The AIstarterkit.
Where to start if you want to learn AI and keep up with it: courses, papers, books, podcasts and communities picked by AI Circle × Data Gradient.
Structured ways to learn the fundamentals.
Cohere LLM University
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.
Start learning ↗AWS Skill Builder
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.
Start learning ↗NVIDIA Deep Learning Institute
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.
Start learning ↗Databricks Academy
Free introductory courses and paid certification tracks covering data engineering, ML, generative AI (including RAG), and SQL analytics on the Databricks platform.
Start learning ↗Microsoft Learn
Free modules and learning paths across Azure AI, Copilot, machine learning, and responsible AI, with sandbox environments and certification exam prep.
Start learning ↗IBM Developer AI
Free courses and guided projects covering machine learning, generative AI, data science, and application development, from foundational concepts to hands-on implementation.
Start learning ↗DeepLearning.AI
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.
Course catalog ↗Coursera
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.
Start learning ↗BlueDot Impact
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.
Start learning ↗All Tech Is Human: Responsible AI Courses
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.
Start learning ↗Labelbox Library
Guides and tutorials on data labeling, model-assisted labeling, active learning, and GenAI evaluation workflows. Best for practitioners working on data-centric AI.
Browse the library ↗Credentials worth having, starting with Data Gradient’s own.
Data Gradient Certificates
Free courses and certificates for AI training and evaluation work, from understanding AI systems to prompt writing and context engineering. Pass the assessment to earn a certificate you can share.
Browse courses ↗United States Artificial Intelligence Institute (USAII)
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.
Get certified ↗Artificial Intelligence Board of America (ARTiBA)
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.
Get certified ↗Intel AI Training
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, with hands-on labs and optional certification exams.
Get certified ↗DataCamp Certifications
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.
Get certified ↗Coursera Professional Certificates
Professional certificates from Google, IBM, Microsoft, Meta, DeepLearning.AI, and more, 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.
Get certified ↗NVIDIA Certification Program
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.
Get certified ↗The papers behind modern AI, worth reading in the original.
ImageNet Classification with Deep Convolutional Neural Networks
2012The 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.
Read the paper ↗Neural Machine Translation by Jointly Learning to Align and Translate
2014Introduces 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.
Read the paper ↗Deep Residual Learning for Image Recognition
2015Introduces ResNets and skip connections, allowing training of networks 100+ layers deep. One of the most cited papers in deep learning; skip connections are now everywhere.
Read the paper ↗Founder pickAttention Is All You Need
2017The foundational paper that introduced the Transformer architecture powering GPT, BERT, Claude, and virtually every modern AI system. Replaced recurrence and convolutions with pure attention, enabling unprecedented parallelization and scale. Must read.
Read the paper ↗GPipe: Easy Scaling with Micro-Batch Pipeline Parallelism
2019Google'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.
Read the paper ↗Founder pickRetrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
2020Introduces 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.
Read the paper ↗Constitutional AI: Harmlessness from AI Feedback
2022Anthropic'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.
Read the paper ↗Founder pickGraph of Thoughts: Solving Elaborate Problems with Large Language Models
2023Extends Chain-of-Thought and Tree of Thoughts by modeling LLM reasoning as a graph, enabling more complex thought patterns like combining, refining, and looping over ideas. Key reading to design advanced reasoning systems.
Read the paper ↗Direct Preference Optimization: Your Language Model is Secretly a Reward Model
2023Introduces 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, achieving better results with just straightforward classification.
Read the paper ↗Sparks of Artificial General Intelligence: Early Experiments with GPT-4
2023Microsoft 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.
Read the paper ↗DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
2024Shows 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.
Read the paper ↗Founder pickGroup Robust Preference Optimization in Reward-free RLHF
2024Solves 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, balancing AI behavior across different demographics and groups.
Read the paper ↗Founder pickAI-Assisted Assessment of Coding Practices in Modern Code Review
2024Research 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, allowing human reviewers to focus on logic and functionality.
Read the paper ↗Founder pickA Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications
2024Comprehensive guide to prompt engineering, 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.
Read the paper ↗Founder pickRLHF Workflow: From Reward Modeling to Online RLHF
2024Complete guide to online iterative RLHF, 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.
Read the paper ↗Longer reads for the big picture.
