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Linux kernel maintainer Greg Kroah-Hartman has released a talk examining how large language models affect software security, particularly for open-source projects like the Linux kernel. The discussion covers both the risks LLMs introduce into code review and vulnerability handling, and their potential as tools for maintainers. It is drawing attention from developers weighing the trustworthiness of AI-assisted code.
- 2Redis creator launches ds4 for running LLMs locally●From the creator of Redis; run LLM locally with ds4
Salvatore Sanfilippo, the creator of Redis, has released ds4, a tool for running large language models on local machines. The project, hosted at dwarfstar.sh, is drawing attention among developers interested in local AI inference, many of whom are following the author's move from databases into the AI tooling space.
- 3Strata launches semantic layer that can refuse LLM requests●Show HN: Strata – an expressive semantic layer that can say no to your LLM
A tool called Strata has been launched, described as an expressive semantic layer that can say no to a large language model. The pitch is that it sits between an LLM and a company's data, allowing the model to be blocked from answering queries it should not handle. Discussion is centred on how such a layer could make AI assistants safer and more reliable when working with structured data.
- 4Janus brings GGUF model support to GPUs via Vulkan●Show HN: Janus – Go binary that runs GGUF models via Vulkan on AMD/Intel/Nvidia
A developer has released Janus, an open-source tool written in Go that runs GGUF language models on AMD, Intel and Nvidia graphics cards using Vulkan. Distributed as a single binary, it removes the need for platform-specific builds or CUDA, letting users deploy local AI models across mixed GPU hardware.