The hottest Machine Learning Substack posts right now

And their main takeaways
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The Sequence Chat: The Transition that Changes Everything. From Pretraining to Post-Training in Foundation Models

TheSequence • 56 implied HN points • 04 Dec 24
🕹 Technology Machine Learning
  1. The transition from pretraining to post-training in AI models is a big deal. This change helps improve how AI can reason and learn from data.
  2. New models like DeepSeek's R1 and Alibaba's QwQ are now using this transition to become smarter and more effective. They can solve complex problems better than before.
  3. The shift is moving away from old methods like reinforcement learning with human feedback. Instead, there are new ways being developed that promise to make AI work even better.

A simple test for AI comprehension

Logos • 19 implied HN points • 21 Jan 24
🕹 Technology Machine Learning
  1. The author tests AI's understanding using a guessing game. The AI struggled and often made mistakes, which leads to questions about their comprehension.
  2. LLMs act like children by mimicking language without true understanding. They can say the right words but might not grasp the ideas behind them.
  3. The argument suggests that while LLMs can analyze complex topics, their understanding is shallow compared to human comprehension.

Launch of Conformal Prediction Book 🦫

Mindful Modeler • 59 implied HN points • 14 Feb 23
🚌 Education Machine Learning
  1. Conformal prediction can be combined with any uncertainty quantification method you already use, making it versatile and not restrictive.
  2. Conformal prediction is model-agnostic, meaning you can implement it without changing your existing models or user interface.
  3. One of the key advantages of conformal prediction is its guarantee of the true outcome coverage, making it a practical and useful addition to predictive modeling.

Open source libraries for Text and Time Series

Gradient Flow • 99 implied HN points • 25 Aug 22
🕹 Technology Machine Learning
  1. Consider incorporating transformer-based language models like BERTopic, PolyFuzz, and KeyBERT in NLP pipelines for text analysis.
  2. Explore new open source libraries like Merlion, Nixtla, Kats, and Greykite for time series analysis and modeling.
  3. Learn about AI toolkits like Ray AI Runtime (AIR) that unify ML libraries, facilitating scaled machine learning workloads with minimal code.
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Popular LLM Datasets

The Beep • 19 implied HN points • 21 Jan 24
🕹 Technology Machine Learning
  1. Datasets are crucial for training machine learning models, including language models. They help the model learn patterns and make predictions.
  2. Popular sources for datasets include Project Gutenberg and Common Crawl, which provide large amounts of text data for training language models.
  3. Instruction tuning datasets are used to adapt pre-trained models for specific tasks. These help the model perform better in given situations or instructions.

Who Gets to Compute?

Technically Optimistic • 19 implied HN points • 19 Jan 24
🕹 Technology Machine Learning
  1. The barrier to training large language models (LLMs) has been a challenge due to the high cost of resources like talent, data, power, and computing; this could lead to a situation where only big tech companies control AI, but there's hope for more diversity with smaller models.
  2. Direct Preference Optimization (DPO) is a potential game-changer in training LLMs as it skips the need for a costly reward model, reducing the barrier to entry for creating new models and potentially allowing for more diverse players in AI development.
  3. While DPO may make training large language models more accessible and less costly, it skips an important step involving human feedback that helps iron out biases and improve understanding of how these systems work, possibly hindering explainability efforts.

Identifying unmaintained open source packages at scale

Once a Maintainer • 5 implied HN points • 20 Nov 25
🕹 Technology Machine Learning
  1. Open source packages can become abandoned when original developers lose interest, meaning they might not get important updates or security fixes.
  2. To find abandoned packages, you can look at factors like how often the package has updates, the activity of commits, and what maintainers say about the package.
  3. Machine learning models can help predict whether a package might be abandoned by combining various factors like release frequency, maintainer communication, and community engagement.

The Bitter Lesson

Artificial Fintelligence • 20 implied HN points • 26 Jun 25
🕹 Technology Machine Learning
  1. Over time, methods that use more computing power will usually do better than those that don't. It's important to think about how to use more compute in AI.
  2. In the short term, adding human knowledge can help achieve good results quickly, but it's often not a good long-term strategy. Relying too much on human input can stall advancement.
  3. Real success in AI comes from focusing on general improvements that can scale, rather than chasing quick wins with expert knowledge. This approach is harder but pays off in the long run.

