AI, indexed

Chat, research, write, and create

Work with a conversational assistant

DeepSeek chat and model APIs

Google’s AI assistant

Explore and organize your sources

Chat with Grok

Watch Andrej Karpathy demonstrate search, document analysis, coding, and voice workflows with the AI tools available in February 2025.

Run open models and local agents

A self-hosted interface for AI models

Search the web with cited answers

Build AI applications and agents on AWS

Claude models and AI research

Build applications with Claude

Enterprise models and search tools

DeepSeek chat and model APIs

Gemini APIs and Gemma model guides

Build and deploy AI on Google Cloud

Prototype with Gemini in your browser

Grok model APIs and developer guides

Develop and deploy AI on Azure

Models, assistants, and AI services

Build with OpenAI models and tools

Access models through a shared API

Learn how to analyze AI failures, design evaluations, validate model judges, and monitor application quality.

Compare models with human preferences

Compare model quality, speed, and cost

Discover models, datasets, and demos

Write and run evaluations for language models and agents.

Evaluate language models across a shared set of tasks.

Test prompts, agents, and retrieval systems with repeatable evaluations.

Reports on AI progress and adoption

Benchmarks for software engineering agents

Pair program with AI in your terminal

Chip Huyen explains how to design, evaluate, and deploy applications built with foundation models.

Build AI applications in TypeScript

Sebastian Raschka teaches how to build, train, and fine-tune a GPT-style model in Python and PyTorch.

Work with Claude in your codebase

An open-source coding agent

Build and maintain software with an agent

Use an open-source coding agent in development workflows.

Develop software with coding agents

Study 2023 lectures on LLM application design, retrieval, evaluation, and user experience.

AI assistance across your coding workflow

Prototype with Gemini in your browser

Jay Alammar and Maarten Grootendorst teach language model concepts and applications with diagrams and Python examples.

Learn to use transformers, prepare datasets, fine-tune language models, and build reasoning models with Python.

Learn to design, train, test, deploy, and monitor machine learning applications with Goku Mohandas's practical course.

Inspect and test Model Context Protocol servers.

Run and fine-tune language models on Apple silicon.

Build neural networks in Python with Andrej Karpathy's video lessons, from backpropagation to GPT and tokenizers.

Run and manage coding agents and software workflows.

Build typed AI agents and workflows in Python.

Build and train neural networks with Python.

Train and evaluate machine learning models in Python.

Learn practical ways to work with coding agents, review their changes, test software, and understand generated code.

Study language model training through Stanford's lectures and assignments on tokenization, data, systems, scaling, and evaluation.

Benchmarks for software engineering agents

Load and train models for text, images, audio, and more.

Run and fine-tune models with local training tools.

Compare agent and workflow patterns in Anthropic's 2024 guide to choosing and combining simple building blocks.

Learn ways to select, organize, and maintain the information that an AI agent receives during its work.

Work with Claude in your codebase

Build and maintain software with an agent

Build and optimize language-model programs against a chosen metric.

Extend an agent with tools for coding and other tasks.

Delegate work to an AI agent

Build retrieval, search, and agent pipelines with reusable components.

An agent with memory and reusable skills

Learn agent concepts, build projects with Python frameworks, and evaluate how your agents perform.

Learn Model Context Protocol concepts and build applications that connect AI models to external tools and data.

Build stateful agent workflows

Build real-time voice agents

Build document and data workflows

Inspect and test Model Context Protocol servers.

Connect AI applications to tools and data

Connect apps in visual workflows

Run and manage coding agents and software workflows.

Build conversational voice applications

Build typed AI agents and workflows in Python.

Learn practical ways to work with coding agents, review their changes, test software, and understand generated code.

Build compact agents that use tools through code.

Speech recognition and voice APIs

Generate voices and build voice agents

Run Whisper transcription through CTranslate2.

Use audio transformers for speech recognition, audio classification, and speech generation through lessons and exercises.

