The hottest Computing Substack posts right now

And their main takeaways
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Top Technology Topics

Chinese AI’s Sputnik Moment

Taipology • 69 implied HN points • 24 Jan 25
🕹 Technology AI Innovation Computing Open Source Research
  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.

Anthropic, WOW

TheSequence • 161 implied HN points • 27 Oct 24
🕹 Technology AI Machine Learning Software Computing Innovation
  1. Anthropic has launched a new AI model named Claude that can interact with computers like a human, allowing it to execute tasks directly on-screen. This opens many new possibilities for AI applications.
  2. Two upgraded versions of Claude have been released, one focusing on coding and tool usage with high performance, and the other emphasizing speed and affordability for everyday applications.
  3. A new analysis tool has been introduced in Claude.ai, enabling the model to write and run JavaScript code for data analysis and visualizations, enhancing its functionality for users.

Undecidability Isn't So Bad

Bzogramming • 22 implied HN points • 07 Dec 24
🕹 Technology Programming Computing Algorithms Mathematics Software
  1. Some problems in computing are called undecidable, which means we can't find a definite solution for them. However, that doesn’t mean we can’t approach them creatively and get some useful results.
  2. When working with programs, understanding their behavior can often reveal hidden bugs. If a program doesn't behave the way we expect, it might be a sign that something is wrong in the code.
  3. There are smarter ways to analyze code than just throwing our hands up and saying it’s impossible. Advanced tools are already in place in many programming environments, but they often work behind the scenes without us being aware of them.

Happy Llama Day 🦙🤩 ✨

Sector 6 | The Newsletter of AIM • 79 implied HN points • 20 Apr 24
🕹 Technology AI Computing Software Innovation Research
  1. Meta launched Llama 3, an advanced open-source language model that outshines its competitors in reasoning and coding tasks. This model is creating a lot of buzz for its performance.
  2. Andrej Karpathy, a former OpenAI scientist, is very excited about Llama 3 and thinks it will be a strong competitor against GPT-4.
  3. Llama 3 is designed with a massive 400 billion parameters, making it a powerful tool for various applications in AI.

Has AI Progress Stalled?

The Future of Life • 19 implied HN points • 21 Jul 24
🕹 Technology AI Machine Learning Software Development Computing Data science
  1. AI improvement has slowed down in terms of new abilities since GPT-4 came out, but other factors like cost and speed have gotten much better.
  2. The focus now is on practical changes and making AI more valuable, which will help set the stage for bigger breakthroughs in the future.
  3. Reaching human-level skills in tests doesn't mean AI will be truly intelligent. Future development will need to incorporate more complex abilities like planning and learning from experiences.
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There's no such thing as "machine learning."

Top Carbon Chauvinist • 19 implied HN points • 20 Jul 24
🕹 Technology AI Machine Learning Philosophy Computing Data science
  1. Machines don't really learn like humans do. They can take in data and improve performance, but they don't understand or experience learning in the same way we do.
  2. The term 'machine learning' can be misleading. It's more about machines mimicking learning processes rather than actually experiencing them.
  3. Understanding how machines operate helps clarify their limitations. They can process large amounts of information but lack conscious experience or true comprehension.

I, Token

Nothing Human • 23 implied HN points • 25 Nov 24
🕹 Technology AI Machine Learning Computing Innovation Philosophy
  1. Tokens are like bits of language that help us express thoughts and feelings. They connect our emotions and experiences across time and space.
  2. The story of survival, like the mother warning her child about the snake, shows how important communication is for human beings. They have always used sounds and symbols to protect and connect with each other.
  3. Now, we create tokens using machines, but they still need human creativity. While technology can produce many tokens, the unique insights and connections come from people.

How Far Can Moore's Law Go?

Tapa’s Substack • 79 implied HN points • 07 Apr 24
🕹 Technology Computing Hardware Engineering AI Electronics
  1. Moore's Law shows that the number of transistors on chips grows, but the real limit to performance is how efficiently we can use power. Even if we add more transistors, we might not get better performance without better power management.
  2. We need to consider the costs of power and cooling when designing chips, not just the cost of the hardware itself. Cooling efforts can be more complex and expensive as we push for higher performance.
  3. New technologies and materials like photonics, 3D chip designs, and even concepts like spintronics might help enhance computing performance, especially for memory-related tasks, but there are many challenges to overcome.

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 AI Models Hardware Generative AI 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.

The Sequence Chat: Thinking About Transformers as Computers

TheSequence • 105 implied HN points • 30 Oct 24
🕹 Technology Artificial Intelligence Computing Machine Learning Natural Language Data science
  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.

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.

