A growing collection of research papers, books, podcasts, thought leaders, and courses to help you learn AI and keep up with the field.
Albert Chun Founder, AI CircleView on LinkedIn ↗
Pratik Mehta AI Architect, NVIDIAView on LinkedIn ↗
Josephine Parquet Head of API Products, VEEDView on LinkedIn ↗
John Larson Founder & CEO, RostraView on LinkedIn ↗
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Zamir Akimbekov Founder, CayuView on LinkedIn ↗Pick a category to jump straight to it.
Founder pick: recommended by AI Circle’s founder. Member pick: recommended by AI Circle members. AI Circle partner: companies we host rooms with.
The AlexNet paper that launched the deep learning revolution. Krizhevsky, Sutskever, and Hinton won ImageNet 2012 by a massive margin using deep convolutional neural networks, ReLU activations, and dropout. Proved neural networks could dominate computer vision and sparked the modern AI era.
Introduces the attention mechanism for sequence-to-sequence models. By Bahdanau, Cho, and Bengio. The breakthrough that enabled neural networks to handle long sequences by learning which parts to focus on. Precursor to transformers.
Introduces ResNets and skip connections. These made it possible to train networks 100+ layers deep. One of the most cited papers in deep learning. Skip connections are now everywhere.
The foundational paper that introduced the Transformer architecture powering GPT, BERT, Claude, and virtually every modern AI system. Replaced recurrence and convolutions with pure attention. This made training far more parallel and scalable. Must read.
Google's technique for training giant neural networks across multiple devices by splitting models into stages and processing micro-batches in parallel. Key infrastructure enabling billion-parameter models that power today's AI.
Introduces RAG (Retrieval-Augmented Generation), which combines AI language models with external knowledge databases. Allows AI to pull in real information instead of relying only on what's in its training. Biggest use case in the industry. Must read.
Anthropic's method for training AI to be helpful and harmless using AI feedback instead of human labeling. Foundation for how Claude and other safe AI assistants are built. Key paper for understanding AI alignment in practice.
Extends Chain-of-Thought and Tree of Thoughts by modeling LLM reasoning as a graph. This enables more complex thought patterns like combining, refining, and looping over ideas. Key reading to design advanced reasoning systems.
Introduces DPO, a simpler & more stable way to train AI than traditional reinforcement learning (PPO). Eliminates the need for a separate reward model and complex optimization. Achieves better results with just straightforward classification.
Microsoft Research's exploration of GPT-4's capabilities across reasoning, math, coding, vision, medicine, and law. Documents emergent abilities, limitations, and failure modes. Essential reading for understanding what frontier models can actually do.
Shows how DeepSeek trained an AI to excel at complex math by carefully selecting training data from the web and using a new optimization technique (GRPO). Achieves near-GPT-4 level performance on competition-level math problems.
Solves the problem where AI ignores minority viewpoints by focusing on the worst-performing groups. Instead of optimizing for average preferences, GRPO improves performance for underrepresented perspectives and balances AI behavior across different demographics and groups.
Research paper from Google on AutoCommenter, an LLM-based system that automatically detects coding best practice violations during code review. Demonstrates how AI can handle nuanced style and readability rules that traditional static analysis tools miss. This lets human reviewers focus on logic and functionality.
Guide to prompt engineering and how to write instructions that get better AI results. Organizes techniques by use case with clear examples of what works and when. Essential reference for building with LLMs.
Complete guide to online iterative RLHF. It's the training method behind top chatbots. Shows how continuous feedback cycles beat one-time training. Includes open-source code and datasets to reproduce state-of-the-art results.
From accessible introductions to hands-on engineering guides.
By Antonio Gulli. A code-backed curriculum covering 21 design patterns for building autonomous AI agents. Includes pattern overviews, practical use cases, hands-on examples, and key takeaways. Your recipe book for implementing agentic AI systems using LangChain/LangGraph, CrewAI, and Google ADK.
NYT bestseller from Wharton professor Ethan Mollick. A practical playbook for working and learning alongside AI. Useful for anyone who wants to work well with AI while staying grounded in what makes us human.
By Jay Alammar and Maarten Grootendorst. A visually-driven guide with nearly 300 custom illustrations that takes you from zero to confident practitioner in LLMs. Covers transformers, tokenizers, semantic search, RAG, and fine-tuning with intuitive explanations and working Python code throughout.
Andriy Burkov's concise guide to ML fundamentals in 100 pages. Covers supervised/unsupervised learning, neural networks, model evaluation and practical implementation without overwhelming theory. The go-to starting point for anyone wanting to understand machine learning without a formal degree program.
Chip Huyen's guide to building applications with foundation models. Covers prompt engineering, RAG, fine-tuning, agents, evaluation, and deployment. Great guide for anyone building AI products.
Also by Chip Huyen. Amazon bestseller covering the end-to-end process of designing ML systems for production. Focuses on data engineering, feature engineering, model deployment, and monitoring. Essential companion to AI Engineering for production ML.
Aurélien Géron's practical guide to building ML systems with Python. Covers classical ML through deep learning with hands-on code examples. The go-to technical reference for practitioners who learn by doing.
One of the most complete books on deep learning. Covers mathematical foundations, neural networks, and modern architectures. Dense but essential for anyone wanting deep theoretical understanding.
By economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb. They reframe AI as a tool that dramatically lowers the cost of prediction and cut through the hype to explain what AI actually does and how to apply it strategically. Named one of "the five best books to understand AI" by The Economist. Essential framework for executives, strategists, and entrepreneurs navigating AI decisions.
