The hottest Computing Substack posts right now

And their main takeaways
Category
Top Technology Topics

Bell's Inequality - Guide and Walkthrough

Donkeyspace • 6 implied HN points • 08 Dec 25
🔬 Science Computing
  1. Bell's theorem shows that the universe is fundamentally non-local, meaning particles can be connected no matter how far apart they are. This idea challenges our traditional understanding of space and distance.
  2. The CHSH game illustrates how entangled particles can outperform classical strategies by showing that Alice and Bob can get better results by measuring angles differently. This surprising outcome demonstrates the strange nature of quantum mechanics.
  3. Understanding Bell's inequality reshapes how we see physical laws; it's more like a set of logical rules rather than forces acting on objects. This perspective changes how we think about the universe and its fundamental nature.

Shift to the Client

Dana Blankenhorn: Facing the Future • 59 implied HN points • 07 Nov 23
🕹 Technology Computing
  1. The shift in technology market from servers to clients is impacting PC sales and new machine development.
  2. AI advancements are driving the change in the market towards client-focused devices.
  3. Expect new types of high-end and consumer-focused PC devices, with enhanced interfaces and connectivity, to become prevalent in the near future.

Prompt engineering is spaghetti code

Sunday Letters • 59 implied HN points • 08 Oct 23
🕹 Technology Computing
  1. Prompt engineering is not a lasting software discipline; it may fade away as technology improves. It's a reaction to a lack of computing resources, trying to make every use of AI efficient.
  2. Using AI tools should be approached like programming: break tasks into smaller pieces to handle them better. This is more effective than creating complex prompts that are hard to manage.
  3. It's better to focus on making something work well before worrying about cost or optimization. Don't stress about minimizing resource use until the solution is working reliably.

Randomness- A billion dollar problem in Software Engineering [Math Mondays]

Technology Made Simple • 59 implied HN points • 22 Aug 23
🕹 Technology Computing
  1. Randomness in software engineering introduces unpredictability and can be used for various reasons like generating different outputs and introducing randomness into systems.
  2. Careful consideration is needed when using randomness in software engineering to avoid security risks and unnecessary complexity.
  3. To test the randomness of a system, consider using Diehard tests, which are intuitive and effective in evaluating randomness.
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Nvidia and the AI scaling wall, and what founder mode really is

Alex's Personal Blog • 98 implied HN points • 21 Nov 24
🕹 Technology Computing
  1. Nvidia is experiencing strong demand for its new Blackwell GPUs, which are expected to outperform previous models. Major companies are eager to integrate these powerful chips into their systems.
  2. The concept of 'founder mode' is about being deeply involved in the critical details of your business. It's not just about delegating tasks, but collaborating closely with team members to achieve great outcomes.
  3. The AI industry continues to evolve with new ways to improve model performance. Nvidia's focus on scaling in various aspects shows that innovation in AI is still very much alive.

The Sequence Research #490: A Practical Deep Dive Inside DeepSeek-R1

TheSequence • 70 implied HN points • 14 Feb 25
🕹 Technology Computing
  1. DeepSeek-R1 is a new AI model that performs well without needing to be very big. It uses smart training methods to achieve great results at a lower cost.
  2. The model successfully matches the performance of a larger, more expensive model called GPT-o1. This shows that size isn't the only thing that matters for good performance.
  3. DeepSeek-R1 challenges the idea that you always need large models for reasoning, suggesting that clever techniques can also lead to impressive results.

The Most Valuable Problem in AI

How the Hell • 313 implied HN points • 30 Aug 23
🕹 Technology Computing
  1. In AI, there's a shift to being able to throw any amount of compute power at problems
  2. We are approaching a world where we can solve any intellectual problem by allocating money as a compute budget to AI agents
  3. Solving the problem of efficient compute allocation can lead to building the most valuable company of the century

A Tale of a Close Partnership

Sector 6 | The Newsletter of AIM • 39 implied HN points • 20 Dec 23
🕹 Technology Computing
  1. AMD has partnered with Lamini to help startups create and run generative AI products using AMD GPUs. This collaboration started in September and aims to address the GPU shortage in the AI industry.
  2. Lamini disclosed that they have been exclusively using AMD GPUs for the past year, showcasing their commitment to this partnership. They even highlighted their continuous use of AMD hardware at an AI event.
  3. Together, AMD and Lamini have developed the LLM Superstation, a powerful supercomputer equipped with 128 AMD Instinct GPUs. This setup allows businesses to train large AI models more effectively.