Speculative RAG By Google Research

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 12 Jul 24
🕹 Technology AI Machine Learning Natural Language Processing Data science Computing
  1. Retrieval Augmented Generation (RAG) is a way to improve answers by using a mix of information from language models and external sources. By doing this, it gives more accurate and timely responses.
  2. The new Speculative RAG method uses a smaller model to quickly create drafts from different pieces of information, letting a larger model check those drafts. This makes the whole process faster and more effective.
  3. Using smaller, specialized language models for drafting helps save on costs and reduces wait times. It can also improve the accuracy of answers without needing extensive training.

Who Runs the World?

Sector 6 | The Newsletter of AIM • 99 implied HN points • 26 Feb 24
🕹 Technology Computing AI Software Hardware Data
  1. NVIDIA is a major player in the tech industry, affecting many computer companies worldwide. They've made big strides in both hardware and software for computing and AI.
  2. The company's recent financial success is impressive, with revenue growing significantly compared to last year. This shows that more businesses and industries are adopting their technology.
  3. NVIDIA's growth signals a shift to a new era in computing. Many experts believe we are entering a transformative phase in technology.

Data Science Weekly - Issue 502

Data Science Weekly Newsletter • 319 implied HN points • 07 Jul 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Analytics Computing
  1. Generative design is making strides in drug discovery, but there are still challenges to address for better outcomes.
  2. The UK government is investing in a Foundation Model Taskforce to harness AI for societal benefits and safety.
  3. Keeping updated with developments in data science, such as new models and applications, is essential for professionals in the field.

Is Earth A Computational Process?

Surfing the Future • 119 implied HN points • 28 Jan 24
🔬 Science Computing AI Sustainability Chemistry
  1. Stephen Wolfram's TED talk on computational thinking explores AI, the universe, and more, opening up new possibilities for the future.
  2. Earth being a computing process is a fascinating concept with implications for sustainability and AI.
  3. The work of James Lovelock, especially his Gaia theory, holds significance and influences the thinking of many individuals.

Complete Summary of Absolute, Relative and Rotary Position Embeddings!

Aziz et al. Paper Summaries • 79 implied HN points • 31 Mar 24
🕹 Technology Artificial Intelligence Machine Learning Natural Language Processing Computing Data science
  1. Transformers can't understand the order of words, so position embeddings are used to give them that context.
  2. Absolute embeddings assign unique values to each word's position, but they struggle with new positions beyond what they trained on.
  3. Relative embeddings focus on the distance between words, which makes the model aware of their relationships, but they can slow down training and searching.

How AI generates images, visually explained 🎨

Year 2049 • 4 implied HN points • 20 Jan 25
🕹 Technology AI Computing Innovation Digital Media Data science
  1. AI creates images using a process called diffusion. This means it starts with random noise and turns it into a clear image step by step.
  2. Understanding how AI generates images helps demystify some of the technology behind AI and art. It's cool to see how computers can make creative expressions!
  3. Learning about AI can open up more conversations about its impact on our everyday lives and the future of creativity. It's important to think about both the benefits and challenges.

Intents: the end-game for computing interfaces?

Router by Dmitry Pimenov • 2 HN points • 11 Sep 24
🕹 Technology Interfaces Programming AI Computing Automation
  1. Computing interfaces are evolving from specific command-based systems to more user-friendly methods that focus on overall goals. This makes it easier for developers to work on what really matters instead of getting bogged down in details.
  2. Intent-driven interfaces allow us to express our thoughts directly to machines, removing the need for complicated steps. This means we can communicate what we want in a more natural way.
  3. The rise of AI and new technologies is shifting how we interact with computers. Soon, we may even communicate our intentions directly from our minds, making technology feel more personal and easier to use.

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.

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 AI Machine Learning Computing Innovation Data science
  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.

The PacBio Vega - More Tech Details

ASeq Newsletter • 21 implied HN points • 07 Nov 24
🕹 Technology Engineering Biotechnology Research Instruments Computing
  1. The PacBio Vega is designed for small labs and minimizes downtime between runs. Users can load new samples while a run is ongoing, making it efficient.
  2. The technology in the Vega seems to be similar to the Revio but aims to reduce costs, likely making high-quality sequencing more accessible to small research centers.
  3. There's curiosity about how PacBio has managed to incorporate advanced computing power into a compact design, which is crucial for producing quality data without needing expensive equipment.

The Sequence Radar #467: NVIDIA AI Software Party at a Hardware Show

TheSequence • 35 implied HN points • 12 Jan 25
🕹 Technology AI Computing Software Hardware Robotics
  1. NVIDIA is focusing more on AI software, not just hardware, which was clear at CES. They launched several new AI software products that make it easier for developers to integrate AI into their apps.
  2. The new NVIDIA NIM microservices allow developers to deploy AI capabilities quickly, cutting down deployment times significantly. This is a game changer for companies looking to adopt AI technologies fast.
  3. NVIDIA's new AI Blueprints are templates that help developers create AI solutions efficiently. This means developers can spend more time innovating instead of starting from scratch.