By Abonia Sojasingarayar and Manan Thakkar. A free eBook covering the full journey from NLP fundamentals to production-ready LLM systems. Walks through text representation, transformers, fine-tuning, RAG, and LLMOps with practical case studies across industries. Designed for beginners to professionals looking to industrialize AI solutions.
By Shekhar Khandelwal. A hands-on guide bridging data architecture and deep learning. Covers neural networks, CNNs, RNNs, GANs, and Transformers with practical Python implementations. Teaches you to transform raw data into actionable insights and build predictive models from scratch. For data architects, data scientists, and engineers applying deep learning to real-world problems.
By Paul Iusztin and Maxime Labonne. A practical guide taking you from LLM fundamentals to production deployment using LLMOps best practices. Covers data engineering, RAG, fine-tuning, preference alignment, evaluation, inference optimization, and AWS deployment through hands-on project building. For AI engineers and LLM practitioners ready to move beyond Jupyter notebooks to production-grade systems.
Guide that starts with NLP basics and moves up to LLMs. Covers mathematical foundations (linear algebra, optimization, probability, statistics) essential for understanding ML and NLP. Includes Python code samples, real-world business problem solutions, and expert insights on future trends. GitHub repository included. Ideal for ML/NLP researchers, practitioners, educators, and STEM students.
Extensive exploration of transformer architectures and their applications in generative AI and LLMs. Covers Transformer, BERT, GPT, T5, ViT, CLIP, DALL-E 2/3, and GPT-4V. Includes data preprocessing, tokenization, fine-tuning, RAG implementation, and risk mitigation strategies for LLMs. GitHub repository with notebooks included. For NLP/CV engineers, data scientists, and technical leaders.
By Marvin Minsky, co-founder of the MIT AI Lab. A classic theory of how intelligence emerges from many simple agents working together. Written as 270 short essays that still shape how people think about minds and multi-agent AI.
Podcasts and channels our members keep in rotation. Pick one and make it a habit.
Interviews with researchers, founders, and investors.
Covers foundation models, code generation, multimodality, AI agents, and GPU infrastructure directly from the founders, builders, and researchers working at the frontier. Features interviews with leaders from OpenAI, Anthropic, Meta, Databricks, Modular, and more. Known for high signal-to-noise ratio, deep technical discussions, and breaking news.
Host Nathan Labenz scouts AI from every angle. Features interviews with AI builders, researchers, and investors, plus original deep-dive "AI Scouting Reports" on critical topics. Covers a wide range from AI safety debates and global AI policy to practical applications in sales, education, healthcare, and more. Good for business, policy, and academic leaders staying current on AI developments and implications.
AI and entrepreneurship podcast hosted by investor Sarah Guo (founder of Conviction) and serial entrepreneur Elad Gil (co-founder of Color Health, author of High Growth Handbook). Features conversations with leading engineers, researchers, and founders about the AI revolution covering topics like AGI timelines, market disruption, state-of-the-art research, and how commerce and society will change. Accessible for non-technical listeners while still diving deep on industry trends.
AI podcast from Andreessen Horowitz (a16z) featuring discussions with leading AI engineers, founders, and experts alongside a16z general partners. Covers how AI is changing everything from art to enterprise IT. Topics include AI agents, coding tools, data infrastructure, pricing models, and security. Good for staying current on where the technology and industry are heading.
Long-form interviews with leading thinkers in AI, science, history, and geopolitics. Features conversations with top AI researchers on topics like AGI timelines, scaling laws, and the future of AI research. Known for substantive, intellectual depth.
Interviews with world-class product leaders, founders, and growth experts on building, launching, and growing products. Guests include CPOs, founders, and researchers from companies like LinkedIn, Slack, Anthropic, and Duolingo. Covers product management, AI tools for PMs, enterprise sales, and leadership.
Digestible and authoritative takes on the latest AI developments, trends, and research. Hosted by Pete Huang. Helps you stay up to speed and become an authority in your own circles. Available on all podcasting platforms and YouTube.
Podcast focused on production-grade AI and machine learning operations. Features interviews with practitioners on topics like deep learning architectures, recommendation systems, voice agents, and deploying AI at scale. Also hosts virtual conferences covering practical themes in enterprise AI.
Practical AI education hosted by Claire Vo, three-time CPO and founder. Each episode features live demonstrations and screen sharing of specific AI workflows guests use to transform their work. Step-by-step processes you can copy immediately. Guests include product leaders, designers, and founders from companies like Whoop, Atlassian, and Gumroad showing real productivity gains with tools like Cursor, v0, and Devin.
Daily briefing on the most significant AI developments, hosted by Nathaniel Whittemore. Cuts through the noise with analytical depth on the AI revolution. Covers technological breakthroughs, AGI development, alignment, and implications for work and creativity.
For developers and entrepreneurs building with LLMs and generative AI. Host Raza Habib connects you with leaders at AI frontier companies who share hard-won stories, lessons, and playbooks from shipping successful AI products. Weekly episodes cover real-world challenges like code review automation, AI agents, multimodal applications, and scaling AI systems.
Host Dan Shipper (CEO of Every) explores how the smartest people use AI to improve their thinking, creativity, and relationships. Interviews founders, filmmakers, writers, and investors on how they incorporate AI tools into their work and daily lives. Features screen-sharing of chats and live experimentation with AI during episodes.
Deeply researched and technical podcast featuring in-depth interviews with AI experts. Hosted by Daniel Bashir as part of The Gradient, a non-profit run by graduate students, researchers, and engineers. Aims to make AI more accessible and facilitate meaningful discussions within the AI community.
Bi-weekly podcast hosted by Noah Kravitz exploring AI's impact across industries. Features 25-minute interviews with experts from healthcare, legal, science, and more. Builds a running oral history of how AI is changing industries.
