The hottest Models Substack posts right now

And their main takeaways
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A few charts on where AI adoption is going

Tanay’s Newsletter • 113 implied HN points • 19 Feb 25
🕹 Technology AI Adoption Models Business Consulting
  1. The cost of using advanced AI models has dropped dramatically, making it easier for businesses to experiment and integrate AI into their products. This change opens up new possibilities for reaching millions of users.
  2. Reinforcement learning is proving effective for tasks with clear outcomes, which could lead to better performance of AI models over time. As these models improve, we can expect more widespread use of AI.
  3. The journey to adopting AI takes time, but it's happening faster than past innovations like electricity or telephones. Today, a significant portion of people are regularly using AI tools.

Which AI to Use Now: An Updated Opinionated Guide

One Useful Thing • 2229 implied HN points • 26 Jan 25
🕹 Technology AI Software Data Models Applications
  1. When choosing an AI, consider using a paid version for better features. Claude, Gemini, and ChatGPT are the top choices right now.
  2. New AI advances include live interaction and reasoning capabilities. This helps AIs understand and respond more naturally, making them feel more human.
  3. Privacy is now better handled by major AI models, and you can customize them for your specific needs. Explore different AIs to find one that fits your style.

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

TheSequence • 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.

Good Enough AI

Teaching computers how to talk • 131 implied HN points • 05 Feb 25
🕹 Technology AI Software Models Open Source Consumer Tech
  1. A new AI model called DeepSeek shows that we can create powerful tools without spending too much money. This could change how we think about making AI.
  2. The average person might not notice a big difference between high-end and cheaper AI models. Many consumers just want something that works well and is affordable.
  3. The AI industry might become more competitive and focused on meeting everyday needs instead of creating super advanced technology. This means consumers may benefit more while companies earn less.

DeepSeek, o3, and AI applications

Generating Conversation • 116 implied HN points • 06 Feb 25
🕹 Technology AI Applications Models Innovation Competition
  1. DeepSeek R1 is a strong AI model that has impressed the industry, but life goes on, and the world hasn't changed drastically because of it. More good models out there mean better choices for those building AI applications.
  2. Competition is heating up in the AI space. Other companies, like OpenAI, are responding by releasing new models quickly to keep up with emerging players like DeepSeek.
  3. The trend of making AI models more affordable is continuing. This can help more people and businesses use AI, solving new problems that weren’t possible before.
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4.5 An Introduction to Electromagnetic Models

Fields & Energy • 279 implied HN points • 28 Aug 24
🔬 Science Physics Energy Electromagnetism Models Theory
  1. Electromagnetic energy can flow along wires due to charge imbalances. This creates electric and magnetic fields that help guide the energy.
  2. There are different viewpoints on what influences electromagnetic behavior the most: charges and currents, fields, or energy itself. Each aspect plays a role in how energy moves.
  3. Understanding these concepts can lead to better insights into electromagnetic models, but it can be complex since many elements are connected and affect each other.

AI predictions for 2025

Artificial Ignorance • 126 implied HN points • 08 Jan 25
🕹 Technology AI Predictions Innovation Models Regulation
  1. In 2025, AI will focus more on improving reasoning abilities rather than just building larger models. This means smarter, more capable AI that can think through problems better.
  2. Expect personalized AI experiences to get better, with chatbots that can truly remember and learn about you. This could change how we interact with AI in our daily lives.
  3. There will likely be more AI 'agents' in workplaces, especially for customer service and sales, but many won't live up to the hype. We may see both benefits and gaps in their performance.

4.2 Early Models of Electricity

Fields & Energy • 179 implied HN points • 19 Jun 24
🔬 Science Physics Electricity Electromagnetism Models History
  1. Electricity can be understood in two ways: as a fluid traveling through wires or as fields in the space around electric charges. This is still a big question in physics.
  2. Different cultures have unique approaches to explaining scientific concepts. For example, English physicists use hands-on models, while French scientists prefer abstract theories.
  3. Benjamin Franklin was key in shaping the idea that electricity is a single fluid. This foundational concept helps us still today in understanding electricity and electronics.

