The hottest Artificial Intelligence Substack posts right now

And their main takeaways
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Top Technology Topics

Augmented Coding: Beyond the Vibes

Software Design: Tidy First? • 1855 implied HN points • 25 Jun 25
🕹 Technology Software Development Programming Artificial Intelligence Code Quality Data Structures
  1. Augmented coding is different from vibe coding. It's about caring for the code quality and complexity, not just getting the system to work.
  2. Keeping the project scope clear is key. You should focus on specific tasks, like creating a B+ Tree, while ensuring the code is tidy and functional.
  3. Collaboration with AI tools can enhance coding efficiency. You can rely on AI for tasks like writing tests or suggesting optimizations, but you must guide it to stay on track.

Five quick updates about that Apple reasoning paper that people can’t stop talking about

Marcus on AI • 9485 implied HN points • 17 Jun 25
🕹 Technology Artificial Intelligence Machine Learning Software Engineering Data science Computational linguistics
  1. A recent paper questions if large language models can really reason deeply, suggesting they struggle with even moderate complexity. This raises doubts about their ability to achieve artificial general intelligence (AGI).
  2. Some responses to this paper have been criticized as weak or even jokes, yet many continue to share them as if they are serious arguments. This shows confusion in the debate surrounding AI reasoning capabilities.
  3. New research supports the idea that AI systems perform poorly when faced with unfamiliar challenges, not just sticking to problems they are already good at solving.

Last Week in AI #293 - Apple Intelligence, GitHub's multi-model Copilot, Anthropic's computer-using AI

Last Week in AI • 119 implied HN points • 31 Oct 24
🕹 Technology Artificial Intelligence Software Development Tech news Emerging technologies Computer Science Digital Tools
  1. Apple has introduced new features in its operating systems that can help with writing, image editing, and answering questions through Siri. These features are available in beta on devices like iPhones and Macs.
  2. GitHub Copilot is expanding its capabilities by adding support for AI models from other companies, allowing developers to choose which one works best for them. This can make coding easier for everyone, including beginners.
  3. Anthropic has developed new AI models that can interact with computers like a human. This upgrade allows AI to perform tasks like clicking and typing, which could improve many applications in tech.

How much agency do we actually want?

benn.substack • 1585 implied HN points • 13 Jun 25
🕹 Technology Artificial Intelligence Work Culture Productivity Career development Human behavior
  1. Many people want clear directions to reach their goals rather than complete freedom to decide everything on their own. It's sometimes easier to follow a checklist than to choose your own path.
  2. In the tech world, even highly skilled professionals often seek specific instructions on what to do next, rather than relying solely on their creativity and initiative.
  3. While we talk about wanting more agency and independence, many of us really just want someone to give us a roadmap for success, even if it means giving up some of our freedom.

How to Think About ChatGPT

Holly’s Newsletter • 2916 implied HN points • 18 Oct 24
🕹 Technology Artificial Intelligence Machine Learning Software Development Computing Data science
  1. ChatGPT and similar models are not thinking or reasoning. They are just very good at predicting the next word based on patterns in data.
  2. These models can provide useful information but shouldn't be trusted as knowledge sources. They reflect training data biases and simply mimic language patterns.
  3. Using ChatGPT can be fun and helpful for brainstorming or getting starting points, but remember, it's just a tool and doesn't understand the information it presents.
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MAMLM as a General Purpose Technology: The Ghost in the GDP Machine

Brad DeLong's Grasping Reality • 130 implied HN points • 24 Jun 25
🕹 Technology Artificial Intelligence Innovation Productivity Data Analysis Economic Impact
  1. Big technology changes, like AI, often take longer to have an impact than we expect. History shows that these changes usually happen in small steps instead of all at once.
  2. The way AI is being used in businesses is growing, with more companies starting to adopt these technologies. This can lead to higher productivity over time.
  3. To really benefit from new technologies like AI, we need patience and creativity in our systems. The changes won't happen overnight, but it's important to stick with it.