Multi-award winning podcast exploring how AI is transforming our world. Hosted by mathematician Hannah Fry. Features DeepMind scientists and thinkers discussing AI foundations, neuroscience, games, safety, and how AI is accelerating science.
AI podcast probing the sharpest minds in AI. Features interviews with leaders from OpenAI, Anthropic, Google, Databricks, Scale, and top researchers on what's real today, what's coming, and what it means for builders and businesses. Covers AI products, infrastructure, agents, reasoning models, and industry trends.
One of the most influential venture capital podcasts, hosted by Harry Stebbings. Features interviews with top VCs, founders, and operators covering fundraising, scaling startups, and market trends. Offers a multi-cycle investor perspective on AI. Episodes cover data labeling, AI coding tools, and the evolving AI landscape.
Tutorials, explainers, and technical deep dives.
Structured AI and ML learning resource. Official YouTube channel from Andrew Ng's DeepLearning.AI. Features free course content from their Coursera programs. Includes full specializations on deep learning, machine learning, CNNs, sequence models, and MLOps. Also hosts recorded events, live streams, and conference talks covering practical topics like building production LLM apps, RAG applications, prompt engineering, and multi-agent systems.
Machine learning researcher, builder, and writer known for visualizing AI/ML concepts. Clear, scaffolded teaching approach with illustrated guides to transformers, GPT-3, BERT, word embeddings, and reasoning models like DeepSeek-R1. Also covers explainable AI, LLM agents, RAG, DSPy, and practical tools.
AI educator and former OpenAI/Tesla AI lead. Features two tracks: a general audience track with in-depth LLM explainers, and a technical "Zero to Hero" series that builds neural networks from scratch. Known for clear, thorough explanations of how modern AI actually works.
One of the best channels for beginners. Educational content from IBM experts covering AI, machine learning, data science, and broader tech topics like cybersecurity, DevOps, and quantum computing. Features clear explainers on foundational concepts (LLMs, RAG, AI agents, transformers, CNNs) plus technical tutorials for building AI solutions. Great for building baseline understanding.
Staff pick and founder favorite. Uses animation to make tricky topics intuitive and difficult problems simple through changes in perspective. Covers math, physics, and CS including linear algebra, calculus, neural networks, and transformers. The emphasis is always on visualizing core ideas.
Deep dives into the biggest AI developments as they happen. Topics range from new model releases to industry shifts. Created by the author of SimpleBench, a benchmark exposing the remaining reasoning gap between humans and LLMs. Breaks down complex AI news with technical depth while remaining accessible.
Fast-paced coverage of the latest AI research, model releases, and industry developments. Combines breaking news with beginner-friendly tutorials on tools like ChatGPT, Claude, and NotebookLM. Covers everything from new robotics announcements to ethical considerations in AI.
Daily digests keep you current. Long-form writing explains why it matters.
Curated updates on research, policy, and practice.
Notes from the rooms on post-training, evaluations, and human data. Get recaps and takeaways from AI Circle events in your inbox.
Daily AI newsletter for business professionals. Covers the latest AI news, product launches, and trends in a digestible format. Also features curated recommendations of the best AI tools by category, explainers on key AI topics, and a free Intro to ChatGPT course.
5-minute technical digest for AI developers. Covers the latest breakthrough models, repos, and papers. Customizable by topic (language models, audio/speech, image/video, agents, robotics, dev tools) and tailored for researchers, developers, data scientists, and ML engineers.
Newsletter about the intersection of AI and Trust & Safety by Alice Goguen Hunsberger of Musubi. Covers AI-powered moderation for social apps, marketplaces, and online communities.
Weekly insights on AI agents, tools, and real-world use cases by Andreas Horn. Covers GenAI innovation, curated tools, and tactical career advice for building and leading in an AI-first world.
Bi-weekly newsletter covering AI policy, AI governance best practices, and responsible AI adoption. From Trustible, a provider of responsible AI governance software for legal/risk and AI/ML teams.
Expert commentary on AI's impacts across technology, research, policy, regulation, jobs, economy, and security. Platform for dialogue and debate featuring perspectives from specialists across fields. Supported by the Center for AI Safety.
Resources and frameworks for startups building responsibly. Covers responsible AI, GTM planning, data minimization, customer discovery, and making responsibility a competitive differentiator. Also offers builder programs, events, and a weekly newsletter on AI policy and responsible innovation.
Technical newsletter on machine learning safety research from Dan Hendrycks and the Center for AI Safety. Covers topics like AI scheming, prompt injection, deceptive reasoning, cyberattack capabilities, and safety benchmarks.
Original analysis from leading voices in AI.
Deeply researched advice on product, growth, and career. Features tactical frameworks and guides on topics like AI prototyping for PMs, evals, user growth strategies, and feedback. Also includes LennyBot, an AI assistant trained on the archive.
Newsletter from OpenAI's Global Affairs team. Covers AI policy, energy infrastructure, US-China AI competition, federal and state AI legislation, and AI's impact on the economy and workforce.
Jay Alammar's Substack covering large language models, their internals, and applications. Features illustrated deep-dives on topics like DeepSeek-R1, transformers, tokenizers, and LLM agents. Companion to his YouTube channel and the book "Hands-On Large Language Models."
Wharton professor Ethan Mollick on the implications of AI for work, education, and life. Research-based essays on AI agents, practical AI usage guides, and how AI is changing how we learn and work.
Original analysis on AI progress, scaling, infrastructure, and geopolitics. Mix of AI-focused pieces (RL efficiency, AGI timelines, fab buildouts) and broader intellectual explorations (history, science).