LLM Links, 2/12/2025 and Live Event Friday

In My Tribe • 212 implied HN points • 12 Feb 25
🕹 Technology AI Models Innovation Business Careers
  1. Reasoning-trained AI models are expected to outperform existing models in tasks like coding and math while still being costlier to run.
  2. DeepSeek is making waves in AI for its engineering efficiency and lower training costs, potentially leading to many companies creating competitive models.
  3. AI might replace numerous jobs, with tax preparers topping the list, highlighting the shift towards automated processes in many fields.

Interpretability of theories

Infinitely More • 17 implied HN points • 11 Jan 25
📖 Philosophy Logic Theory Interpretation Models Semantics
  1. You can understand one theory by interpreting it through another theory. This means translating ideas from one set of concepts to another.
  2. Interpreting theories involves a consistent method to show how one theory fits within the framework of another. It connects the ideas and structures from both.
  3. The host theory provides a detailed explanation of how the interpreted theory operates, using only its own language and concepts. This helps clarify the relationships between different theories.

Deep Dive on Aleph Alpha - Towards European AI Sovereignty

AI Supremacy • 491 implied HN points • 08 Feb 24
🕹 Technology AI Startups Funding Models Ethics
  1. Aleph Alpha is a German AI startup focusing on AI governance, privacy, and ethics aligning with EU standards.
  2. Aleph Alpha's flagship product, Luminous, offers language models in multiple sizes and is known for its ability to explain outputs.
  3. Aleph Alpha's collaborative and 'sovereignty first' approach sets it apart from US AI companies, emphasizing data privacy and transparency.

Things don't only get better

imperfect offerings • 379 implied HN points • 26 Feb 24
🕹 Technology AI Climate Workforce Education Models
  1. Improvements in AI models are not always guaranteed, as evidenced by instances of models getting worse over time due to tweaks and updates.
  2. Investment in AI technology is booming, generating wealth for billionaires while possibly hindering investment in viable low-carbon tech solutions for climate change.
  3. The narrative surrounding AI portrays it as a powerful force for the future, but practical solutions for climate crisis require more than just technological advancements - they also need systemic changes and investments.

Open-source reasoning models, OpenAI's Operator, Bytedance's free Cursor alternative, Spell 3D worlds, Smallest VLM, Perplexity Assistant, open-source native GUI agent model, Kling's Elements & more

AI Brews • 17 implied HN points • 24 Jan 25
🕹 Technology AI Software Open Source Models Innovation
  1. DeepSeek released a new open-source reasoning model that performs as well as some of the top AI systems. It's free to use and has a chat feature on their website.
  2. OpenAI launched a new tool called Operator that can do tasks on the web for you, using its own browser to interact with websites directly.
  3. Hugging Face introduced the smallest Vision Language Model, which can answer questions about images. This could be useful for a lot of applications, especially in learning or assisting with image analysis.

The Reasoning Race: Can Small Models Reason?

TheSequence • 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.

Google's Gemini Advanced: Tasting Notes and Implications

One Useful Thing • 861 implied HN points • 08 Feb 24
🕹 Technology AI Artificial Intelligence Models Future
  1. Gemini Advanced is a GPT-4 class model, offering strengths and weaknesses compared to other advanced AI models.
  2. Gemini Advanced reveals the potential for emergent properties in large AI models, showing hints of 'ghosts' or unique intelligence.
  3. Google's Gemini Advanced hints at a future where AI serves as powerful integrated personal assistants, differentiating itself from other AI models.

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

TheSequence • 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.

Import AI 362: Amazon's big speech model; fractal hyperparameters; and Google's open models

Import AI • 299 implied HN points • 26 Feb 24
🕹 Technology AI Models Language Models Fiction
  1. The full capabilities of today's AI systems are still not fully explored, with emerging abilities seen as models scale up.
  2. Google released Gemma, small but powerful AI models that are openly accessible, contributing to the competitive AI landscape.
  3. Understanding hyperparameter settings in neural networks is crucial as the fine boundary between stable and unstable training is found to be fractal, impacting the efficiency of training runs.

