Here's all the info to use Anthropic's Claude 3.7 Sonnet with Amazon Bedrock: how to use “extended thinking”, tool use with reasoning, updates on computer use, thinking blocks, and more.

https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-37.html

#AWS #AI #GenAI #SDK #Bedrock

I updated the multimodal chat to support Anthropic's Claude 3.7 Sonnet with extended thinking mode. Note that by default it uses 16K of budget tokens for reasoning. Edit config.ini to disable/enable model reasoning and set a different budget.

https://github.com/danilop/multimodal-chat

#AWS #AI #GenAI #Bedrock #Python

Inspired by the latest AI Demo Days in London, I built this:

Run any AWS Lambda function as a Large Language Model (LLM) tool without code changes using Anthropic's Model Control Protocol (MCP)

https://github.com/danilop/MCP2Lambda

#AI #GenAI #MCP #AWS #Serverless

Nice video from Mike on the new Anthropic's Claude 3.7 Sonnet model and how to use extended thinking mode in Amazon Bedrock 👉 Including with code and how to set up budget tokens for thinking https://www.youtube.com/watch?v=B_iaicsxCYc

#AWS #AI #GenAI #Python #SDK

Evaluating AI Agents – Nice short course to learn how to set up evaluations and add observability to an AI agent for insights and debugging

https://www.deeplearning.ai/short-courses/evaluating-ai-agents/

I went down the rabbit hole of high dimensional orthogonal vectors analysis:

https://github.com/danilop/high-dim-orthogonal-vectors-analysis

With higher dimensions, most random vectors are almost (for example, within 1 degree of tolerance) orthogonal between each other.

Because vectors are used as machine learning embeddings, and cross product is often used to compare the similarity of vector embeddings, this results show that higher dimensions provide exponentially more expressiveness for the concepts linked to those embeddings.