"Forge Your Master Artificial Intelligence" โ The Ultimate Foundry v2.0
Total_Resources: 215 Items
4-8 Weeks // 45 Items
8-16 Weeks // 60 Items
12-24 Weeks // 80 Items
Ongoing // 30 Items
21 Lessons, Get Started Building with Generative AI
12 Lessons to Get Started Building AI Agents
12 Weeks, 24 Lessons, AI for All!
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
๐ Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.
๐งฎ A collection of resources to learn mathematics for machine learning
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
The 30 Days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than 100 days. Follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw
Collection of awesome LLM apps with AI Agents and RAG using OpenAI, Anthropic, Gemini and opensource models.
Essential AI logic.
Interactive roadmaps, guides and other educational content to help developers grow in their careers.
Essential AI logic.
This repository provides tutorials and implementations for various Generative AI Agent techniques, from basic to advanced. It serves as a comprehensive guide for building intelligent, interactive AI systems.
A production-ready template to kickstart your Generative AI projects with structure and scalability in mind.
A smart multi-agent system to turn prompts into GitHub PRs.
The fast, Pythonic way to build MCP servers and clients.
Dev All through LLM-powered Multi-Agent Collaboration.
The first agentic LLM for autonomous data science.
All-in-one Multimodal Document Processing RAG system built on LightRAG.
Expose secure FastAPI endpoints as MCP tools with minimal setup and authentication.
Turns LLMs into an instant execution partner that writes and runs code in a preconfigured sandbox.
Visual workflow builder for AI agents powered by Firecrawl.
The absolute trainer to light up AI agents.
Ship AI Agents to Google Cloud in minutes with built-in CI/CD and observability.
Semantic code editing and retrieval for agent-driven coding.
Swift code analysis that turns screen content into actionable AI context.
Pulls up-to-date, version-specific documentation straight from your code.
Get up and running with Kimi-K2.5, GLM-5, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models.
Production-ready platform for agentic workflow development.
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses.
Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
Summaries and resources for Designing Machine Learning Systems book (Chip Huyen, O'Reilly 2022)
Learn how to develop, deploy and iterate on production-grade ML applications.
Essential AI logic.
An educational resource to help anyone learn deep reinforcement learning.
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
Natural Language Processing Tutorial for Deep Learning Researchers
A repo lists papers related to LLM based agent
Official code repo for the O'Reilly Book - "Hands-On Large Language Models"
Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning
Essential AI logic.
Curated coding interview preparation materials for busy software engineers
If you want to become good at system design, join this newsletter now ๐
A collective list of free APIs.
:books: Freely available programming books
FULL Augment Code, Claude Code, Cluely, CodeBuddy, Comet, Cursor, Devin AI, Junie, Kiro, Leap.new, Lovable, Manus, NotionAI, Orchids.app, Perplexity, Poke, Qoder, Replit, Same.dev, Trae, Traycer AI, VSCode Agent, Warp.dev, Windsurf, Xcode, Z.ai Code, Dia & v0. (And other Open Sourced) System Prompts, Internal Tools & AI Models
Documentation created to better understand open projects.
A list of free LLM inference resources accessible via API.
GitHub Agentic Workflows
Nuxt developer tools for route inspection and SSR debugging.
Interface with game engine APIs for AI-assisted game development.
Tool for testing and debugging MCP servers by inspecting protocol handshakes.
Enhances n8nโs workflow automation by streamlining creation and orchestration.
Codes and Notebooks for AI Projects.
The agent engineering platform
AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.
Stable Diffusion web UI
A Gemini 2.5 Flash Level MLLM for Vision, Speech, and Full-Duplex Multimodal Live Streaming on Your Phone
A minimal, secure Python interpreter written in Rust for use by AI
Open Vision Agents by Stream. Build Vision Agents quickly with any model or video provider. Uses Stream's edge network for ultra-low latency.
ๅบไบๅคๆบ่ฝไฝLLM็ไธญๆ้่ไบคๆๆกๆถ - TradingAgentsไธญๆๅขๅผบ็
66 Specialized Skills for Full-Stack Developers. Transform Claude Code into your expert pair programmer.
Build databases, automations, apps & agents with AI โ no code. Open source platform available on cloud and self-hosted. GDPR, HIPAA, SOC 2 compliant. Best Airtable alternative.
Shannon Lite is an autonomous, white-box AI pentester for web applications and APIs. It analyzes your source code, identifies attack vectors, and executes real exploits to prove vulnerabilities before they reach production.
Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | ๐ Star if you like it!
A collection of MCP servers.
List of books, blogs, newsletters and people!
AI engineering updates, tooling, and agent ecosystem news.
Structured roadmap for mastering LLM systems.
Creating personal AI replicas using NotebookLM.
Using Gemini and NotebookLM for visual workflows.
End-to-end guide for building AI agents.
LangChainโs DeepAgents CLI for structured agent design.
Condensed overview of AI agent architectures.
High-level introduction to LLM concepts.
Core ML algorithms and theory.
Progression of language model usage into agents.
Critical evaluation of agent reliability.
Best practices for robust agent systems.
Combining reasoning and acting in LLM agents.
Simulated human-like agents with memory and planning.
Teaching LLMs to use tools autonomously.
Improves reasoning via intermediate steps.
Multi-branch reasoning strategy.
Self-reflective agent learning.
Comprehensive overview of retrieval-augmented generation.
From transformers to production LLM deployment.
Production-grade AI agent development.
Train agents through reinforcement learning.
Hands-on PyTorch-based deep learning.
Build GPT models from scratch.
