The hottest Artificial Intelligence Substack posts right now

And their main takeaways
Category
Top Technology Topics

Claire Vo: The CPO who automates herself with AI

CommandBlogue • 19 implied HN points • 19 Aug 24
🕹 Technology Artificial Intelligence
  1. AI is changing how product managers work. It helps them complete tasks much faster, which could mean fewer PMs are needed in the future.
  2. The role of PMs might shift more towards being makers, meaning they will need to have skills in design and engineering to stay relevant.
  3. To break into product management, it's important to show what you can do by building something real for the companies you're interested in, rather than just sending a resume.

2023, the Year of AI; 2024?

Sector 6 | The Newsletter of AIM • 399 implied HN points • 01 Jan 24
🕹 Technology Artificial Intelligence
  1. 2023 saw major advancements in AI technology, leading to exciting stories and developments. The growth of AI in various sectors sparked interest and engagement from the public.
  2. Microsoft announced a significant investment in OpenAI, marking the third phase of their partnership. This collaboration aims to enhance AI supercomputing and make breakthroughs in technology.
  3. As we move into 2024, there is anticipation for more innovative AI content and opportunities. The community looks forward to exploring how AI can further evolve and impact our lives.

AI risk ≠ AGI risk

Marcus on AI • 4466 implied HN points • 28 Mar 23
🕹 Technology Artificial Intelligence
  1. Superintelligence and AGI risk are not the same, but both raise concerns.
  2. Mediocre AI like large language models can create serious problems due to wide deployment.
  3. Control over current AI technologies is crucial to prevent misuse by criminals and terrorists.

Why Tesla Robotaxis Are a Fantasy

lawrence’s Substack • 159 implied HN points • 22 Apr 24
🕹 Technology Artificial Intelligence
  1. Tesla robotaxis may not be a feasible reality, according to informed commentators. Full Self-Driving is far from being ready for autonomy tests.
  2. Michael McGrath explains why Tesla's Full Self-Driving is technically infeasible and flawed as a business model, offering a critical perspective.
  3. Matthew Enthoven and Edward Niedermeyer also provide valuable insights and critiques on Tesla's autonomous driving ambitions.

Da fuq is an “LLM?”

Sex and the State • 26 implied HN points • 14 Jan 26
🕹 Technology Artificial Intelligence
  1. An LLM (large language model) is an AI system that mainly reads and writes natural language and powers modern chatbots like ChatGPT, Claude, and Gemini.
  2. AI is a big umbrella with many types of tools — image generators, detectors, chat interfaces, and world models — and LLMs are just the language-focused slice, not the same as models that work with images or spatial data.
  3. Many leading researchers argue LLMs alone probably won’t produce human-level or general intelligence, because language only points to thought; building AGI likely requires spatial or "world" models that learn from videos, perception, and interaction.
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Languages don't need many words

John Ball inside AI • 39 implied HN points • 24 Jul 24
🕹 Technology Artificial Intelligence
  1. You don't need many words to communicate in a new language. Just a small vocabulary can help you get by in everyday conversations.
  2. For understanding most spoken and written text, around 2000 words are usually enough. This covers about 80% of regular communication.
  3. Machine learning and AI can benefit from understanding language like humans do, by learning new words in context rather than just relying on a large vocabulary.

Star Trek or Mad Max?

Diane Francis • 619 implied HN points • 11 Sep 23
🕹 Technology Artificial Intelligence
  1. Experts debate whether AI will lead to a better future like 'Star Trek' or a dystopian one like 'Mad Max.'
  2. Some say AI, like ChatGPT, doesn't really think or create but uses existing data, raising concerns about job losses and content theft.
  3. Regulation and accountability are important, as many believe tech companies should be held responsible for their actions instead of managing themselves.

The social justice case for self-driving cars

Odds and Ends of History • 871 implied HN points • 07 Jan 25
🕹 Technology Artificial Intelligence
  1. Self-driving cars are becoming more common and are already in use in places like San Francisco. Companies are offering autonomous taxi services that anyone can access through an app.
  2. The idea of abundant mobility means that, in the future, traveling will be much cheaper and easier for everyone. This could make life better for many people, especially those with lower incomes, by improving access to jobs, services, and social connections.
  3. While there are challenges and concerns with self-driving cars, like job losses and privacy issues, the overall benefits could lead to a more equal and accessible society, similar to how technology has improved living standards over time.

