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TheSequence Substack focuses on the latest trends and innovations in AI, covering open source LLM models, generative AI advancements, and multimodal generative AI. It discusses new research, frameworks, and tools, highlighting their impact on software development and AI applications' efficiency and capabilities.

Artificial Intelligence Generative AI Open Source AI Models Language Models Machine Learning Frameworks AI Research AI Applications in Software Development Multimodal Generative AI

The hottest Substack posts of TheSequence

And their main takeaways

The Sequence Research #663: The Illusion of Thinking, Inside the Most Controversial AI Paper of Recent Weeks

105 implied HN points • 13 Jun 25
🕹 Technology AI Research Innovation Computing Data science
  1. Large Reasoning Models (LRMs) can show improved performance by simulating thinking steps, but their ability to truly reason is questioned.
  2. Current tests for LLMs often miss the mark because they can have flaws like data contamination, not really measuring how well the models think.
  3. New puzzle environments are being introduced to better evaluate these models by challenging them in a structured way while keeping the logic clear.

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

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

The Sequence Engineering #661: Create Your Own Deep Research Agent with DeerFlow

119 implied HN points • 11 Jun 25
🕹 Technology Software AI Automation Research Development
  1. DeerFlow is an open-source tool that helps automate research tasks. It uses multiple agents to make research faster and easier.
  2. The framework can do many tasks, like searching the web and creating reports, with little help from people. This makes it very efficient.
  3. It's designed for developers and engineers who want to build research systems that can grow and adapt easily.

The Sequence Knowledge #560: The Amazing World of Agentic Benchmarks

49 implied HN points • 10 Jun 25
🕹 Technology AI Machine Learning Software Data science Automation
  1. Agentic benchmarks are new ways to evaluate AI that focus on decision-making rather than just answering questions. They look at how well AI can plan and adapt to different tasks.
  2. Traditional evaluation methods aren't enough for AI that acts like agents. We need tests that measure how AI can handle complex situations and multi-step processes.
  3. One exciting example of these benchmarks is the Web Arena, which helps assess AI's ability to perform tasks on the web. This includes how well they interact with online tools and environments.

The Sequence Radar #559 : Two Remarkable Papers This Week: Self-Improving Agents and the Limits of LLM Memorization

56 implied HN points • 08 Jun 25
🕹 Technology AI Research Development Innovation Models
  1. The Darwin Gödel Machine is a new AI system that can improve itself by changing its own code, leading to better performance in coding tasks. This approach mimics evolution by letting different versions of the AI compete and innovate.
  2. A recent study found that large language models have a limited capacity for memorizing information, roughly 3.6 bits per parameter. This helps us understand how these models learn and remember data.
  3. Both papers highlight how AI can evolve and learn, with one focusing on self-improvement and the other on what models can and cannot remember. Together, they show the potential and limits of AI development.
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The Sequence Research #558: The New Reinforcement Learning from Internal Feedback Allows LLMs to Reason Without External Rewards

70 implied HN points • 06 Jun 25
🕹 Technology AI Machine Learning Computing Research Innovation
  1. Reinforcement learning is a key way to help large language models think and solve problems better. It helps models learn to align with what people want and improve accuracy.
  2. Traditional methods like RLHF require a lot of human input and can be slow and costly. This limits how quickly models can learn and grow.
  3. A new approach called Reinforcement Learning from Internal Feedback lets models learn on their own using their own internal signals, making the learning process faster and less reliant on outside help.

The Sequence Opinion #557: Millions of GPUs, Zero Understanding: The Cost of AI Interpretability

49 implied HN points • 05 Jun 25
🕹 Technology AI Machine Learning Computing Data science Cybersecurity
  1. AI models are becoming super powerful, but we don't fully understand how they work. Their complexity makes it hard to see how they make decisions.
  2. There are new methods being explored to make these AI systems more understandable, including using other AI to explain them. This is a fresh approach to tackle AI interpretability.
  3. The debate continues about whether investing a lot of resources into understanding AI is worth it compared to other safety measures. We need to think carefully about what we risk if we don't understand these machines better.