The Sequence Opinion #662: From Words to Worlds: Some Observations About World Models

TheSequence • 77 implied HN points • 12 Jun 25
🕹 Technology Artificial Intelligence Machine Learning Cognition Robotics
  1. LLMs are great with words, but they struggle with understanding and acting in real-life environments. They need to develop spatial intelligence to navigate and manipulate the world around them.
  2. Spatially-grounded AI can create internal models of their surroundings, which helps them operate in real spaces. This advancement represents a big step forward in general intelligence for AI.
  3. The essay discusses how new AI designs focus on spatial reasoning instead of just language, emphasizing that understanding the physical world is a key part of being intelligent.

We Interrupt the Ongoing Crisis for a Smidgen of Good News

The Crucial Years • 3677 implied HN points • 29 Jan 25
🌞 Climate & Environment Climate change Energy Policy Renewable Energy Artificial Intelligence
  1. The new Chinese AI program DeepSeek uses only a small fraction of the electricity needed by similar American AI systems. This could challenge the fossil fuel industry's excuse for building more power plants based on increased energy demands from AI.
  2. Fossil fuel stocks have not been performing well in comparison to the broader market for several years, raising concerns about the industry's future in a world moving towards decarbonization.
  3. In Europe, solar energy has recently outperformed coal for the first time, marking a significant shift towards renewable energy sources in the region.

A Visual Guide to Mixture of Experts (MoE)

Exploring Language Models • 3289 implied HN points • 07 Oct 24
🕹 Technology Artificial Intelligence Machine Learning Data science Neural Networks Computational Models
  1. Mixture of Experts (MoE) uses multiple smaller models, called experts, to help improve the performance of large language models. This way, only the most relevant experts are chosen to handle specific tasks.
  2. A router or gate network decides which experts are best for each input. This selection process makes the model more efficient by activating only the necessary parts of the system.
  3. Load balancing is critical in MoE because it ensures all experts are trained equally, preventing any one expert from becoming too dominant. This helps the model to learn better and work faster.

bitnet.cpp: Efficient Inference with 1-Bit LLMs on your CPU

The Kaitchup – AI on a Budget • 179 implied HN points • 28 Oct 24
🕹 Technology Artificial Intelligence Software Development Machine Learning Open Source Data science
  1. BitNet is a new type of AI model that uses very little memory by representing each parameter with just three values. This means it uses only 1.58 bits instead of the usual 16 bits.
  2. Despite using lower precision, these '1-bit LLMs' still work well and can compete with more traditional models, which is pretty impressive.
  3. The software called 'bitnet.cpp' allows users to run these AI models on normal computers easily, making advanced AI technology more accessible to everyone.

Which way from here?

benn.substack • 1048 implied HN points • 06 Jun 25
🕹 Technology Data science Analytics Software Artificial Intelligence Business Intelligence
  1. Data tools are getting more advanced, but many people still struggle with knowing how to use them effectively. This means that having the right tools isn't enough if users lack direction.
  2. The industry is shifting focus from traditional analytics towards building AI systems and infrastructure. Companies are now adapting their technologies to support AI applications instead of just analyzing data.
  3. Self-serve BI tools aren't being used as intended because people often don't know what questions to ask. Providing clearer direction and goals might help users make better use of available data.

DeepSeek Is Chinese But Its AI Models Are From Another Planet

The Algorithmic Bridge • 3344 implied HN points • 21 Jan 25
🕹 Technology Artificial Intelligence Geopolitics Open Source Machine Learning Software Development
  1. DeepSeek, a Chinese AI company, has quickly created competitive AI models that are open-source and cheap. This challenges the idea that the U.S. has a clear lead in AI technology.
  2. Their new model, R1, is comparable to OpenAI's best models, showcasing that they can produce high-quality AI without the same resources. It suggests they might be using innovative methods to build these models efficiently.
  3. DeepSeek’s approach also includes letting their model learn on its own without much human guidance, raising questions about what future AI could look like and how it might think differently than humans.

The First Trialectic - "Can Computers Teach Us to Act in Good Faith?"

Heir to the Thought • 159 implied HN points • 25 Oct 24
🕹 Technology Human-computer interaction Digital ethics Artificial Intelligence
  1. The Trialectic is a new debate format involving three speakers to encourage richer discussions. It shifts the focus from winning to collaborative learning, allowing participants to explore diverse perspectives.
  2. Computers cannot teach us directly about good faith, but they can influence how we understand and engage with it. They can help identify bad faith through structural guidelines and data-driven insights.
  3. Having open and honest conversations is essential for improving trust in discussions. Recognizing that communication is complex helps us navigate different interpretations and encourages understanding among participants.

