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Pick the right generative AI customization approach on AWS with an 8-step decision framework, from prompt engineering and RAG to fine-tuning, continued pre-training, and Amazon Nova Forge. Start simple and escalate only when you must.
The Swedish automaker gives its small SUV a sympathetic makeover.
Foundation models made catalog-wide demand forecasting easy; the hard part is now acting on the forecast. This post builds a closed detect-decide-act loop on Databricks and Amazon Quick that reconciles demand surges against live supplier availability and places replenishment orders unattended, escalating to a human only when no supplier can cover a surge.
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The Problem: Tracking Request Latency Without Slowing Things Down For a cloud data warehouse, performance is not just about average query time. What often matters more is tail latency, predictability, and the ability to pinpoint where things go wrong. In a cloud-native data warehouse like Databend, a single request may pass through multiple stages: SQL planning, distributed execution, remote storage, Raft logging, and state machine apply. Tail latency in any one of these stages can affect the qu
AI keeps showing up in the wrong places. Attackers are using it to speed up exploits, test defenses, and automate more of the job. Some models are also crossing lines on their own. That is not a great combination. The rest of the week is more familiar: old bugs still working, fresh exploit chains, exposed systems, weak defaults, and simple paths that should have been harder to abuse. A few of
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