Build real-time voice agents

Build conversational voice applications

Memory for voice agents

Transcribe speech with OpenAI’s released Whisper models.

Run Whisper speech recognition in C and C++.

Align transcript words with audio and separate speaker turns.

Watch Stephen Welch explain diffusion models and CLIP in this 2025 guest lesson on 3Blue1Brown.

Build visual AI workflows with nodes

Run and train diffusion models for image, video, and audio generation.

Run image, video, and audio models

Explore AI image creation

Run AI models through an API

Generate and edit video with AI

Pair program with AI in your terminal

Store embeddings and retrieve context for AI applications.

An open-source coding agent

Build visual AI workflows with nodes

Use an open-source coding agent in development workflows.

Run and train diffusion models for image, video, and audio generation.

Convert documents into structured data for AI workflows.

Build and optimize language-model programs against a chosen metric.

Run Whisper transcription through CTranslate2.

Extend an agent with tools for coding and other tasks.

Build retrieval, search, and agent pipelines with reusable components.

An agent with memory and reusable skills

Discover models, datasets, and demos

Write and run evaluations for language models and agents.

Search and store multimodal data for AI applications.

Trace, evaluate, and improve language-model applications.

Evaluate language models across a shared set of tasks.

Route model requests through a shared gateway and SDK.

Run language models in C and C++

Run open models and local agents

Inspect and test Model Context Protocol servers.

Machine learning on Apple silicon

Run and fine-tune language models on Apple silicon.

Run open models

Run and manage coding agents and software workflows.

A self-hosted interface for AI models

Add vector similarity search to PostgreSQL.

Test prompts, agents, and retrieval systems with repeatable evaluations.

Build typed AI agents and workflows in Python.

Build and train neural networks with Python.

Search vectors and semantic data

Train and evaluate machine learning models in Python.

Build embeddings, semantic search, and reranking.

Build compact agents that use tools through code.

Load and train models for text, images, audio, and more.

Run and fine-tune models with local training tools.

Serve language models

Transcribe speech with OpenAI’s released Whisper models.

Run Whisper speech recognition in C and C++.

Align transcript words with audio and separate speaker turns.

Build AI applications in TypeScript

Build AI applications and agents on AWS

Inspect traces and evaluate AI application behavior.

Store embeddings and retrieve context for AI applications.

Build applications with Claude

Run AI models on Cloudflare

Speech recognition and voice APIs

Chip Huyen covers data pipelines, model deployment, monitoring, and the design of production ML systems.

Convert documents into structured data for AI workflows.

Run image, video, and audio models

Gemini APIs and Gemma model guides

Build and deploy AI on Google Cloud

Grok model APIs and developer guides

Hosted AI inference

Search and store multimodal data for AI applications.

Trace, evaluate, and improve language-model applications.

Route model requests through a shared gateway and SDK.

Learn to design, train, test, deploy, and monitor machine learning applications with Goku Mohandas's practical course.

Convert PDFs and other documents into Markdown and JSON.

Develop and deploy AI on Azure

Run AI workloads on cloud compute

Connect AI applications to tools and data

Build with OpenAI models and tools

Access models through a shared API

Add vector similarity search to PostgreSQL.

Vector search for AI applications

Search vectors and semantic data

Run AI models through an API

Build embeddings, semantic search, and reranking.

Train, fine-tune, and serve AI models

Serve language models

See how a simple neural network recognizes handwritten digits in Grant Sanderson's visual introduction.

Explore cross-entropy in the second video of 3Blue1Brown's 2026 series on compression and intelligence.

Watch Stephen Welch explain diffusion models and CLIP in this 2025 guest lesson on 3Blue1Brown.

Follow tokens, word embeddings, and predictions through a transformer with Grant Sanderson's animated explanation.

Chip Huyen explains how to design, evaluate, and deploy applications built with foundation models.

Learn how to analyze AI failures, design evaluations, validate model judges, and monitor application quality.