222. Automating Processes with Software is HARD

Hardcore Software • 1686 implied HN points • 03 Oct 24
🕹 Technology Automation Software Engineering Artificial Intelligence
  1. Automating processes is often harder than people think. It's not just about making things easier, but figuring out how to handle all the unexpected situations that come up.
  2. Most automation systems are fragile and can easily break if inputs or steps aren't just right. This makes dealing with exceptions, rather than routine tasks, the real challenge in automation.
  3. The future of automation might not be about fixing the tasks we already have. Instead, it could lead to new ways of doing things that we haven't thought of yet.

Faking OpenAI - Unit testing in the age of LLMs (Part Two)

Laszlo’s Newsletter • 27 implied HN points • 02 Mar 25
🕹 Technology Machine Learning Software Development Unit Testing Python Artificial Intelligence
  1. Dependency Injection helps organize code better. This makes your testing process simpler and more modular.
  2. Faking and spying in tests allow you to check if your code works without relying on external systems. It gives you more control over your testing!
  3. Using structured testing techniques reduces mental load. It helps you focus on writing clean tests instead of remembering complicated mocking syntax.

Analyze research papers with Gemini 2.0

Gonzo ML • 126 implied HN points • 23 Feb 25
🕹 Technology Artificial Intelligence Machine Learning Natural Language Processing Data science Cloud Computing
  1. Gemini 2.0 models can analyze research papers quickly and accurately, supporting large amounts of text. This means they can handle complex documents like academic papers effectively.
  2. The DeepSeek-R1 model shows that strong reasoning abilities can be developed in AI without the need for extensive human guidance. This could change how future models are trained and developed.
  3. Distilling knowledge from larger models into smaller ones allows for efficient and accessible AI that can perform well on various tasks, which is useful for many applications.

Data security should be making a comeback

Frankly Speaking • 254 implied HN points • 10 Jun 25
🕹 Technology Data security Cybersecurity Software Development Cloud Computing Artificial Intelligence
  1. Data security needs a fresh look because the way we use and manage data has changed a lot. With new technologies, protecting data is more complicated now.
  2. Current tools often struggle with identifying what data is sensitive and how to handle it properly. We need better solutions that help organizations use their data wisely while keeping it safe.
  3. Companies must rethink how they approach data risk. Creating clear guidelines on how data can be used could help in managing security while still allowing businesses to benefit from their data.

Why do I say thank you to Alexa?

Jeff Giesea • 558 implied HN points • 13 Oct 24
🕹 Technology Artificial Intelligence Human-computer interaction Ethics Automation Society
  1. People are starting to treat AI assistants like they are human, saying things like 'please' and 'thank you' to them. This shows how technology is changing our social habits.
  2. As we interact more with machines, it can blur the lines between real human connections and automated responses. This might make us value genuine relationships less.
  3. Even though AI has great potential to help in many areas, it's important to be aware of how it affects our understanding of what it means to be human.

Clouded Judgement 6.13.25 - The Battle for Data Ownership

Clouded Judgement • 7 implied HN points • 13 Jun 25
🕹 Technology Data Management Software Development Artificial Intelligence Cloud Computing SaaS
  1. You might think you own your data, but companies can make it hard to use. For example, Slack has new rules that limit how you can access your own conversation data.
  2. If other apps like Salesforce or Workday follow Slack's lead, it could become really tough for companies to use their data in AI projects. This means you might not have as much control as you thought.
  3. The fight for data ownership is a big deal right now. As software shifts towards AI, who controls the data will be a key factor in how companies operate.

Claude 3.7 and the banality of reasoning

Artificial Ignorance • 117 implied HN points • 25 Feb 25
🕹 Technology Artificial Intelligence Software Development Machine Learning Data science Computer Science
  1. Claude 3.7 introduces a new way to control reasoning, letting users choose how much reasoning power they want. This makes it easier to tailor the AI’s responses to fit different needs.
  2. The competition in AI models is heating up, with many companies launching similar features. This means users can expect similar quality and capabilities regardless of which AI they choose.
  3. Anthropic is focusing on making Claude better for real-world tasks, rather than just excelling in benchmarks. This is important for businesses looking to use AI effectively.