Claiming LLMs are merely "next token predictors" is a fundamental misunderstanding

The Future of Life • 19 implied HN points • 18 Jan 24
🕹 Technology Machine Learning
  1. LLMs are more than just next-token predictors. They use complex internal algorithms that let them understand and create language beyond simple predictions.
  2. The process that powers LLMs, like token prediction, is just a tool that leads to their true capabilities. These systems can evolve and learn in many sophisticated ways.
  3. Understanding LLMs isn't easy because their full potential is still a mystery. What limits them could be anything from their training methods to the data they learn from.

Data Prepare of Basic Retrieval Augmented Generation

The Beep • 19 implied HN points • 18 Jan 24
🕹 Technology Machine Learning
  1. Retrieval Augmented Generation (RAG) helps combine general language models with specific domain knowledge. It acts like a plugin that makes models smarter about particular topics.
  2. To prepare data for RAG, you need to load, split, and create vector stores from your documents. This process helps in organizing and retrieving relevant information efficiently.
  3. Using RAG can improve the accuracy of responses from language models. By providing context from relevant documents, you can reduce errors and make the information shared more reliable.

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

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

OpenAI isn’t Exciting Any Longer

Sector 6 | The Newsletter of AIM • 39 implied HN points • 27 Jun 23
🕹 Technology Machine Learning
  1. OpenAI is losing talented employees to Google, indicating a shift in the competitive landscape of AI.
  2. Some former OpenAI staff are unhappy with leadership, feeling that the company's vision is too focused on ChatGPT.
  3. There are concerns about the lack of direction at OpenAI, with rumors about the CEO's understanding of the business being superficial.

The Anatomy of the Least Squares Method, Part Four

The Palindrome • 5 implied HN points • 17 Nov 25
🕹 Technology Machine Learning
  1. You can use the least-squares method to understand and analyze regression models well. It's a handy tool for data scientists.
  2. Large language models like GPT-2 aren't as complex as they seem. A basic understanding of math can help you learn how they work.
  3. Using Python to model LLMs allows you to see how the math applies in real time. Following along with code can really boost your learning.

Catechizing the Bots, Part 2: Reinforcement Learning and Fine-Tuning With RLHF

jonstokes.com • 206 implied HN points • 10 Jun 23
🕹 Technology Machine Learning
  1. Reinforcement Learning is a technique that helps models learn from experiencing pleasure and pain in their environment over time.
  2. Human feedback plays a crucial role in fine-tuning language models by providing ratings that indicate how a model's output impacts users' feelings.
  3. To train models effectively, a preference model can be used to emulate human responses and provide feedback without the need for extensive human involvement.

Edge 364: About COSP and USP: Two New LLM Reasoning Methods Built by Google Research

TheSequence • 133 implied HN points • 25 Jan 24
🕹 Technology Machine Learning
  1. Two new LLM reasoning methods, COSP and USP, have been developed by Google Research to enhance common sense reasoning capabilities in language models.
  2. Prompt generation is crucial for LLM-based applications, and techniques like few-shot setup have reduced the need for large amounts of data to fine-tune models.
  3. Models with robust zero-shot performance can eliminate the need for manual prompt generation, but may have less potent results due to operating without specific guidance.

Could language models change language?

The Counterfactual • 119 implied HN points • 22 Jul 22
🕹 Technology Machine Learning
  1. Language is shaped by how we use it, and machine learning models might influence our language by suggesting words or phrases. Over time, these suggestions could change the way we communicate.
  2. The widespread use of predictive text and language models could either slow down language change by promoting similar expressions, or lead to new and unexpected language innovations.
  3. We could see personalized language models that adapt to individual users, potentially changing how we write and understand language, and encouraging less need for clarity in communication.

The CHAT Stack, GPT-4, And The Near-Term Future Of Software

jonstokes.com • 237 implied HN points • 15 Mar 23
🕹 Technology Machine Learning
  1. Developers will build apps on top of ChatGPT and similar models to create interactive and knowledgeable AI assistants
  2. The CHAT stack approach involves Context, History, API, and Token window, enabling how software applications will operate in the near future
  3. GPT-4 introduces an enlarged token window, improved control surfaces, and better ability to follow human instructions

Thinking Time

New World Same Humans • 42 implied HN points • 26 Jan 25
🕹 Technology Machine Learning
  1. Giving AI more time to think can greatly improve its performance, just like it helps humans think better. This 'thinking time' could be key in advancing artificial intelligence.
  2. Being busy doesn't always mean you're being productive; it's important to take breaks and allow space for creative thinking. Sometimes the best ideas come when you're not actively working.
  3. To truly innovate, focus on depth and originality instead of just producing a lot of work. It's about finding valuable insights that add to the conversation, rather than just adding to the noise.