The Sequence Radar #554 : The New DeepSeek R1-0528 is Very Impressive

77 implied HN points • 01 Jun 25
🕹 Technology AI Computing Machine Learning Software Engineering
  1. The DeepSeek R1-0528 model is really good at math and reasoning, showing big improvements in understanding complicated problems.
  2. This new model can handle large amounts of data at once, making it perfect for tasks that need lots of information, like technical documents.
  3. DeepSeek is focused on making advanced AI accessible to everyone, not just big companies, which is great for developers and researchers with limited resources.

The Sequence Engineering #556: Inside Anthropic's New Open Source AI Interpretability Tools

49 implied HN points • 04 Jun 25
🕹 Technology AI Interpretability Open Source Research Development
  1. Anthropic is becoming a leader in AI interpretability, which helps explain how AI systems make decisions. This is important for understanding and trusting AI outputs.
  2. They have developed new tools for tracing the thought processes of language models, helping researchers see how these models work internally. This makes it easier to improve and debug AI systems.
  3. Anthropic's recent open source release of circuit tracing tools is a significant advancement in AI interpretability, providing valuable resources for researchers in the field.

The Sequence Research #553: Self-Evaluating LLMs Are Here: Inside Meta AI’s J1 Framework

63 implied HN points • 30 May 25
🕹 Technology AI Machine Learning Software Innovation Research
  1. LLMs are now used as judges, which is an exciting new trend in AI. This can help improve how we evaluate AI outputs.
  2. Meta AI's J1 framework is a significant development that makes LLMs more like active thinkers rather than just content creators. This means they can make better evaluations.
  3. Using reinforcement learning, J1 allows AI models to learn effective ways to judge tasks. This helps ensure that their evaluations are both reliable and understandable.

The Sequence Opinion #552: Seriously, What is an Agent?

70 implied HN points • 29 May 25
🕹 Technology AI Software Innovation Automation Ethics
  1. The term 'AI agent' can mean many things, and different experts have different definitions. This shows that there is still a lot of discussion about what really makes an AI an agent.
  2. Some people think an AI agent should be able to plan and act on its own, while others see it as any system that uses language models or performs tasks. There is no clear agreement on this.
  3. The lines between traditional AI models and agents might be blurring, suggesting that future AI systems could include features of agents directly within them.

The Sequence Research #543: The Leaderboard Illusion Challenges Chatbot Arena Type Benchmarks

119 implied HN points • 16 May 25
🕹 Technology AI Machine Learning Benchmarks Data science Research
  1. Leaderboards in AI help direct research by showing who is doing well, but they can also create problems. They might not show the whole picture of how models really perform.
  2. The Chatbot Arena is a way to judge AI models based on user choices, but it has issues that make it unfair. Some big labs can take advantage of the system more than smaller ones.
  3. To make AI evaluations better, there need to be rules that ensure fairness and transparency. This way, everyone gets a fair chance in the AI race.

The Sequence Engineering #546: You Know MCP, but What About ACP

84 implied HN points • 21 May 25
🕹 Technology AI Protocols Communication Systems Autonomy
  1. The Agent Communication Protocol (ACP) allows different AI agents to talk to each other easily. This makes their interactions more advanced and effective.
  2. ACP builds on the Model Context Protocol (MCP) but adds features for more complex conversations. It supports things like agent discovery and message management.
  3. Understanding both MCP and ACP is important for grasping how AI agents work together. They each play a unique role in improving AI communication.

The Sequence Opinion #542 : Some Ideas About the Future of MCP

112 implied HN points • 15 May 25
🕹 Technology AI Networking Protocols Integration Systems
  1. Model Context Protocol (MCP) is becoming really important for how AI models connect with tools and data. It's like how USB-C has made it easier for devices to connect with each other.
  2. MCP is evolving from just being a way to connect models to creating networks of AI systems that can work together and find resources dynamically. It's moving towards smarter and more flexible AI interactions.
  3. The future of MCP involves areas like better discovery methods and securing trust between AI agents. This is a shift towards creating more complex and coordinated systems that understand and use context effectively.

The Sequence Knowledge #550: Let's Talk About Safety Benchmarks

42 implied HN points • 27 May 25
🕹 Technology AI safety Machine Learning Benchmarks Evaluation Risk Assessment
  1. Safety benchmarks are important tools that help evaluate AI systems. They make sure these systems are safe as they become more advanced.
  2. Different organizations have created their own frameworks to assess AI safety. Each framework focuses on different aspects of how AI systems can be safe.
  3. Understanding and using safety benchmarks is essential for responsible AI development. This helps manage risks and ensure that AI helps, rather than harms.

