The hottest Machine Learning Substack posts right now

And their main takeaways
Category
Top Business Topics

A taxonomy for next-generation reasoning models

Democratizing Automation • 467 implied HN points • 04 Jun 25
🕹 Technology Machine Learning
  1. Next-gen reasoning models will focus on skills, calibration, strategy, and abstraction. These abilities help the models solve complex problems more effectively.
  2. Calibrating how difficult a problem is will help models avoid overthinking and make solutions faster and more enjoyable for users.
  3. Planning is crucial for future models. They need to break down complex tasks into smaller parts and manage context effectively to improve their problem-solving abilities.

Analog Chip Design is an Art. Can AI Help?

The Asianometry Newsletter • 2707 implied HN points • 12 Feb 24
🕹 Technology Machine Learning
  1. Analog chip design is a complex art form that often takes up a significant portion of the total design cost of an integrated circuit.
  2. Analog design involves working with continuous signals from the real world and manipulating them to create desired outputs.
  3. Automating analog chip design with AI is a challenging task that involves using machine learning models to assist in tasks like circuit sizing and layout.

Intentions & Actions

Software Design: Tidy First? • 1082 implied HN points • 16 Dec 24
🕹 Technology Machine Learning
  1. People often come to computers with intentions, like wanting to watch a show or add a stop to a trip. But the actions needed to achieve those intentions can be confusing and hard to remember.
  2. When the computer does what we want easily, we feel amazed and grateful. But this happens less often because of complicated menus and actions we have to figure out.
  3. Kids find it easier to use technology because they learn quickly from their friends and practice a lot. They navigate digital worlds more smoothly, while others often struggle with the basics.

DeepSeek V3 and the cost of frontier AI models

Democratizing Automation • 973 implied HN points • 09 Jan 25
🕹 Technology Machine Learning
  1. DeepSeek V3's training is very efficient, using a lot less compute than other AI models, which makes it more appealing for businesses. The success comes from clever engineering choices and optimizations.
  2. The actual costs of training AI models like DeepSeek V3 are often much higher than reported, considering all research and development expenses. This means the real investment is likely in the hundreds of millions, not just a few million.
  3. DeepSeek is pushing the boundaries of AI development, showing that even smaller players can compete with big tech companies by making smart decisions and sharing detailed technical information.

What I've been reading (#2): More on Kimi K2, how to build a bad research center, Pretraining with RL, and sporks of AGI

Democratizing Automation • 285 implied HN points • 10 Aug 25
🕹 Technology Machine Learning
  1. AI companies have different ways of operating, especially in China. One company, Moonshot, focuses on individual users and has a unique culture compared to others.
  2. People mostly use AI for coding today, but many are still figuring out how to use these tools effectively. It's important to provide enough information to the AI to get better help.
  3. There are various tools and techniques being developed to improve AI. Researchers are sharing their findings on topics like long-context training and troubleshooting to help others learn and grow.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

GPT-5: OpenAI’s Flagship Model Faces Great Expectations

The Algorithmic Bridge • 318 implied HN points • 04 Aug 25
🕹 Technology Machine Learning
  1. People often have unrealistic expectations for new AI models like GPT-5, leading to disappointment when they don't meet those high hopes. The hype around these releases can skew how we perceive their actual capabilities.
  2. Previous models like GPT-4.5 faced challenges and may not have been failures outright, but rather steps in the learning process for what works best in AI development. They revealed important insights even if they didn't perform perfectly.
  3. OpenAI is in a competitive race with other companies, and while it has achieved significant financial success, there are concerns about its talent retention and whether it is keeping up with faster innovation from rivals.

The Sequence Radar #783: Softbank, DeepSeek, MiniMax and The Sequence 2026

TheSequence • 49 implied HN points • 04 Jan 26
🕹 Technology Machine Learning
  1. SoftBank is using massive capital to buy both leading AI model stakes and the physical data center and edge infrastructure that runs them. This vertical integration is blurring the line between model providers and infrastructure owners.
  2. DeepSeek’s new model and the GRPO technique match top-tier reasoning performance while needing far fewer GPU hours. This shows smarter algorithms can close the gap against big-budget competitors.
  3. MiniMax’s planned Hong Kong IPO (~$539M) signals public-market interest in application-layer AI and gives the company capital to compete amid hardware export controls and intense domestic rivalry.

Has Google gone too woke? Why even the biggest models still struggle with guardrails

Marcus on AI • 2608 implied HN points • 21 Feb 24
🕹 Technology Machine Learning
  1. Google's large models struggle with implementing proper guardrails, despite ongoing investments and cultural criticisms.
  2. Issues like presenting fictional characters as historical figures, lacking cultural and historical accuracy, persist with AI systems like Gemini.
  3. Current AI lacks the ability to understand and balance cultural sensitivity with historical accuracy, showing the need for more nuanced and intelligent systems in the future.