Quant Letter: June 2025, Week-1

The Parlour • 21 implied HN points • 04 Jun 25
💰 Finance Machine Learning
  1. New methods are being developed to test asset pricing anomalies, showing that different paths on the same dataset can lead to similar outcomes. This means we need to be cautious about our assumptions in finance.
  2. Deep reinforcement learning is being used to improve risk management in life insurance. This method helps in making better decisions about profits and losses related to different risk factors.
  3. Large language models struggle with accuracy in specialized fields due to lack of specific training data. To improve their performance, fine-tuning techniques are essential.

Understanding Google's GPT-Killer- The Revolutionary Pathways Architecture [Storytime Saturdays]

Technology Made Simple • 39 implied HN points • 19 Feb 23
🕹 Technology Machine Learning
  1. Google's Bard is designed to be more versatile than ChatGPT, with a unique model architecture called Pathways.
  2. Google's approach includes training a single model for multiple tasks, working with different modalities like images and text, and using sparse activation to specialize network parts.
  3. The Pathways architecture sets Google apart by enabling their AI models to handle a wide range of tasks, making them cost-effective and versatile.

Unearthing Datasets Preparation for LLM

The Beep • 19 implied HN points • 11 Jan 24
🕹 Technology Machine Learning
  1. Good datasets are really important for training large language models (LLMs). If the data isn't well prepared, the model won't perform well.
  2. To prepare a dataset, you need to gather data, clean it up, and then convert it into a format the model can understand. Each step is crucial.
  3. While training LLMs, it's important to think about issues like data bias and privacy. This can affect how well the model works and who it might unfairly impact.

Google ships it: Gemma open LLMs and Gemini backlash

Democratizing Automation • 118 implied HN points • 22 Feb 24
🕹 Technology Machine Learning
  1. Google released Gemma, an open-weight model, which introduces new standards with 7 billion parameters and has unique architecture choices.
  2. The Gemma model addresses training issues with a unique pretraining annealing method, REINFORCE for fine-tuning, and a high capacity model.
  3. Google faced backlash for image generations from its Gemini series, highlighting the complexity in ensuring multimodal RLHF and safety fine-tuning in AI models.

🏆 Context Engineering > Prompts: What I Learned Building AI Products That Actually Work

The Product Channel By Sid Saladi • 16 implied HN points • 20 Jul 25
🕹 Technology Machine Learning
  1. Context engineering is key for making AI products work well. It's about providing the right information to the AI so it can solve problems effectively.
  2. The four important steps in context engineering are: writing for memory, selecting relevant info, compressing data to fit limits, and isolating different contexts.
  3. Using context engineering helps improve how AI understands tasks and delivers better results by managing the information it uses.

Key Components to Understand the LLM Models

The Beep • 19 implied HN points • 07 Jan 24
🕹 Technology Machine Learning
  1. Large language models (LLMs) like Llama 2 and GPT-3 use transformer architecture to process and generate text. This helps them understand and predict words based on previous context.
  2. Emergent abilities in LLMs allow them to learn new tasks with just a few examples. This means they can adapt quickly without needing extensive training.
  3. Techniques like Sliding Window Attention help LLMs manage long texts more efficiently by breaking them into smaller parts, making it easier to focus on relevant information.

Active Prompting with Chain-of-Thought for Large Language Models

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 05 Jan 24
🕹 Technology Machine Learning
  1. AI can help improve language models by using a four-step process: estimating uncertainty, selecting uncertain questions, annotating them, and making final inferences. This helps ensure better answers.
  2. Using human annotations along with AI makes the training data clearer and reduces confusion. It allows us to focus on the most important information for the models.
  3. Companies can benefit from this approach by streamlining how they handle data. It promotes a more organized way of discovering, designing, and developing data.

DataFrame

davidj.substack • 35 implied HN points • 20 Feb 25
🕹 Technology Machine Learning
  1. Polars Cloud allows for scaling across multiple machines, making it easier to handle large datasets than using just a single machine. This helps in processing data faster and more efficiently.
  2. Polars is simpler to use compared to Pandas and often performs better, especially when transforming data for machine learning tasks. It supports familiar methods that many users already know.
  3. Unlike SQL, which runs well on cloud services, using Pandas and R for large-scale transformations has been challenging. The new Polars Cloud aims to bridge this gap, providing more scalable solutions.

