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Red Hat and Nvidia expand their partnership to secure autonomous AI agents

Red Hat Inc. today expanded its collaboration with Nvidia Corp. to help enterprises secure autonomous artificial intelligence agents, govern access to AI models and deploy workloads on Nvidia’s upcoming Vera Rubin data center infrastructure. Announced at Nvidia’s GTC event in Berlin, the updates center on Red Hat AI Factory with Nvidia, combining agent runtime controls, […] The post Red Hat and…

Red Hat and Nvidia expand their partnership to secure autonomous AI agents

We haven't written up this one. SiliconANGLE has the full story — the link below goes straight to it.

Read the original at siliconangle.com →

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4 Accessibility Mistakes AI Coding Assistants Make, and How to Catch Them

AI coding assistants are good at producing working components quickly. They learned from a huge amount of public code, and public code is full of accessibility mistakes — so the same mistakes come…

  • Clickable divs lacking keyboard support
  • Inaccurate aria-labels replacing visible text
  • Placeholders as input field labels

A quick site hardening pass: email DNS, AI-crawler rules and security headers

Three things go quietly wrong on a lot of small sites: mail that fails authentication, a robots.txt that says something different from what you meant, and missing security headers.

  • Email DNS should include SPF, DKIM, DMARC, and MX records
  • Review robots.txt for AI crawler rules like OpenAI's GPTBot
  • Implement security headers like Strict-Transport-Security

Four big local LLMs on one RTX 5090 and 128 GB of RAM, benchmarked side by side

One RTX 5090, 128 GB of RAM, and four open models from 125B to 748B parameters. Over the past weeks I got each of them running on the same workstation, on three different runtimes.

  • All four models are mixture-of-experts, allowing them to run on a single GPU with limited VRAM.
  • Performance measured by decoding speed, time to first token, prefill speed, and drafting quality.

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