The best free AI learning resources
on the internet.
Every tutorial, doc, GitHub repo, and YouTube channel we use to build, ship, and teach. Organized beginner → advanced. Updated for July 2026. Curated by 2BFT.
Three places to start. Pick one.
Elements of AI
University of Helsinki. 30 hours. Free certificate. The single best intro for absolute beginners  no math, no code, just the ideas.
Start the courseMicrosoft AI for Beginners
12 weeks, 24 lessons, 36K+ GitHub stars. Code + exercises. The most-used free AI course on the internet. All in a GitHub repo.
Clone the repo3Blue1Brown  Neural Networks
The best visual explanation of how neural networks actually work. Watch the series. You’ll finally get backpropagation.
Watch on YouTubeFour steps. Pick where you are.
Use the apps
Open Claude, ChatGPT, or Gemini. Ask real questions every day for a week.
- Sign up for Claude, ChatGPT, and Gemini (all free)
- Replace 3 Google searches a day with a chat
- Use it for email, planning, summaries
- Notice where it lies, where it shines
Learn to prompt
Move from "I asked and got something" to "I asked and got what I wanted".
- Take Andrew Ng's ChatGPT Prompt Engineering course (1.5h free)
- Read Anthropic's prompt engineering docs
- Practice: write 10 prompts, eval them, rewrite them
- Learn: system prompt, few-shot, structured output, temperature
Build with APIs
Call a model from your own code. Ship one tool.
- Pick one provider  Anthropic, OpenAI, or Gemini  get an API key
- Build a CLI tool that uses it (1 day)
- Build a RAG app over your own notes (1 weekend)
- Learn evals: how do you know it works?
Ship an agent
Build a multi-step system that does a job without you.
- Pick a framework  OpenClaw default, Mastra for TS, LangGraph for state
- Wire up MCP so it can use your tools
- Build a daily-runner: scraper → summariser → emailer
- Add evals, add logs, ship to someone else
12 topics. Pick what you need.
Prompt Engineering
The skill every other skill rests on. How to talk to a model so it actually does what you meant.
LLM Engineering
Move past prompting. Build with models  APIs, context windows, structured output, evals.
Coding with AI
Ship code with Claude Code, Cursor, Codex, and friends. The new IDE is a chat box.
Agent Frameworks
From a single prompt to a multi-agent system. The 2026 stack, ranked by stars and fit.
MCP  Model Context Protocol
The missing protocol. The standard for letting models talk to your tools, files, and APIs.
Visual AI
Image and video, the 2026 stack. Nano Banana 2 for stills, Veo 3.1 for video.
Voice AI
Audio content at scale. Voiceovers, podcasts, narration, real-time voice agents.
RAG & Vector DBs
Teach a model your data. Retrieval-Augmented Generation, from prototype to enterprise.
Multimodal AI
Models that see, hear, and read. The 2026 frontier: text + image + audio + video in one call.
AI for Coding Beginners
No code? No problem. These tools turn ideas into shipped apps  no IDE required.
For Indian Builders
India is the world's 3rd largest AI ecosystem. These are the local starting points.
For Personal Productivity
The everyday tools. Research, writing, planning, learning  all with AI in the loop.
Every topic. Fully linked.
Prompt Engineering
The skill every other skill rests on. How to talk to a model so it actually does what you meant.
LLM Engineering
Move past prompting. Build with models  APIs, context windows, structured output, evals.
Coding with AI
Ship code with Claude Code, Cursor, Codex, and friends. The new IDE is a chat box.
Agent Frameworks
From a single prompt to a multi-agent system. The 2026 stack, ranked by stars and fit.
MCP  Model Context Protocol
The missing protocol. The standard for letting models talk to your tools, files, and APIs.
Visual AI
Image and video, the 2026 stack. Nano Banana 2 for stills, Veo 3.1 for video.
Voice AI
Audio content at scale. Voiceovers, podcasts, narration, real-time voice agents.
RAG & Vector DBs
Teach a model your data. Retrieval-Augmented Generation, from prototype to enterprise.
Multimodal AI
Models that see, hear, and read. The 2026 frontier: text + image + audio + video in one call.
AI for Coding Beginners
No code? No problem. These tools turn ideas into shipped apps  no IDE required.
For Indian Builders
India is the world's 3rd largest AI ecosystem. These are the local starting points.
For Personal Productivity
The everyday tools. Research, writing, planning, learning  all with AI in the loop.
Beginner. Intermediate. Advanced. Pick your level.