The Sequence Opinion #547: Best Practices I am Learning While Coding with AI Agents

63 implied HN points • 22 May 25
🕹 Technology Software AI Engineering Coding Testing
  1. Software engineering is changing rapidly with the use of AI agents. Teams are now using AI to help speed up their work and take on new roles.
  2. AI agents are moving beyond just helping with code completion. They now can generate entire code bases, run tests, and manage pull requests automatically.
  3. Developers are shifting their focus from hands-on coding to more strategic tasks like code review and creating documentation, as AI handles more of the coding work.

The Sequence Radar #549: Google, Microsoft and Anthropic Monster AI Week

49 implied HN points • 25 May 25
🕹 Technology AI Software Research Development Conferences
  1. Google is making big strides towards creating Artificial General Intelligence (AGI) with new models like Gemini 2.5 and features such as a universal AI assistant called Project Astra.
  2. Microsoft is focusing on 'agentic AI', which means they're developing AI that can work independently to complete complex tasks, supported by their new Azure AI Foundry.
  3. Anthropic introduced the Claude 4 series, which improves reasoning abilities in AI models and emphasizes safety and ethical behavior, helping developers build smarter AI systems.

The Sequence Research #548: Why I Can't Stop Thinking About AlphaEvolve

56 implied HN points • 23 May 25
🕹 Technology AI Software Research Innovation Algorithms
  1. AlphaEvolve is a new tool that uses AI to create and improve algorithms, which could be a big step toward achieving artificial general intelligence (AGI).
  2. It combines evolutionary methods with large language models, allowing it to discover and refine algorithms more efficiently.
  3. AlphaEvolve not only makes significant math discoveries but also helps improve Google's technology operations.

The Sequence Engineering #551: Magentic-UI Push The Boundaries of Agentic User Experience

35 implied HN points • 28 May 25
🕹 Technology AI UX Software Automation Web Development
  1. Magentic-UI is a new web interface by Microsoft that helps with complex tasks using AI. It allows people to work together with AI in a more effective way.
  2. This interface combines large language models with real-time feedback, making automation dynamic and secure. Users can complete multi-step tasks more easily.
  3. Agentic user experience is an emerging area in generative AI, and Magentic-UI aims to improve how we interact with AI beyond just chat interfaces.

The Sequence Knowledge # 555: Not All Benchmark are that Simple: An Intro to Multiturn Benchmarks

14 implied HN points • 03 Jun 25
🕹 Technology AI Machine Learning Evaluation Natural Language Benchmarks
  1. Multi-turn benchmarks are important for testing AI because they make AIs more like real conversation partners. They help AIs keep track of what has already been said, making the chat more natural.
  2. These benchmarks are different from regular tests because they don’t just check if the AI can answer a question; they see if it can handle ongoing dialogue and adapt to new information.
  3. One big challenge for AIs is remembering details from previous chats. It's tough for them to keep everything consistent, but it's necessary for good performance in conversations.

The Sequence Radar #544: The Amazing DeepMind's AlphaEvolve

63 implied HN points • 18 May 25
🕹 Technology AI Machine Learning Computing Research Innovation
  1. AlphaEvolve is a new AI model from DeepMind that helps discover new algorithms by combining language models with evolutionary techniques. This allows it to create and improve entire codebases instead of just single functions.
  2. One of its big achievements is finding a faster way to multiply certain types of matrices, which has been a problem for over 50 years. It shows how AI can not only generate code but also make important mathematical discoveries.
  3. AlphaEvolve is also useful in real-world applications, like optimizing Google's systems, proving it's not just good in theory but has practical benefits that improve efficiency and performance.

The Sequence Knowledge #545 : Beyond Language, Learning About Multimodal Benchmarks

28 implied HN points • 20 May 25
🕹 Technology AI Machine Learning Computer Vision Data science Benchmarking
  1. Multimodal benchmarks are tools to evaluate AI systems that use different types of data like text, images, and audio. They help ensure that AI can handle complex tasks that combine these inputs effectively.
  2. One important benchmark in this area is called MMMU, which tests AI on 11,500 questions across various subjects. This benchmark needs AI to work with text and visuals together, promoting deeper understanding rather than just shortcuts.
  3. The design of these benchmarks, like MMMU, helps reveal how well AI understands different topics and where it may struggle. This can lead to improvements in AI technology.

