chore: sync content to repo (#9686)

Co-authored-by: kamranahmedse <4921183+kamranahmedse@users.noreply.github.com>

src/data/roadmaps/ai-engineer/content/ai-safety-and-ethics@8ndKHDJgL_gYwaXC7XMer.md

@@ -5,4 +5,5 @@ AI safety and ethics involve establishing guidelines and best practices to ensur
 Visit the following resources to learn more:
 
 - [@article@Understanding Artificial Intelligence Ethics and Safety](https://www.turing.ac.uk/news/publications/understanding-artificial-intelligence-ethics-and-safety)
+- [@article@The Hidden Security Risks of LLMs](https://towardsdatascience.com/the-hidden-security-risks-of-llms/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
 - [@video@What is AI Ethics?](https://www.youtube.com/watch?v=aGwYtUzMQUk)

src/data/roadmaps/ai-engineer/content/anthropic-claude@hy6EyKiNxk1x84J63dhez.md

@@ -4,7 +4,7 @@ Anthropic's Claude is an AI language model designed to facilitate safe and scala
 
 Visit the following resources to learn more:
 
-- [@official@Claude](https://claude.ai)
 - [@course@Claude 101](https://anthropic.skilljar.com/claude-101)
+- [@official@Claude](https://claude.ai)
 - [@video@How To Use Claude Pro For Beginners](https://www.youtube.com/watch?v=J3X_JWQkvo8)
 - [@video@Claude FULL COURSE 1 HOUR (Build & Automate Anything)](https://www.youtube.com/watch?v=KrKhfm2Xuho)

src/data/roadmaps/ai-engineer/content/hugging-face@v99C5Bml2a6148LCJ9gy9.md

@@ -4,6 +4,6 @@ Hugging Face is a leading AI company and open-source platform that provides tool
 
 Visit the following resources to learn more:
 
-- [@official@Hugging Face](https://huggingface.co)
 - [@course@Hugging Face Official Video Course](https://www.youtube.com/watch?v=00GKzGyWFEs&list=PLo2EIpI_JMQvWfQndUesu0nPBAtZ9gP1o)
+- [@official@Hugging Face](https://huggingface.co)
 - [@video@What is Hugging Face? - Machine Learning Hub Explained](https://www.youtube.com/watch?v=1AUjKfpRZVo)

src/data/roadmaps/ai-engineer/content/meta-llama@OkYO-aSPiuVYuLXHswBCn.md

@@ -4,5 +4,5 @@ Meta Llama is a family of large language models (LLMs) developed by Meta AI. The
 
 Visit the following resources to learn more:
 
-- [@official@Llama](https://www.llama.com/)
-- [@course@Building with Llama 4](https://www.deeplearning.ai/short-courses/building-with-llama-4/)
+- [@course@Building with Llama 4](https://www.deeplearning.ai/short-courses/building-with-llama-4/)
+- [@official@Llama](https://www.llama.com/)

src/data/roadmaps/ai-engineer/content/model-context-protocol-mcp@AeHkNU-uJ_gBdo5-xdpEu.md

@@ -7,5 +7,4 @@ Visit the following resources to learn more:
 - [@course@Model Context Protocol (MCP) Course](https://huggingface.co/learn/mcp-course/en/unit0/introduction)
 - [@official@Model Context Protocol](https://modelcontextprotocol.io/)
 - [@opensource@Model Context Protocol](https://github.com/modelcontextprotocol)
-- [@article@Model Context Protocol (MCP): A Guide With Demo Project](https://www.datacamp.com/tutorial/mcp-model-context-protocol)
-- [@video@What is MCP? Integrate AI Agents with Databases & APIs](https://www.youtube.com/watch?v=eur8dUO9mvE)
+- [@article@Discover more aritlces on MCP](https://towardsdatascience.com/tag/mcp/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)

src/data/roadmaps/ai-engineer/content/multi-agents@kG1bkF2oY21CJOm9zhdpn.md

@@ -6,4 +6,5 @@ Visit the following resources to learn more:
 