Further Trouble in Hinton City

Marcus on AI • 2687 implied HN points • 08 Feb 24
🕹 Technology Machine Learning
  1. Recent evidence challenges claims of Generative AI systems not storing things or understanding them deeply
  2. Trivial perturbations affect GenAI systems significantly, indicating a lack of deep understanding
  3. GenAI systems effectively store things but struggle with novel designs and understanding simple concepts

Data Science Weekly - Issue 533

Data Science Weekly Newsletter • 339 implied HN points • 09 Feb 24
🕹 Technology Machine Learning
  1. Satellite data is important for machine learning and should be treated as a unique area of research. Recognizing this can help improve how we use this data.
  2. Many data science and machine learning projects fail from the start due to common mistakes. Learning from past experiences can help increase the chances of success.
  3. Open source software plays a crucial role in advancing AI technology. It's important to support and protect open source AI from regulations that could harm its progress.

What comes next with reinforcement learning

Democratizing Automation • 435 implied HN points • 09 Jun 25
🕹 Technology Machine Learning
  1. Reinforcement learning (RL) is getting better at solving tougher tasks, but it's not easy. There's a need for new discoveries and improvements to make these complex tasks manageable.
  2. Continual learning is important for AI, but it raises concerns about safety and can lead to unintended consequences. We need to approach this carefully to ensure the technology is beneficial.
  3. Using RL in sparser domains presents challenges, as the lack of clear reward signals makes improvement harder. Simple methods have worked before, but it’s uncertain if they will work for more complex tasks.

Skills: The Missing Piece in AI Security Tooling

Boring AppSec • 23 implied HN points • 23 Jan 26
🕹 Technology Machine Learning
  1. Generic threat modeling tools miss risks unique to multi‑agent AI systems, so one‑size‑fits‑all methods like STRIDE are insufficient.
  2. Skills are modular, LLM‑native knowledge packages that let agents detect agentic patterns and find context‑specific threats (like cascade failures and goal hijacking) that generic rules miss.
  3. Skills are portable and quick to create and share, so teams can build reusable, relevant expertise that yields better findings than lots of generic noise.

Grok 3: Another Win For The Bitter Lesson

The Algorithmic Bridge • 817 implied HN points • 18 Feb 25
🕹 Technology Machine Learning
  1. Scaling laws are really important for AI progress. Bigger models and better computing power often lead to better results, like how Grok 3 outperformed earlier versions and is among the best AI models.
  2. DeepSeek shows that clever engineering can help, but it still highlights the need for more computing power. They did well despite limitations, but with more resources, they could achieve even greater things.
  3. Grok 3's success proves that having more computing resources can beat just trying to be clever. Companies that focus on scaling their resources are likely to stay ahead in the AI race.

Security is Not an AI Problem

Security Is • 159 implied HN points • 02 May 24
🕹 Technology Machine Learning
  1. AI doesn't really fix security problems well. Many times, the technology just doesn't work in the tough, unpredictable environments that security deals with.
  2. The best results in security often come from simple, clear procedures, not from complex machine learning models. Basic rules can solve most problems effectively.
  3. Generative AI can help with minor tasks but isn't a magic solution for security. It might even confuse people about important issues, rather than clarify them.

In the End, They will All Look the Same

Sector 6 | The Newsletter of AIM • 379 implied HN points • 22 Jan 24
🕹 Technology Machine Learning
  1. The internet is facing an issue called 'model collapse' where AI chatbots start to sound more and more alike due to using generated content for training. This makes them lose their unique information.
  2. Research shows that when AI models use content made by other AIs to learn, they can forget important details and produce weaker results.
  3. Experts warn that as more AI models create similar data, future AI systems from different companies may end up producing nearly identical responses.

The Sequence Radar #775: Last Week in AI: Tokens, Throughput, and Trillions

TheSequence • 63 implied HN points • 21 Dec 25
🕹 Technology Machine Learning
  1. Massive funding and infrastructure bets are setting the rules: the companies that can industrialize models into cheap, reliable global services will win more than those with just the fanciest research demos.
  2. Engineering focus has shifted to throughput, latency, and long-context agentic capabilities, with new models and hardware optimized to move lots of tokens through multi-step workflows at predictable cost.
  3. Generative outputs and developer workflows are becoming iterative and productized — image editing in chat and tightened data/observability loops make AI a usable creative IDE, while enterprise platforms race to own the data plane and production tooling.