The Sequence Radar #477: The R1 Moment

546 implied HN points • 26 Jan 25
🕹 Technology AI Machine Learning Open Source Innovation Data science Research
  1. DeepSeek-R1 is a new AI model that shows it can perform as well or better than big-name AI models but at a much lower cost. This means smaller companies can now compete in AI innovation without needing huge budgets.
  2. The way DeepSeek-R1 is trained is different from traditional methods. It uses a new approach called reinforcement learning, which helps the model learn smarter reasoning skills without needing a ton of supervised data.
  3. The open-source nature of DeepSeek-R1 means anyone can access and use the code for free. This encourages collaboration and allows more people to innovate in AI, making technology more accessible to everyone.

The Sequence Opinion #480: What is GPT-o1 Actually Doing?

161 implied HN points • 30 Jan 25
🕹 Technology AI Machine Learning Deep Learning Software Development Data science
  1. GPT models are becoming more advanced in reasoning and problem-solving, not just generating text. They are now synthesizing programs and refining their results.
  2. There's a focus on understanding how these models work internally through ideas like hypothesis search and program synthesis. This helps in grasping the real innovation they bring.
  3. Reinforcement learning is a key technique used by newer models to improve their outputs. This shows that they are evolving and getting better at what they do.

The Sequence Opinion #489: CRAZY: How DeepSeek R1 Bypassed CUDA with Lower-Level GPU Optimization Techniques

112 implied HN points • 13 Feb 25
🕹 Technology Computing Programming GPU Optimization Innovation
  1. DeepSeek R1 has found new ways to optimize GPU performance without using NVIDIA's CUDA. This is impressive because CUDA is widely used for GPU programming.
  2. The team utilized PTX programming and NCCL to improve communication efficiency. These lower-level techniques help in overcoming GPU limitations.
  3. These innovations show that there are still creative ways to enhance technology, even against established systems like CUDA. It's exciting to see where this might lead in the future.

The Reasoning Race: Can Small Models Reason?

182 implied HN points • 05 Jan 25
🕹 Technology AI Models Research Engineering Philosophy
  1. The Sequence newsletter is evolving to offer more focused content, catering to both AI scientists and engineers. This means you'll get richer discussions on research and practical applications.
  2. There will be new editions each week that cover a variety of topics like education, engineering, interviews, and insights. This change aims to make the content shorter and easier to digest.
  3. The discussions around reasoning in AI are expanding to include smaller models, challenging the idea that only large models are capable of complex reasoning. It's an exciting area of exploration.

Moving Past RLHF: In 2025 We Will Transition from Preference Tuning to Reward Optimization in Foundation Models

189 implied HN points • 29 Dec 24
🕹 Technology Artificial Intelligence Machine Learning Neural Networks Modeling Data science
  1. Artificial intelligence is moving from preference tuning to reward optimization for better alignment with human values. This change aims to improve how models respond to our needs.
  2. Preference tuning has its limits because it can't capture all the complexities of human intentions. Researchers are exploring new reward models to address these limitations.
  3. Recent models like GPT-o3 and Tülu 3 showcase this evolution, showing how AI can become more effective and nuanced in understanding and generating language.

📝 Guest Post: Augmented SBERT: A Data Augmentation Method to Enhance Bi-Encoders for Pairwise Sentence Scoring*

126 implied HN points • 31 Jan 25
🕹 Technology Natural Language Processing Data science Machine Learning Artificial Intelligence Software Development
  1. Augmented SBERT (AugSBERT) improves sentence scoring tasks by using data augmentation to create more sentence pairs. This means it can perform better even when there's not much training data available.
  2. Traditional methods like cross-encoders and bi-encoders have limitations, like being slow or needing a lot of data. AugSBERT addresses these issues, making it more efficient for large-scale tasks.
  3. The approach combines the strengths of different models to enhance performance, especially in specific domains. It shows significant improvements over existing models, making it a useful tool for various natural language processing applications.