Mathematical foundation of ML.
Bias detection and responsible AI design.
Deployment and monitoring of LLM systems.
Deploy ML models into production environments.
Anthropic engineer Barry Zhang on designing effective multi-step agents.
Full MCP workshop (Mahesh Murag, Anthropic) on model-context for agents.
Hands-on tutorial building an agent from first principles.
LangChain DeepAgents overview for JavaScript.
Demo: a voice agent that can attend and summarize calls.
Case study automating social media using a single agent.
Using Perplexity Comet agents to automate common tasks.
Create VFX-style ads using VEO 3 JSON prompts.
Build voice-call automations and follow-ups using n8n and AI.
Clear explanation of model internals, training and inference.
Short overview of Perplexity Pro features.
Full course on AI agents (Simplilearn live session).
Guide to building voice AI assistants that reason, plan and act.
Introductory explainer on agentic AI concepts and design.
Karpathyโs practical workflow and tips for LLMs.
Pattern for pipelines that detect and auto-fix runtime errors.
How generative AI works โ foundations and limitations.
Practical prompting techniques and prompt engineering principles.
System prompts, retrieval-augmented workflows, and use-cases.
CS50-style AI material: neural nets, LLMs, and projects.
Prompt design and evaluation from CS50 extension materials.
Deep dive into GPT-4 internals and capabilities.
How prompting changes the software development interface.
Collection exploring how AI reshapes teaching and learning.
Practical resources for educators using AI in classrooms.
A concise, low-jargon introduction to generative AI.
Write better prompts for Vertex AI and Gemini models.
Cloud fundamentals for data, ML and AI workloads.
Foundational prealgebra lessons for beginners.
Core algebra concepts and worked examples.
Precalculus topics bridging algebra and calculus.
Differential and integral calculus explained with examples.
Vectors, matrices and linear transforms โ AI foundations.
Visual and proof-based geometry lessons.
Angles, identities and trig applications.
Probability, descriptive stats and inference basics.
Probability theory and statistical reasoning.
Limit concepts that underpin calculus.
Rate-of-change and differentiation techniques.
Area-under-curve, accumulation and applications.
Dimension reduction, factor analysis, and scaling techniques.
Practical statistics with R for data analysis.
Model training, validation, and real-world ML pipelines.
Foundations of programming, algorithms and problem solving.
Beginner-friendly introduction to programming concepts.
CS fundamentals tailored for business professionals.
Overview of internet, cybersecurity and multimedia.
Intro to AI algorithms and Python implementations.
Core Python programming concepts and exercises.
Build full-stack projects with Python, JS and SQL.
Playlist covering philosophical and technical agent topics.
Open-access book explaining deep learning concepts and practice.
Googleโs agent whitepaper resources hosted on Kaggle.
Companion materials and dataset references for agent research.
Best practices and docs for agentic coding with Claude Code.
OpenAIโs practical guide and patterns for building agents.
Short course on using MCP to build rich-context AI apps.
Using Pinecone and vector DBs to power retrieval apps.
From embeddings to production vector-database applications.
Design patterns for memory in agent systems.
Advanced retrieval-augmented generation system design.
Create browser-based agents that perform web tasks.
Methods and metrics for testing and validating agents.
Enabling AI systems to perform structured computer tasks.
Designing and coordinating multiple AI agents in production.
Practical techniques to increase LLM reliability and accuracy.
Design patterns for agent orchestration using Autogen.
Architecting and training systems of cooperating agents.
Practical prompt engineering techniques for developers.
Coding with AI assistance using Replit.
Hands-on examples of using Claude Code for agentic coding.
Course on using Claude Code as an agentic coding assistant.
Use OpenAI Canvas to collaborate with AI on writing and code.
Project write-up and creative directions from Jam with AI.
Lifecycle approach to prompt design for production systems.
Notes and experiments on continual RL techniques.
Google Cloud training paths for ML and AI services.
Compare AI technologies and real-world use cases.
AWS learning plan for decision-makers on generative AI.
Beginner-friendly course on generative AI concepts.
Hands-on agent building on Salesforce Agentforce.
Free training and certification track for OCI AI foundations.
Hands-on GenAI app building using WatsonX and LangChain.
Stanfordโs 2025 CV lecture series.
Foundational textbook for machine learning theory.
Practical algorithmic explanations for ML tasks.
The canonical deep learning textbook.
Classic RL textbook covering foundational algorithms.
Advanced material on distributional approaches in RL.
Concepts and algorithms for multi-agent RL.
Advanced reinforcement learning chapters and references.
Advanced probabilistic machine learning topics.
Additional CS229 lecture content and deep dives.
Fast, structured overview of major ML concepts.
Template and structure for repeatable ML projects.
Full project walkthrough from data to deployment.
Build and monitor ML pipelines with real data.
Reality check on timelines and how to learn ML effectively.
Practical regression project with evaluation and trade-offs.
Clear overview of common ML model families and when to use them.
Guidance on building maintainable, production ML systems.
Beginner-friendly introduction to ML concepts.
Comprehensive guide to agentic design patterns (PDF/Doc).
Coding with AI helpers and automation in Replit.
Practical examples of using Claude Code for agents.
Workflows for collaborative AI-assisted writing and coding.
Practical patterns and examples for building agents.
Project notes and tutorials from the Jam with AI team.
Operational lifecycle approach to prompt development.
Continual RL experiments and practical notes.
Google Cloud training for machine learning practitioners.
Paper reviews, project showcases, and a direct line to the frontier. Forge with us.
Curated papers, tool reviews, and roadmap updates. Direct to your terminal.