Technical newsletter for AI engineers covering AI UX, agents, dev tools, infrastructure, and open source models. Features deep-dives on coding agents, context engineering, and the AI startup landscape. Includes reading lists, event recaps, and exclusive interviews. Companion to the Latent Space Podcast.
Latest news on AI safety from the Center for AI Safety. Covers developments in AI policy, regulation, frontier model evaluations, and safety research. No technical background required.
Essays, audio, and interviews on AI developments from Nathan Lambert. Covers open models, reinforcement learning, reasoning, and model training. Sits at the border between high-level and technical thinking.
Duke Law professor Nita Farahany shares her AI Law & Policy course syllabus, reading list, and lecture summaries. Covers AI definitions, training data, deepfakes, and regulatory approaches. Paid subscription for full access.
In-depth writing from practitioners.
Blog and resources from Louis-François Bouchard, co-founder at Towards AI. Covers LLMs, AI agents, prompt engineering, and practical guides for building with AI. Offers free learning guides, courses (Python for AI, LLM development), and a newsletter. Accessible for both beginners and developers.
Blog from Women in AI Ethics (WAIE). The group has elevated diverse voices in responsible AI since 2018. Features career journey interviews, the annual "100 Brilliant Women in AI Ethics" list, book recommendations, and commentary on AI ethics issues like deepfakes, accessibility, and inclusion.
The fastest way to keep up is to follow practitioners directly and then talk with them. These are AI Circle members, accounts, and communities worth your time.
Researchers and operators from the community.
Founder of Human in the Loop and author of The Generative AI Career Masterplan. Lectures in the AI master's program at ZIGURAT and advises AI companies. Previously Head of AIOps for EMEA at IBM.
Leads go-to-market for generative AI services across EMEA at AWS. Previously led go-to-market for MLOps at AWS and was General Manager AI at Vodafone.
Leads partner solutions architecture for EMEA at Mistral AI. Previously built and led the EMEA generative AI solutions architect team at MongoDB. Earlier worked at Murex, Huawei, and Orange.
Leads AI capability at Arena, formerly LMArena. Previously Head of AI at Moonpig and AI Strategy Lead at NatWest Group.
Principal technical product manager at Microsoft's AI Frontiers lab in New York. Previously technical product manager for incubation at IBM Research. Writes about AI agents and human-agent collaboration.
Co-author of the foundational RAG and Dense Passage Retrieval papers while at Meta AI Research. Previously Lead AI Research Engineer at Arcee.ai and Senior ML Engineer at Meta.
Works on growth at ElevenLabs. Previously VP of Marketing at Bland and Lamini and VP of Growth Marketing at Remote. Created the State of DevOps Report at Puppet and co-founded DORA, acquired by Google Cloud.
Founder of Perle, a human data company. Previously Head of Supply and Growth at Scale AI. Earlier worked at Amazon, McKinsey, and Oracle.
CEO and co-founder of Across AI and associate professor at UC Berkeley. Writes Nilou’s Notes on building AI agents that are safe in practice.
Co-founder of Carbon Arc, a New York company that makes alternative data accessible for analysis and AI. Previously Head of Proprietary Research at Point72.
Leads robotics product at Applied Intuition and advises physical AI startups. Previously VP of Operations and VP of Engineering at Sanctuary AI.
Head of API Products at VEED and Head of Ecosystem and London Chair at AI Circle. Previously product manager at Stability AI.
Member of technical staff at Primitive (YC P26), which gives AI agents their own email inboxes. Previously co-founded a stealth startup.
Founder and lead maintainer of OpenMed, an open-source project releasing clinical AI models. Also a principal AI engineer on the post-training team at Arcee AI. Previously led healthcare AI at John Snow Labs.
Staff research scientist at Google DeepMind in London working on Gemini pre- and mid-training. Previously worked on SynthID watermarking and medical AI agents.
Senior researcher at Snowflake working on agents, continual learning, reasoning, retrieval, and evals. Previously member of technical staff at X.
Leads personalization and recommendations ML at Instacart. Builds recommender systems that combine generative AI and traditional machine learning.
Product manager at Perplexity. Previously chief of staff at SparkAI through its acquisition by John Deere.
Principal technical program manager in Microsoft's Industry Solutions group, helping enterprises adopt AI. Previously an engineering manager at IBM.
Research scientist at Google in New York. Previously worked on climate and energy research at Google in London.
Leads GenAI infrastructure strategy and decision science at Apple. Previously chief of staff for Apple's AI and ML infrastructure org and a founding data scientist at QuantCo. Former NASA Hubble Fellow in astrophysics.
Manages product engineering technical program management at Cohere and is a London Chair at AI Circle. Previously Business Lead to the Chief Scientist at DeepMind.
Senior manager of technical program management for machine learning at Cohere. Previously at LinkedIn and Apple.
Program manager for frontier AI research at Google DeepMind in New York. Previously led go-to-market strategy for generative media in Google Ads and led PwC's consulting AI Factory.
Staff product manager at Meta Superintelligence Labs working on post-training and the Muse Spark API. Previously product lead at Google DeepMind on Gemini agentic coding.
Member of technical staff at Anthropic. Previously worked across data and evals for agents at Amazon's AGI Lab.
Member of technical staff at Anthropic. Previously data TPM for Gemini post-training at Google DeepMind, focused on computer use, tool use, and RL environments.
High-signal accounts for research, product, and industry updates.
Co-founder of The Narrative Company, investor, and entrepreneur. Shares threads, posts, and breakdowns on AI, narratives, and communication. Playbooks are shared openly.