Agentic Design Patterns
2025By Antonio Gulli. A comprehensive, 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.
Amazon ↗Co-Intelligence: Living and Working with AI
2024NYT bestseller from Wharton professor Ethan Mollick. The definitive playbook for working, learning, and thriving alongside AI. Essential for anyone looking to harness AI's potential while staying grounded in what makes us human.
Amazon ↗Hands-On Large Language Models
2024By 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.
llm-book ↗The Hundred-Page Machine Learning Book
2019Andriy 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.
Amazon ↗AI Engineering
2025Chip Huyen's comprehensive guide to building applications with foundation models. Covers prompt engineering, RAG, fine-tuning, agents, evaluation, and deployment. Great guide for anyone building AI products.
Amazon ↗Designing Machine Learning Systems
2022Also 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.
Amazon ↗Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
2019Aurélien 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.
Amazon ↗Deep Learning
2016One of the most comprehensive books on deep learning. Covers mathematical foundations, neural networks, and modern architectures. Dense but essential for anyone wanting deep theoretical understanding.
Amazon ↗Prediction Machines: The Simple Economics of Artificial Intelligence
2022By economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb, who reframe AI as a tool that dramatically lowers the cost of prediction, cutting 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.
Amazon ↗Member pickFounder Brand
Dave Gerhardt's tactical guide to using your founder story as a competitive advantage. Covers personal branding, social media strategy, platform selection, and measuring results. Practical playbook for founders and CEOs looking to build credibility and trust through authentic storytelling.
Amazon ↗Member pickThe Great CEO Within
Matt Mochary's tactical playbook for scaling startups, based on his experience coaching CEOs at top Silicon Valley companies. Covers accountability systems, problem-solving frameworks, transparent feedback, and operational efficiency. Concise, actionable guide for founding CEOs navigating rapid growth.
Amazon ↗Member pickThe Advantage
Patrick Lencioni's case for why organizational health is the greatest competitive advantage in business. Covers building cohesive leadership teams, creating clarity, overcommunicating, and reinforcing through systems. Foundational reading for leaders building resilient company cultures.
Amazon ↗Member pickDare to Lead
Brené Brown's research-backed guide to courageous leadership. Covers vulnerability, values-driven decision-making, building trust, and having difficult conversations. Practical framework for leaders who want to cultivate brave cultures and whole-hearted teams.
Amazon ↗Member pickFrom Research to Production - Industrializing NLP and Large Language Models
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.
Link (Free) ↗Member pickDeep Learning for Data Architects
2024By Shekhar Khandelwal. A hands-on guide bridging data architecture and deep learning, covering 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.
Amazon ↗Member pickThe LLM Engineer's Handbook: Master the art of engineering LLMs from concept to production
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.
Amazon ↗Member pickMastering NLP from Foundations to LLMs
Comprehensive guide starting with NLP basics and advancing 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.
E-Book ↗Member pickTransformers for Natural Language Processing
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.
E-Book ↗Listen while you work.
Latent Space Podcast: The AI Engineer Podcast
Covers foundation models, code generation, multimodality, AI agents, and GPU infrastructure directly from the founders, builders, and researchers pushing the cutting edge. 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.
Spotify ↗Member pickCognitive Revolution Podcast
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.
Spotify ↗Member pickNo Priors Podcast: AI, Machine Learning, Tech, & Startups
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.
Spotify ↗Member pickAI + a16z Podcast
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, including topics like AI agents, coding tools, data infrastructure, pricing models, and security. Good for staying current on where the technology and industry are heading.
Spotify ↗Member pickDwarkesh Podcast
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.
Spotify ↗Staff pickLenny's Podcast
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.
Spotify ↗Member pickThe Neuron
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.
Spotify ↗MLOps Community
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.
Spotify ↗How I AI: Podcast with Claire Vo
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.
Spotify ↗The AI Daily Brief: Artificial Intelligence News and Analysis
Daily briefing on the most significant AI developments, hosted by Nathaniel Whittemore. Cuts through the noise with analytical depth on the AI revolution, covering technological breakthroughs, AGI development, alignment, and implications for work and creativity.
Spotify ↗High Agency Podcast
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.
Spotify ↗AI & I
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.
Spotify ↗The Gradient Podcast
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.