 - [@article@What is a multi-agent system?](https://www.ibm.com/think/topics/multiagent-system)
 - [@article@Multi-Agent Systems](https://huggingface.co/learn/agents-course/en/unit2/smolagents/multi_agent_systems)
-- [@article@Guide to multi-agent systems (MAS)](https://cloud.google.com/discover/what-is-a-multi-agent-system)
+- [@article@Guide to multi-agent systems (MAS)](https://cloud.google.com/discover/what-is-a-multi-agent-system)
+- [@article@Agentic AI 103: Building Multi-Agent Teams](https://towardsdatascience.com/agentic-ai-103-building-multi-agent-teams/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)

src/data/roadmaps/ai-engineer/content/nanobanana-api@6y73FLjshnqxV8BTGUeiu.md

@@ -4,4 +4,5 @@ The NanoBanana API is a tool designed to facilitate the integration and processi
 
 Visit the following resources to learn more:
 
-- [@official@NanoBanana API](https://nanobananaapi.ai/)
+- [@official@NanoBanana API](https://nanobananaapi.ai/)
+- [@article@Generating Consistent Imagery with Gemini](https://towardsdatascience.com/generating-consistent-imagery-with-gemini/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)

src/data/roadmaps/ai-engineer/content/openai-agentkit@Sm0Ne5Nx72hcZCdAcC0C2.md

@@ -6,4 +6,4 @@ Visit the following resources to learn more:
 
 - [@official@Introducing AgentKit](https://openai.com/index/introducing-agentkit/)
 - [@official@Build every step of agents on one platform](https://openai.com/agent-platform/)
-- [@video@Build Hour: AgentKit](https://www.youtube.com/watch?v=sAitLFLbgDA)
+- [@video@OpenAI Agents SDK Tutorial (FULL SERIES)](https://www.youtube.com/watch?v=gFcAfU3V1Zo)

src/data/roadmaps/ai-engineer/content/role--behavior@N3TzWYxU0jgv1l99Ts58n.md

@@ -1,4 +1,4 @@
-# Role & Behavior 
+# Role & Behavior
 
 System prompting involves crafting instructions that define the AI model's role, personality, and overall behavior when interacting with users. This allows you to shape the AI's responses, ensuring they are consistent with desired guidelines, such as adopting a specific persona (e.g., a helpful assistant, an expert) or adhering to constraints on tone and style. By carefully defining these aspects, you can significantly influence the AI's output and guide it towards more relevant and effective interactions.
 

src/data/roadmaps/ai-engineer/content/type-of-models@2X0NDcq2ojBJ0RxY_U6bl.md

@@ -1,3 +1,7 @@
 # Types of AI Models
 
-AI models come in various forms. Open models provide transparent access to their architecture and training data, fostering collaboration and customization, while closed models keep these details proprietary. Pre-trained models are trained on massive datasets and can be fine-tuned for specific tasks, saving time and resources. Self-hosted models, on the other hand, offer greater control and privacy as they are deployed and managed on your own infrastructure.
+AI models come in various forms. Open models provide transparent access to their architecture and training data, fostering collaboration and customization, while closed models keep these details proprietary. Pre-trained models are trained on massive datasets and can be fine-tuned for specific tasks, saving time and resources. Self-hosted models, on the other hand, offer greater control and privacy as they are deployed and managed on your own infrastructure.
+
+Visit the following resources to learn more:
+
+- [@article@Recap of all types of LLM Agents](http://towardsdatascience.com/recap-of-all-types-of-llm-agents/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)

src/data/roadmaps/ai-engineer/content/what-are-rags@lVhWhZGR558O-ljHobxIi.md

@@ -5,4 +5,5 @@ Retrieval-Augmented Generation (RAG) combines information retrieval with languag
 Visit the following resources to learn more:
 
 - [@article@What is RAG?](https://aws.amazon.com/what-is/retrieval-augmented-generation/)
+- [@article@RAG Explained: Understanding Embeddings, Similarity, and Retrieval](https://towardsdatascience.com/rag-explained-understanding-embeddings-similarity-and-retrieval/?utm_source=roadmap&utm_medium=Referral&utm_campaign=TDS+roadmap+integration)
 - [@video@What is Retrieval-Augmented Generation? IBM](https://www.youtube.com/watch?v=T-D1OfcDW1M)