Beginner
8 picksIntermediate
10 picks32 terms. No jargon.
Large Language Model. The class of model  GPT-5.6, Claude Sonnet 5, Gemini 3.1 Pro, Zia  that takes text and produces text. Trained on huge corpora of human writing.
The input you give a model. Includes system prompt (sets behaviour), user prompt (the actual ask), and often few-shot examples.
How much text the model can read at once. GPT-5.6 = 1M tokens, Claude Sonnet 5 = 1M, Gemini 3.1 Pro = 2M+. Bigger window = more memory, more cost.
A chunk of text  roughly 4 characters or ¾ of a word. Models price per token. 1M tokens ≈ 750K English words.
Retrieval-Augmented Generation. You fetch relevant documents, paste them into the prompt, ask the model to answer using them. The model "reads" your data without fine-tuning.
A way to turn text into a vector (a list of numbers) so you can search by meaning, not by keywords. The math behind RAG.
A database that stores embeddings and finds the closest ones fast. Pinecone, Weaviate, Qdrant. The plumbing for RAG.
Training a model further on your own data. Expensive, slow, but teaches the model a style or domain deeply. Most teams don't need it.
Model Context Protocol. An open standard (Anthropic, 2024) for models to call your tools. Think USB-C for AI integrations.
A model running in a loop with tools. It decides what to do, calls a tool, reads the result, decides again. OpenClaw, LangGraph, Mastra are agent frameworks.
Several agents, each with a role, talking to each other. A "researcher" agent finds sources, a "writer" agent drafts, a "critic" agent edits. CrewAI, AutoGen.
When the model is allowed to call functions you wrote  fetch a URL, query a database, send a message. The bridge from LLM to real software.
Evaluations. Tests you run on the model to measure quality. "Does my prompt still answer X correctly?" Essential once you ship a prompt to production.
Forcing the model to reply in JSON or a schema. Use it when you're parsing the response in code.
The older name for tool use. Same idea  model returns a JSON object describing which function to call with which arguments.
Hidden instructions at the start of the conversation. "You are a helpful Tamil tutor." Sets the model's behaviour for the whole chat.
How random the model's next-token pick is. 0 = deterministic, 1 = creative, 2 = chaotic. For evals and code, set 0. For brainstorming, set 0.7–1.
When the model confidently states something false. RAG, evals, and "say I don't know" prompts all help. There's no fix, only mitigation.
Asking the model to "think step by step" before answering. Boosts accuracy on math, logic, and multi-step tasks. Don't print the chain to the user.
Putting 2–5 examples in the prompt so the model imitates the pattern. Cheaper than fine-tuning, often good enough.
Asking with no examples. Works for simple tasks. Falls apart on edge cases.
The thing that splits your text into tokens. Different models tokenize differently. The OpenAI tokenizer and the Claude tokenizer disagree by ~10% on the same text.
The architecture all modern LLMs are built on. "Attention is all you need" (2017). You don't need the math  just know the name.
Running the model. Training = learning. Inference = answering. Cost is per inference call, not per training.
Models you can download and run yourself  Llama 4, Mistral, Qwen, DeepSeek. You keep the data, you pay the GPU bill.
Training a small model to imitate a big model. GPT-5.6-mini is a distillation of GPT-5.6. Cheaper, faster, almost as good.
Reinforcement Learning from Human Feedback. The post-training step that makes models polite and helpful. The reason Claude won't tell you how to make a bomb.
When untrusted text (an email, a doc) hijacks your prompt. The #1 security concern for production LLM apps. Defend with structured prompts and tool whitelists.
Returning the model's answer token-by-token as it's generated. The thing that makes ChatGPT look like it's typing.
How long the model takes to start answering. Streaming first-token latency for Claude Sonnet 5 ≈ 0.5s. Plan your UI around it.
Submit a million requests, get them back in 24 hours, pay 50% less. For evals, bulk classification, anything that doesn't need to be live.
Most LLM APIs let you cache long system prompts. Claude prompt caching: 10% of the cost on cache reads. Big save on RAG-heavy apps.
Copy-paste prompts. Model picks.
Plan a feature
Claude Code, Cursor, CodexI'm building a <WHAT>. The current behaviour is <BEFORE>. I want <AFTER>. Constraints: <LIMITS>. Before writing code, ask me any clarifying questions. Then propose 3 approaches, ranked by simplicity. Then wait for me to pick one.
Debug an error
Claude Code, ChatGPTI'm getting this error: <PASTE FULL STACK TRACE>. I expected <EXPECTED>. I tried <WHAT YOU TRIED>. Don't fix it yet  first list 3 most likely causes, ranked by probability. Then ask me which one to investigate.
