links 930 AI financing jitters. Slow loan growth in China. The grimness of Penn Station & Bible apps.

Chartbook • 472 implied HN points • 23 Nov 25
🕹 Technology Computing
  1. Oracle is investing heavily in AI, promising to spend billions on chips and data centers. This shows they're serious about competing in the AI market.
  2. China is experiencing slow loan growth, which might indicate economic challenges ahead. It's important to watch how this trend unfolds.
  3. There's a feeling of gloom about places like Penn Station, suggesting that urban areas could be facing tougher times ahead. It's a reminder to pay attention to our public spaces.

The End of Simplification: Intel Abandons X86S

The Chip Letter • 2402 implied HN points • 05 Jun 25
🕹 Technology Computing
  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.
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The Bitter Lesson versus The Garbage Can

One Useful Thing • 1675 implied HN points • 28 Jul 25
🕹 Technology Computing
  1. Organizations often work in messy and chaotic ways, not always following clear processes. This can lead to confusion and frustration for employees trying to understand how things really get done.
  2. AI can sometimes perform better when it learns through experience rather than from human-defined rules. Instead of trying to teach it specific steps, letting it learn from outcomes can be more effective.
  3. When using AI in companies, instead of getting bogged down by trying to map every process, it may be smarter to focus on defining what good results look like. The AI can then figure out the best way to get there, even through the chaos.

LLMs Know More Than What They Say

LLMs for Engineers • 120 HN points • 15 Aug 24
🕹 Technology Computing
  1. Using latent space techniques can improve the accuracy of evaluations for AI applications without requiring a lot of human feedback. This approach saves time and resources.
  2. Latent space readout (LSR) helps in detecting issues like hallucinations in AI outputs by allowing users to adjust the sensitivity of detection. This means it can catch more errors if needed, even if that results in some false alarms.
  3. Creating customized evaluation rubrics for AI applications is essential. By gathering targeted feedback from users, developers can create more effective evaluation systems that align with specific needs.

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

Desystemize • 3933 implied HN points • 16 Feb 25
🕹 Technology Computing
  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.

Breaking news: AGI is not imminent!

Marcus on AI • 4466 implied HN points • 20 Jan 25
🕹 Technology Computing
  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.

Vote for the December 2025 + Post Topic

Computer Ads from the Past • 256 implied HN points • 24 Dec 25
🕹 Technology Computing
  1. Readers are invited to vote on the December 2025 + post topic from several options.
  2. The choices are magazine images and ads spanning decades (1977, 1986, 1992, 1995), showing a wide range of retro computing products.
  3. The post will be published before the end of the year, supporters are thanked, and readers can claim the free post or subscribe to access paid content.

A Nonconstructive Existence Proof of Aligned Superintelligence

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

DeepSeek: What the Headlines Miss

ChinaTalk • 4121 implied HN points • 26 Jan 25
🕹 Technology 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.

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

Marcus on AI • 4189 implied HN points • 09 Jan 25
🕹 Technology Computing
  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.

o3 Turns Pro

Don't Worry About the Vase • 1881 implied HN points • 17 Jun 25
🕹 Technology Computing
  1. o3-pro can handle bigger problems but can be slow, which might disrupt your workflow. It’s often better to queue up questions for later use instead of waiting for immediate answers.
  2. Many users see o3-pro as slightly better than o3 but still not perfect, especially in areas like coding where its performance can be inconsistent. It works well for in-depth analysis, but may not be the best for all tasks.
  3. The significant price drop for o3 makes it a more appealing choice for general use compared to o3-pro, which is seen as special-case only. This change could lead to more ambitious AI projects with the same budget.

Through the Ages: Apple Concurrency APIs

Jacob’s Tech Tavern • 1749 implied HN points • 24 Jun 25
🕹 Technology Computing
  1. Apple's concurrency APIs have evolved significantly since 1977, with each new version reflecting advancements in technology. Today, developers can handle complex tasks easily, thanks to modern tools.
  2. In the early days of computing, like with the Apple ][, parallel processing was nearly impossible because machines had limited capabilities. Over the years, technology grew, leading to better tools for developers.
  3. Swift Concurrency is considered a major breakthrough for the Swift programming language, making it easier to manage multiple tasks and streamline code.

