The hottest Computing Substack posts right now

And their main takeaways
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Top Technology Topics

Disassembling Crossroads, part 1

Dan’s MEGA65 Digest • 11 implied HN points • 15 Nov 24
🕹 Technology Gaming Software Emulation Programming Computing
  1. The game Crossroads is a fast-paced maze shoot'em up where players collect items and battle various enemies. It's a classic Commodore 64 game that invokes nostalgia for many fans.
  2. Reverse engineering games like Crossroads can help understand how they work, especially their graphics and sound mechanics. Using modern tools, you can inspect the game’s code and see how it produces effects.
  3. New features for gaming boards, like high score tables for MEGA65 games, enhance competitive play. These tools suggest an active community looking to improve gaming experiences on older hardware.

C57BL/6 (Beta)

Niko McCarty • 19 implied HN points • 25 May 24
🔬 Science Biotechnology Research Ethics Pharmaceuticals Computing
  1. In 2032, scientists created computer emulations of mice, including their entire anatomy and brain. This was only possible for a few organizations with strong computing power.
  2. The military used these emulators to test how drugs could enhance mouse performance, but some results were secretly tested on prisoners, raising ethical concerns.
  3. The NIH gave access to emulators mainly to select academic institutions, leading to a flood of biomedical papers. This made their findings influential in clinical trials, affecting millions of people.

☞ The Garden of Computational Delights

Cabinet of Wonders • 231 implied HN points • 02 Aug 23
🕹 Technology Computing Innovation Data Digital Preservation Emerging Tech
  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.

Understanding Big Oh, Big Theta, and Big Omega Notations[Math Mondays]

Technology Made Simple • 99 implied HN points • 16 May 23
🕹 Technology Algorithms Computing Education
  1. Time complexity refers to the number of instructions a software executes, not the actual time taken to run the code.
  2. Three common asymptotic notations for computing time complexity are Big Oh, Big Theta, and Big Omega.
  3. Understanding time complexity bounds is essential in computer science and software engineering, as they are fundamental concepts that appear regularly.
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Battery Ventures' Max Schireson: To Build Value in Tech, Build Different

Condensing the Cloud • 98 implied HN points • 31 Aug 23
🕹 Technology Innovations Startups Data Computing Tech Companies
  1. To build value in the tech industry, aim to do things differently, not just better or faster.
  2. Doing something different can polarize users, with some finding it better and others not.
  3. Success in tech often comes from being unique and offering something new, not just improving existing technologies.

A Linear Algebra Trick for Computing Fibonacci Numbers Fast

Confessions of a Code Addict • 158 HN points • 05 Nov 23
🔬 Science Mathematics Computing Algorithms Linear Algebra Complexity
  1. A linear algebra technique can be applied to compute Fibonacci numbers quickly with a logarithmic time complexity.
  2. Efficient algorithms like repeated squaring can compute powers of matrices in logarithmic time, improving performance for Fibonacci number calculations.
  3. A closed form expression using the golden ratio offers a direct method to compute Fibonacci numbers, showing different approaches with varied performance.

Beat your Bot: Building your Moat against AI

Musings on Markets • 2 HN points • 28 Aug 24
🕹 Technology AI Computing Data Disruption Automation
  1. AI is getting better at doing mechanical tasks, but it struggles with intuitive ones. This means jobs that rely on creativity and adaptability are safer than those that are purely formulaic.
  2. Jobs that follow strict rules can be easily replaced by AI, while those that need human judgement and understanding of principles will be harder for AI to take over. This shows the value of being skilled in areas that require more complex thinking.
  3. To protect your job from AI, be a generalist instead of a specialist, practice telling stories around your work, and try not to rely too much on technology for reasoning. This can help you stay unique and valuable in a changing job landscape.

Edge 445: A New Series About Knowledge Distillation

TheSequence • 35 implied HN points • 05 Nov 24
🕹 Technology AI Data science Machine Learning Computing Engineering
  1. Knowledge distillation helps make large AI models smaller and cheaper. This is important for using AI on devices like smartphones.
  2. A key goal of this process is to keep the accuracy of the original model while reducing its size.
  3. The series will include reviews of research papers and discussions on frameworks like Google's Data Commons that support factual knowledge in AI.

HyperX Topology for Photonic Wafer Scale Integration

Tapa’s Substack • 59 implied HN points • 17 Dec 23
🕹 Technology Networking Electronics Optics Computing Engineering
  1. Using the HyperX topology can be a good choice for connecting photonic wafer-scale systems, helping to improve efficiency and lower costs. It focuses on making connections quicker and cheaper in long-distance scenarios on wafers.
  2. Photonic wafer-scale integration offers benefits like reduced energy use and lower latency compared to traditional electrical methods, but the right network setup has been a challenge. Finding a suitable layout is important for maximizing performance.
  3. The HyperX design has advantages like fewer layers and a straightforward layout, which can help minimize complications in building these systems. It's a simple yet effective way to boost the performance of interconnects in photonic setups.

Moore's Law Changed Our Lives Forever: Rest in Peace Gordon Moore

Mule’s Musings • 256 implied HN points • 25 Mar 23
🕹 Technology Semiconductors Computing Innovation Engineering Legacy
  1. Moore's Law drove massive technological progress and changed our lives significantly
  2. Moore's Law enabled the rapid advancement of communication, entertainment, and healthcare
  3. Moore's Law was an aspiration upheld by the semiconductor industry, not a scientific law, but its impact on technology and progress remains profound

S&T - Bret Victor, Realtalk and the ‘laboratory of the future’ vision [updated with proposal]

James W. Phillips' Newsletter • 78 implied HN points • 14 May 23
🕹 Technology Innovation Computing Visualization Collaboration
  1. Bret Victor envisions a future where the laboratory is a communal computational system.
  2. Personal computing history, led by figures like Alan Kay, envisioned computers as 'intellectual amplifiers'.
  3. Realtalk is a system where physical spaces are transformed into computational systems, allowing collaborative work without screens.

