Collection
32 resources worth bookmarking in 2026
OpenAI
For: Knowledge workers, managers, and professionals beginning their AI journey
Why explore: OpenAI Academy course on workplace AI foundations—opens the Academy course page (structured path through applied use, agents, and adoption topics).
Microsoft
For: Beginners interested in the technical foundations of generative AI
Why explore: Open-source curriculum combining concepts, labs, and code examples for how generative AI applications are built.
Hugging Face
For: Learners interested in language models, open-source AI, and machine learning
Why explore: Introduces transformers, tokenization, and open-source libraries used to work with language models in practice.
NVIDIA
For: Technical learners and educators
Why explore: Curated NVIDIA training portal of freely licensed AI and accelerated computing courses—pick modules for self-study or internal workshops.
Amazon Web Services
For: Professionals exploring AI applications and cloud infrastructure
Why explore: Curated AWS training portal for introductory through specialist AI, ML, and generative AI learning in a cloud context.
Google Cloud
For: Beginners and professionals seeking structured AI education
Why explore: Structured learning paths for generative AI concepts, deployment, and application development on Google Cloud.
Databricks
For: Business professionals, analysts, and prospective AI practitioners
Why explore: Connects model capabilities to enterprise data, governance, and implementation considerations.
Learn Prompting
For: Beginners and intermediate users developing practical AI skills
Why explore: Accessible prompt engineering material from introductory techniques toward security and advanced prompting topics.
OpenAI
For: Builders who need reliable outputs from OpenAI models
Why explore: Official guidance on clear instructions, context, output formats, and testing prompts for reliable outputs.
OpenAI
For: Everyday ChatGPT users improving instruction quality
Why explore: Practical recommendations for everyday ChatGPT use: specificity, examples, and iterative refinement.
Anthropic
For: Teams building with Claude who need consistent task outcomes
Why explore: Claude-focused guidance on clarity, examples, success criteria, and handling complex tasks.
For: Practitioners designing prompts for Gemini and Vertex AI
Why explore: How structure, context, and examples influence Gemini behavior, including grounded and tool-assisted use.
GitHub
For: Developers using Copilot for production-quality code assistance
Why explore: Principles for reliable AI-assisted code through requirements, context, and project-aware instructions.
GitHub
For: Teams standardizing reusable prompt assets in repos
Why explore: Documents reusable Markdown prompt templates as project assets for repeatable team workflows.
DeepLearning.AI
For: Professionals applying prompting to research, writing, and creation
Why explore: Course connecting prompting techniques to everyday professional research, writing, and creation tasks.
OpenAI
For: Developers implementing OpenAI APIs, agents, and evaluation patterns
Why explore: Implementation examples for APIs, structured outputs, agents, evaluation, and common application patterns.
Anthropic
For: Developers building document and tool workflows with Claude
Why explore: Recipes for tool use, context management, document workflows, and other Claude application patterns.
For: Developers integrating Gemini APIs and multimodal features
Why explore: Code examples for prompting, function calling, multimodal inputs, and Gemini API integration.
Microsoft
For: Builders completing hands-on generative AI application labs
Why explore: Entry point to Lesson 06 in Microsoft's open Generative AI for Beginners course—hands-on text-generation apps (the full repo has 21 lessons).
Pinecone
For: Engineers designing retrieval-augmented knowledge applications
Why explore: Tutorials on retrieval-augmented generation: embeddings, reranking, and evaluation for knowledge-backed apps.
fast.ai
For: Practitioners who learn by building deep learning projects
Why explore: Practical deep learning course emphasizing implementation intuition alongside underlying ML concepts.
Full Stack Deep Learning
For: Advanced builders architecting LLM applications and operations
Why explore: Recorded lectures on LLM application architecture, prompting, augmented models, and operational concerns.
DeepLearning.AI & OpenAI
For: Developers connecting prompt design to API-based products
Why explore: Connects prompt design to API-based application development with OpenAI models.
LangChain
For: Developers learning LangChain, LangGraph, and agent deployment
Why explore: Self-paced courses on LangChain, LangGraph, agent evaluation, deployment, and monitoring.
Microsoft
For: Developers new to agent concepts and tool integration
Why explore: Open curriculum on agent concepts, design patterns, tool integration, and practical development considerations.
Databricks
For: Teams exploring agents on enterprise data platforms
Why explore: Introductory agent components and organizational workflow applications on enterprise data platforms.
NVIDIA
For: Advanced learners studying agentic AI and infrastructure
Why explore: Curated NVIDIA learning portal spanning agentic AI, foundation models, and infrastructure for advanced AI systems.
OpenAI
For: Leaders driving AI adoption and governance
Why explore: OpenAI Academy course on leading organizational AI adoption—opens the Academy course page (a structured learning path, not a single static doc).
OpenAI
For: Operators rolling out Academy-style programs in organizations
Why explore: Guidance on rolling out Academy-style learning programs with sponsorship and reinforcement in organizations.
NIST
For: Leaders and risk owners implementing trustworthy AI governance
Why explore: Voluntary playbook for Govern, Map, Measure, and Manage functions in trustworthy AI governance.
Databricks
For: Practitioners who need a sandbox beyond theory-only learning
Why explore: Sandbox environment for experimenting with data and AI workflows beyond purely theoretical learning.
DeepLearning.AI
For: Non-technical leaders understanding AI opportunities and workflows
Why explore: Non-technical introduction to AI opportunities, project workflows, and organizational implications.