The new AI scaling law shell game

Marcus on AI • 4663 implied HN points • 24 Nov 24
🕹 Technology Computing
  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.

GPT Agent Is Standing By

Don't Worry About the Vase • 1299 implied HN points • 23 Jul 25
🕹 Technology Computing
  1. OpenAI's ChatGPT Agent can now perform tasks like managing your calendar or shopping for groceries. It uses a combination of web browsing, research skills, and conversational abilities to help users with more complex requests.
  2. Although the ChatGPT Agent shows promise and can do some tasks well, like spreadsheet work, it still faces limitations. For now, it feels more like a helpful assistant rather than a full replacement for humans in many tasks.
  3. Safety is a top priority with the new capabilities of the ChatGPT Agent. OpenAI is taking steps to prevent misuse and ensure that the technology is used responsibly, especially in sensitive areas like biology and chemistry.

o3, Oh My

Don't Worry About the Vase • 3852 implied HN points • 30 Dec 24
🕹 Technology Computing
  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 #123: Moratorium Moratorium

Don't Worry About the Vase • 1433 implied HN points • 03 Jul 25
🕹 Technology Computing
  1. The recent AI moratorium vote showed strong support for removing the regulation, signaling that many lawmakers may want to proceed with AI development without heavy restrictions.
  2. AI models can provide useful assistance, but they often struggle with mundane tasks and can make big mistakes, especially in high-stakes situations.
  3. As AI continues to evolve, it's essential to ensure safer regulations and maintain a balance between innovation and managing potential risks that AI might pose.

Big O vs Hardware: Better Complexity ≠ Better Performance

Confessions of a Code Addict • 1106 implied HN points • 03 Aug 25
🕹 Technology Computing
  1. Not all algorithms with lower time complexity perform better in the real world. Hardware efficiency also plays a big role in how fast they run.
  2. An algorithm may have a good time complexity but if it relies on expensive operations, it won't win in performance. It's important to consider how the algorithm works with the CPU.
  3. Some algorithms can perform better on hardware depending on their design. A well-optimized algorithm can take advantage of hardware strengths, leading to faster results compared to those with similar complexity.

Go Grok Yourself

Don't Worry About the Vase • 2777 implied HN points • 19 Feb 25
🕹 Technology 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.

Wafer Scale - Trilogy Systems: Part 2

The Chip Letter • 4149 implied HN points • 27 Oct 24
🕹 Technology Computing
  1. Trilogy Systems, founded by Gene Amdahl in 1979, aimed to revolutionize the mainframe market with a new technology called Wafer Scale Integration, which promised to be faster and cheaper than existing solutions. However, the company struggled with technical challenges and internal issues.
  2. As delays mounted and financial troubles grew, Trilogy abandoned its mainframe plans and, ultimately, its Wafer Scale technology. Distractions like personal tragedies and a lack of cohesive vision contributed to the company's downfall.
  3. After losing credibility and facing mounting losses, Trilogy merged with Elxsi, but that too did not lead to success. Amdahl felt a deep personal responsibility for the failure, which haunted him even after the company's collapse.

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

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

o1 Turns Pro

Don't Worry About the Vase • 3449 implied HN points • 10 Dec 24
🕹 Technology Computing
  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.

Alan Sugar of Amstrad Speaks to Practical Computing (1985)

Computer Ads from the Past • 896 implied HN points • 07 Aug 25
🕹 Technology Computing
  1. Alan Sugar wanted to create practical and affordable computers, focusing on what most users needed like word processing.
  2. He believed that many expensive computers had features that people weren't using, so he aimed to provide good value through integration.
  3. Sugar was cautious about expanding into the U.S. market, preferring to find committed customers before making large investments.