An AI Haunted World

One Useful Thing • 972 implied HN points • 19 Dec 23
🕹 Technology AI Models Applications Development Autonomy
  1. The development of open source AI models is democratizing AI usage and allowing for easier modification and widespread deployment.
  2. The efficiency and affordability of LLMs will lead to AI being incorporated into various products for troubleshooting, monitoring, and interaction, potentially creating an 'AI haunted world'.
  3. Future AI integration may involve hierarchies of various AI models working together, with smart generalist AIs delegating tasks to cheaper, specialized AIs.

Agent AI: Agentic Applications Are Software Systems With A Foundation Model AI Backbone & Defined Autonomy via Tools

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 05 Aug 24
🕹 Technology AI Software Models Applications Data
  1. Agentic Applications are advanced software systems that use AI models to operate more independently. They can navigate and process information effectively using tools.
  2. The MindSearch framework helps break down complex questions into simpler parts, making it easier to find answers online. It simulates how humans think and search for information.
  3. There are special agents in this system, like WebPlanner and WebSearcher, that work together to gather and organize information from the web, enhancing the problem-solving process.

Import AI 348: DeepMind defines AGI; the best free LLM is made in China; mind controlling robots

Import AI • 339 implied HN points • 13 Nov 23
🕹 Technology AI Robotics Models Investment Internet
  1. DeepMind defines AGI levels and the risks they pose, highlighting the potential societal impacts of increasingly autonomous AI systems.
  2. Researchers have created smart glasses with object detection capabilities powered by a miniaturized YOLO model, showcasing the possibilities of on-device AI processing.
  3. Stanford's NOIR project demonstrates how brain-scanning signals can be used to control robots for a variety of tasks, paving the way for a future where humans interact with robotic systems through brain commands.

Import AI 323: AI researcher warns about AI; BloombergGPT; and an open source Flamingo

Import AI • 519 implied HN points • 03 Apr 23
🕹 Technology AI Finance Open Source Models Data Centers
  1. Bloomberg has developed BloombergGPT, a powerful language model trained on proprietary financial data with significant performance improvements on financial tasks.
  2. AI researcher Dan Hendrycks warns about future AI systems potentially out-competing humans due to natural selection favoring AI traits that may not align with human interests.
  3. Open source initiatives like OpenFlamingo and Cerebras-GPT show how companies and collectives are replicating and releasing advanced AI models, presenting a trend in the industry towards open collaboration and competition.

Building LLM-powered Apps: What You Need to Know

Gradient Flow • 519 implied HN points • 06 Apr 23
🕹 Technology AI Machine Learning Data science Applications Models
  1. Developers can now create AI-powered applications without deep machine learning knowledge, opening up opportunities for rapid experimentation and innovation.
  2. Building custom large language models (LLMs) is becoming more accessible through startups offering resources for model fine-tuning or training from scratch.
  3. Integration of custom LLMs with third-party services, utilizing knowledge bases, and serving models efficiently are key areas of focus for developers in the AI application space.

Import AI 330: Palantir's AI-War future; BLOOMChat; and more money for distributed AI training

Import AI • 399 implied HN points • 22 May 23
🕹 Technology AI Models Funding Regulation Training
  1. Palantir is making a big bet on AI for defense and intelligence, integrating it with large language models to enhance capabilities for conflict-based scenarios.
  2. SambaNova introduces BLOOMChat as a competitor to chatGPT, showcasing the ongoing race between open source models and proprietary ones in the field of AI development.
  3. Startup Together.xyz secures $20m in funding to promote open source and decentralized AI development, aiming to make AI training more accessible and widespread.

Mutual and bi-interpretation of models

Infinitely More • 17 implied HN points • 14 Dec 24
📖 Philosophy Logic Interpretation Mathematics Theories Models
  1. Mutual interpretation means that two models can understand each other. Each model can be explained using the features of the other.
  2. When you interpret one model within another, it creates a loop of understanding. You can go back and forth between the two models, revealing deeper connections.
  3. Bi-interpretability is when both models not only understand each other but are actually related in a stronger way. This offers even more insights into their structure.

