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Anthropic has published new research measuring how well large language models can develop working software exploits, and the findings are drawing serious attention from the security community. The work, hosted on Anthropic's red-teaming platform, attempts to quantify a risk that many researchers have warned about for years — that AI systems could meaningfully lower the barrier to cyberattack. How you measure that capability, and what you do with the answer, turns out to be a genuinely hard problem.
On a very different note, AWS is reportedly preparing to add Elon Musk's Grok model to its Bedrock platform, which offers enterprises access to a range of AI systems. The catch is that enterprise customers don't appear to want it. One security lead at a major bank reportedly described Grok as the "revenge porn edgelord LLM" and said the answer was a hard no. It raises a fair question about whether cloud platforms are curating for quality or simply collecting inventory.
And rounding out today's broadcast, a GitHub project is making the rounds that claims to tune CPU-only inference for Qwen three, a large open-weight model, using an IBM Quantum sampling loop. The details are sparse and the claims are ambitious, but it speaks to a broader moment where developers are hunting for any edge they can find to run powerful models on ordinary hardware.
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