Black Friday/Happy Birthday Sale 2025

Computer Ads from the Past • 256 implied HN points • 28 Nov 25
🕹 Technology Computing
  1. Get 39% off annual plans for life if you buy a paid membership between now and December 8.
  2. If you prefer not to use Substack, you can support with one-time donations via Ko‑Fi, SubscribeStar, Cash App, PayPal, Liberpay, or Patreon.
  3. Gift subscriptions are available and on sale for the holidays, and subscribing helps support the reader-supported publication.

LLMs Die Every Time You Close the Chat Window

The Algorithmic Bridge • 997 implied HN points • 18 Jul 25
🕹 Technology Computing
  1. When you close a chat window with an AI, it forgets everything, like it never existed. This means that every time you reopen it, it's like starting from scratch.
  2. Humans experience memory and consciousness differently; when we sleep, we retain our memories and essence, while LLMs lose everything overnight.
  3. The mystery of dreams and consciousness in humans is still a big question, but it's clear that the way we perceive our identity is different from how AI operates.

Demystifying GPU Compute Software

The Chip Letter • 6989 implied HN points • 10 Mar 24
🕹 Technology Computing
  1. GPU software ecosystems are crucial and as important as the GPU hardware itself.
  2. Programming GPUs requires specific tools like CUDA, ROCm, OpenCL, SYCL, and oneAPI, as they are different from CPUs and need special support from hardware vendors.
  3. The effectiveness of GPU programming tools is highly dependent on support from hardware vendors due to the complexity and rapid changes in GPU architectures.

Turing's Yearning, Part 1

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

DeepSeek v3: The Six Million Dollar Model

Don't Worry About the Vase • 2777 implied HN points • 31 Dec 24
🕹 Technology Computing
  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.

DeepSeek isn't a victory for the AI sceptics

Odds and Ends of History • 2345 implied HN points • 28 Jan 25
🕹 Technology Computing
  1. DeepSeek, a new AI model from China, is much more efficient than existing models, meaning it can do more with less resources. This could lead to more widespread use of AI technology.
  2. Even if this new model appears better, it doesn't mean demand for computing power will decrease. Instead, it might increase as more uses for AI are discovered.
  3. The release of DeepSeek highlights the growing competition in AI technology, especially between China and the West. This might push companies to invest more in developing even smarter models.

The Swift Compiler

Jacob’s Tech Tavern • 2624 implied HN points • 24 Dec 24
🕹 Technology Computing
  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 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.

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

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

A new generation of AIs: Claude 3.7 and Grok 3

One Useful Thing • 1968 implied HN points • 24 Feb 25
🕹 Technology Computing
  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.

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 Computing
  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.

Efficient Computer's Electron E1 CPU

More Than Moore • 653 implied HN points • 24 Jul 25
🕹 Technology Computing
  1. The Electron E1 CPU by Efficient Computer uses a unique design that aims to be much more energy-efficient than traditional chips. It does this by changing how data moves and is processed, reducing energy waste.
  2. This CPU has a special architecture called 'Fabric' that lets data flow directly between computing nodes. This design is supposed to save a lot of energy that typical CPUs lose moving data around.
  3. Efficient Computer believes their chip could be 10 to 100 times more efficient than the best ARM CPUs. However, until more independent tests are done, it's hard to say how well it’ll really perform in the real world.

The ads are coming

benn.substack • 920 implied HN points • 23 May 25
🕹 Technology Computing
  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.

Mourning for GPT-4o Boyfriends + Beijing Hates the H20?

ChinaTalk • 504 implied HN points • 15 Aug 25
🕹 Technology Computing
  1. China is worried about foreign chips, especially Nvidia's H20 GPUs, and suspects they might have hidden surveillance features. They think these chips could jeopardize their security and want to promote local alternatives.
  2. Many people in China are emotional about losing access to GPT-4o, a version of an AI they felt connected to. They believe new versions lack the warmth and emotional depth they valued in older models.
  3. Chinese state media is calling out local electric vehicle makers for their poor safety in testing. This is surprising since state media often praises domestic products, but it shows they want to improve industry standards.

Why reasoning models will generalize

Democratizing Automation • 1535 implied HN points • 28 Jan 25
🕹 Technology 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.