Shift-Left Analytics

SUP! Hubert’s Substack • 50 implied HN points • 22 Nov 24
🕹 Technology Machine Learning
  1. Shift-left analytics means doing analysis early in the data process. This helps in getting insights faster and making quick decisions.
  2. It focuses on checking data quality right away, so only reliable data is used. This leads to more accurate insights and avoids problems caused by bad data.
  3. Collaboration between teams is encouraged in this approach. By working together from the start, everyone can ensure their analyses are useful and aligned with business goals.

Looking back on AI in 2024

Generating Conversation • 46 implied HN points • 19 Dec 24
🕹 Technology Machine Learning
  1. AI companies need to show clear value to succeed. This means saving money or making profits, not just improving productivity.
  2. Building customer trust is key for AI products. Letting customers test and experience the product firsthand is often more effective than complicated evaluation tools.
  3. User experience with AI tools is really important. Good AI needs to be easy and enjoyable to use, which is a challenge that still needs solving.

How to Create Good Documentation in Software Engineering and Tech[Technique Tuesdays]

Technology Made Simple • 59 implied HN points • 19 Oct 22
🕹 Technology Machine Learning
  1. Good documentation in software engineering is crucial as it provides clarity to the team about goals and work done, enhancing productivity.
  2. Key pillars of good documentation include having a vision for the company and products, outlining resource/situational constraints, detailing data sources and processing, tracking projects in progress, sharing actual code, and establishing ownership.
  3. Benefits of good documentation in tech include aligning teams, clarifying vision and plans, reducing onboarding time, and promoting asynchronicity in an increasingly remote working environment.

GroupBy #16: Uber's Anomaly Detection & Alerting System, many layers of data lineage

VuTrinh. • 19 implied HN points • 02 Jan 24
🕹 Technology Machine Learning
  1. Uber has developed an anomaly detection system called uVitals, which helps identify issues before they become major problems. It analyzes data patterns to catch anomalies early.
  2. Data modeling is essential for creating structured databases that allow for better analysis and comparisons. It's important for data projects to have clear designs.
  3. As the field of data engineering evolves, new roadmaps and resources are emerging to guide professionals in developing necessary skills. Staying updated can help engineers advance their careers.

And AI took that personally

networked • 215 implied HN points • 22 Mar 23
🕹 Technology Machine Learning
  1. Artificial intelligence is the revolutionary technology that crypto tried and failed to be.
  2. Many of today's popular AI products are effectively loss leaders, not fully-fledged solutions.
  3. AI will often be mindlessly stapled onto legacy formats, creating unoriginal implementations.

Scalable Embedding based retrieval for target side value

Recommender systems • 23 implied HN points • 17 May 25
🕹 Technology Machine Learning
  1. Scalability is key for embedding-based recommendation systems, especially when dealing with billions of users. Finding effective ways to limit the search can help manage this challenge.
  2. It’s important to deliver value not just to viewers but also to the recommended targets, as this can improve user retention. Balancing recommendations for both sides can create a better experience.
  3. Using advanced algorithms can help ensure viewers don’t get overwhelmed with too many recommendations while also making sure that every target gets the attention they need. This balance is crucial for effective recommendations.

AI Roundup 097: Model Mayhem

Artificial Ignorance • 46 implied HN points • 13 Dec 24
🕹 Technology Machine Learning
  1. Google has launched new AI models such as Gemini 2.0, which can create text, images, and audio quickly. They also introduced tools to summarize video content and help users with web tasks.
  2. OpenAI released several features, including a text-to-video model named Sora for paying users. They also improved ChatGPT's digital editing tool and added new voice capabilities for video interactions.
  3. Meta and other companies are also advancing in AI with new models for cheaper yet effective performance and tools for watermarking AI-generated videos, showing that competition in AI is heating up.

Why reward models are key for alignment

Democratizing Automation • 110 implied HN points • 14 Feb 24
🕹 Technology Machine Learning
  1. Reward models provide a unique way to assess language models without relying on traditional prompting and computation limits.
  2. Constructing comparisons with reward models helps identify biases and viewpoints, aiding in understanding language model representations.
  3. Generative reward models offer a simple way to classify preferences in tasks like LLM evaluation, providing clarity and performance benefits in the RL setting.