Wharton professor researching AI's impact on education, work, and innovation. Co-founder of Wharton Interactive. Shares practical insights on using AI effectively and how it's changing how we learn and work.
Research-backed insights on leadership, management, and the future of work. Includes AI's impact on business strategy.
Shares updates on his books and visual guides, celebrates open-source ML tools, and posts interactive visualizations of AI research.
Shares thoughts on AI industry developments, how programming is changing with AI tools, and reflections on learning. Also announces his YouTube deep dives here.
Creators of the internet's first prompt engineering guide. Shares breakdowns of prompting techniques (like Chain-of-Thought), summaries of the latest prompting research papers, updates on HackAPrompt competitions, and events.
Chief AI Officer at Meta and head of Meta Superintelligence Labs. Founded Scale AI.
Senior research engineer at Google DeepMind working on memory and personalization for Gemini.
Co-founder of Adaption. Previously led Cohere For AI and its open multilingual research.
Machine learning engineer at Hugging Face building the Hugging Face Hub and open-source tooling.
Co-founder of Cohere. Previously a researcher at Google Brain.
Co-founder and CEO of World Labs and professor at Stanford. Creator of ImageNet.
Co-founder and CEO of Anthropic.
Stanford PhD student working on ML systems. Teaches CS336 and built the Gigatoken tokenizer library.
Co-founder of Anthropic and lead author of the GPT-3 paper.
Co-founder and CEO of Contra, the platform for independent creative work.
Co-founder and CEO of Mercor, the AI recruiting and expert data company.
Research scientist at AMI Labs working on world models. Previously led Movie Gen work at Meta.
Researcher at OpenAI sharing work on the GPT models.
Co-founder and CEO of Prolific, the platform for high-quality human data and feedback.
Founder of AMI Labs and Turing Award winner. Former chief AI scientist at Meta.
Co-founder and CEO of Hugging Face.
Co-founder and CEO of LlamaIndex.
Alignment researcher at Anthropic. Previously co-led superalignment at OpenAI.
Leads product for Google AI Studio and the Gemini API at Google DeepMind.
Head of post-training at Liquid AI and author of the open LLM Course.
Co-founder and CEO of Thinking Machines Lab. Former CTO of OpenAI.
Chairman and CEO of Microsoft.
Co-founder and CEO of OpenAI.
Founder of DeepLearning.AI and co-founder of Coursera. Former head of Google Brain.
Co-founder and CEO of Encord, the data layer for AI teams building with multimodal data.
Servers where builders trade ideas in real time.
A network of researchers, operators, and founders working on post-training and model evaluation. Members meet at chapter events in San Francisco, London, New York, Seattle, Toronto, Paris, Stanford, and MIT.
Official community server for Cohere. Features channels for API discussions and troubleshooting, a "Live Builds" section where members showcase their Cohere-powered projects, and research discussions covering LLMs and NLP advances. Good for developers building with Cohere's API and anyone interested in enterprise AI applications.
Official OpenAI community server. Themed galleries for sharing AI-generated images, channels for prompt help, general discussion of AI news, community Q&A, and updates on OpenAI announcements.
Official community for the AI image generation tool Midjourney and one of the largest Discord servers in the world. Features prompt help channels, community galleries organized by theme, and spaces to share edits and real-world projects. Free to join and browse. A subscription is required to create images.
Community server focused on prompt engineering education and prompt hacking competitions. Learn prompt engineering fundamentals, compete in HackAPrompt challenges, or browse channels for AI image prompts, resource sharing, and AI news. Skill levels range from GenAI beginners to ML engineers.
Community server for discussing AGI, futurism, and the future of AI development. Channels for sharing AI news, making predictions, and discussing robotics, space exploration, virtual reality, life extension, and philosophy. Casual and speculative rather than technical.
Large educational community covering the full spectrum of AI/ML learning. Reading groups, applied workshops, learning seminars, and live coding sessions across NLP, computer vision, generative AI, reinforcement learning, and ethics. Structured roadmaps for beginners, career help, and project sharing. Welcomes all levels.
Practical help community for data science, ML, programming, and analytics. Members ask and answer questions across Python, R, math and stats, MLOps, generative AI, and career advice. Includes a resource library with recommended books and courses.
Unofficial community server for users of TensorFlow, Google's open-source machine learning framework. Support channels for Python and JavaScript, plus broader ML topics including CNNs, GANs, reinforcement learning, NLP, and AI ethics. Includes ML job boards and a show-and-tell channel for projects.
Structured learning for when you want depth, credentials, or both. Most start free.
Free and paid learning paths from the companies building the tools.
Free curriculum covering LLM fundamentals (embeddings, transformers, attention) alongside practical modules on semantic search, prompt engineering, RAG, and tool use. Good for learning core LLM concepts with hands-on API practice.
AWS's official learning platform with 1,000+ free resources plus subscription labs and certification prep. Covers AI/ML, generative AI, cloud architecture, and all AWS services.
Free and paid training in deep learning, AI, and GPU-accelerated computing with hands-on cloud GPU environments. Self-paced courses (1-8 hours) and instructor-led workshops across computer vision, NLP, robotics, and data science.
Free introductory courses and paid certification tracks covering data engineering, ML, generative AI (including RAG), and SQL analytics on the Databricks platform.
Free modules and learning paths across Azure AI, Copilot, machine learning, and responsible AI. Includes sandbox environments and certification exam prep.
Free courses and guided projects covering machine learning, generative AI, data science, and application development. Levels run from foundational concepts to hands-on implementation.
AI education company founded by Andrew Ng offering specializations on Coursera and free short courses on its own platform. Covers machine learning fundamentals through advanced topics like LLMs, prompt engineering, and agentic AI.
