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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

📝 Guest Post: How to Build the Right Team for Generative AI*

994 implied HN points • 19 Jan 24
🕹 Technology AI Engineering Product Management Tooling
  1. You may not need ML engineers for Generative AI projects due to the availability of pre-trained models like GPT-4.
  2. Prompt engineering, the clear articulation of needs in natural language, is a crucial skill for AI application development.
  3. Product managers and domain experts play a significant role in shaping AI products through prompt engineering, reducing the need for technical experts.

Edge 462: What is Fast-LLM. The New Popular Framework for Pretraining your Own LLMs

126 implied HN points • 02 Jan 25
🕹 Technology AI Models Open Source Scalability Research Innovation
  1. Fast-LLM is a new open-source framework that helps companies train their own AI models more easily. It makes AI model training faster, cheaper, and more scalable.
  2. Traditionally, only big AI labs could pretrain models because it requires lots of resources. Fast-LLM aims to change that by making these tools available for more organizations.
  3. With trends like small language models and sovereign AI, many companies are looking to build their own models. Fast-LLM supports this shift by simplifying the pretraining process.

The Sequence Knowledge #473: Not All RAGs are Created Equal

98 implied HN points • 21 Jan 25
🕹 Technology Machine Learning Artificial Intelligence Data science Information Retrieval Natural Language Processing
  1. RAG stands for Retrieval Augmented Generation. It's a way for machines to pull in outside information, helping them give better and more accurate answers.
  2. There are many kinds of RAG, like Standard RAG and Fusion RAG. Each type helps machines deal with different problems and has its special strengths.
  3. Understanding these RAG types is important for anyone working in AI. It helps them choose the right approach for different challenges.

The Sequence Research #490: A Practical Deep Dive Inside DeepSeek-R1

70 implied HN points • 14 Feb 25
🕹 Technology AI Machine Learning Software Computing Innovation
  1. DeepSeek-R1 is a new AI model that performs well without needing to be very big. It uses smart training methods to achieve great results at a lower cost.
  2. The model successfully matches the performance of a larger, more expensive model called GPT-o1. This shows that size isn't the only thing that matters for good performance.
  3. DeepSeek-R1 challenges the idea that you always need large models for reasoning, suggesting that clever techniques can also lead to impressive results.

eBook: Mastering AI Agents

77 implied HN points • 07 Feb 25
🕹 Technology AI agents Machine Learning Automation Software Development Data Analysis
  1. You can learn to create effective AI agents with the right guidance. There's a helpful eBook that covers how these agents work and when to use them.
  2. The book reviews three frameworks for developing AI agents, helping you choose what's best for your needs. It also shares case studies to show real-life applications.
  3. It addresses common reasons AI agents fail and provides solutions to avoid these problems. This can help ensure your AI projects succeed.
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The Sequence Knowledge #482: An Introduction to Corrective RAG

77 implied HN points • 04 Feb 25
🕹 Technology Artificial Intelligence Machine Learning Data science Software Development Information Systems
  1. Corrective RAG is a smarter way of using AI that makes it more accurate by checking its work. It helps prevent mistakes or errors in the information it gives.
  2. This method goes beyond basic retrieval-augmented generation (RAG) by adding feedback loops that refine and improve the output as it learns.
  3. The goal of Corrective RAG is to provide answers that are factually accurate and coherent, reducing confusion or incorrect information.

Edge 460: Anthropic's New Protocol to Link AI Assistants to Data Sources

119 implied HN points • 26 Dec 24
🕹 Technology AI Software Open Source Data Frameworks
  1. Anthropic has created the Model Context Protocol (MCP) to help AI assistants connect with different data sources. This means AI can access more information to assist users better.
  2. MCP is open-source, which allows developers to use and improve the protocol freely. This encourages collaboration and innovation in AI tools.
  3. Anthropic is expanding its focus beyond AI models to include workflows and developer tools, showing that they're growing in new areas within AI technology.