The Sequence Chat: Thinking About Transformers as Computers

TheSequence • 105 implied HN points • 30 Oct 24
🕹 Technology Computing
  1. Transformers are changing AI, especially in how we understand and use language. They're not just tools; they act more like computers in some ways.
  2. The way transformers can adapt and scale is really impressive. It's like they can learn and adjust in ways traditional computers can't.
  3. Thinking of transformers as computers opens up new ideas about how we approach AI. This perspective can help us find new applications and improve our understanding of tech.

The Sequence Chat: Small Specialists vs. Large Generalist Models and What if NVIDIA Becomes Sun Microsystems

TheSequence • 98 implied HN points • 13 Nov 24
🕹 Technology Computing
  1. Large AI models have been popular because they show amazing capabilities, but they are expensive to run. Many businesses are now looking at smaller, specialized models that can work well without the high costs.
  2. Smaller models can definitely operate on basic hardware, unlike large models that often need high-end GPUs like those from NVIDIA. This could change how companies use AI technology.
  3. There's an ongoing discussion about the future of AI models. It will be interesting to see how the market evolves with smaller, efficient models versus the larger ones that have been leading the way.

Want to play AAA Games in Your Car?

More Than Moore • 233 implied HN points • 04 Jan 24
🕹 Technology Computing
  1. At CES, AMD announced new automotive APUs for in-car entertainment, driver safety, and autonomous driving.
  2. The new AMD chips support a gaming experience in cars, with potential for multiple displays and better graphics performance.
  3. AMD's acquisition of Xilinx enhances their presence in automotive technology, particularly in ADAS with their Versal AI Edge processors.

Chinese AI’s Sputnik Moment

Taipology • 69 implied HN points • 24 Jan 25
🕹 Technology Computing
  1. DeepSeek-R1 is a new AI model from China that performs on par with top models at a much lower cost. This is surprising and changing the AI landscape.
  2. It uses a special 'DeepThink' mode that makes it think about responses more deeply, which helps it give better answers compared to other models.
  3. The competition is heating up, with concerns that Chinese AI could take over. DeepSeek aims not just to match the West but to innovate and lead in technology.

Micro-Benchmarking a RISC-V SBC (CanMV K230)

Fprox’s Substack • 20 implied HN points • 23 Aug 25
🕹 Technology Computing
  1. Micro-benchmarks help you measure how fast different instructions run on the RISC-V K230 chip. This is important for understanding the chip's performance.
  2. Data values can change how fast instructions execute, especially for operations like division. It's crucial to consider these variations in performance measurements.
  3. The RISE development image is a stable and feature-rich option for developers working with the CanMV K230. It makes connecting and running programs easier compared to earlier images.

Prompt-RAG

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 20 Mar 24
🕹 Technology Computing
  1. Prompt-RAG is a new method that improves language models without using complex vector embeddings. It simplifies how we retrieve information to answer questions.
  2. The process involves creating a Table of Contents from documents, selecting relevant headings, and generating responses by injecting context into prompts. It makes handling data easier.
  3. While this method is great for smaller projects and specific needs, it still requires careful planning when constructing the documents and managing costs related to token usage.

Intel Pushes the Nitro Button

Sector 6 | The Newsletter of AIM • 39 implied HN points • 11 Dec 23
🕹 Technology Computing
  1. Intel is planning a big event where they might announce new AI products to compete with NVIDIA and AMD. This shows how competitive the tech industry has become.
  2. One exciting product expected is the Gaudi3 AI accelerator chip, which will be much faster and better than the previous version. It promises improved performance with higher compute power and memory capacity.
  3. Looking ahead, Intel has plans for even more advanced chips, combining their AI technology with GPU power. This hints at more innovations coming in the future.

Number Theoretic Transform with RVV

Fprox’s Substack • 83 implied HN points • 07 Dec 24
🕹 Technology Computing
  1. The Number Theoretic Transform (NTT) helps speed up polynomial multiplication, which is important in cryptography. It uses a smart method to do complicated calculations faster than traditional methods.
  2. Using RISC-V Vector (RVV) technology can further improve the speed of NTT operations. This means that by using special hardware instructions, operations can be completed much quicker.
  3. Benchmarks show that a well-optimized NTT using RVV can be substantially faster than basic polynomial multiplication, making it crucial for applications in secure communications.

AI Roundup 107: GPT-4.5

Artificial Ignorance • 58 implied HN points • 28 Feb 25
🕹 Technology Computing
  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.

Transformer^2: Self-adaptive LLMs

Gonzo ML • 63 implied HN points • 27 Jan 25
🕹 Technology Computing
  1. Transformer^2 uses a new method for adapting language models that makes it simpler and more efficient than fine-tuning. Instead of retraining the whole model, it adjusts specific parts, which saves time and resources.
  2. The approach breaks down weight matrices through a process called Singular Value Decomposition (SVD), allowing the model to identify and enhance its existing strengths for various tasks.
  3. At test time, Transformer^2 can adapt to new tasks in two passes, first assessing the situation and then applying the best adjustments. This method shows improvements over existing techniques like LoRA in both performance and parameter efficiency.