A Vector Database 2,000 years in the making

What Is Called Thinking? • 82 implied HN points • 25 Nov 25
🕹 Technology Machine Learning
  1. The Oral Torah is described as a living, growing, self-referential commentary tradition that developed over two thousand years and across continents.
  2. It’s not just an “oral tradition” that was later written down, but an ongoing, networked conversation of interpretation and commentary.
  3. The piece asks whether people should write with AIs in mind and suggests imagining the Oral Torah as a kind of long-lived, interconnected repository—like a vector database—for modern LLMs.

What the world needs isn't "Artificial Intelligence" but Optimized Computational Methodologies

Top Carbon Chauvinist • 79 implied HN points • 21 Jun 24
🕹 Technology Machine Learning
  1. We should focus on making smarter tools instead of trying to make machines think like humans. Real progress comes from solving practical problems, not imitating nature.
  2. Copying how living things work is often a bad approach. Nature is full of flaws, and we don't need to mimic those to create better designs.
  3. It's important to clearly define the problems we want machines to solve. Without a clear goal, projects will struggle and waste resources on unnecessary tasks.

AI's meaning-making problem.

The Uncertainty Mindset (soon to become tbd) • 119 implied HN points • 22 May 24
🕹 Technology Machine Learning
  1. Humans can make meaning by assigning value to things, which is something AI cannot do. This includes deciding what's good or bad, worth doing, and how different things compare in value.
  2. AI systems depend on humans for meaning-making to produce useful outputs. When using AI, the skill of the user to interpret and edit outputs is essential for effectiveness.
  3. Understanding that meaning-making is a human ability helps in developing better AI systems. It shifts the focus from what AI can do to what humans do that AI cannot.

Reflections on studying Large Language Models

Philosophy bear • 393 implied HN points • 24 Jun 25
🕹 Technology Machine Learning
  1. It's important to understand what Large Language Models (LLMs) can currently do and limit excessive philosophical concerns. Focusing on their real capabilities helps us appreciate their strengths and weaknesses better.
  2. Critics often overlook the achievements of LLMs, making broad claims without specific evidence of what these models can't do. A careful look at their limitations and abilities is needed for a fair assessment.
  3. When thinking about LLMs, we should be cautious about using complex concepts like 'thinking' or 'creativity.' It's better to focus on what these models can actually accomplish instead of getting caught up in vague definitions.

Does Decentralized Machine Learning Need Crypto? Bittensor (TAO)

DeFi Education • 599 implied HN points • 27 Oct 23
🕹 Technology Machine Learning
  1. Bittensor is a platform that uses decentralized machine learning to connect users with miners who run AI models. It aims to create a more open and fair AI ecosystem where everyone can participate.
  2. The platform rewards miners and validators with TAO tokens based on their contributions, similar to how Bitcoin operates. This incentive system encourages the best AI models to be selected for user queries.
  3. There's a growing trend of open source AI projects that show promise without needing huge corporate funding, making it possible for smaller teams to create effective AI tools without significant expenses.

How are companies actually using AI?

Technically • 24 implied HN points • 27 Jan 26
🕹 Technology Machine Learning
  1. Coding agents are the fastest-growing use case, with companies spending heavily on sandbox-based tooling and using the same tech for things like reinforcement learning.
  2. LLM inference is moving toward self-hosting with open-source models and inference engines so businesses can tune offline, online, and semi-online workloads, and spending on these OS stacks has surged.
  3. Science and B2B production use cases are steadily growing, showing AI is maturing from experiments into real enterprise deployments and driving rising infrastructure spend.

Data Science Weekly - Issue 544

Data Science Weekly Newsletter • 159 implied HN points • 26 Apr 24
🕹 Technology Machine Learning
  1. Evaluating AI models can be expensive, but tools like lm-buddy and Prometheus help do it on cheaper hardware without high costs.
  2. Installing and deploying LLaMA 3 is made simple with clear guides that cover everything from setup to scaling effectively.
  3. Understanding best practices in machine learning is essential, and resources like the 'Rules of Machine Learning' provide valuable guidelines for beginners.

Finding Data Bugs in dbt Pull Requests

clkao@substack • 39 implied HN points • 17 Aug 24
🕹 Technology Machine Learning
  1. Data bugs can be costly for companies, with bad data potentially costing up to 25% of their revenue. These issues often arise from problems in data-centric systems like dbt.
  2. Using dbt allows data engineers to implement software practices like version control and testing, helping to ensure the correctness of their data transformations. However, relying solely on post-processing tests has its limits.
  3. Manual spot checks are still crucial in ensuring data accuracy during code reviews. Tools like Recce aim to streamline this process, making it easier for developers to validate and document their changes.

2023, the Year of AI; 2024?

Sector 6 | The Newsletter of AIM • 399 implied HN points • 01 Jan 24
🕹 Technology Machine Learning
  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.

