LLM Agents in Production: Architectures, Challenges, and Best Practices – ZenML Blog https://
bit.ly/4gGcPtW
#AI #MachineLearning #DeepLearning #LLMs #DataScience
AGENTS
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LLM Agents Production: Architectures, Challenges, Best Practices
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Fireside Chat on LLM-Based Customer Support Agent Systems
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If you love diving deep into the technical details of highly complex, continually improving LLM-based systems… then boy do I have an event for you! Next Tuesday, in SF, fireside chat with Decagon – one of the leading customer support agent builders https://
lu.ma/w7y0bqwr -
Chaining Multiple AI Models for Optimized Workflow Performance
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@RodrigoLiang shared with @jeremyakahn of @FortuneMagazine his insights on #AI: The next phase involves chaining multiple models to create seamless workflows
Our chips are 100x faster, with 1/10th the power
In workflows with multiple models, delays add up Read more -
Build Beautiful AI Apps with 21st.dev and Replit Agent
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Need a beautiful design for your next Replit app?
— Replit ⠕ (@Replit) 30 janvier 2025
Head to 21st.dev and:
– Find a design
– Copy the prompt into Replit Agent
– Iterate and add Replit’s built-in tools and databases
– Deploy your app to the public
– Profit
Share what you build and tag us! pic.twitter.com/PoEREiNtveNeed a beautiful design for your next Replit app? Head to 21st.dev and: – Find a design
– Copy the prompt into Replit Agent
– Iterate and add Replit’s built-in tools and databases
– Deploy your app to the public
– Profit Share what you build and tag us! -
LLM Agents Production: Architectures Challenges Best Practices
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LLM Agents in Production: Architectures, Challenges, and Best Practices – ZenML Blog https://
bit.ly/4gGcPtW
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
Local AI Models: Internet Search Capabilities Explained
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Does it means that the model will not search on internet if used locally on your data. Or is it still possible ?
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BAIR RAIL Lab AI Research Led by Sergey Levine
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Work from BAIR researchers at RAIL lab led by @svlevine
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