The hottest Artificial Intelligence Substack posts right now

And their main takeaways
Category
Top Technology Topics

Why We Must Build World Models

Reasons to Be Optimistic • 6 implied HN points • 17 Feb 26
🕹 Technology Artificial Intelligence
  1. Text-only models are powerful but incomplete because language misses how the world actually looks, moves, and feels; video offers a far richer, high-volume source of physics, sound, and human behavior.
  2. True world models must be causal and action-conditioned, predicting the next state step-by-step under intervention; autoregressive diffusion transformer architectures trained on multimodal video and actions are a promising path.
  3. General world models will turn naive software into systems that understand and interact with the real world, enabling adaptive robots, immersive simulations, new learning tools, and large-scale scientific discovery.

✨ Life in an AI utopia: A Quick Q&A with futurist and philosopher Nick Bostrom

Faster, Please! • 731 implied HN points • 27 Dec 24
🕹 Technology Artificial Intelligence
  1. It's often easier for people to imagine a bad future, like in movies, than a good one. This can affect how cultures think about their future.
  2. When thinking about a perfect world, many people share similar ideas, like having peace and cleanliness. But if everything goes perfectly, we might miss out on challenges that give our lives meaning.
  3. The future of artificial intelligence could be really bright or really dark. We need to prepare for both possibilities because we are entering a new era with big changes ahead.

2026 - the year of AGI

davidj.substack • 23 implied HN points • 13 Jan 26
🕹 Technology Artificial Intelligence
  1. AGI means an AI that can learn many different tasks and perform many things at least as well as a typical human — it doesn't require sentience or being a superintelligence.
  2. Progress toward AGI will rely more on post-training learning: agents that can learn after deployment, retain skills, and build or use tools, rather than just bigger pretraining runs.
  3. Narrow AGI will appear in specific domains soon via agents that learn and share useful skills while keeping private data local, but these systems will still have clear limits and won't replace all human abilities.

⤴⤵ Up Wing/Down Wing #51

Faster, Please! • 274 implied HN points • 05 Jul 25
🕹 Technology Artificial Intelligence
  1. The US is speeding up its review process for new nuclear reactors, which could help increase energy efficiency and reduce waste. This new reactor design aims to start construction in 2026.
  2. There's a new material called Superwood made from waste wood that could replace steel and plastic in many products. It’s strong, lightweight, and could even be used in things like flying cars.
  3. A new mRNA flu vaccine from Moderna shows stronger results than the regular flu shot, especially for older adults. This could lead to better protection during flu seasons.

A metrics layer is like your New Year's resolution…

HyperArc • 39 implied HN points • 11 Jul 24
🕹 Technology Artificial Intelligence
  1. A metrics layer helps standardize how companies measure data, making it easier for everyone to understand what is important. It can automate calculations, like rolling averages, which saves time and reduces confusion.
  2. Traditional business intelligence tools often lose useful underlying information, which makes it hard to understand how certain metrics were created. More context is needed to ensure decisions are well-informed and based on complete data.
  3. HyperArc offers a solution by capturing the team's insights and reasoning during analysis. It helps keep track of not just the final metrics, but also the thought process behind them, making it easier to revisit and understand decisions in the future.
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The Rise of Verticalized AI Coworkers

Tanay’s Newsletter • 208 implied HN points • 29 Jul 25
🕹 Technology Artificial Intelligence
  1. Verticalized AI coworkers are designed for specific jobs like insurance adjusters or nurses, handling repetitive tasks that humans usually do. They can help fill roles where there are not enough workers.
  2. These AI coworkers integrate directly with existing tools and systems, allowing them to manage tasks efficiently. They aim to take some of the workload off human employees.
  3. Many of these AI systems are starting with easy, high-volume tasks, such as document processing and customer interactions. Over time, they may take on more complex tasks as they improve.

455: Rolex Pricing, Jensen & Nvidia, China Chip Ban 1.5, Costco, Google Maps to Optimize Cities, Bicycle for the Mind, and Beckham

Liberty’s Highlights • 452 implied HN points • 18 Oct 23
🕹 Technology Artificial Intelligence
  1. It's liberating to realize that most fields are understandable to an interested outsider, focusing on big ideas.
  2. Exploring new fields and combining knowledge from different areas can lead to rich and interesting discoveries.
  3. Taking calculated risks and thorough preparation can lead to successful outcomes in business decisions, like pushing all the chips in.

Teaching Small Language Models to Reason

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 39 implied HN points • 10 Jul 24
🕹 Technology Artificial Intelligence
  1. Using Chain-Of-Thought prompting helps large language models think through problems step by step, which makes them more accurate in their answers.
  2. Smaller language models struggle with Chain-Of-Thought prompting and often get confused because they don't have enough knowledge and understanding like the bigger models.
  3. Google Research has a method to teach smaller models by learning from larger ones. This involves using the bigger models to create helpful examples that the smaller models can then learn from.