The Sequence Engineering #488: Txtai, Maybe the Simplest Way to do Embeddings

63 implied HN points • 12 Feb 25
🕹 Technology AI Software Open Source Databases Development
  1. Embeddings are important for generative AI applications because they help with understanding and processing data. A good embedding framework should be simple and easy for developers to use.
  2. Txtai is an open-source database that combines different tools to make working with embeddings easier. It allows for semantic search and supports creating various AI applications.
  3. This framework can help build advanced systems like autonomous agents and search tools, making it a versatile choice for developers creating LLM apps.

Microsoft's New Framework for Multi-Agent Systems

175 implied HN points • 10 Nov 24
🕹 Technology AI Software Research Innovation Systems
  1. Magentic-One is a new tool from Microsoft that helps manage multiple AI agents to tackle complex tasks. It acts like a conductor guiding different musicians, making it easier to complete different jobs together.
  2. This system allows for flexibility by using different AI models for different tasks, which means it can be customized based on what you need. It's designed to improve efficiency in our daily tasks, like ordering food or doing research.
  3. While Magentic-One is powerful, it's still being improved to reduce errors and ensure it acts safely. The goal is to make sure these AI agents help us reliably without causing problems.

The Race for AI Reasoning is Challenging our Imagination

112 implied HN points • 22 Dec 24
🕹 Technology AI Machine Learning Research Innovation Computing
  1. OpenAI and Google are in a fierce competition to improve AI reasoning capabilities. Their advancements could lead to machines that think and solve problems more like humans.
  2. Better reasoning in AI could transform many fields, such as healthcare and law. Imagine AI helping doctors diagnose diseases with high accuracy or assisting lawyers in complex cases.
  3. As AI models become smarter at reasoning, they will change the way we live and work. This could open up many new opportunities and challenges for society.

The Sequence Engineering #474: The Super Popular Eliza Framework for Building AI Agents

77 implied HN points • 22 Jan 25
🕹 Technology AI Web3 Development Frameworks Automation
  1. The Eliza framework is becoming very popular, especially in the web3 and crypto spaces. It helps developers create AI applications by automating essential tasks.
  2. Despite not being widely known, Eliza has gained a lot of attention on platforms like GitHub, showing its growing appeal.
  3. Eliza offers a flexible design, making it a strong choice for building agentic apps. It's more than just a tool for crypto; it's useful for various types of AI projects.

The Sequence Knowledge #468: A New Series About RAG

84 implied HN points • 13 Jan 25
🕹 Technology Artificial Intelligence Machine Learning Data science Natural Language Processing Research
  1. Retrieval Augmented Generation, or RAG, helps AI models use outside information to improve their answers. This makes the responses more accurate and relevant.
  2. RAG works in two steps: first, it finds useful information, and then it uses that information to create better responses. This method is great for applications that need quick and correct answers.
  3. A key paper introduced RAG and showed that combining different types of memory can lead to better results in language tasks, like answering questions or generating text.

The Sequence Radar #472: Remember this Name: Ndea

77 implied HN points • 19 Jan 25
🕹 Technology AI Research Startups Innovation Machine Learning Data science
  1. Ndea is a new AI lab aiming to create artificial general intelligence (AGI) with a unique approach called guided program synthesis. This approach allows models to learn efficiently from fewer examples.
  2. Francois Chollet, a well-known AI expert, is leading Ndea. He believes current deep learning methods have limitations and wants to explore new ideas for better AI development.
  3. The goal of Ndea is to drive quick scientific advancements by combining program synthesis with deep learning, aiming to tackle tough challenges and possibly discover new scientific frontiers.

The Sequence Research #471: One of the New Techniques Powering in OpenAI GPT-o3

77 implied HN points • 17 Jan 25
🕹 Technology AI Research Model Training
  1. Deliberate Alignment is a new method to make AI safer and more trustworthy. It helps AI systems better understand and follow safety rules.
  2. This technique is different from older training methods because it teaches the AI explicitly about safety. This means the AI can use that knowledge when responding, especially in tricky situations.
  3. By focusing on this direct instruction, the AI can handle new challenges better and learn from them more efficiently.