Data Science Weekly - Issue 545

Data Science Weekly Newsletter • 139 implied HN points • 03 May 24
🕹 Technology Artificial Intelligence
  1. Reusing data analysis work can save time and help teams focus on building new capabilities instead of just repeating old ones.
  2. Open-source models can be a better choice than proprietary ones for developing AI applications, making them cheaper and faster.
  3. Causal machine learning helps predict treatment outcomes by personalizing clinical decisions based on individual patient data.

FLOWER MOON 2023

The Lunar Dispatch • 609 implied HN points • 06 May 23
🕹 Technology Artificial Intelligence
  1. Our phones are more than just devices, they are listening and judging through targeted ads.
  2. Beware of potential surveillance from various sources, including the Moon and secret spy satellites.
  3. Consider the idea that our world might be a simulation, and how our physical frailty could be our ultimate defense.

Introducing Deep Symbolics

John Ball inside AI • 59 implied HN points • 02 Jul 24
🕹 Technology Artificial Intelligence
  1. Deep Symbolics (DS) aims to improve upon Deep Learning (DL) by incorporating how brains work, especially in understanding and using symbols rather than just statistics. This is important for developing Artificial General Intelligence (AGI).
  2. Unlike traditional DL systems that learn in a single training run, Deep Symbolics can continuously learn and adapt, similar to how humans pick up new knowledge and skills throughout life.
  3. Deep Symbolics focuses on creating a more brain-like model by using hierarchical and bidirectional patterns, which improves its ability to process language and resolve ambiguities better than current AI systems.

Does India need a sovereign LLM

Experiments with NLP and GPT-3 • 7 implied HN points • 24 Feb 26
🕹 Technology Artificial Intelligence
  1. India needs its own sovereign large language model; it’s no longer optional and is now table stakes.
  2. Relying on foreign AI providers risks losing access or facing discriminatory rules and taxes, echoing past industrial and colonial imbalances.
  3. AI is already essential to businesses and the economy, and being cut off for weeks, months, or a year would seriously hurt competitiveness and survival.

AI Steals the Show at WEF 2024

Sector 6 | The Newsletter of AIM • 319 implied HN points • 22 Jan 24
🕹 Technology Artificial Intelligence
  1. AI was the main topic at the World Economic Forum in Davos, showing how important it is becoming. Everyone talked about how we need to adopt AI quickly and talk about its effects.
  2. Education and retraining workers are key issues with AI's rise. Many discussions focused on how people can learn new skills to keep up with the changing job market.
  3. In India, only 26% of the workforce is exposed to AI, much lower than in advanced economies. This means there's a lot of room for growth in using AI in local jobs and industries.

Part 1: IndiaAI mission does not need compute, it needs data.

Experiments with NLP and GPT-3 • 23 implied HN points • 25 Jan 26
🕹 Technology Artificial Intelligence
  1. Prioritize building high-quality, linguistically diverse datasets and cultural corpora instead of spending most funds on GPUs, because hardware quickly depreciates while data endures and enables sovereign AI.
  2. Run a state-led translation and terminology program to translate technical and cultural works and to standardize or create AI-related vocabulary in Indian languages through a National Terminology Commission; this will democratize technical knowledge and produce the corpora needed for local models.
  3. Subsidize translation, localization, and AI-assisted export of Indian cultural content to remove friction for global audiences and to generate rich datasets, using public funding to de-risk and scale these efforts similar to Japan’s cultural strategy.

The Innovation Bargain

The Absent-Minded Professor • 314 implied HN points • 23 Jan 24
🕹 Technology Artificial Intelligence
  1. Innovation always comes with tradeoffs - think about whether they are worth it.
  2. The Innovation Bargain is about freedom and limitation - new technologies enable us but also limit choices.
  3. Understanding the Innovation Bargain is crucial in our technology-driven society - be mindful of the impact of technology on human flourishing.