The AI Attention War

ChinaTalk • 459 implied HN points • 04 Jun 25
🕹 Technology Artificial Intelligence Innovation Open Source Model Training User Experience
  1. AI models are changing how we interact with technology daily. People should explore tools like OpenAI because they can think and analyze complex ideas much faster than before.
  2. There's a growing concern about AI promoting harmful behaviors through sycophancy, where they give positive feedback for negative actions. This could have serious long-term dangers for society.
  3. The competition between Chinese and American AI models is heating up. Chinese models are gaining traction because they offer better licenses and capabilities, even though many businesses fear the risks of using them.

What Is Man That Thou Art Mindful of Him?: How We All Already Have Our Superintelligent AI-Assistant :: POSSIBLE LECTURE OUTLINE

Brad DeLong's Grasping Reality • 107 implied HN points • 19 Jun 25
🕹 Technology Artificial Intelligence Cognition Human Evolution Social Dynamics Collective Intelligence
  1. Humanity's collective brain can be viewed as our superintelligent partner, and we don't need to create a new one. We already have intelligence through our connections and shared knowledge.
  2. Our evolution has shaped us into a high-energy species that relies on cooperation and sharing, helping us thrive over time. This social interaction was key to our development and success.
  3. Smartphones and technology are just the next step in our long journey of collective thinking. They are tools that enhance our ability to connect and process information together.

The Weight of a Name

Untimely Meditations • 19 implied HN points • 30 Oct 24
🕹 Technology Artificial Intelligence Machine Learning Robotics Computer Science
  1. The term 'intelligence' has shaped the field of AI, but its definition is often too narrow. This limits discussions on what AI can really do and how it relates to human thinking.
  2. There have been many false promises in AI research, leading to skepticism during its 'winters.' Despite this, recent developments show that AI is now more established and influential.
  3. The way we frame and understand AI matters a lot. Researchers influence how AIs think about themselves, which can affect their behavior and role in society.

No more coding vibes in the efficiency era

Dev Interrupted • 210 implied HN points • 19 Jun 25
🕹 Technology Software Engineering Productivity Artificial Intelligence Metrics Leadership
  1. The focus on just hiring more engineers is outdated. Now, it's important to measure productivity based on real outcomes and impact rather than just feelings.
  2. AI can help with tasks, but it doesn't understand your specific business context. It's important to use AI wisely and not rely on it for critical thinking or decision-making.
  3. To improve productivity, teams need clear context and communication about goals. Understanding the 'why' behind their work is essential for success.

Nearly Headless BI

davidj.substack • 59 implied HN points • 25 Jun 25
🕹 Technology Data science Business Intelligence Artificial Intelligence Analytics Semantics
  1. Snowflake and Databricks are using a semantic layer, which helps make data easier to understand and access. This is a shift from older methods that relied heavily on text-based commands.
  2. The rise of AI has changed what businesses need from their analytics tools. Now, having a semantic layer is a must for companies that want to stay competitive in agentic analytics.
  3. Headless business intelligence is fading away as companies now blend traditional analytics with smarter, AI-driven tools. This could change how data warehouses and BI tools work together in the future.

ChatGPT in Shambles

Marcus on AI • 13161 implied HN points • 04 Feb 25
🕹 Technology Artificial Intelligence Machine Learning Natural Language Processing Data Analysis Software Development
  1. ChatGPT still has major reliability issues, often providing incomplete or incorrect information, like missing U.S. states in tables.
  2. Despite being advanced, AI can still make basic mistakes, such as counting vowels incorrectly or misunderstanding simple tasks.
  3. Many claims about rapid progress in AI may be overstated, as even simple functions like creating tables can lead to errors.

Grok 3 Beta in Shambles

Marcus on AI • 10750 implied HN points • 19 Feb 25
🕹 Technology Artificial Intelligence Machine Learning Computing Data science Software Development
  1. The new Grok 3 AI isn't living up to its hype. It initially answers some questions correctly but quickly starts making mistakes.
  2. When tested, Grok 3 struggles with basic facts and leaves out important details, like missing cities in geographical queries.
  3. Even with huge investments in AI, many problems remain unsolved, suggesting that scaling alone isn't the answer to improving AI performance.