Write a test
Claude Code, Cursor, CodexHere's a function: <PASTE>. Write a test file. Cover: happy path, edge cases, the 3 most likely bugs. Use <TEST FRAMEWORK>. Run them. If any fail, fix and re-run. Don't ask for confirmation between steps.
RAG answer with sources
Claude, GPT, Gemini  production RAGAnswer the user's question using ONLY the documents below. If the answer isn't in the documents, say "I don't know"  don't make it up. Cite the document name after every sentence.
<DOCUMENTS>
{{retrieved_chunks}}
</DOCUMENTS>
User question: {{question}}Extract structured data
Any model  with structured output enabledExtract the following from the text below into JSON matching the schema. If a field is missing, use null. Don't explain. Just JSON.
Schema: {{json_schema}}
Text:
"""
{{text}}
"""Indian-language rewrite
Gemini 3.1 Pro is best for Indian languages in 2026Translate this to <TAMIL / HINDI / TELUGU>. Keep the technical terms in English. Match the register: <CASUAL / FORMAL>. Don't transliterate  actually translate. Then explain any cultural adaptations you made.
The right tool for the job. 2026 edition.
| Task | Pick | Why |
|---|---|---|
| Long docs / RAG | Gemini 3.1 Pro | 2M+ context, strong on long inputs |
| Code / agents | Claude Sonnet 5 | Best tool use, longest sustained reasoning |
| Everyday chat | GPT-5.6 | Default in ChatGPT, fastest iteration |
| Image gen | Nano Banana 2 | 4K in 4–6s, free tier, India-friendly |
| Video gen | Veo 3.1 Lite | 8s clips, audio included |
| Voice / TTS | ElevenLabs | Most natural prosody in 2026 |
| Hindi / Tamil / Telugu | Gemini 3.1 Pro | Best Indian-language code-mixing |
| Self-hosted / privacy | Llama 4 70B | Open weights, runs on 1× A100 |
| Cheap bulk | GPT-5.6 mini / Claude Haiku | 10× cheaper, 90% of the quality |
| Tamil Nadu SMB use | Zia LLM | Made in India, 100M+ Zoho users, India data residency |
The 10 GitHub repos every AI builder should star.
Ranked by stars, production usage, and the number of times we’ve actually opened the README. Star them all in one sitting. You’ll come back to them within a month.
- 01↗openclaw/openclaw376K stars
The most-starred repo in GitHub history. Self-hosted AI agent with 50+ integrations.
- 02↗langchain-ai/langgraph24.8K stars
Stateful, graph-based agent workflows. The production pick.
- 03↗langflow-ai/langflowVisual builder
Drag-drop agent builder. The fastest way to prototype a multi-agent flow.
- 04↗langgenius/dify136K stars
Production agentic workflows. The LLMOps platform many teams ship on.
- 05↗infiniflow/ragflow70K stars
Enterprise RAG. Built for serious document ingestion and retrieval.
- 06↗anthropics/anthropic-cookbookClaude recipes
Claude code + prompts. The closest thing to official Claude patterns.
- 07↗anthropics/awesome-claude-skills1,000+ skills
Claude Code extensions. A library of skills you can drop in.
- 08↗caramaschiHG/awesome-ai-agents-2026Curated list
The freshest, most-curated list of AI agent projects and frameworks.
- 09↗ashishpatel26/500-AI-Agents-Projects500+ examples
Real production agent examples across industries.
- 10↗msitarzewski/agency-agents147 agents
A multi-agent framework and 147 ready-to-use agent specs.
What are you actually trying to do? Pick the closest match.
Personal growth
Claude, ChatGPT, NotebookLM, Perplexity, Copilot. The five tools we use daily.
Build a website
v0, Bolt.new, Lovable, Cursor, Claude Code. From idea to deployed site in a day.
Code faster
Claude Code, Cursor, Codex, Antigravity, Trae. Pick one and ship.
Start a business
LLM APIs, OpenClaw, Dify, n8n, Mastra. The 2026 founder's stack.
Create content
Nano Banana 2 for stills, Veo 3.1 for video, ElevenLabs for audio, NotebookLM for research.
For students
Elements of AI, Microsoft AI for Beginners, 3Blue1Brown, Khan Academy + AI. Start with the basics.