Edge 448: Meta AI's Technique For Building LLMs that "Think Before they Speak"

140 implied HN points • 14 Nov 24
🕹 Technology AI Research Machine Learning Language Models Generative AI
  1. Meta AI is developing new techniques to make AI models better at reasoning before giving answers. This could help them become more like humans in problem-solving.
  2. The research focuses on something called Thought Preference Optimization, which could lead to breakthroughs in how generative AI works.
  3. Studying how AI can 'think' before speaking might change the future of AI, making it smarter and more effective in conversation.

Anthropic, WOW

161 implied HN points • 27 Oct 24
🕹 Technology AI Machine Learning Software Computing Innovation
  1. Anthropic has launched a new AI model named Claude that can interact with computers like a human, allowing it to execute tasks directly on-screen. This opens many new possibilities for AI applications.
  2. Two upgraded versions of Claude have been released, one focusing on coding and tool usage with high performance, and the other emphasizing speed and affordability for everyday applications.
  3. A new analysis tool has been introduced in Claude.ai, enabling the model to write and run JavaScript code for data analysis and visualizations, enhancing its functionality for users.

The Sequence Opinion #485: What's Wrong With AI Benchmarks

56 implied HN points • 06 Feb 25
🕹 Technology AI Data Evaluation Machine Learning Benchmarking
  1. AI benchmarks are currently facing issues like data contamination and memorization, which affect how accurately they evaluate models. It's important to find better ways to test these systems.
  2. New benchmarks are popping up all the time, making it hard to keep track of what each one measures. This could lead to confusion in understanding AI capabilities.
  3. There's a need for clearer and more standard methods in AI evaluation to really see how well these models perform and improve their reliability.

The Transformer Robots are Here, Just a Different Kind

693 implied HN points • 07 Jan 24
🕹 Technology Robotics AI ML Research AI Radar
  1. Advancements in foundation models like language and computer vision are shaping a new era of robotic applications.
  2. Google DeepMind introduced innovative methods like AutoRT and SARA-RT to enhance robotic actions using vision-language models.
  3. The integration of foundation models in image, language, and video is accelerating robotics to new levels of efficiency.

The Toughest Math Benchmark Ever Built

133 implied HN points • 17 Nov 24
🕹 Technology AI Machine Learning Mathematics Benchmarks Research
  1. Frontier Math is a really tough math test designed for AI. It has new, unique problems that are hard for AI to solve, testing deeper reasoning skills.
  2. Many AI models do well on easier math problems but struggle with Frontier Math. They often can't combine ideas creatively like a human can.
  3. This benchmark shows the big gap between current AI abilities and true mathematical understanding, highlighting the need for better AI reasoning.

Edge 455: Building Smaller Foundation Models Using Graph-Based Distillation

105 implied HN points • 10 Dec 24
🕹 Technology Artificial Intelligence Machine Learning Data science Software Development Graph Theory
  1. Graph-based distillation helps smaller models learn better by using the connections between data points. Instead of just focusing on individual data, it looks at how they relate to one another.
  2. This technique uses attention networks to improve how student models understand data, making them more effective in learning.
  3. There’s a new framework called Hugging Face Autotrain that allows for easier training of foundation models without needing too much coding knowledge.

The Sequence Knowledge #487: A RAG that Assesses Itself

49 implied HN points • 11 Feb 25
🕹 Technology AI Software Machine Learning Data science Innovation
  1. Self-RAG is a new method that helps improve how retrieval-augmented generation works by letting models check their own work.
  2. It uses special tokens that help the model decide when it should look for information and how to review its own answers.
  3. This technique aims to make the process more thoughtful compared to regular methods that just pull information randomly.

📽 Webinar: How Convirza Scaled SLMs for Real-Time Call Analytics – Without Breaking the Bank

126 implied HN points • 15 Nov 24
🕹 Technology AI Analytics Webinars Infrastructure Scalability
  1. Convirza found a way to analyze call data quickly and affordably. They combined many tools into one setup, making everything run smoother.
  2. Their response time for customers is now under two seconds, even when many people are using the service. This helps workers get the info they need fast.
  3. By switching to a new system, they reduced costs a lot. They no longer need expensive machines for each task, which keeps their expenses low while still providing accurate results.