Major online learning platform hosting courses and certificates from Google, IBM, Microsoft, Meta, and universities. Many courses are free to audit. Coursera Plus provides unlimited access.
Nonprofit offering free cohort-based courses on AI safety, alignment, and governance with facilitated group discussions. Includes a short self-paced intro, "The Future of AI," and intensive 25-30 hour programs with strong career support.
Free series of five short courses on responsible AI covering principles, history, business case, roles, and operational best practices. Completable in a few hours with a certificate of completion.
Guides and tutorials on data labeling, model-assisted labeling, active learning, and GenAI evaluation workflows. Best for practitioners working on data-centric AI.
Credentials recognized by employers.
Free courses and certificates for AI training and evaluation work. Topics range from understanding AI systems to prompt writing and context engineering. Pass the assessment to earn a certificate you can share.
Globally recognized AI certification body offering four credential tracks for Engineers, Consultants, Scientists, and Transformation Leaders. Self-paced programs (4-25 weeks) require no coding prerequisites and cater to professionals from entry-level to C-suite.
Global AI credentialing body offering engineer, chartered engineering, and business professional certifications built on vendor-neutral standards. Validates applied competence in ML pipelines, model deployment, and system architecture.
Free, self-paced courses for developers and data scientists to optimize AI on Intel hardware. Covers computer vision, deep learning inference, edge AI, and OpenVINO. Includes hands-on labs and optional certification exams.
Industry-recognized certifications for Data Analyst, Data Scientist, Data Engineer, and AI Engineer roles. Combines timed exams with practical case-study assessments in Python, R, or SQL. Included with a premium subscription.
Professional certificates from Google, IBM, Microsoft, Meta, DeepLearning.AI, and more. Each is designed to make learners with no prior experience job-ready in 3-6 months. Covers data science, AI/ML, and more with hands-on projects.
Professional credentials validating expertise in NVIDIA technology. Associate (NCA) and Professional (NCP) exams cover tracks like agentic AI, InfiniBand networking, and accelerated data science. The standard for engineers working in AI factories.
Free certificates · Data Gradient
Data Gradient offers free courses and certificates for AI training and evaluation work. Learn the craft, pass the assessment, and get a certificate you can share.
Reference sheets and guides you can use today.
Spreadsheet tracking 700+ AI foundation models with key specs: parameters, training tokens, benchmark scores (MMLU, GPQA, HLE), and ALScore ratings. Useful for comparing model capabilities and scale.
OpenAI's official framework for building AI agents. Covers when to use agents, how to design them (model selection, tools, instructions), and how to scale from single to multi-agent systems. Includes essential guardrails like safety classifiers, PII filters, and risk-scored tool access.
FirstMark's map of 1,400+ companies across the Machine Learning, AI, and Data ecosystem. Categories include applications and agents, infrastructure, ML and AI (build, validate, operate, research), and data sources. The standard reference for who's who in the AI landscape.
AI Circle’s map of the companies selling human data for physical AI. Vendors are grouped by format. Search and filter across egocentric video, teleoperation, synthetic data, annotation, and infrastructure.
AI Circle’s map of the companies selling multilingual human data for AI. Search and filter vendors across localization, evals, speech, RLHF and SFT, and low-resource languages.
A living market map of who provides human data for physical AI. Organized by data type and compiled by AI Circle.
A working map of 140+ companies across the stack. Filter by layer or search by name. AI Circle partners are marked.
AI Developer Platforms
Open-source framework for building AI agents. LangSmith adds observability, evaluation, and deployment.
AI Developer Platforms
Platform for enterprise document processing and document-powered AI agents. Its open-source framework covers RAG and workflows.
AI Developer Platforms
Open-source hub hosting over a million ML models, datasets, and demo apps.
AI Developer Platforms
Cloud platform for building, training, and deploying AI models from the browser with zero infrastructure setup.
AI Developer Platforms
Enterprise RAG platform for building agents grounded in company data. Founded by the creators of RAG.
AI Developer Platforms
Frontend cloud platform whose AI SDK powers AI apps and generated UI.
AI Developer Platforms
AI lab building efficient, open-weight language models for production from edge to cloud.
AI Developer Platforms
Compute platform from the creators of Ray for scaling training, inference, and data processing.
Local LLM Runtimes
Run LLMs locally with simple terminal commands and a local API.
Local LLM Runtimes
Desktop app for running LLMs locally with a built-in chat UI and OpenAI-compatible API.
Local LLM Runtimes
On-device GenAI for running LLM, vision, and audio models across CPUs, GPUs, and NPUs. Formerly Nexa AI, now part of Qualcomm.
Agent Platforms
Open-source framework for multi-agent systems with asynchronous messaging and built-in observability.
Agent Platforms
Open-source framework and cloud platform for building role-based multi-agent systems.
Agent Platforms
Low-code platform for building agents connected to business data and published across Teams and the web.
Agent Platforms
Google Cloud platform for building, deploying, and governing enterprise agents with access to 200+ models.
Agent Platforms
Autonomous agents across Salesforce Customer 360 for sales, service, marketing, and commerce.
Agent Platforms
Framework for complex single- and multi-agent workflows with loops, branches, and human-in-the-loop checks.
Agent Platforms
Now Assist embeds AI search, summarization, and agents across IT, HR, and customer service workflows.
Agent Platforms
Enterprise generative AI platform for custom agents and workflows with brand-voice guardrails.
Agent Platforms
Enterprise search, assistant, and agents on a permission-aware knowledge graph across 100+ workplace apps.
Agent Platforms
Low-code agent framework with pre-built RAG pipelines that deploys on private cloud.