- Elements of AI (Helsinki)
- Microsoft AI for Beginners
- 3Blue1Brown (YouTube)
- Khan Academy
- Khanmigo (tutor)
For teachers
Claude for lesson plans, NotebookLM for research, Khanmigo for tutoring. Free, practical.
For Indian SMBs
Zia LLM, self-hosted OpenClaw, Nano Banana 2 for catalogue shoots. Built for tier-3 towns.
- Zia LLM (Zoho, India)
- OpenClaw (self-host)
- Nano Banana 2 (catalogue)
- IndiaAI portal
- 2BFT Jewellery use case
India is now the world’s 3rd largest AI ecosystem.
3,100+ AI startups. ₹10,372 Cr IndiaAI Mission. 100M+ users on Zoho’s Zia LLM. The full numbers, the 2BFT angle, and the Tamil Nadu layer  all on the /india-ai page.
The 6 questions. The 6 answers.
01What are the best free AI courses in 2026?
For absolute beginners: Elements of AI (University of Helsinki, 30 hours, free cert) and Google AI Essentials on Coursera (free first chapter). For code-first learners: Microsoft AI for Beginners on GitHub (12 weeks, 36K+ stars). For prompt engineering: DeepLearning.AI's ChatGPT Prompt Engineering for Developers (Andrew Ng, 1.5 hours, free). For visual AI: Nano Banana 2's official docs and Higgsfield's intro guide. For agent building: Anthropic Academy and LangChain Academy ship production-grade content free in 2026.
02What's the best way to learn AI in 2026?
The fastest path: (1) Start with Elements of AI or Google AI Essentials to build vocabulary. (2) Pick one model  Claude, GPT, or Gemini  and use it daily for a month. (3) Learn prompt engineering from Andrew Ng's free DeepLearning.AI course. (4) Build one project with Claude Code, Cursor, or Trae. (5) Read Anthropic's Building Effective Agents and awesome-ai-agents-2026. (6) Ship something. The skill is shipping, not collecting tutorials. 2BFT's Academy follows this exact arc.
03What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open standard, released by Anthropic in late 2024, that lets AI models talk to your tools, files, and APIs in a structured way. Instead of writing custom glue code for every integration, MCP gives you a universal protocol  a server exposes tools (Notion, WhatsApp, Google Sheets, GitHub), and any MCP-compatible model (Claude, GPT, Gemini) can call them. In 2026 MCP is the closest thing the AI industry has to a USB-C standard for tools. Start at modelcontextprotocol.io, the Anthropic MCP course on DeepLearning.AI, and microsoft/mcp-for-beginners (16K stars).
04Which AI agent framework should I use?
In July 2026, the default is OpenClaw (347–376K GitHub stars, self-hosted). For TypeScript shops: Mastra (RAG, MCP, 40+ model providers, 21K stars). For graph-based production orchestration: LangGraph (24.8K stars). For sub-2-microsecond agent runs: Agno. For visual workflow building: Dify (136K stars). For no-code: Langflow. For pure Python: Pydantic AI (type-safe) or Smolagents. For Fortune 500 production: CrewAI. 2BFT picks: OpenClaw default, Mastra for Next.js, LangGraph for state.
05Where can I learn prompt engineering for free?
Start with DeepLearning.AI's ChatGPT Prompt Engineering for Developers (Andrew Ng + Isa Fulford, 1.5 hours, free). Then read Anthropic's Prompt Engineering Overview. For depth, work through the Anthropic Prompt Engineering Masterclass on GitHub (9 chapters). Anthropic's Skilljar hosts 15+ free courses. OpenAI's Prompt Engineering Guide covers GPT-5.6. Google's Prompting Essentials is the Gemini counterpart. For an Indian-language angle, try the same prompts across GPT-5.6, Claude Sonnet 5, and Gemini 3.1 Pro in Tamil and Hindi  the models behave very differently on register and code-mixing.
06What's the best free resource to learn Claude?
Start at Anthropic Academy (anthropic.skilljar.com)  15+ free courses. Then read the Anthropic docs (docs.anthropic.com)  the prompt engineering overview, tool use docs, Claude Agent SDK overview. For practical workflows, clone the anthropic-cookbook repo. For Claude Code specifically, read Anthropic's Claude Code best practices guide. For community, r/ClaudeAI and Anthropic's Discord. The Claude app at claude.ai is the fastest way to build vocabulary before writing code.
Free resources got you this far. Now go deeper.
2BFT Academy is the structured version of this page. 4 phases, 18 lessons, ~17 hours. Phase 0 is free, forever. The other 3 phases are paid  and we use every rupee to train more Indian builders.