Edge 452: The AI Magic Behind Google's NotebookLM Audio Features

112 implied HN points • 28 Nov 24
🕹 Technology Artificial Intelligence Machine Learning Innovation Tech Tools
  1. NotebookLM is a popular AI tool for generating podcasts, using clever techniques that combine humor and realistic dialogue. People are starting to recognize the voices in these generated podcasts.
  2. The audio features of NotebookLM are powered by technologies from Google DeepMind, notably SoundStorm and AudioLM, which focus on creating realistic sounds and speech.
  3. Research in audio generation is advancing quickly, aiming to develop systems that can produce coherent and realistic speech and music. Google DeepMind is leading the way in this exciting field.

Edge 458: From Pre-training to Post-training. Inside the Amazing Tülu 3 Framework

91 implied HN points • 19 Dec 24
🕹 Technology AI Machine Learning Frameworks Open Source Data Training
  1. There is a new focus in AI from pre-training models to post-training methods. This change is happening because it's now easier to train models with data from the internet.
  2. The Tülu 3 framework is designed to improve existing language models after their initial training. It highlights how important the post-training process is for making models work better.
  3. By making post-training techniques more open and accessible, Tülu 3 aims to help the open-source community compete with top-performing private models.

The Sequence Research #466: Small but Migthy, Diving Into Microsoft Phi-4

70 implied HN points • 10 Jan 25
🕹 Technology AI Software Data science Research Innovation
  1. Microsoft's Phi-4 is a new language model that's smaller in size but powerful in performance. It shows that high-quality data can make a big difference in AI.
  2. Phi-4 has 14 billion parameters, which means it can handle complex language tasks effectively. This model builds on the success of earlier Phi models.
  3. The innovations in Phi-4 come from its unique approach to training, focusing on pre-training, mid-training, and post-training stages to enhance its capabilities.

Alibaba QwQ Really Impresses at GPT-o1 Levels

105 implied HN points • 01 Dec 24
🕹 Technology AI Models Machine Learning Data science Generative AI Open Source
  1. Alibaba's new AI model called QwQ is doing really well in reasoning tasks, even better than some existing models like GPT-o1. This shows that it's becoming a strong competitor in the AI field.
  2. QwQ is designed to think carefully and explain its reasoning step by step, making it easier for people to understand how it reaches its conclusions. This transparency is a big deal in AI development.
  3. The rise of models like QwQ indicates a shift towards focusing on reasoning abilities, rather than just making models bigger. This could lead to smarter AI that can learn and solve problems more effectively.

Edge 443: EVERYTHING you Need to Know About State Space Models

133 implied HN points • 29 Oct 24
🕹 Technology AI Machine Learning Neural Networks Computational efficiency Data science
  1. State space models (SSMs) are a promising alternative to transformers for processing data. They handle long sequences more efficiently without losing important information.
  2. SSMs are designed to be computationally efficient, scaling linearly with context windows unlike transformers which scale quadratically. This makes them better for tasks needing a lot of information.
  3. Recent models like Mamba show that SSMs can outperform transformers in performance and efficiency, especially for tasks that require understanding long contexts.

Edge 375: Meta's System 2 Attention is a Very Unique LLM Reasoning Method

462 implied HN points • 05 Mar 24
🕹 Technology AI Machine Learning Cognitive Psychology
  1. Meta's System 2 Attention method in LLM reasoning is inspired by cognitive psychology and immediately impacts reasoning.
  2. LLMs excel in reasoning by focusing intensely on the context to predict the next word, but they can be misled by irrelevant correlations in context.
  3. Understanding Meta's System 2 Attention helps in comprehending the functioning of Transformer-based LLMs.

Edge 459: Quantization Plus Distillation

77 implied HN points • 24 Dec 24
🕹 Technology Machine Learning AI Models Data science Model optimization Deep Learning
  1. Quantized distillation helps make deep neural networks smaller and faster by combining two techniques: knowledge distillation and quantization.
  2. This method transfers knowledge from a high-precision model (teacher) to a low-precision model (student) without losing much accuracy.
  3. Using soft targets from the teacher model can reduce problems that often come with using simpler models, keeping performance strong.