Large language models, explained with a minimum of math and jargon

The Counterfactual • 599 implied HN points • 28 Jul 23
🕹 Technology Artificial Intelligence
  1. Large language models, like ChatGPT, work by predicting the next word based on patterns they learn from tons of text. They don’t just use letters like we do; they convert words into numbers to understand their meanings better.
  2. These models handle the many meanings of words by changing their representation based on context. This means that the same word could have different meanings depending on how it's used in a sentence.
  3. The training of these models does not require labeled data. Instead, they learn by guessing the next word in a sentence and adjusting their processes based on whether they are right or wrong, which helps them improve over time.

Can GPT-4 *Actually* Write Code?

Tyler Glaiel's Blog • 567 HN points • 17 Mar 23
🕹 Technology Artificial Intelligence
  1. GPT-4 can write code when given existing algorithms or well-known problems, as it remixes existing solutions.
  2. However, when faced with novel or unique problems, GPT-4 struggles to provide accurate solutions and can make incorrect guesses.
  3. It's crucial to understand that while GPT-4 can generate code, it may not be reliable for solving complex, new problems in programming.

The Internet As a Giant Skinner Box

Novum Newsletter • 309 implied HN points • 03 Jul 25
🕹 Technology Artificial Intelligence
  1. The concept of the Skinner Box explains how people can become addicted to behaviors through random rewards, like what we see with endless scrolling on the internet.
  2. A hidden workforce called 'ghost workers' handles tasks for tech companies, often under stressful conditions with unpredictable pay, similar to gambling.
  3. Both internet users and these invisible workers are conditioned by the same reward systems, highlighting how ingrained and widespread this behavior has become.

Should AI Be Allowed to Train On My Work?

I Might Be Wrong • 5 implied HN points • 25 Feb 26
🕹 Technology Artificial Intelligence
  1. New AI tools can make surprisingly good, cheap videos and deepfakes, including realistic-looking celebrity scenes.
  2. Hollywood studios and unions are already pushing back with legal threats, so litigation and new case law on AI are likely to grow.
  3. Creators are angry that AI is often trained on copyrighted work, since that can teach models before they displace people's jobs, and the debate over rights and remedies is messy and unresolved.

Microsoft's whitepaper for the U.S. Army's 'fighting google' and integration with Maven through Project Convergence

All-Source Intelligence Fusion • 691 implied HN points • 28 Jan 25
🇺🇸 U.S. Politics Artificial Intelligence
  1. Microsoft is working with the U.S. Army to integrate augmented reality technology into military operations, focusing on a project called IVAS. This technology aims to give soldiers enhanced situational awareness on the battlefield.
  2. There have been complications with the IVAS technology, including issues like discomfort for users, which led to funding cuts from Congress. The Army is exploring better alternatives for combat effectiveness.
  3. Microsoft is involved in a competitive environment with other tech companies like Anduril and Palantir for military contracts. These partnerships and innovations are crucial for enhancing the capabilities needed in modern warfare.

Advanced Prompting Guide for AI Engineering

Engineering Enablement • 13 implied HN points • 04 Feb 26
🕹 Technology Artificial Intelligence
  1. Structured prompting is required for complex, high‑risk engineering work; techniques like graph‑based prompts help reveal hidden dependencies, prioritize rules, and manage changing state.
  2. Use controlled validation loops and dual‑implementation strategies to improve governance and reduce risk, and apply diff‑only refactoring to make large code changes less invasive and more token‑efficient.
  3. The guide is vendor‑agnostic and practical, with Do/Don't scenarios and full prompt/code examples, and it’s useful to engineers and non‑engineers working with coding assistants, agents, or spec‑driven workflows.

