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The funding for the CVE database runs out on April 16th, and MITRE itself will no longer be issuing CVEs. CNAs will still be issuing CVEs for now. It’s also mentioned downthread that the funding renewal has come down to the wire before. Comments

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There are a ton of llm/AI tools for nvim now, but what’s everyone here using? I know of:

• codecompanion.nvim https://github.com/olimorris/codecompanion.nvim

• ChatGPT.nvim https://github.com/jackMort/ChatGPT.nvim

• llm.nvim https://github.com/Kurama622/llm.nvim

… and several others - but I’m curious what y’all are using. I’m especially interested in any that incorporate multiple models - e.g., llama4 for simple autocompletes, o4 for design, etc.

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High performance, strongly consistent applications are beginning to require scalable sub-microsecond clock synchronization. State-of-the-art clock synchronization focuses on improving accuracy or frequency of synchronization, ignoring the properties of the local clock: lost of connectivity to the remote clock means synchronization failure. Our system, Graham, leverages the fact that the local clock still keeps time even when connectivity is lost and builds a failure model using the characteristics of the local clock and the desired synchronization accuracy. Graham characterizes the local clock using commodity sensors present in nearly every server and leverages this data to further improve clock accuracy, increasing the tolerance of Graham to failures. Graham reduces the clock drift of a commodity server by up to 2000×, reducing the maximum assumed drift in most situations from 200ppm to 100ppb. Comments

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