Gavin Purcell and Kevin Pereira discuss AI news, tools, and their effects on everyday life.

Geoff Woods offers AI leadership training, advisory services, and practical resources for business teams.

Claude models and AI research

Compare agent and workflow patterns in Anthropic's 2024 guide to choosing and combining simple building blocks.

Learn ways to select, organize, and maintain the information that an AI agent receives during its work.

Melanie Mitchell examines AI history, core methods, and the gap between machine performance and understanding.

Stuart Russell and Peter Norvig cover search, planning, learning, and reasoning in this AI textbook.

Recent artificial intelligence preprints

Sebastian Raschka teaches how to build, train, and fine-tune a GPT-style model in Python and PyTorch.

Short courses on building with AI

Chip Huyen covers data pipelines, model deployment, monitoring, and the design of production ML systems.

Learn AI fundamentals, problem solving, machine learning, and social implications through an introductory course from the University of Helsinki and MinnaLearn.

Practical deep learning for coders

Study 2023 lectures on LLM application design, retrieval, evaluation, and user experience.

Explore and organize your sources

Learn machine learning through animated lessons, interactive explanations, and exercises on models, data, and evaluation.

Jay Alammar and Maarten Grootendorst teach language model concepts and applications with diagrams and Python examples.

Learn agent concepts, build projects with Python frameworks, and evaluate how your agents perform.

Use audio transformers for speech recognition, audio classification, and speech generation through lessons and exercises.

Courses on models, agents, and more

Learn to use transformers, prepare datasets, fine-tune language models, and build reasoning models with Python.

Learn Model Context Protocol concepts and build applications that connect AI models to external tools and data.

Watch Andrej Karpathy explain how language models are trained and why they behave as they do in this 2025 lecture.

Watch Andrej Karpathy demonstrate search, document analysis, coding, and voice workflows with the AI tools available in February 2025.

Swyx and guests discuss how AI models, developer tools, and applications are built.

Tim Scarfe and guests examine AI research, cognitive science, and the nature of intelligence.

Learn to design, train, test, deploy, and monitor machine learning applications with Goku Mohandas's practical course.

Learn deep learning through MIT's 2026 videos, slides, and Python labs on language, vision, generation, and reinforcement learning.

Build neural networks in Python with Andrej Karpathy's video lessons, from backpropagation to GPT and tokenizers.

NVIDIA interviews guests about AI applications in science, industry, and everyday life.

Learn practical AI skills

Daniel Whitenack and Chris Benson discuss AI engineering and practical uses with expert guests.

Learn practical ways to work with coding agents, review their changes, test software, and understand generated code.

Reports on AI progress and adoption

Watch Stanford's public 2024 lecture series on neural networks for natural language processing.

Start Chelsea Finn's 2025 Stanford lecture series with Markov decision processes and the foundations of deep reinforcement learning.

Study language model training through Stanford's lectures and assignments on tokenization, data, systems, scaling, and evaluation.

Nathan Labenz interviews AI builders and researchers about capabilities, applications, and risks.

Use Jay Alammar's diagrams to understand the original transformer's attention, encoders, and decoders.

Sam Charrington interviews researchers and practitioners about machine learning and AI systems.

Memory for voice agents

Michael Stelzner explores practical AI uses for marketers, creators, and business owners.

Gavin Purcell and Kevin Pereira discuss AI news, tools, and their effects on everyday life.

Swyx and guests discuss how AI models, developer tools, and applications are built.

Tim Scarfe and guests examine AI research, cognitive science, and the nature of intelligence.

MIT Sloan Management Review interviews leaders about how their organizations use AI.

Sarah Guo and Elad Gil interview AI researchers and founders about technology and business.

NVIDIA interviews guests about AI applications in science, industry, and everyday life.

Daniel Whitenack and Chris Benson discuss AI engineering and practical uses with expert guests.

Nathaniel Whittemore explains AI news and its effects on work, business, and society.

Nathan Labenz interviews AI builders and researchers about capabilities, applications, and risks.