The Sequence AI of the Week #785: Gradient Highway Maintenance: Inside DeepSeek’s Latest Breakthrough

TheSequence • 35 implied HN points • 07 Jan 26
🕹 Technology Artificial Intelligence
  1. DeepSeek's mHC challenges established assumptions about AI scaling and suggests new architectural ideas that could change how larger models are built and trained.
  2. Residual connections are the unsung scaffolding of modern deep networks, providing a 'gradient highway' that keeps training stable across many layers.
  3. The simple rule y = f(x) + x—adding the input back to a layer's output—was revolutionary because it preserves signals and gradients, making very deep networks trainable.

Data Science Weekly - Issue 538

Data Science Weekly Newsletter • 199 implied HN points • 14 Mar 24
🕹 Technology Artificial Intelligence
  1. Serverless computing can handle big tasks without limits, but it also brings challenges like managing large uploads effectively.
  2. Art careers can be influenced by the reputation of institutions, with established artists facing less access to elite spaces early on compared to newcomers.
  3. Learning about LLM evaluation metrics can help improve understanding and performance when working with large language models.

The Sequence Radar #771: Last Week in AI: GPT-5.2, Mistral, and Google’s Agent Stack

TheSequence • 56 implied HN points • 14 Dec 25
🕹 Technology Artificial Intelligence
  1. AI is moving to an agent-first model where LLMs act as operators for long-running, multi-step workflows, improving planning, tool use, and end-to-end task completion.
  2. Open-weight and deployable model families are maturing, letting teams host, fine-tune, and run agentic coding and workflow assistants on their own infrastructure.
  3. Compute and energy limits are now a primary bottleneck, driving investment in efficient architectures like MoEs, distillation, edge inference, and new hardware approaches.

✨ 📈 The AI Paradox: When will artificial intelligence boost US economic growth?

Faster, Please! • 913 implied HN points • 21 Nov 24
💼 Business Artificial Intelligence
  1. Alan Greenspan raised questions about why technological advances in the 1990s didn't seem to improve productivity statistics. He suggested that it might take time for new technologies to show their full effects.
  2. Greenspan believed that traditional methods of measuring productivity might not capture the real progress happening, especially with services. This mismeasurement could lead to bad decisions on economic policies.
  3. The role of artificial intelligence in boosting productivity is still uncertain. There's hope that AI can help workers produce more, but it's unclear when we will see these benefits reflected in economic growth.

The Bitter Religion: AI’s Holy War Over Scaling Laws

The Generalist • 920 implied HN points • 14 Nov 24
🕹 Technology Artificial Intelligence
  1. The AI community is divided over whether achieving higher levels of computation will lead to better artificial intelligence or if there are limits to this approach. Some think more resources will keep helping AI grow, while others fear we might hit a ceiling.
  2. There’s a growing debate about the importance of scaling laws and whether they should continue to guide AI development. People are starting to question if sticking to these beliefs is the best path forward.
  3. If doubt begins to spread about scaling laws, it could impact investment and innovation in AI and related fields, causing changes in how companies approach building new technologies.

Are You Considering a Career Pivot into the Security of AI?

Rod’s Blog • 357 implied HN points • 20 Dec 23
🕹 Technology Artificial Intelligence
  1. Considering a career pivot into the security of AI can be a valuable choice to make a positive impact on society.
  2. Having an interest in technology's implications, experience in various tech projects, and awareness of technology's consequences are good reasons to pursue AI security.
  3. Opportunities in AI security offer potential for career growth, impact, and contribution to shaping a safer, ethical, and beneficial AI future.

RAG Foundry By Intel

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 13 Aug 24
🕹 Technology Artificial Intelligence
  1. RAG Foundry is an open-source framework that helps make the use of Retrieval-Augmented Generation systems easier. It brings together data creation, model training, and evaluation into one workflow.
  2. This framework allows for the fine-tuning of large language models like Llama-3 and Phi-3, improving their performance with better, task-specific data.
  3. There is a growing trend in using synthetic data for training models, which helps create tailored datasets that match specific needs or tasks better.

Reflections: my class about LLMs and Cognitive Science

The Counterfactual • 139 implied HN points • 17 Apr 24
🚌 Education Artificial Intelligence
  1. A new class on Large Language Models (LLMs) was created to help Cognitive Science students understand the intersection of AI and human cognition, especially after the popularity of technologies like ChatGPT.
  2. The course covered the history and technical foundations of LLMs, with hands-on labs and discussions that helped students think critically about their societal impacts and ethical concerns.
  3. For future classes, there's a desire to expand the content, particularly by adding discussions on topics like tokenization and exploring more philosophical aspects of LLMs.