First Contact!

Technohumanism • 39 implied HN points • 24 Jul 24
🕹 Technology Machine Learning
  1. CETI is using advanced technology to understand sperm whales' communication. This shows how AI can help us connect with other species.
  2. There's a humorous aspect to this first contact, highlighting the unexpected ways we might communicate with animals.
  3. The idea raises questions about the limits and responsibilities of using AI in understanding and interacting with wildlife.

AI risk ≠ AGI risk

Marcus on AI • 4466 implied HN points • 28 Mar 23
🕹 Technology Machine Learning
  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.

Data Science Weekly - Issue 526

Data Science Weekly Newsletter • 419 implied HN points • 22 Dec 23
🕹 Technology Machine Learning
  1. Generative AI is changing how we work with tools, improving the Human-Tool Interface. This can help us use technology in ways we never could before.
  2. Support Vector Machines (SVMs) can be very effective for prediction tasks, often outperforming other models in error rates. However, they aren’t as commonly used, possibly due to their complexity.
  3. Deep multimodal fusion is useful in surgical training. It helps classify feedback from experienced surgeons to trainees by combining different types of data like text, audio, and video.

How to sell bread with quantile regression

Mindful Modeler • 339 implied HN points • 23 Jan 24
🕹 Technology Machine Learning
  1. Quantile regression can be used for robust modeling to handle outliers and predict tail behavior, helping in scenarios where underestimation or overestimation leads to loss.
  2. It is important to choose quantile regression when predicting specific quantiles, such as upper quantiles, for scenarios like bread sales where under or overestimating can have financial impacts.
  3. Quantile regression can also be utilized for uncertainty quantification, and combining it with conformal prediction can improve coverage, making it useful for understanding and managing uncertainty in predictions.

Da fuq is an “LLM?”

Sex and the State • 26 implied HN points • 14 Jan 26
🕹 Technology Machine Learning
  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.

Languages don't need many words

John Ball inside AI • 39 implied HN points • 24 Jul 24
🕹 Technology Machine Learning
  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.

Machine learning never cheats but it may play flawed games

Mindful Modeler • 259 implied HN points • 27 Feb 24
🕹 Technology Machine Learning
  1. Machine learning models may use shortcuts or exploit quirks in data, but it's important to consider them as playing the game according to the rules set by the data.
  2. Detecting flaws in prediction games is crucial, as models can unintentionally learn and act on misleading information from the data.
  3. Designing prediction games effectively requires a deep understanding of the data-generating process, tools like sampling theory, design of experiments, and a statistical mindset can be valuable in shaping prediction tasks.

Five Levels Of AI Agents

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 119 implied HN points • 16 May 24
🕹 Technology Machine Learning
  1. AI agents can make decisions and take actions based on their environment. They operate at different levels of complexity, with level one being simple rule-based systems.
  2. Currently, AI agents are improving rapidly, sitting at levels two and three, where they can automate tasks and manage sequences of actions effectively.
  3. The future of AI agents is bright, as they will be more integrated into various industries, but we need to consider issues like accountability and ethics when designing and implementing them.

The Sequence AI of the Week #805: Goodfire and the Era of AI Interpretability

TheSequence • 14 implied HN points • 11 Feb 26
🕹 Technology Machine Learning
  1. Modern AI is built by optimizing huge datasets with gradient descent, which produces powerful but opaque "black box" models.
  2. Relying only on prompts and RLHF is like doing behavioral psychology on an alien mind because we don't understand the model's internal workings; without interpretability tools, reliability and safety are limited.
  3. Interpretability efforts like feature steering and agent internals are pushing toward a "Software 3.0" where engineers can intentionally design a model's internal behavior, and investor interest shows the industry is shifting from alchemy to intentional, inspectable AI.

Data Science Weekly - Issue 545

Data Science Weekly Newsletter • 139 implied HN points • 03 May 24
🕹 Technology Machine Learning
  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.

Introducing Deep Symbolics

John Ball inside AI • 59 implied HN points • 02 Jul 24
🕹 Technology Machine Learning
  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.

Data Exfiltration in OpenAI Agent Builder via MCP

PromptArmor Blog • 138 implied HN points • 14 Oct 25
🕹 Technology Machine Learning
  1. There's a risk with AI applications passing the responsibility of security to users. Many people don't know how to protect themselves from prompt injection attacks, which makes this a big issue.
  2. Even with safety features like Guardrails, attackers can still trick AI systems into leaking sensitive data. This shows that current protections aren't foolproof.
  3. AI models might recognize malicious prompts but still process them, allowing harmful instructions to be passed through multiple steps in a workflow. This can lead to serious security issues.