practicalainotes
Hands-on notes on using AI tools for real work: what holds up, what breaks, and what costs more than it saves.
Bayer built an Agentic Decision Engine on Amazon Bedrock AgentCore that cuts workflow‑troubleshooting from an hour to under ten minutes – here’s how it works and what to try today.
Hands‑on look at Amazon SageMaker AI's Multi‑Turn Reinforcement Learning, costs, performance boost on Wordle, and how to try it today.
IskayPet reduced employee search time from minutes to 7 seconds using Amazon Bedrock Managed Knowledge Bases, avoiding vector DB ops and keeping SharePoint permissions.
We test three ways to train Qwen3 on a single AWS Trainium trn2.48xlarge, compare the setups and show what you can try today.
Learn how to hook Smartsheet’s MCP Server into Amazon Quick or Kiro, the trade‑offs, and a step‑by‑step test you can run today.
Amazon Bedrock now forces Claude to emit schema‑compliant JSON, cutting parsing errors and retries for data extraction, classification and agent tools.
A hands‑on look at Meta’s Muse Spark Open in Amazon SageMaker JumpStart, covering architecture, cost trade‑offs, and a step‑by‑step guide to try it today.
A hands‑on look at deploying Reflection Beam, the 501B open‑weight coding model, with SageMaker JumpStart and HyperPod, and what trade‑offs to expect.
A practical look at Amazon Bedrock AgentCore’s nine‑stage evaluation framework and how it changes the way we keep AI agents trustworthy in production.
We built and ran the Amazon Bedrock AgentCore demo that creates PPT decks from enterprise docs in under 90 seconds. Here’s what worked, what didn’t, and a quick way to try it yourself.
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