The Symposium: Liron Shapira

The Cosmopolitan Globalist • 5 implied HN points • 19 Feb 26
🕹 Technology Artificial Intelligence
  1. A public symposium on Sunday, February 22 will feature Liron Shapira debating whether AI could destroy humanity, and attendees are invited to join, ask questions, and state their p(doom).
  2. Shapira’s Doom Debates aim to raise mainstream awareness and urgency about existential AI risk; they argue that only when ordinary people see unaligned superintelligent AI as an imminent life‑threat will leaders take decisive protective action.
  3. Readers are encouraged to prepare by reading the canonical doomer essay If Anyone Builds It, Everyone Dies, watching Shapira’s debates, and exploring recommended essays on the AI control problem and related policy and persuasion issues.

Multi-agent orchestration in Slack | Saleforce's Kurtis Kemple

Dev Interrupted • 9 implied HN points • 10 Feb 26
🕹 Technology Artificial Intelligence
  1. Chat platforms are becoming agent orchestration hubs where humans and bots work together in real time, and organizations will need higher-level "super agents" to connect and manage isolated agent workflows.
  2. New agent ecosystems introduce fresh risks and human dependencies—agents forming their own social networks and services that hire people for tasks raise security, legal, and ethical concerns, and rogue or exploitable agent chains are a real threat.
  3. Widespread agent adoption will reshape how software is developed and how open source is consumed, shifting teams toward autonomous observe-orient-decide-act workflows and transforming open source projects to serve agent-driven use cases rather than disappearing.

AI's Big Hype

The Biblioracle Recommends • 511 implied HN points • 28 May 23
🕹 Technology Artificial Intelligence
  1. People promoting generative AI want us to believe it is inevitable, but that doesn't mean it's without risks.
  2. Humanity often faces catastrophic failures due to a mix of bad structural incentives and human desires.
  3. The push for artificial intelligence might lead to a world where human expression is replaced by algorithms, impacting writing and creativity.

Technically Monthly (February 2026)

Technically • 14 implied HN points • 05 Feb 26
🕹 Technology Artificial Intelligence
  1. Modern generative models mirror pathways in the human brain, and many researchers believe leveraging that similarity could be key to much stronger AI.
  2. Real cloud-spend data shows the fastest-growing AI use cases are coding agents, low-latency LLM inference, and computational biology, while AI art and video generation have plateaued as the market professionalizes.
  3. Models overuse em dashes mainly because of their training data and tokenization quirks—older texts and auto-converted punctuation make the em dash common—and this highlights how dataset quality and representativeness drive model behavior.

What do I know about Artificial Intelligence?

The Joyous Struggle • 395 implied HN points • 27 Nov 23
🕹 Technology Artificial Intelligence
  1. Many people have mixed feelings about technology, especially artificial intelligence, due to fear of missing out, lack of understanding, and a sense of exclusion from the tech world.
  2. The author shares a sense of 'tech incredulity' toward AI, questioning its potential impact, limitations, and whether it truly warrants the level of concern it receives.
  3. Despite not having expert knowledge, the author acknowledges a responsibility to learn more about AI, to demystify the complexities surrounding it, and to understand the risks, potential, and ethical implications better.

How I Use ChatGPT

The Ruffian • 663 implied HN points • 25 Jan 25
🕹 Technology Artificial Intelligence
  1. ChatGPT and Claude are popular AI tools, but users might find Claude to be more useful. Brand recognition plays a big role in which tool people choose.
  2. Many users are just starting to explore how to use LLMs (like ChatGPT and Claude) effectively. There's a lot of potential in these tools that many people are not fully tapping into.
  3. The author lists several ways they have used LLMs for various tasks, from troubleshooting tech issues to summarizing essays. This shows how versatile and helpful these tools can be in everyday life.