Spotify ↗The AI Podcast by NVIDIA
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. Creates a real-time oral history of AI and its transformative influence on our world.
Spotify ↗Google DeepMind: The Podcast
Multi-award winning podcast exploring how AI is transforming our world. Hosted by mathematician Hannah Fry, featuring DeepMind scientists and thinkers discussing AI foundations, neuroscience, games, safety, and how AI is accelerating science.
Spotify ↗Member pickUnsupervised Learning: Redpoint's AI Podcast
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.
Spotify ↗Member pick20VC (The Twenty Minute VC)
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, with episodes on data labeling, AI coding tools, and the evolving AI landscape.
Website ↗Writers worth subscribing to.
Lenny's Newsletter
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.
Substack ↗OpenAI Global Affairs
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.
Substack ↗Language Models & Co (Jay Alammar)
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."
Substack ↗Member pickOne Useful Thing (Ethan Mollick)
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.
Substack ↗Dwarkesh
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).
Substack ↗Latent Space
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.
Substack ↗AI Safety Newsletter
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.
Substack ↗Member pickInterconnects (Nathan Lambert)
Essays, audio, and interviews on AI developments from Nathan Lambert. Covers open models, reinforcement learning, reasoning, and model training, at the border between high-level and technical thinking.
Substack ↗AI Law and Policy Syllabus (Nita Farahany)
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.
Substack ↗Lectures, walkthroughs and interviews.
DeepLearning.AI
Comprehensive resource for structured AI/ML learning. Official YouTube channel from Andrew Ng's DeepLearning.AI, featuring 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.
YouTube ↗Jay Alammar
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.
YouTube ↗Founder pickAndrej Karpathy
AI educator and former OpenAI/Tesla AI lead. Features two tracks: a general audience track with comprehensive LLM deep dives, and a technical "Zero to Hero" series that builds neural networks from scratch. Known for clear, thorough explanations of how modern AI actually works.
YouTube ↗IBM Technology
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.
YouTube ↗Founder pick3Blue1Brown
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, all with an emphasis on visualizing core ideas.
YouTube ↗AI Explained
Deep dives into the biggest AI developments as they happen, 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.
YouTube ↗The AiGrid
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.
YouTube ↗Research blogs from the labs and the people in them.
What's AI (Louis-François Bouchard)
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 ↗Women in AI Ethics
Blog from Women in AI Ethics (WAIE), elevating 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.
Blog ↗Researchers and builders who post what they are working on.
Aadit Sheth
Co-founder of The Narrative Company, investor and entrepreneur. Shares threads, posts, and breakdowns on AI, narratives, and communication, with playbooks shared openly.
Follow on X ↗Member pickEthan Mollick
Wharton professor researching AI's impact on education, work, and innovation, and co-founder of Wharton Interactive. Shares practical insights on using AI effectively, with a focus on how it's changing how we learn and work.
Follow on X ↗Member pickMIT Sloan Management Review
Research-backed insights on leadership, management, and the future of work, including AI's impact on business strategy.
Follow on X ↗Jay Alammar
Shares updates on his books and visual guides, celebrates open-source ML tools, and posts interactive visualizations of AI research.
Follow on X ↗Andrej Karpathy
Shares thoughts on AI industry developments, how programming is changing with AI tools, and reflections on learning. Also announces his YouTube deep dives here.
Follow on X ↗Learn Prompting
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.
Follow on X ↗Places to ask questions and learn with others.
Cohere
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.
Join the server ↗OpenAI
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.
Join the server ↗Midjourney
Official community for Midjourney, the AI image generation tool, 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.
Join the server ↗Learn Prompting
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.
Join the server ↗r/Singularity
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.
Join the server ↗Learn AI Together
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.
Join the server ↗Data Science
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.
Join the server ↗TensorFlow
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.
Join the server ↗Hands-on resources for building and experimenting.
List of 700+ AI Foundation Models
Comprehensive 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.
Google Sheet ↗OpenAI: A Practical Guide to Building Agents
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.
PDF ↗FirstMark MAD Landscape 2025
FirstMark's map of 1,400+ companies across the Machine Learning, AI, and Data ecosystem, organized by category: applications and agents, infrastructure, ML and AI (build, validate, operate, research), and data sources. The definitive reference for who's who in the AI landscape.
PDF ↗