Agent Platforms
No-code enterprise agents connected to company knowledge for sales, marketing, and support.
Agent Platforms
Build agents via chat, low-code, or code. Includes evaluations and production monitoring.
Agent Infrastructure
Open-source SDK for integrating LLMs and agents into C#, Python, or Java code bases.
Agent Infrastructure
Python framework for building Model Context Protocol servers and clients.
Agent Infrastructure
Web scraping API that turns websites into clean, LLM-ready markdown or structured data.
Agent Infrastructure
Observability, session replays, and cost tracking for AI agents across 400+ frameworks.
Agent Infrastructure
Declarative framework for building modular AI systems with code instead of prompts. It compiles modules into optimized prompts and weights.
Observability & Evaluation
End-to-end platform for evaluating LLM apps: logging, evals, prompt management, and datasets in one place.
Observability & Evaluation
Tracing, evaluation, and prompt optimization for LLM apps and agents in production.
Observability & Evaluation
Observability and evaluation platform that turns offline evals into production guardrails.
Observability & Evaluation
Evaluation platform for LLMs, RAG, and agents. Its Lynx model handles hallucination detection.
Observability & Evaluation
Pre-production evaluation, runtime guardrails, and monitoring for ML, GenAI, and agentic systems.
Observability & Evaluation
Open-source LLM engineering platform for tracing, evals, and prompt management.
AI Safety & Security
Open-source toolkit for content moderation, jailbreak prevention, topic control, and PII detection.
AI Safety & Security
Open-source framework for validating and constraining LLM inputs and outputs.
AI Safety & Security
Proxy between apps and model providers that moderates prompts and responses with compliance logging.
AI Safety & Security
Discover, inventory, test, and remediate enterprise AI systems for vulnerabilities and compliance.
AI Safety & Security
Protects employees, apps, code assistants, and agents from prompt injection, data leaks, and jailbreaks.
AI Safety & Security
Runtime security, red-teaming, and observability for GenAI models and agents. Formerly CalypsoAI, acquired by F5 in 2025.
AI Safety & Security
Custom supervisor models, guardrails, and automated red-teaming to get AI from POC into production.
AI Safety & Security
Real-time security for GenAI applications against prompt attacks and data leakage.
Routing & Prompt Management
Unified gateway to govern models and agents with guardrails and usage tracking.
Routing & Prompt Management
LLM routing that picks the optimal model per request by balancing cost, latency, and quality.
Routing & Prompt Management
Log, version, and iterate on prompts in production.
Routing & Prompt Management
One API for hundreds of models across providers.
AI Governance
Lifecycle management, risk monitoring, and regulatory compliance for models, apps, and agents.
AI Governance
Governance for generative AI, agents, and third-party vendors with automated risk assessment and reporting.
AI Governance
Privacy, risk, data, and compliance controls across the AI lifecycle.
AI Governance
Unified data security, AI governance, access controls, and LLM firewalls.
MLOps
Experiment tracking, artifacts, LLM fine-tuning, and GenAI tracing with Weave.
MLOps
Open-source platform for the full ML lifecycle across traditional ML and GenAI.
MLOps
Enterprise MLOps platform for building, deploying, and monitoring models across hybrid cloud.
MLOps
Open-source inference platform to package, optimize, and serve any model with autoscaling.
MLOps
Real-time feature serving for ML and AI agents with train/serve consistency. Includes Tecton, acquired by Databricks in 2025.
Data Generation & Labeling
Human data platform connecting AI teams with vetted participants and domain experts for evaluation and training data.
Data Generation & Labeling
AI data and services company supporting model training, evaluation, and deployment.
Data Generation & Labeling
Human-annotated training and evaluation data across languages and modalities.
Data Generation & Labeling
AI-powered translation and multilingual data. Services include hyper-localized evaluation for global models.
Data Generation & Labeling
Delivers coding, reasoning, and post-training data to frontier labs and helps enterprises deploy AI. Led by an AI Circle member.
Data Generation & Labeling
Expert human data for training and evaluating frontier models. Led by an AI Circle member.
Data Generation & Labeling
Data engine for frontier AI: labeling, RLHF, model evaluation, and fine-tuning.
Data Generation & Labeling
Data factory for AI teams: labeling services, RLHF, model evaluation, and alignment data.
Data Generation & Labeling
Data annotation for GenAI, computer vision, and RLHF with built-in QA.
Data Generation & Labeling
Expert annotation, RLHF, and red-teaming across 90+ domains and 40+ languages.
Data Generation & Labeling
High-skill human data and RLHF for frontier model training.
Data Generation & Labeling
Labeling and document processing for images, video, and documents. Also includes extraction agents.
Data Generation & Labeling
Curate, visualize, and evaluate image and video datasets at scale.
Data Generation & Labeling
Search, classify, and tag visual content at scale.
Data Generation & Labeling
Synthetic data with de-identification and redaction for testing and ML training.
Data Science
Open-source interactive notebooks for data science across 40+ languages.
Data Science
Visual, open-source platform for data blending, analytics, and ML workflows.
Data Science
RStudio, Shiny, and other open-source data science tooling for R and Python.
Data Science
MATLAB and Simulink for analysis, algorithm development, and model-based design.
Data Science
Collaborative data notebooks with built-in AI assistance.
Enterprise AI Platforms
Build analytics, ML models, and agents in one governed environment.
Enterprise AI Platforms
AI data cloud for data agents, unstructured data analysis, and ML inside a governed perimeter.
Enterprise AI Platforms
Privately deployable AI workspace combining LLMs, search, and agents.
Enterprise AI Platforms
OpenAI's enterprise workspace with admin controls, SSO, and no training on your data.