How could we know if Large Language Models understand language?

The Counterfactual • 219 implied HN points • 18 Oct 22
🕹 Technology Artificial Intelligence Machine Learning Natural Language Processing Computing Data science
  1. There's a big debate about whether large language models truly understand language or if they're just mimicking patterns from the data they were trained on. Some people think they can repeat words without really grasping their meaning.
  2. Two main views exist: One says LLMs can't understand language because they lack deeper meaning and intent, while the other argues that if they behave like they understand, then they might actually understand.
  3. As LLMs become more advanced, we need to create better ways to test their understanding. This will help us figure out what it really means for a machine to 'understand' language.

🔮E08: Optical Computing

Let Us Face the Future • 238 implied HN points • 14 Jul 23
🕹 Technology Computing Manufacturing Chip Design Artificial Intelligence
  1. Optical computing uses light particles instead of electrons for computations, promising faster processing speeds and energy efficiency.
  2. Opto-electronic computing is close to commercialization, combining optical and electronic functions to leverage speed and bandwidth advantages.
  3. Optical computing faces challenges in adoption due to the need for changing components and manufacturing processes, but has potential for high-performance tasks like AI training.

Prompt engineering is spaghetti code

Sunday Letters • 59 implied HN points • 08 Oct 23
🕹 Technology AI Software Programming Computing Optimization
  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 Software Math Computing Engineering Programming
  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.

A Tale of a Close Partnership

Sector 6 | The Newsletter of AIM • 39 implied HN points • 20 Dec 23
🕹 Technology AI Hardware Software Startups 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.

Prompt-RAG

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 20 Mar 24
🕹 Technology AI Machine Learning Data science Natural Language Processing 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 AI Chips Computing Trends Innovation
  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.

Groq's inference strategy

Axial • 7 implied HN points • 22 Oct 24
🕹 Technology AI Hardware Semiconductors Innovation Computing
  1. Groq is designing chips that speed up AI by using a special kind of memory called SRAM, which is faster but also more expensive. This helps them run complex AI models more efficiently.
  2. Their choice of using separate cards for each chip instead of smaller, cheaper chips means they might face higher costs and power use. This choice could limit how easily they can grow their technology.
  3. Other companies like Microsoft are trying different approaches that might be cheaper and easier to scale. Groq needs to find a balance between speed and practicality to succeed in the competitive AI market.

Understanding the Power of Bitwise Operators. No math needed

Deus In Machina • 145 implied HN points • 11 May 23
🕹 Technology Programming Emulation Computing Software
  1. Bitwise operators manipulate binary data without the need for math, making them powerful tools in programming.
  2. Understanding binary representation is crucial in computer programming, allowing for efficient manipulation of data.
  3. Bitwise operators like AND, OR, XOR, and shift operations are essential in tasks like setting specific bits, masking off bits, or shifting binary numbers.

Wen Babelfish? feat. Aron of SEMRON who is making an analog AI chip

State of the Future • 7 implied HN points • 12 Feb 25
🕹 Technology AI Computing Semiconductors Energy Efficiency
  1. Edge AI needs efficient computing because it's important for energy conservation. The best designs will combine processing and storage to save power.
  2. CapRAM is a promising technology since it uses standard materials, making it easier and cheaper to produce. This could help it succeed where other technologies struggle.
  3. CapRAM could lead to smaller, more powerful edge devices by minimizing data movement and energy use. This means devices can perform better without overheating.

Claude 3 Ignites Cloud Wars

Sector 6 | The Newsletter of AIM • 19 implied HN points • 06 Mar 24
🕹 Technology AI Cloud Software Computing Data
  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 Innovation Devices Personalization
  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.

When Jensen Was in Town

Sector 6 | The Newsletter of AIM • 39 implied HN points • 10 Sep 23
🕹 Technology AI Computing Innovation Partnerships Startups
  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.

XGBoost is the Secret of ML Energy

Sector 6 | The Newsletter of AIM • 39 implied HN points • 06 Sep 23
🕹 Technology Artificial Intelligence Machine Learning Data science Algorithms 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.

Implementing Softmax using RISC-V Vector

Fprox’s Substack • 41 implied HN points • 12 Feb 24
🕹 Technology Programming Algorithms Computing Simulation Hardware
  1. Softmax is a non-linear normalization layer commonly used in neural networks to compute probabilities of multiple classes.
  2. When implementing Softmax, numerical stability is crucial due to exponential function's rapid growth, requiring clever techniques to prevent overflow.
  3. RISC-V Vector (RVV) can be used to efficiently implement complex functions like Softmax, with stable and accurate results compared to naive implementations.

LLM Hallucinations 🤦

Sector 6 | The Newsletter of AIM • 39 implied HN points • 27 Jul 23
🕹 Technology AI Computing Software Development Innovation
  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.

Getting Faster for Your Own LLM Inference

The Beep • 19 implied HN points • 28 Jan 24
🕹 Technology AI Computing Machine Learning Software Development Data processing
  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.

GPT-4 invalidates the Turing test

Gray Mirror • 110 implied HN points • 13 Apr 23
🕹 Technology AI Computing Intelligence Models Algorithms
  1. Large language models like GPT-4 are not AI, but they are powerful tools that connect patterns and rely on intuition.
  2. The Turing test is not a valid test for AGI, as machines like LLMs can invalidate it by excelling in certain tasks while lacking in others.
  3. Understanding the difference between general and special intelligence is key to not overestimating the capabilities of tools like GPT-4.