RLHF learning resources in 2024

Democratizing Automation • 435 implied HN points • 12 Jan 24
🕹 Technology Research Code Models Datasets
  1. The post shares a categorized list of resources for learning about Reinforcement Learning from Human Feedback (RLHF) in 2024.
  2. The resources include videos, research talks, code, models, datasets, evaluations, blog posts, and other related materials.
  3. The aim is to provide a variety of learning tools for individuals with different learning styles interested in going deeper into RLHF.

Import AI 345: Facebook uses AI to mindread; MuJoCo v3; Amazon adds bipedal robots to its warehouses

Import AI • 339 implied HN points • 23 Oct 23
🕹 Technology AI Robotics Simulation Models
  1. Facebook has developed an AI system that uses brain scan data to roughly predict visual representations, demonstrating convergence between AI and human behavior.
  2. Amazon is testing bipedal robots in its warehouses, potentially streamlining the integration of robots into human-centric environments.
  3. Adept released Fuyu-8B, a multimodal model to help AI systems understand and interact with visual elements, expanding the range of tasks AI systems can perform beyond text.

Machine learning interpretability from first principles

Mindful Modeler • 359 implied HN points • 26 Sep 23
🕹 Technology Machine Learning Interpretability Models Methods
  1. Machine learning models can be understood as mathematical functions that can be broken down into simpler parts
  2. Interpretation methods address the behavior of these simplified components to enhance model interpretability
  3. Techniques like Permutation Feature Importance (PFI), SHAP values, and Accumulated Local Effect Plots use decomposition to explain the importance of features in prediction models

Custom embedding models with small training dataset

TechTalks • 216 implied HN points • 08 Jan 24
🕹 Technology AI Models Research Technique
  1. Custom embedding models are important for certain applications to match user prompts to relevant documents.
  2. A new technique by Microsoft researchers simplifies the training process of embedding models, making it cost-effective.
  3. By using autoregressive models and avoiding expensive pre-training, companies can create custom embedding models efficiently.

A safe harbor for AI evaluation and red teaming

AI Snake Oil • 307 implied HN points • 05 Mar 24
🕹 Technology AI Research Safety Evaluation Models
  1. Independent evaluation of AI models is crucial for uncovering vulnerabilities and ensuring safety, security, and trust
  2. Terms of service can discourage community-led evaluations of AI models, hindering essential research
  3. A legal and technical safe harbor is proposed to protect and encourage public interest research into AI safety, removing barriers and improving ecosystem norms

How Generative AI is Transforming Healthcare

Gradient Flow • 139 implied HN points • 22 Feb 24
🕹 Technology AI Healthcare Data Podcasts Models
  1. Generative AI in healthcare can transform patient care by providing personalized treatment suggestions, streamlining documentation, and enhancing communication.
  2. Generative AI enables the development of privacy-assured synthetic medical data for research and prediction of health outcomes through data analysis.
  3. Specialized models tailored to specific tasks through fine-tuning offer more efficient and accurate solutions compared to broader capabilities, highlighting the importance of personalized AI approaches.

1.5 The Paradigm Shift to a New Model

Fields & Energy • 239 implied HN points • 29 Nov 23
🔬 Science Physics Education Cognition Models Innovation
  1. People often prefer sticking to familiar ideas instead of embracing new ones, which can create mental barriers to understanding change. To overcome this, simplifying complex concepts is important.
  2. Models are tools we use to understand the world around us. Having multiple models allows us to tackle problems from different angles, making us better problem solvers.
  3. Understanding basic principles in science can help anyone grasp more complex ideas without needing extensive knowledge. For example, knowing atoms make up everything can help explain many scientific concepts.

State-space LLMs: Do we need Attention? 

Democratizing Automation • 395 implied HN points • 20 Dec 23
🕹 Technology AI Research Models
  1. Non-attention architectures for language modeling are gaining traction in the AI community, signaling the importance of considering different model architectures.
  2. Different language model architectures will be crucial based on the specific tasks they aim to solve.
  3. Challenges remain for non-attention technologies, highlighting that it is still early days for these advancements.

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

TheSequence • 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.