The Most Amazing Week in Gen AI Releases

84 implied HN points • 15 Dec 24
🕹 Technology AI Software Innovation Research Modeling
  1. Several major tech companies like OpenAI, Google, and Microsoft launched new AI models in a single week. This shows how quickly AI technology is progressing.
  2. OpenAI's Sora model allows users to create videos from text descriptions, but it has some limitations. It's an exciting step for video generation!
  3. Google's Gemini 2.0 has improved capabilities, allowing it to handle more complex tasks and interact more effectively with users.

The Sequence Chat: The End of Data. Or Maybe Not

105 implied HN points • 20 Nov 24
🕹 Technology AI Data Generative AI Machine Learning Models
  1. There's a big debate about whether we're running out of data for AI. Some people believe that as AI keeps growing, we might hit a point where there's just not enough new data to use.
  2. Many AI models have already used a lot of data from the internet. This raises concerns that without fresh and vast data sources, these models might not improve much anymore.
  3. To tackle the data issue, some suggest focusing on getting better quality data or even creating new, artificial datasets. This could help keep AI development moving forward.

Edge 454: Meet Magentic-One, Microsoft's New Framework for Building Multi Agent Systems

91 implied HN points • 05 Dec 24
🕹 Technology AI Frameworks Systems Innovation Research
  1. Microsoft has introduced a new framework called Magentic-One for building multi-agent systems. It allows different AI agents to work together on tasks that can change or evolve.
  2. This framework is built upon another Microsoft technology called AutoGen, which helps agents collaborate effectively. It aims to manage tasks using information from the web and files from various fields.
  3. Magentic-One is part of a growing trend in AI where multi-agent systems are gaining popularity. This reflects the diverse and innovative landscape of AI development today.

Edge 369: LLM Reasoning with Chain-Of-Code

476 implied HN points • 13 Feb 24
🕹 Technology AI Programming Research
  1. LLMs can potentially use code generation to tackle complex tasks by breaking them down into manageable steps.
  2. Understanding the concept of Chain-of-Code (CoC) is crucial for LLM reasoning.
  3. The Embedchain RAG framework is an important tool introduced in this post for enhancing LLM reasoning processes.

Edge 457: Can we Distill Specific Knowledge in LLMs? An Intro to Attention-Based Distillation

77 implied HN points • 17 Dec 24
🕹 Technology Machine Learning Artificial Intelligence Data science Software Development Natural Language Processing
  1. Attention-based distillation (ABD) is a method that helps smaller models learn from larger models by mimicking their attention patterns. This can make the smaller models perform better with fewer resources.
  2. Unlike traditional methods that just look at output predictions, ABD focuses on the reasoning process of the larger model. This leads to a deeper understanding and better results for the smaller model.
  3. Using ABD can produce student models that perform well even when they have less complexity. This is useful for applications where efficiency is key.

World Models are Coming and They are Awesome

84 implied HN points • 08 Dec 24
🕹 Technology AI Machine Learning Generative AI 3D Modeling Data science
  1. This week saw the release of two exciting world models that can create 3D environments from simple prompts. These models are important for advancing AI's abilities in various fields.
  2. DeepMind's Genie 2 can generate interactive 3D worlds and simulate realistic object interactions, making it very useful for AI training and game development.
  3. World Labs has introduced a user-friendly system for designing 3D spaces, allowing artists to create and manipulate environments easily, which can help in game prototyping and creative workflows.

Edge 441: SSMs Beyond Language

119 implied HN points • 22 Oct 24
🕹 Technology AI Machine Learning Research Computational Models
  1. SSMs can be used in areas beyond just language, like audio processing. This makes them very useful for handling complex and irregular data.
  2. Meta AI is researching how SSMs can improve speech recognition, showing their potential in understanding spoken language better.
  3. The Llama-Factory framework helps in pretraining large language models, making them more efficient and powerful.