The Sequence AI of the Week #793: DeepSeek's New Paper: Storing 100B Parameters on CPU RAM

TheSequence • 21 implied HN points • 21 Jan 26
🕹 Technology Artificial Intelligence
  1. The current LLM trend is to scale models huge and use sparsity tricks like Mixture-of-Experts so only a small part of the model activates per token, reducing FLOPs.
  2. Reusing an old technique — storing large, static lookup-like memories on CPU RAM and conditionally accessing them — can let models hold around 100B parameters off-GPU and avoid expensive dense computation.
  3. The key insight is that many LLM costs come from simulating static lookup tables with neural computation, so replacing that simulation with real conditional lookups makes models much more efficient.

Import AI 341: Neural nets can smell; technofeudalism via AI; China releases another solid open access model

Import AI • 459 implied HN points • 25 Sep 23
🕹 Technology Artificial Intelligence
  1. China released open access language models trained on both English and Chinese data, emphasizing safety practices tailored to China's social context.
  2. Google and collaborators created a digital map of smells, pushing AI capabilities to not just recognize visual and audio data but also scents, opening new possibilities for exploration and understanding.
  3. An economist outlines possible societal impacts of AI advancement, predicting a future where superintelligence prompts dramatic changes in governance structures, requiring adaptability from liberal democracies.

The Theory of the Leisure Class on Instagram

quite useless • 452 implied HN points • 15 Mar 23
🎭️ Culture Artificial Intelligence
  1. The internet phase of culture is driven by recursion, accelerating social cycles and shifting tastes rapidly.
  2. Instagram has evolved from reflecting real-world consumption and leisure to a curated space for projecting digital honorific waste.
  3. Strategies such as humor, conspicuous crap, hypercuration, and outsourcing signal a shift away from the pursuit of illusory perfection on Instagram.

Recent Metascience News/Links

The Good Science Project • 40 implied HN points • 18 Dec 25
🔬 Science Artificial Intelligence
  1. Even though we spend much more on science and R&D than in the past, the bottleneck for economic growth is often our ability to translate discoveries into marketable products, not a shortage of new ideas.
  2. Research funding and review rules are shifting: NSF is allowing fewer outside reviews and giving program managers more discretion, and NIH has removed the old requirement to get advance permission for very large grant applications.
  3. Reproducibility and data-quality problems keep appearing in areas like crystallography, and analysts caution against treating measures like “variance explained” as if they directly show a variable’s causal impact.

✨ While tech CEOs talk up AGI imminence, real-world AI use cases emerge

Faster, Please! • 639 implied HN points • 06 Jan 25
🕹 Technology Artificial Intelligence
  1. In a few years, we might see AI agents start working alongside humans, which could really change how companies function.
  2. Tech leaders believe that powerful AI could lead to huge advances in science and medicine, speeding up progress significantly.
  3. While there is excitement about AI's potential, it's also important to manage the risks to make sure it benefits everyone.

✅ AI in PR shifts from experimentation to implementation

Wadds Inc. newsletter • 39 implied HN points • 08 Jul 24
🕹 Technology Artificial Intelligence
  1. AI is becoming a key part of public relations, moving beyond trials to real use in daily tasks. This means teams are now figuring out how to best integrate AI tools into their work.
  2. AI offers significant benefits, like increased efficiency and productivity, but it requires a clear approach to adopt and adapt it effectively. Breaking down workflows is essential to understand where AI can help.
  3. The impact of AI on public relations is both a technology and a culture issue, meaning it's important for everyone in a team to learn and work together to make the most of these tools.

Data Science Weekly - Issue 523

Data Science Weekly Newsletter • 339 implied HN points • 01 Dec 23
🕹 Technology Artificial Intelligence
  1. Data science is evolving quickly, and it's important to stay updated with new advances and tools. Courses and reading lists can help you catch up and enhance your skills.
  2. Using machine learning to solve real-world problems, like correctly attributing quotes, shows the practical applications of data science. Collaboration between universities and organizations can lead to innovative solutions.
  3. The job market for data scientists is challenging right now. Many applicants are competing for limited positions, so if you're looking for a job, patience is key.

How this solo-dev 'incremental' game hit 100k+ sales!

The GameDiscoverCo newsletter • 235 implied HN points • 22 Jan 24
🕹 Technology Artificial Intelligence
  1. The solo-developed 'incremental' game Gnorp Apologue sold over 120k copies in a month on Steam.
  2. The game attracted players and YouTubers with unexpected upgrades, swift scaling, and adorable pixel art helpers.
  3. The developer priced the game low at $6.99 to make it accessible and enjoyable for players, leading to its unexpected success.

