panaversity/learn-agentic-ai: Hands‑on LLM app examples with Dapr
Why does a GitHub list of “10 Most Popular Repositories for Learning AI” matter to anyone building real‑world language‑model services? Because most tutorials stop at theory, leaving developers to guess how to stitch together the pieces that actually run in production. While the hype around “agentic AI” swirls online, few open‑source projects give you a step‑by‑step playground that mirrors the infrastructure you’ll need tomorrow.
Here’s where panaversity/learn-agentic-ai steps in. The repo bundles Dapr‑backed examples, letting you see how cloud‑native patterns translate into code you can clone, run, and extend. It’s not just a collection of notebooks; it’s a systems‑first curriculum that pushes you from a single prompt to a coordinated set of microservices.
If you’ve ever wondered how to move from a toy chatbot to a production‑grade LLM app, the material in this repository offers a concrete bridge. The focus is on learning by example, exploring modern agentic patterns, and accelerating hands‑on development of production‑style LLM apps. panaversity/learn-agentic-ai Learn Agentic AI using Dapr Agentic Cloud Ascent (DACA) is a cloud‑native, systems‑first learning program focused on designing and
The focus is on learning by example, exploring modern agentic patterns, and accelerating hands-on development of production-style LLM apps. panaversity/learn-agentic-ai Learn Agentic AI using Dapr Agentic Cloud Ascent (DACA) is a cloud-native, systems-first learning program focused on designing and scaling planet-scale agentic AI systems. It teaches how to build reliable, interoperable multi-agent architectures using Kubernetes, Dapr, OpenAI Agents SDK, MCP, and A2A protocols, with a strong emphasis on workflows, resiliency, cost control, and real-world execution.
The goal is not just building agents, but training developers to design production-ready agent swarms that can scale to millions of concurrent agents under real constraints. dair-ai/Mathematics-for-ML Mathematics for Machine Learning is a curated collection of high-quality books, papers, and video lectures that cover the mathematical foundations behind modern ML and deep learning. It focuses on core areas such as linear algebra, calculus, probability, statistics, optimization, and information theory, with resources ranging from beginner-friendly to research-level depth.
The goal is to help learners build strong mathematical intuition and confidently understand the theory behind machine learning models and algorithms. ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code 500+ Artificial Intelligence Project List with Code is a massive, continuously updated directory of AI/ML/DL project ideas and learning resources, grouped across areas like computer vision, NLP, time series, recommender systems, healthcare, and production ML. It links out to hundreds of tutorials, datasets, GitHub repos, and "projects with source code," and encourages community contributions via pull requests to keep links working and expand the collection.
armankhondker/awesome-ai-ml-resources Machine Learning & AI Roadmap (2025) is a structured, beginner-to-advanced guide that maps out how to learn AI and machine learning step by step. It covers core concepts, math foundations, tools, roles, projects, MLOps, interviews, and research, while linking to trusted courses, books, papers, and communities.
The repository offers a concrete set of examples, letting learners move from theory to code without a steep ramp‑up. By pairing LLM‑centric patterns with Dapr’s building blocks, it demonstrates one way to assemble production‑style agents. Yet the material stops short of detailing how well these snippets scale beyond toy projects, leaving that question open.
The focus on “learning by example” aligns with the broader push toward hands‑on AI education, but the depth of coverage for underlying math or system design remains unclear. For newcomers, the curated list of popular GitHub repos provides a useful entry point, spanning fundamentals, computer vision, and agentic workflows. Still, the description of the Dapr Agentic Cloud Ascent (DACA) program is truncated, offering no concrete outcomes or assessment criteria.
In short, the collection supplies practical scaffolding for building LLM apps, while the extent of its pedagogical rigor and real‑world applicability awaits further evidence.
Further Reading
- Papers with Code - Latest NLP Research - Papers with Code
- Hugging Face Daily Papers - Hugging Face
- ArXiv CS.CL (Computation and Language) - ArXiv
Common Questions Answered
What is the primary purpose of the panaversity/learn-agentic-ai repository?
The repository provides hands‑on, production‑style LLM app examples that demonstrate how to combine Dapr's building blocks with modern agentic patterns. It focuses on learning by example, allowing developers to move from theory to working code quickly.
Which technologies does the Learn Agentic AI using Dapr Agentic Cloud Ascent (DACA) program teach developers to use?
DACA teaches developers to design and scale planet‑scale agentic AI systems using Kubernetes, Dapr, the OpenAI Agents SDK, MCP, and A2A protocols. These cloud‑native components are emphasized to build reliable, interoperable multi‑agent architectures.
How does the repository aim to bridge the gap between theory and production‑ready LLM services?
By pairing LLM‑centric patterns with Dapr’s building blocks, the repository offers concrete code snippets that illustrate how to assemble production‑style agents. This approach lets learners see real‑world infrastructure decisions rather than just abstract concepts.
What limitation does the article note about the scalability of the provided examples?
The article points out that the material stops short of demonstrating how the snippets perform beyond toy projects. Consequently, it leaves open the question of whether these examples can scale to planet‑scale agentic AI workloads.