Enterprise AI Platforms
Build, operate, and govern AI agents at enterprise scale.
Enterprise AI Platforms
Generative and predictive AI with air-gapped and on-prem deployment.
Enterprise AI Platforms
Turnkey enterprise AI applications for maintenance, forecasting, fraud, and supply chain.
Speech & Voice
Real-time voice AI on state space models: ultra-low-latency TTS (Sonic) and STT (Ink).
Speech & Voice
Text-to-speech, voice cloning, dubbing, and conversational voice agents.
Speech & Voice
Research company building lifelike conversational voice companions.
Speech & Voice
Developer platform for voice agents with sub-500ms latency and telephony integrations.
Speech & Voice
Speech-to-text and voice AI APIs for real-time applications.
Speech & Voice
Speech AI models for transcription and audio understanding.
Computer Vision
Multimodal data platform to manage, curate, and annotate image, video, and document data for training and aligning models.
Computer Vision
Dataset management, AI-assisted annotation, training, and deployment for visual AI.
Computer Vision
Open-source computer vision library with 2,500+ optimized algorithms.
Frontier Labs
Research lab behind GPT models, ChatGPT, and the OpenAI API.
Frontier Labs
AI safety and research company building the Claude family of models.
Frontier Labs
Google's AI research lab behind Gemini, AlphaFold, and foundational work in reinforcement learning.
Frontier Labs
Meta's AI research organization behind the open-weight Llama models.
Frontier Labs
Enterprise-focused foundation models for generation, search, and retrieval.
Frontier Labs
Research company building the Grok frontier reasoning model with real-time search and tool use.
Frontier Labs
French lab building open and commercial models, the Le Chat assistant, and the Codestral coding model.
Frontier Labs
Chinese lab developing cost-efficient open-weight models such as DeepSeek-V3 and R1.
Frontier Labs
Research lab building customizable, collaborative AI systems.
Frontier Labs
Research company building tools for human-AI collaboration. Its Sculptor tool coordinates coding agents.
Frontier Labs
Multimodal foundation models for text, image, audio, and video.
Frontier Labs
Tokyo lab developing nature-inspired models through evolutionary model merging.
Research Institutes
Allen Institute for AI: open language models (OLMo, Tülu), multimodal models (Molmo), and AI for science.
Research Institutes
Paris open-science lab behind the real-time speech-native dialogue model Moshi.
Research Institutes
Quebec AI Institute founded by Yoshua Bengio. It has 1,400+ researchers.
Research Institutes
Toronto nonprofit advancing AI research, trustworthy AI, and talent development.
Research Institutes
UK national institute for data science and AI research and policy.
Research Institutes
Pan-European network of 1,800+ machine learning researchers across 17 countries.
Research Institutes
Nonprofit researching and reducing societal-scale AI risks.
Research Institutes
Stanford's Human-Centered AI institute and publisher of the annual AI Index.
Research Institutes
Consortium behind MLPerf benchmarks and the AILuminate safety evaluations.
Research Institutes
Research on trends in AI compute, data, and capabilities.
Research Institutes
Nonprofit research collective focused on open models and interpretability.
GPU Cloud
AI-native cloud with NVIDIA H100, H200, and Blackwell GPUs, InfiniBand networking, and managed Kubernetes.
GPU Cloud
On-demand GPU instances and 1-Click Clusters for training and inference.
GPU Cloud
Energy-first AI data centers with GPU cloud and managed inference.
GPU Cloud
AI cloud with Blackwell-class GPUs and managed Kubernetes and Slurm clusters.
GPU Cloud
On-demand and reserved H100 capacity with deployment blueprints.
GPU Cloud
On-demand and serverless GPUs across 30+ GPU types and global regions.
GPU Cloud
Python-driven serverless cloud for AI workloads with sub-second cold starts.
GPU Cloud
Inference platform with optimized model APIs and multi-cloud autoscaling.
GPU Cloud
European sovereign cloud with GPU clusters and managed inference.
Inference & Serving
Fast, cost-efficient inference for open models with fine-tuning and OpenAI-compatible APIs.
Inference & Serving
Inference, fine-tuning, and GPU clusters for open-source models.
Inference & Serving
Low-latency inference on custom LPU hardware via GroqCloud.
Inference & Serving
Inference on custom dataflow chips via cloud APIs or on-prem.
Inference & Serving
Open-source high-throughput LLM inference engine built on PagedAttention.
Inference & Serving
Open-source serving framework for LLMs and multimodal models.
Inference & Serving
Inference compiler and runtime. Its TensorRT-LLM version targets large language models.
AI Hardware
GPUs, the CUDA ecosystem, and full-stack systems for AI training and inference.
AI Hardware
Instinct accelerators and the open-source ROCm software stack.
AI Hardware
Custom tensor processing units for training and inference on Google Cloud.
AI Hardware
Custom AWS chips for lower-cost training and inference.
AI Hardware
Wafer-scale processors for fast training and inference.
AI Hardware
CPUs and Gaudi AI accelerators for data centers.
AI Hardware
RISC-V based AI processors and open software.
Edge AI
Mobile and PC processors with Hexagon NPUs for on-device inference.
Edge AI
Machine-learning system-on-chip processors for physical AI at the edge.
Edge AI
Build and deploy ML models on embedded devices. The workflow runs from data collection to optimization.
Edge AI
Ruggedized mobile data centers bringing compute and AI to remote sites.
This guide points you in the right direction, but community keeps you on the path alongside other researchers, engineers, founders, and operators actively shaping the frontier.
Join AI Circle to learn faster, meet the right people, and stay close to what is happening at the frontier:
