ReAct (Reasoning and Acting) ReAct combines Reflection and Tool use patterns. Now the agent can reflect on both the LLM-generated outputs and the results of its interactions with the external world using tools. One of the most popular and powerful patterns used today.
GENERATIVE AI
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Reflection Pattern: AI Self-Critique and Iterative Refinement Cycle
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Reflection Pattern In this pattern, the AI “reviews” its own work—spotting mistakes and iterating until the final outcome is polished. Think of it like a self-critique cycle where the model refines each draft before presenting the result!
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Top 5 Agentic AI Design Patterns Clearly Explained
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Top 5 Agentic AI design patterns, clearly explained (with visuals):
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Reference Framework on When and How to Use AI Today
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a simple reference framework on when and how to use AI today with almost a million views already, one of the most viewed posts in recent days on
@LinkedIn -
o3-mini Developer Features Match o1-mini Capabilities
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Yes, o3-mini will have the same developer features as o1-mini—including function calling and structured outputs.
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OpenAI o3-mini: Fast, Smart Model Available in API Soon
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OpenAI o3-mini is fast and incredibly smart. Developers, you’ll be able to build with it in the API on day one—coming soon!
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Google Agentspace: Bringing AI Agents to Enterprises
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Bringing AI Agents to Enterprises with Google Agentspace | Google Cloud Blog https://
bit.ly/3ZTZ1pg
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
LLMs Drive Innovation in Healthcare, Finance, Education
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6/ Why This Book Is a Game-Changer
LLMs are driving innovation in: Healthcare Finance Education
This book equips you to lead the AI revolution with tools, insights, and strategies for real-world impact. -
Scaling LLMs: Long-Sequence Modeling and Distributed Training
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3/ Chapter 2: Generative Models Learn how LLMs are scaled for massive datasets
Discover long-sequence modeling & distributed training
Decode the scaling laws behind state-of-the-art models Build systems that go BIG! -
Pre-training Foundations: Self-supervised Learning and LLM Architectures
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2/ Chapter 1: Pre-training Foundations
Demystify self-supervised learning
Explore architectures like encoder-decoder models
See how pre-training powers scalability in AI Master the backbone of LLMs like BERT!