Join our waitlist! http://
ai21.com/maestro
LLMS
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Anthropic releases token-saving updates and cache management
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Anthropic token-saving updates Yesterday Anthropic released multiple token-saving updates, including simpler cache management and token-efficient tool use (and a new built-in tool)! We've updated our documentation to showcase these features, link in !
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Panel on AI Application Building: Infrastructure and Agentic Workflows
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Panel with Replit’s Michele Catasta, Stanford’s @percyliang , Nebius’ Roman Chernin and Hugging Face’s @Thom_Wolf
, moderated by @lmoroney
, on application building. Lots of tips on infra, open source, agentic workflows, benchmarking and code gen. Particular interest in how to take -
Meta’s Chaya Nayak discusses open Llama models and best practices
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Meta’s Chaya Nayak talking about the open Llama models and Llama Stack, and best practices for using them. Great tips and I saw lots of people pulling out phones to take pictures of her slides!
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Long-term Agentic Memory with LangGraph Course
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New @DeepLearningAI course!! Long-term Agentic Memory with LangGraph Covers different types of memory (semantic, episodic, and procedural) and how to add those to a real world application We've been super interested in memory, this course shares some of our learnings
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Claude Celebrates Second Anniversary Since Public Announcement
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Two years ago today we announced Claude to the world. Happy second birthday, Claude!
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Building Agentic Memory with LangGraph Course
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💫 New @DeepLearningAI course: Long-term Agentic Memory with LangGraph
— LangChain (@LangChain) 14 mars 2025
Learn about how to build an agent with long term memory from @AndrewYNg and @hwchase17 (@LangChainAI CEO). In this course, you'll learn to:
✔️ How to apply three types of memory — semantic, episodic, and… pic.twitter.com/C1HJOrPWMlNew @DeepLearningAI course: Long-term Agentic Memory with LangGraph Learn about how to build an agent with long term memory from @AndrewYNg and @hwchase17 (
@langchain CEO). In this course, you'll learn to: How to apply three types of memory — semantic, episodic, and -
Gemini 2.0 Models Performance Comparison Analysis
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Any of the Gemini 2.0 models near the blue line to anything to the left.
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Intent Analysis Boosts LLM Accuracy Through ARR Method
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Can intent analysis boost LLM accuracy? ARR—analyzing intent, retrieving data, and reasoning—offers a fresh take on AI responses. Read more: https://
bit.ly/4hF3ZwB #AI #MachineLearning -
LLM Distillation: Optimizing AI Model Development Techniques
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Which of these is most important to you when building AI models? Find out how LLM distillation optimizes them all in our latest blog, where we dive into how the technique streamlines AI model development https://
snorkel.ai/blog/llm-disti
llation-demystified-a-complete-guide/
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