The Sequence Opinion #476: The DeepSeek Effect: The Remarkable Innovations and Controversies Surrounding the New Challenger in Open-Source AI

133 implied HN points • 24 Jan 25
🕹 Technology AI Open Source Innovation Controversies Models
  1. DeepSeek is a new player in open-source AI, quickly gaining attention for its innovative models. They have released powerful AI tools that can think and reason well, challenging the idea that only big models can do this.
  2. The company was founded in May 2023 and has shown rapid progress by continually improving its technology. This quick success highlights their commitment to pushing the limits of AI performance and efficiency.
  3. However, the fast advancements by DeepSeek have raised some controversies. People are discussing the implications of their rapid growth in the AI space, suggesting that it might impact the future of AI development.

Edge 360: Meet Ghostbuster: An AI Technique for Detecting LLM-Generated Content

1310 implied HN points • 11 Jan 24
🕹 Technology AI Detection Research
  1. Berkeley University developed a method to detect AI-generated tokens in documents using probability distribution.
  2. Ghostbuster is an AI technique for identifying AI-generated text by calculating token likelihood and using a conclusive classifier.
  3. The technique by Berkeley AI Research aims to tackle challenges in differentiating between human and AI-generated content.

The Sequence Radar #481: Humanity's Last Exam

112 implied HN points • 02 Feb 25
🕹 Technology AI Research Frameworks Benchmarks Innovation
  1. HLE is a new test for AI that has 3,000 tough questions covering many subjects. It helps to see how well AI can perform on academic topics, especially where current tests are too easy.
  2. The questions used in HLE are carefully checked and revised to make sure they truly challenge AI models, ensuring they can't just memorize answers from the internet.
  3. AI is currently struggling with HLE, often getting less than 10% of questions correct. This shows there's still a big gap between AI and human knowledge that needs to be addressed.

The Sequence Engineering #479: Dify.AI: A Deep Dive into its Open-Source LLM Application Development Platform

112 implied HN points • 29 Jan 25
🕹 Technology AI Software Development Open Source Platforms
  1. Dify.AI is an open-source platform that helps developers create applications using large language models (LLMs). Its user-friendly setup makes it easier to build AI solutions like chatbots or complex workflows.
  2. The platform is designed to be flexible and keeps evolving to meet the needs of developers in the fast-paced world of generative AI. This adaptability is key when choosing a tech stack for projects.
  3. Dify.AI includes advanced features like Retrieval Augmented Generation (RAG), which enhances how applications gather and use information. This makes it a powerful tool for building sophisticated AI applications.

Transformers are Eating Quantum

217 implied HN points • 24 Nov 24
🕹 Technology AI Quantum Computing Machine Learning Innovation
  1. Quantum computing faces challenges due to noise affecting performance. AI, specifically AlphaQubit, helps improve error correction in quantum systems.
  2. AlphaQubit uses a neural network design from language models to better decode quantum errors. It shows greater accuracy and adapts to various data types effectively.
  3. While AlphaQubit is a major step forward, there are still issues to tackle, mainly concerning its speed and ability to scale for larger quantum systems.

The Sequence Engineering #483: Block's goose is a Brand New Framework for Building Agentic Applications

91 implied HN points • 05 Feb 25
🕹 Technology AI Software Frameworks Applications Development
  1. Block has introduced a new framework called goose, which helps connect large language models to actions. This means it can make LLMs do things more effectively.
  2. The release of goose shows that big companies are really getting into building applications that can act on their own. It's changing how we look at AI and its capabilities.
  3. The ongoing development of agentic workflows is significant, and it hints that AI will continue to grow and improve in how it helps us solve problems.

📝 Guest Post: Advanced RAG Techniques: Bridging Text and Visuals for More Accurate Responses*

175 implied HN points • 09 Dec 24
🕹 Technology AI Data science Machine Learning Software Development Information Retrieval
  1. RAG techniques combine the power of language models with external data to improve accuracy. This means AI can give better answers by using real-world information.
  2. Advanced methods like Small to Slide RAG make it easier for AI to work with visual data, like slides and images. This helps AI understand complex information that is not just text.
  3. ColPali is a new approach that focuses on visuals directly, avoiding mistakes from converting images to text. It's useful for areas like design and technical documents, ensuring important details are not missed.