The hottest Computing Substack posts right now

And their main takeaways
Category
Top Technology Topics

Sam Altman Wants $7 Trillion

Astral Codex Ten • 16656 implied HN points • 13 Feb 24
🕹 Technology AI Machine Learning Artificial Intelligence Big Data 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.

Demystifying GPU Compute Software

The Chip Letter • 6577 implied HN points • 10 Mar 24
🕹 Technology Computing Programming Hardware Frameworks Development
  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.

Not so fast, Mr. Fourier!

lcamtuf’s thing • 2332 implied HN points • 12 Mar 24
🔬 Science Computing Signal Processing Mathematics Algorithms Engineering
  1. The discrete Fourier transform (DFT) is a crucial algorithm in modern computing, used for tasks like communication, image and audio processing, and data compression.
  2. DFT transforms time-domain waveforms into frequency domain readings, allowing for analysis and manipulation of signals like isolating instruments or applying effects like Auto-Tune in music.
  3. Fast Fourier Transform (FFT) optimizes DFT by reducing the number of necessary calculations, making it more efficient for large-scale applications in computing.

100 Years of 'IBM'

The Chip Letter • 1849 implied HN points • 15 Feb 24
🕹 Technology Computing Companies History
  1. IBM has had a significant impact on the development of computer systems over 100 years.
  2. IBM's influence extends to technologies like mainframes, personal computers, and databases.
  3. The history of IBM shows both positive contributions to technology and darker aspects like the association with controversial events.

When the Mac was a Munition

Pessimists Archive Newsletter • 648 implied HN points • 24 Jan 24
🕹 Technology Computing History Regulation Innovation AI
  1. The US government classified the Power Mac G4 as a super-computer due to its computing power surpassing 1 GIGAFLOP.
  2. In 1979, a GIGAFLOP was seen as powerful and scary, but now we carry thousands of GIGAFLOPs in our pockets with modern devices.
  3. The marketing genius of Apple used the munition classification of the G4 to promote it as a 'Personal Supercomputer', leveraging the restrictions to market the product.
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Why Didn't Stanley Kubrick & Arthur C. Clarke Predict The iPad?

1517 Fund • 121 implied HN points • 07 Mar 24
🕹 Technology Predictions Computing History Innovation
  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.

Intel APX

The Chip Letter • 2466 implied HN points • 25 Jul 23
🕹 Technology Computing Hardware Software Architecture Innovation
  1. Intel announced APX, the next evolution of Intel architecture, with improvements in registers and performance
  2. The introduction of APX includes doubling the number of general purpose registers, new instructions, and enhancements for better performance
  3. Intel also revealed a new vector ISA, AVX10, to establish a common vector instruction set across all architectures

Want to play AAA Games in Your Car?

More Than Moore • 233 implied HN points • 04 Jan 24
🕹 Technology Computing Automotive Gaming AI Hardware
  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.

Implementing Softmax using RISC-V Vector

Fprox’s Substack • 39 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.

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.

A quick introduction to Reinforcement Learning [Math Mondays]

Technology Made Simple • 159 implied HN points • 17 Oct 23
🕹 Technology Machine Learning AI Computing Education
  1. Reinforcement Learning is a big part of Machine Learning, focused on maximizing rewards for models.
  2. Setting up Reinforcement Learning involves components like RL agents, suitable for teaching AI to play games and develop various skills.
  3. Reinforcement Learning is valuable because it can show unexpected system vulnerabilities by behaving differently from humans.

3 Tests AGI Must Pass

Insight Axis • 237 implied HN points • 27 Aug 23
🕹 Technology AI Computing Testing Reasoning Creativity
  1. Computers must excel at calculations to form the foundation for any further intelligence programming.
  2. After calculation, computers need to progress to reasoning - the ability to evaluate information and use it to make value-based decisions.
  3. The ultimate test for artificial intelligence is creativity - the capability to acknowledge rules but break them intuitively to create something new.

☞ The Garden of Computational Delights

Cabinet of Wonders • 230 implied HN points • 02 Aug 23
🕹 Technology Computing Innovation Data 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.

Geometric Constraints on Computing

Bzogramming • 30 implied HN points • 29 Jan 24
🕹 Technology Computing Physics Architecture Optimization Future Trends
  1. The physical constraints of computing, such as distance and volume, significantly impact performance and efficiency.
  2. Parallelism at different scales within a program can affect latency and performance, offering opportunities for optimization.
  3. Considerations like curvature of computation, square-cube law, and heat generation play a crucial role in the design and limitations of computer chips.

🔮E08: Optical Computing

Let Us Face the Future • 235 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.

203. Software As Part of Everything Is Regulated…As Part of Everything

Hardcore Software • 334 implied HN points • 19 Apr 23
🕹 Technology Regulation Software AI Innovation Computing
  1. Software has become a fundamental part of our lives, evolving from its origins in math to touching every aspect of human endeavors.
  2. Regulations have always been key in governing software, ensuring safety, reliability, and functionality in various industries.
  3. The introduction of AI should follow the established regulatory frameworks for software, without seeking a separate or special exemption.

Non-Newtonian Computing?

Bzogramming • 30 implied HN points • 07 Jan 24
🕹 Technology Computing Physics Cryptography Algorithms AI
  1. Physics has alternative framings like Lagrangian and Hamiltonian mechanics, which could inspire new ways of viewing computation.
  2. Reversible computing, preserving information by having bijective gates, is crucial for energy efficiency and future computing technologies.
  3. Studying constraint solvers and NP-complete problems can lead to insights for accelerating search algorithms and developing new computing approaches.

The Iceberg that sank the Snowflake?

Brick by Brick • 9 implied HN points • 01 Mar 24
🕹 Technology Data Warehousing Open Source Computing
  1. Snowflake's stock dropped significantly after the announcement of CEO Frank Slootman's retirement, with a key concern being the impact of Apache Iceberg on moving data out of Snowflake.
  2. Apache Iceberg is a powerful technology that allows for the efficient migration of data out of Snowflake to other systems for processing, causing revenue loss in both storage and compute for Snowflake.
  3. The paradigm shift towards technologies like Iceberg takes time in enterprise settings but can have a significant impact, highlighting the importance of capturing the compute dollars in data processing.

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

[TI-06] people and computers

spencer's paradoxes • 137 implied HN points • 13 Jul 23
🕹 Technology Computing Internet Software Web Innovation
  1. The show Halt and Catch Fire explores the history of personal computers and the early days of the World Wide Web.
  2. Computing can be a tool for creating human connection and meaningful interactions on the internet.
  3. Focusing on creating a computing environment that encourages collaboration, creativity, and shared experiences can lead to a more positive online space.

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.

Understanding the Power of Bitwise Operators. No math needed

Deus In Machina • 145 implied HN points • 11 May 23
🕹 Technology Programming 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.

[TI-04] gestures at alternative internets

spencer's paradoxes • 117 implied HN points • 27 May 23
🕹 Technology Internet Art Computing
  1. Creating internet spaces that highlight humanity and promote real dialogue between humans and technology is important.
  2. Speculative research and creating 'art' pieces are essential in understanding and envisioning the kind of world we want to build.
  3. Technology should be used to create spaces that acknowledge growth, decay, and change while promoting close attention and quality of interaction.

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.

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.