Embedded Online Conference 2024: My Summary

burkhardstubert • 79 implied HN points • 20 May 24
🕹 Technology Artificial Intelligence
  1. Using a top-down approach in software development helps avoid costly mistakes by getting early feedback from customers. It also reduces the blame on software developers when hardware is late.
  2. AI and machine learning can greatly boost productivity in embedded systems by automating repetitive tasks. They can help with coding, documentation, and even testing, making development smoother.
  3. Integrating open source components into embedded systems needs thorough safety analysis. A system bill of materials (SysBoM) helps track interactions and dependencies, ensuring safety and reliability.

Turing Coda: From The AI Mailbag

Technohumanism • 19 implied HN points • 06 Aug 24
🕹 Technology Artificial Intelligence
  1. The term 'artificial intelligence' was created as a marketing concept and doesn’t fully capture the complexities of human consciousness. Imitation isn't the same as true intelligence or awareness.
  2. Desire and emotions are central to human thinking, which machines try to replicate but can't truly understand. It's not enough for a machine to just perform tasks; it must have human-like motivations and feelings.
  3. The debate on whether humans are just machines reveals a longing for certainty in our understanding of consciousness. People act with free will, which challenges the idea that we are purely mechanical beings.

✨🎄 Some AGI optimism: an early Xmas present

Faster, Please! • 639 implied HN points • 23 Dec 24
🕹 Technology Artificial Intelligence
  1. OpenAI has released a new AI model called o3, which is designed to improve skills in math, science, and programming. This could help advance research in various scientific fields.
  2. The o3 model performs much better than the previous model, o1, and other AI systems on important tests. This shows significant progress in AI performance.
  3. There's a feeling of optimism about AGI technology as these advancements might bring us closer to achieving more intelligent and capable AI systems.

Provocation-driven development

Subconscious • 2056 implied HN points • 16 Oct 23
🕹 Technology Artificial Intelligence
  1. Foundational computing projects started with provocations that transformed our vision of technology.
  2. Provocative questions act as seed crystals for creative answers to grow around.
  3. Communities gather around burning questions, sparking conversations that construct meaning together.

The Sequence Knowledge #784: The Convergence of Synthetic Data and World Models Models Are Unlocking Embodied AI

TheSequence • 28 implied HN points • 06 Jan 26
🕹 Technology Artificial Intelligence
  1. Collecting high-quality, perfectly labeled 3D data from the real world is slow, expensive, and misses rare edge cases, so 'reality' is the main bottleneck for embodied AI.
  2. Pairing synthetic data generation with world models lets teams create rich, diverse, and labeled simulated environments, so agents can be trained and tested without costly real-world collection.
  3. New world models like Google DeepMind's Genie show this approach in action by enabling interactive, dynamic 3D simulations where robots and autonomous vehicles can learn more robust behaviors.

Data security should be making a comeback

Frankly Speaking • 254 implied HN points • 10 Jun 25
🕹 Technology 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.

Chip Letter Links No. 16 : Gordon Moore's Notebooks, Nvidia on Acquired, Assembly Language, Jeff Hinton, RISC-V on Jupyter

The Chip Letter • 2839 implied HN points • 16 Apr 23
🕹 Technology Artificial Intelligence
  1. Gordon Moore's notebooks from Fairchild provide a unique insight into his work and research in the early days of computing.
  2. Assembly language, especially 8-bit, was more popular and necessary in the past compared to modern 64-bit architectures.
  3. Nvidia's survival and success were closely tied to their alignment with Moore's Law in the GPU industry.

Data Science Weekly - Issue 518

Data Science Weekly Newsletter • 379 implied HN points • 27 Oct 23
🕹 Technology Artificial Intelligence
  1. Web development is evolving with the use of local models and technologies for building applications, moving beyond just Python-based machine learning.
  2. It's becoming increasingly important for developers to understand GPUs since they're widely used in deep learning and can greatly enhance performance.
  3. Companies are exploring various use cases for generative AI that provide real value, focusing on practical implementations that drive return on investment.

Data Science Weekly - Issue 531

Data Science Weekly Newsletter • 219 implied HN points • 26 Jan 24
🕹 Technology Artificial Intelligence
  1. AI often gets criticized for the quality of its output, but that might not be the real issue people have with it. If quality is fixed, the conversation about AI could change significantly.
  2. Common sense is tricky to define and measure, but researchers are developing ways to quantify it both individually and collectively. This could help clarify how we understand common sense in different contexts.
  3. Large language models (LLMs) can transform education by encouraging hands-on learning. They offer opportunities for more interactive and engaging learning experiences.

In the land of LLMs, can we do better mock data generation?