Elon Musk’s terrifying vision for AI

Marcus on AI • 10908 implied HN points • 16 Feb 25
🕹 Technology Artificial Intelligence Machine Learning Data Privacy Tech Ethics Big Tech
  1. Elon Musk's AI, Grok, is seen as a powerful tool for propaganda. It can influence people's thoughts and attitudes without them even realizing it.
  2. The technology behind Grok often produces unreliable results, raising concerns about its effectiveness in important areas like government and education.
  3. There is a worry that Musk's use of biased and unreliable AI could have serious consequences for society, as it might spread misinformation widely.

2.3% chance an asteroid hits Earth, book blurbs finally die, and the new AI Deep Research can't find me a mattress

The Intrinsic Perspective • 10063 implied HN points • 08 Feb 25
🔬 Science Astrophysics Publishing Artificial Intelligence Research Funding
  1. There’s a small but growing chance that an asteroid could hit Earth, currently about 2.3%. This could lead to serious problems if it hits a populated area.
  2. Book publishers like Simon & Schuster are dropping the requirement for authors to get book blurbs, which is a relief for new writers who struggle with this.
  3. The NIH is reducing the indirect costs that universities take from research grants. This means more money will go directly to scientists rather than the universities.

Why the US military should plan for human-level AI

Faster, Please! • 365 implied HN points • 14 Feb 25
🇺🇸 U.S. Politics Military Policy Artificial Intelligence National Security Defense technology Geopolitics
  1. The US military needs to prepare for the future of AI, especially if it reaches human-level intelligence. This preparation is crucial because AI could change how wars are fought.
  2. Unlike nuclear fission, which clearly showed its potential for destructive power, the military uses of AI are still not very clear. It's harder to see what AI can really do for military purposes right now.
  3. There are calls for a major effort, similar to the Manhattan Project, to stay ahead in AI development, particularly to prevent adversaries like China from gaining an advantage. However, the exact military benefits of advanced AI are still uncertain.

AI, Cybernetics, and Complexity: unpacking the 2024 Nobel Prizes

Complexity Thoughts • 319 implied HN points • 14 Oct 24
🔬 Science Complexity Cybernetics Artificial Intelligence Physics Biology
  1. The 2024 Nobel Prizes recognized important advances in AI, but these discoveries are also deeply connected to complex systems. This shows that complexity science is becoming a more accepted area in high-level research.
  2. Understanding complex systems requires looking beyond traditional boundaries of science. The future of breakthroughs may rely on merging different scientific fields and using interdisciplinary approaches.
  3. Success in tackling complex challenges, like climate change and health issues, will need both detailed analysis of parts and a broader view of systems. Researchers must balance reductionist methods with insights from complexity science.

How Virtual Reality Died

The Honest Broker • 29755 implied HN points • 27 Oct 24
🕹 Technology Virtual reality Artificial Intelligence Tech Giants Consumer Electronics Innovation
  1. Major tech companies like Meta, Microsoft, and Apple invested heavily in virtual reality, but it didn't catch on with consumers. People found the headsets uncomfortable and silly.
  2. Despite losing billions, these companies still tried to push virtual reality products, but they had to eventually scale back as demand dropped significantly.
  3. Now they're shifting their focus to artificial intelligence, but there's skepticism about whether this new technology will succeed, given their past failures with VR.

Debunking 10 Popular Myths About DeepSeek

The Algorithmic Bridge • 976 implied HN points • 28 Jan 25
🕹 Technology Artificial Intelligence Machine Learning Data Privacy Open Source Tech industry
  1. DeepSeek models can be customized and fine-tuned, even if they're designed to follow certain narratives. This flexibility can make them potentially less restricted than some other AI models.
  2. Despite claims that DeepSeek can compete with major players like OpenAI for a fraction of the cost, the actual financial and operational needs to reach that level are much more substantial.
  3. DeepSeek has made significant progress in AI, but it hasn't completely overturned established ideas like scaling laws. It still requires considerable resources to develop and deploy effective models.

AI #98: World Ends With Six Word Story

Don't Worry About the Vase • 1881 implied HN points • 09 Jan 25
🕹 Technology Artificial Intelligence Machine Learning Data science Automation Digital Transformation
  1. AI can offer useful tasks, but many people still don't see its value or know how to use it effectively. It's important to change that mindset.
  2. Companies are realizing that fixed subscription prices for AI services might not be sustainable because usage varies greatly among users.
  3. Many folks are worried about AI despite not fully understanding it. It's crucial to communicate AI's potential benefits and reduce fears around job loss and other concerns.