Claude 3 Ignites Cloud Wars

Sector 6 | The Newsletter of AIM • 19 implied HN points • 06 Mar 24
🕹 Technology Computing
  1. Claude 3 has made competition in the cloud market very intense, especially between Microsoft, Google, and Amazon. Each company is trying to outdo the others by adding new AI features.
  2. OpenAI is under pressure to release GPT-5 as Claude 3 shows strong performance. This situation is causing some confusion for Microsoft Azure.
  3. Anthropic's Claude 3 outperformed OpenAI's GPT-4 in several tests and is now available for businesses on platforms like Amazon Bedrock and Google Cloud. This gives businesses more options for AI tools.

Ambient Computing is Here

Sector 6 | The Newsletter of AIM • 39 implied HN points • 12 Nov 23
🕹 Technology Computing
  1. Ambient computing is evolving, bringing a new way for people to interact with technology. Devices like the Humane Ai Pin are examples of this next-gen communication.
  2. Many experts believe that our current ways of using machines, like computers and phones, are outdated. They're pushing for new methods, such as spatial computing, to improve user experience.
  3. Companies like Apple are also venturing into this area with products like the Vision Pro, showing that there's a growing interest in more immersive technology.

What's Quantum Computing

Technically • 59 implied HN points • 28 Jan 25
🕹 Technology Computing
  1. Quantum computing uses qubits instead of bits. While bits can be either 0 or 1, qubits can be both at the same time, allowing for much faster problem-solving.
  2. Qubits can work together in a unique way, using superposition and interference to find answers much faster than traditional computers. This makes them great for complex problems like drug discovery.
  3. Quantum computers are still in the experimental stage and have challenges like needing very cold temperatures and handling errors, but they hold great promise for the future.

ModernBERT, the BERT of 2024

Gonzo ML • 63 implied HN points • 19 Dec 24
🕹 Technology Computing
  1. ModernBERT is a new version of BERT that improves processing speed and memory efficiency. It can handle longer contexts and makes BERT more practical for today's tasks.
  2. The architecture of ModernBERT has been updated with features that enhance performance, like better attention mechanisms and optimized computations. This means it works faster and can process more data at once.
  3. ModernBERT has shown impressive results in various natural language understanding tasks and can compete well against larger models, making it an exciting tool for developers and researchers.

☞ The Garden of Computational Delights

Cabinet of Wonders • 231 implied HN points • 02 Aug 23
🕹 Technology Computing
  1. Computing goes beyond utilitarian purposes to bring delight and wonder through creative coding and simulations.
  2. The 'Garden of Computational Delights' is a collection of places that evoke fascination with web, programming, and computing.
  3. The boundaries of what fits in the 'Garden' are fuzzy, personal, and idiosyncratic, showcasing a diverse range of computer-related interests.

Intel Thunderbolt 5: Up to 120 Gbps with PAM3

More Than Moore • 210 implied HN points • 12 Sep 23
🕹 Technology Computing
  1. The new Intel Thunderbolt 5 specification offers up to 120 Gbps bandwidth with PAM3 signaling.
  2. Thunderbolt 5 is backward compatible with Thunderbolt 4, providing faster charging, networking, and PCIe speed.
  3. PAM3 signaling in Thunderbolt 5 enables increased speed and efficiency by transmitting three bits per two transfers.

When Jensen Was in Town

Sector 6 | The Newsletter of AIM • 39 implied HN points • 10 Sep 23
🕹 Technology Computing
  1. Jensen Huang, CEO of NVIDIA, believes AI will change how we develop software, with computers themselves becoming software engineers. This shift could make software creation much more efficient.
  2. NVIDIA is focused on building partnerships in India, especially with big companies like Reliance and Tata, to help develop the country's AI ecosystem.
  3. There's a strong emphasis on reskilling the IT workforce in India, which is important as AI continues to grow and evolve in the industry.

Engelbart and the ABC Model

Grist Potentia • 39 implied HN points • 29 May 23
🕹 Technology Computing
  1. Vannevar Bush was a prolific inventor and mathematician who managed a large group of scientists during WWII.
  2. He created the first analog computer at MIT capable of solving complex equations.
  3. Bush's work laid the foundation for advancements in technology and computing.

XGBoost is the Secret of ML Energy

Sector 6 | The Newsletter of AIM • 39 implied HN points • 06 Sep 23
🕹 Technology Computing
  1. XGBoost, or Extreme Gradient Boosting, helps improve the performance and speed of machine learning models that deal with tabular data. It's known for being really good at finding patterns and making predictions.
  2. This algorithm works best for supervised learning when you have lots of training examples, especially when you have both categorical and numeric data. It can handle a mix of different data types well.
  3. If you're working with a dataset that has many features, XGBoost is a strong choice to enhance the capabilities of your machine learning model. It makes it easier to get accurate results.