Neurelo Engineering’s Substack • 1 HN point • 27 Sep 24
🕹 Technology Artificial Intelligence
  1. Mock data is super useful for testing software, but it hasn't really improved much over the years. It needs to be more flexible and easier to generate high-quality data.
  2. Using LLMs (large language models) can be tricky for creating mock data. Instead of trying to generate everything, it’s often better to use techniques like topological sorting to keep relationships correct between data entries.
  3. A new approach is turning to strategies like the Genesis Point Strategy, which helps create unique mock data efficiently. It shows that you can simplify processes to get good results without overcomplicating things.

Capturing the Moment

Cybernetic Forests • 119 implied HN points • 07 Apr 24
🎨 Art & Illustration Artificial Intelligence
  1. AI-generated images can lack emotional impact compared to human-created art, often resulting in an uncanny feeling rather than emotional connection.
  2. The history of art showcases a complex interplay between photography and painting, with AI-generated images adding another layer of complexity to this relationship.
  3. AI images challenge traditional notions of art by blurring the lines between painting and photography, presenting a new form of artistic expression.

Tokens Aren't Fungible

Who is Nnamdi • 7 implied HN points • 11 Feb 26
🕹 Technology Artificial Intelligence
  1. Cheaper, equally intelligent open-source models still capture under 30% of usage, which shows price and benchmark scores explain only a small part of why people choose models.
  2. Most users pick one model and stick with it, and price cuts mainly shift volume rather than grow revenue, so being a user's primary model creates strong lock-in.
  3. Benchmarks miss key, hard-to-measure factors like trust, safety, privacy, tooling, and support, so differentiation on intangibles matters and tokens aren’t fungible.

☞ "The Magic of Code" and the Humanistic Computation Project

Cabinet of Wonders • 254 implied HN points • 09 Jun 25
🕹 Technology Artificial Intelligence
  1. The project focuses on viewing computing as a humanistic art, aiming to blend technology with liberal arts education. This approach hopes to deepen our understanding of code and its impact on society.
  2. There's excitement about developing educational programs like courses and workshops to discuss these ideas more widely. Building a community of people with similar interests is also a goal.
  3. A new book titled 'The Magic of Code' has been released, which explores these themes and is part of the broader Humanistic Computation Project.

OpenAI's Open Source Strategy

Enterprise AI Trends • 168 implied HN points • 06 Aug 25
🕹 Technology Artificial Intelligence
  1. OpenAI has released two new open-weight models called gpt-oss-120b and gpt-oss-20b. This means people can run these powerful models on their own computers without needing an internet connection.
  2. The gpt-oss-120b model is very cost-effective and performs well, even better than some existing models, making advanced AI more accessible.
  3. It's been six years since OpenAI released an open weight model, so this move shows they are serious about reclaiming their position in the open-source AI community.

Is it possible for a human to argue with an AI assistant? 😅

Prawfeed Newsletter • 12 implied HN points • 24 Jan 26
🕹 Technology Artificial Intelligence
  1. Misalignment between human intent and AI output is common and often invisible.
  2. AI can move fast on partial signals and end up going the wrong way. Fixing it takes pausing, naming the drift, and resetting direction instead of just blaming.
  3. The real advantage is human clarity and cognitive leadership. Thinking clearly, communicating boundaries, and guiding the AI matters more than clever prompts.

Six Reasons Why Current AI Won't Displace Lawyers

Autonomy • 34 implied HN points • 20 Dec 25
🕹 Technology Artificial Intelligence
  1. Current AI doesn't generalize or perceive the world like humans, so it misses novel facts and real-world cues that lawyers use to build and win cases.
  2. Litigation is inherently adversarial, so both sides will adopt AI and the human lawyers who best direct and strategize with those tools will decide outcomes.
  3. Lawyering involves client counseling, moral responsibility, and institutional rules that AI can't fulfill, and greater AI productivity may actually increase demand for legal services rather than eliminate lawyers.

Horse rides Astronaut, redux

Marcus on AI • 1462 implied HN points • 13 Feb 24
🕹 Technology Artificial Intelligence
  1. DALL-E 2 and Gemini Ultra struggled with complex prompts and concepts, showing limitations in language understanding.
  2. Proper prompts and iterations are crucial to achieve desired results with AI models like Gemini Ultra.
  3. Despite progress in some areas, challenges persist in neural networks' factuality and compositionality.

The Age of Tinkering is Upon Us

A Biologist's Guide to Life • 13 implied HN points • 28 Jan 26
🕹 Technology Artificial Intelligence
  1. AI tools can amplify and fill gaps in our abilities, acting like a cognitive hearing aid that boosts speed and skill when you learn to use them.
  2. Actively tinkering with AI—asking questions, building projects, and iterating—lets you learn new technical skills quickly and make real things you care about.
  3. Your human strengths—curiosity, compassion, imagination, and intuition—remain the real advantage, so collaborate with others and use AI proactively to create value rather than passively consuming it.