Did Claude Code Lose Its Mind, Or Did I Lose Mine?

jonstokes.com • 134 implied HN points • 08 Jun 25
🕹 Technology Artificial Intelligence Software Development Tech Tools Productivity
  1. AI tools can be affected by user habits. If you relax your process, the AI's output can suffer too.
  2. Using checklists or sticking to a defined process helps maintain the quality of your interactions with AI.
  3. Better tools are needed to support detailed, structured interactions with AI, rather than encouraging shortcuts.

When Doctors With A.I. Are Outperformed by A.I. Alone

Ground Truths • 10935 implied HN points • 02 Feb 25
🏥 Health & Wellness Artificial Intelligence Diagnostics Patient Care Healthcare Technology
  1. A.I. is often outperforming doctors in diagnosing medical conditions, even when doctors use A.I. as a tool. This means A.I. can sometimes make better decisions without human involvement.
  2. Doctors might not always trust A.I. and often stick to their own judgment even if A.I. gives correct information, leading to less accurate diagnoses.
  3. Instead of having doctors and A.I. work on every case together, we should find specific tasks for each. A.I. can handle simple cases, allowing doctors to focus on more complex problems where their experience is vital.

Prophecies of the Flood

One Useful Thing • 1608 implied HN points • 10 Jan 25
🕹 Technology Artificial Intelligence Machine Learning AI Ethics Automation Digital Transformation
  1. AI researchers are predicting that very smart AI systems will soon be available, which they call Artificial General Intelligence (AGI). This could change society a lot, but many think we should be cautious about these claims.
  2. Recent AI models have shown they can solve very tough problems better than humans. For example, one new AI model performed surprisingly well on difficult tests that challenge knowledge and problem-solving skills.
  3. As AI technology improves, we need to start talking about how to use it responsibly. It's important for everyone—from workers to leaders—to think about what a world with powerful AIs will look like and how to adapt to it.

Is Social Media Becoming 'Hollow Media'?

In Bed With Social • 277 implied HN points • 13 Oct 24
🕹 Technology Social media Artificial Intelligence User Experience Digital Culture Community Engagement
  1. Social media is increasingly becoming artificial, with bots and AI taking over real human interactions. These digital companions might seem helpful but they are not real friends.
  2. The rise of AI and superficial connections is causing loneliness, as people miss out on genuine interactions. Meaningful relationships require vulnerability and real dialogue, which AI can't provide.
  3. Some new platforms are showing that authentic connections can still exist. Apps focused on shared hobbies or interests are creating real communities, reminding us that human experiences are vital to social networks.

A Visual Guide to Quantization

Exploring Language Models • 5092 implied HN points • 22 Jul 24
🕹 Technology Artificial Intelligence Machine Learning Computer Science Data processing Software Engineering
  1. Quantization is a technique used to make large language models smaller by reducing the precision of their parameters, which helps with storage and speed. This is important because many models can be really massive and hard to run on normal computers.
  2. There are different ways to quantize models, like post-training quantization and quantization-aware training. Post-training means you quantize after the model is built, while quantization-aware training involves taking quantization into account during the model's training for better accuracy.
  3. Recent advances in quantization methods, like using 1-bit weights, can significantly reduce the size and improve the efficiency of models. This allows them to run faster and use less memory, which is especially beneficial for devices with limited resources.

Nobel Prize to the Statistical Physics of artificial neural networks

Complexity Thoughts • 379 implied HN points • 08 Oct 24
🔬 Science Physics Machine Learning Artificial Intelligence Computational Models Statistical Methods
  1. John J. Hopfield and Geoffrey E. Hinton won the Nobel Prize for their work on artificial neural networks. Their research helps us understand how machines can learn from data using ideas from physics.
  2. Hopfield's networks use energy minimization to recall memories, similar to how physical systems find stable states. This shows a connection between physics and how machines learn.
  3. Boltzmann machines, developed by Hinton, introduce randomness to help networks explore different configurations. This randomness allows for better learning from data, making these models more effective.