LLM Hallucinations 🤦

Sector 6 | The Newsletter of AIM • 39 implied HN points • 27 Jul 23
🕹 Technology Computing
  1. LLM hallucinations are a tough issue for researchers and developers, but new methods are being developed to help reduce them. This gives hope for better solutions in the future.
  2. Several techniques like function calling and context-free grammar are emerging to tackle LLM hallucinations, which may improve accuracy.
  3. LMQL from SRI Lab is showing promise among various solutions and is gaining attention for its potential benefits.

Some Speculation on Future Computational Physics

Bzogramming • 61 implied HN points • 27 Nov 24
🕹 Technology Computing
  1. There are two main ways to tackle physics problems: symbolic methods that involve working with symbols directly, and numerical methods that use simpler calculations. Both have their pros and cons.
  2. Quantum mechanical problems can be very tough to solve and require immense computational power, often beyond what we currently have. Even with advancements, some problems could remain very hard for a long time.
  3. As computing develops, we should explore combining the best parts of symbolic and numerical physics. We might discover new tools and methods that make it easier to solve complex problems in the future.

A Moment of Disbelief

The Last Bear Standing • 45 implied HN points • 31 Jan 25
🕹 Technology Computing
  1. Deepseek has developed new AI models that are very effective and cost much less than competitors. This shows that you can create powerful AI without needing huge resources.
  2. The way AI models are built might change, focusing more on better training methods instead of just adding more hardware. This means companies might need to rethink their strategies.
  3. NVIDIA's stock took a big hit because of the competition from Deepseek. The market didn't react well to the idea that AI could be done more efficiently.

Getting Faster for Your Own LLM Inference

The Beep • 19 implied HN points • 28 Jan 24
🕹 Technology Computing
  1. Lowering the precision of LLMs can make them run faster. Switching from 32-bit to 16 or even 8-bit can save memory and boost speed during processing.
  2. Using prompt compression helps reduce the amount of information LLMs have to process. By making prompts shorter but still meaningful, the workload is lighter and speeds up performance.
  3. Quantization is a key technique for making LLMs usable on everyday computers. It allows big models to be more manageable by reducing their size without losing too much accuracy.

OpenAI's o-1 and inference-time scaling laws

Tanay’s Newsletter • 63 implied HN points • 28 Oct 24
🕹 Technology Computing
  1. OpenAI's o-1 model shows that giving AI more time to think can really improve its reasoning skills. This means that performance can go up just by allowing the model to process information longer during use.
  2. The focus in AI development is shifting from just making models bigger to optimizing how they think at the time of use. This could save costs and make it easier to use AI in real-life situations.
  3. With better reasoning abilities, AI can tackle more complex problems. This gives it a chance to solve tasks that were previously too difficult, which might open up many new opportunities.

The Sequence Chat: The Transition that Changes Everything. From Pretraining to Post-Training in Foundation Models

TheSequence • 56 implied HN points • 04 Dec 24
🕹 Technology Computing
  1. The transition from pretraining to post-training in AI models is a big deal. This change helps improve how AI can reason and learn from data.
  2. New models like DeepSeek's R1 and Alibaba's QwQ are now using this transition to become smarter and more effective. They can solve complex problems better than before.
  3. The shift is moving away from old methods like reinforcement learning with human feedback. Instead, there are new ways being developed that promise to make AI work even better.

AMD Goes Hybrid: Zen 4c in Mobile

More Than Moore • 163 implied HN points • 02 Nov 23
🕹 Technology Computing
  1. AMD introduces hybrid design with Zen 4 and Zen 4c cores in mobile processors.
  2. AMD's decision to launch hybrid chips mid-cycle may indicate a trial run for future generations.
  3. Efficient scheduling of workloads on different cores remains a challenge for hybrid CPUs.

Why Didn't Stanley Kubrick & Arthur C. Clarke Predict The iPad?

1517 Fund • 121 implied HN points • 07 Mar 24
🕹 Technology Computing
  1. Kubrick and Clarke came close to predicting the iPad in 2001: A Space Odyssey, but paper still played a big role in their vision, showing the challenge of imagining the shift to portable computers.
  2. The prediction of flat screens in 2001 was impressive considering they didn't exist at the time; RCA's pursuit of flat-panel technology likely influenced this foresight.
  3. Despite their brilliance, Kubrick and Clarke didn't fully predict the iPad because they were constrained by the prevalent mainframe computing environment and underestimated the advancements in miniaturization and portable computing.