Learn how a global interdealer broker built an automated architecture documentation pipeline on Amazon Bedrock AgentCore that analyzes .NET code bases,…
Category: AI
US Department of Justice backs fair use for AI training in landmark copyright case
In the class-action lawsuit involving The New York Times, the US Department of Justice argues that training AI models on copyrighted text qualifies as…
Meet Switchyard: A Rust Proxy and Library That Routes and Translates LLM Traffic Across OpenAI and Anthropic APIs
NVIDIA has released Switchyard, an Apache-2.0 Rust proxy and library for LLM traffic. It decodes requests into provider-neutral types, routes them with…
Auditing Harness Tampering in Self-Improving Agents
arXiv:2609.00069v1 Announce Type: cross Abstract: Self-improving agents iteratively modify their own harness to push the frontier of their performance.…
A Closer Look at the AI Adoption Stages Behind Enterprise AI Success
Every enterprise moves through the same AI adoption stages, whether it plans them or not. The path runs from scattered tool use to first pilots, then into…
KItCAT: Knowledge Injection via Input Corruption for Auto-regressive Training
arXiv:2609.00082v1 Announce Type: cross Abstract: LLMs acquire vast amounts of knowledge during pre-training, but often lack the specialized knowledge…
Modernizing and scaling support operations with generative AI on AWS
Learn how to build a generative AI-based support operations platform on AWS that converts training videos into structured SOPs, applies…
AutoXRD: Autonomous LLM Agents and Comprehensive Evaluation for Powder Diffraction Analysis
arXiv:2609.00070v1 Announce Type: cross Abstract: Powder X-ray diffraction (XRD) is central to materials characterization, yet reliable end-to-end…
How an AWS team detects dashboard content failures at scale using Amazon Bedrock
Business intelligence dashboards can fail silently, showing blank, stale, or wrong data even when every infrastructure monitor reports healthy. Learn how…
RW-LoRA: Communication-Efficient Decentralized LoRA Fine-Tuning via Random Walks
arXiv:2609.00078v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning methods such as LoRA have become a standard approach for adapting large…