Greg Isenberg and guests explore startup ideas, AI tools, and ways to build internet businesses.

Sam Charrington interviews researchers and practitioners about machine learning and AI systems.

Chip Huyen explains how to design, evaluate, and deploy applications built with foundation models.

Melanie Mitchell examines AI history, core methods, and the gap between machine performance and understanding.

Stuart Russell and Peter Norvig cover search, planning, learning, and reasoning in this AI textbook.

Sebastian Raschka teaches how to build, train, and fine-tune a GPT-style model in Python and PyTorch.

Ethan Mollick explores how to work, learn, and create with AI while using human judgment.

Marco Iansiti and Karim Lakhani examine how data and AI change company operations and competition.

Chip Huyen covers data pipelines, model deployment, monitoring, and the design of production ML systems.

Jay Alammar and Maarten Grootendorst teach language model concepts and applications with diagrams and Python examples.

Paul Daugherty and H. James Wilson explore how people and AI can share work and reshape business processes.

Agrawal, Gans, and Goldfarb examine how AI changes decisions, business systems, and who holds power.

Ajay Agrawal, Joshua Gans, and Avi Goldfarb explain AI through the economics of cheaper prediction.

Geoff Woods explains how leaders can use AI to question assumptions and work through business decisions.

See how a simple neural network recognizes handwritten digits in Grant Sanderson's visual introduction.

Explore cross-entropy in the second video of 3Blue1Brown's 2026 series on compression and intelligence.

Watch Stephen Welch explain diffusion models and CLIP in this 2025 guest lesson on 3Blue1Brown.

Follow tokens, word embeddings, and predictions through a transformer with Grant Sanderson's animated explanation.

Gavin Purcell and Kevin Pereira discuss AI news, tools, and their effects on everyday life.

Study 2023 lectures on LLM application design, retrieval, evaluation, and user experience.

Watch Andrej Karpathy explain how language models are trained and why they behave as they do in this 2025 lecture.

Watch Andrej Karpathy demonstrate search, document analysis, coding, and voice workflows with the AI tools available in February 2025.

Tim Scarfe and guests examine AI research, cognitive science, and the nature of intelligence.

Learn deep learning through MIT's 2026 videos, slides, and Python labs on language, vision, generation, and reinforcement learning.

Build neural networks in Python with Andrej Karpathy's video lessons, from backpropagation to GPT and tokenizers.

Sarah Guo and Elad Gil interview AI researchers and founders about technology and business.

Watch Stanford's public 2024 lecture series on neural networks for natural language processing.

Start Chelsea Finn's 2025 Stanford lecture series with Markov decision processes and the foundations of deep reinforcement learning.

Study language model training through Stanford's lectures and assignments on tokenization, data, systems, scaling, and evaluation.

Greg Isenberg and guests explore startup ideas, AI tools, and ways to build internet businesses.

Michael Stelzner explores practical AI uses for marketers, creators, and business owners.

Geoff Woods offers AI leadership training, advisory services, and practical resources for business teams.

Ethan Mollick explores how to work, learn, and create with AI while using human judgment.

Marco Iansiti and Karim Lakhani examine how data and AI change company operations and competition.

Paul Daugherty and H. James Wilson explore how people and AI can share work and reshape business processes.

Greg Isenberg and Theo Tabah's holding company builds internet businesses around communities.

MIT Sloan Management Review interviews leaders about how their organizations use AI.

Sarah Guo and Elad Gil interview AI researchers and founders about technology and business.

Agrawal, Gans, and Goldfarb examine how AI changes decisions, business systems, and who holds power.

Ajay Agrawal, Joshua Gans, and Avi Goldfarb explain AI through the economics of cheaper prediction.

Nathaniel Whittemore explains AI news and its effects on work, business, and society.

Geoff Woods explains how leaders can use AI to question assumptions and work through business decisions.

Greg Isenberg and guests explore startup ideas, AI tools, and ways to build internet businesses.