Edge 451: Is One Teacher Enough? Understanding Multi-Teacher Distillation

TheSequence • 56 implied HN points • 26 Nov 24
🕹 Technology Machine Learning Artificial Intelligence Data science Computing Software Development
  1. Using multiple teachers in distillation is better than just one. This method helps combine different areas of knowledge, making the student model more powerful.
  2. Each teacher can focus on a specific type of knowledge, like understanding features or responses. This specialization leads to a more balanced learning process.
  3. Although this approach might be more expensive to implement, it creates a stronger and less biased model overall.

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.

English is NOT a Programming Language 🤦

Sector 6 | The Newsletter of AIM • 79 implied HN points • 07 Feb 24
🕹 Technology Programming AI Software Natural Language Computing
  1. English has too many ambiguities to be a programming language. Programming needs precise rules, and English doesn't always follow them.
  2. Douglas Crockford, the creator of JSON, is worried about pushing English as a coding language. He believes that code must be perfect, which English is not.
  3. Using natural language through AI for programming might lead to confusion. Clarity and accuracy are crucial for writing successful code.

Everything AI Is Old Is New Again

Gradient Ascendant • 5 implied HN points • 07 Jan 25
🕹 Technology AI Computing Innovation Business Trends
  1. AI technology today has strong parallels with the computing advancements from the 1980s, showing that history can repeat itself. It's essential to recognize these similarities to better understand our tech landscape.
  2. The major players in AI can be compared to historical companies like Microsoft and Apple, with their own distinct positions and market reactions. This framing helps us see how competition is shaping the AI world now.
  3. Google's situation in AI mirrors IBM's struggles back then, but Google has more opportunities to learn from those past mistakes. This could give them a better chance for success moving forward.

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

Hardcore Software • 337 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.

Is Step Back Prompting The Best Prompting Strategy?

Aziz et al. Paper Summaries • 59 implied HN points • 20 Mar 24
🕹 Technology AI Machine Learning Data science Computing Engineering
  1. Step Back Prompting helps models think about big ideas before answering questions. This method shows better results than other prompting techniques.
  2. Even with Step Back Prompting, models still find it tricky to put all their reasoning together. Many errors come from the final reasoning step which can be complicated.
  3. Not every question works well with Step Back Prompting. Some questions need quick, specific answers instead of a longer thought process.

[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.

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.

The Smartest Bear vs the Dumbest Human: The Implications of AI's Growing Problem-Solving Abilities

The Future of Life • 19 implied HN points • 04 Jun 24
🕹 Technology AI Machine Learning Computing Automation Engineering
  1. AI is getting really good at problem-solving, even beating humans at some tasks, like solving CAPTCHAs. This shows that AI can reason better than many humans, especially in certain situations.
  2. The Turing test isn't just one hurdle to jump over; it's a series of challenges that measure how closely AI can act like a human. As AI improves, it passes more of these challenges, showing its capabilities.
  3. While current AI isn't fully intelligent like a human, it's almost ready to solve a lot of problems. The only big limitation is how much computing power is available for training these AI systems.

Not so fast, Mr. Fourier!

lcamtuf’s thing • 119 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.

[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.

Bonus Clouded Judgement - Inference Time Compute

Clouded Judgement • 20 implied HN points • 28 Jan 25
🕹 Technology AI Computing Machine Learning Software Innovation
  1. DeepSeek has released a new AI model called R1 that is smaller, cheaper, and faster, while still being able to handle complex reasoning tasks. This marks a shift in how AI models are being developed and used.
  2. Inference-time compute is becoming increasingly important, as it refers to how much computation power models need to think and solve problems after being trained. This can lead to a significant increase in the demand for compute resources.
  3. There's an ongoing debate about the future of AI models—whether smaller, efficient models or larger, more powerful ones will dominate. Both types have their advantages, and it seems likely that we'll see a balance of both in the market.

Chain-of-Instructions (CoI) Fine-Tuning & Going Beyond Instruction Tuning

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 39 implied HN points • 21 Mar 24
🕹 Technology Artificial Intelligence Machine Learning Data science Natural Language Computing
  1. Chain-of-Instructions (CoI) fine-tuning allows models to handle complex tasks by breaking them down into manageable steps. This means that a task can be solved one part at a time, making it easier to follow.
  2. This new approach improves the model's ability to understand and complete instructions it hasn't encountered before. It's like teaching a student to tackle complex problems by showing them how to approach each smaller task.
  3. Training with minimal human supervision leads to efficient dataset creation that can empower models to reason better. It's as if the model learns on its own, becoming smarter and more capable through well-designed training.