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Tim Berners-Lee Says AI Will Not Destroy the Web
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Subsea Cable Investment Set To Double As Tech Giants Accelerate AI Buildout
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[$] Magic kernel functions for BPF
When programs written in BPF (the kernel's hot-loadable virtual-machine bytecode) call kernel functions (kfuncs), it may be useful for those functions to have additional information about the context in which those BPF programs are executing. Rather than requiring it to supply that information, it would be convenient to let the BPF verifier pass that information to the called function automatically. That is already possible, but a recent patch set from Ihor Solodrai would make it more ergonomic. It allows kernel developers to specify that a kfunc should be passed additional parameters inferred by the verifier, invisibly to the BPF program. The discussion included concerns that Solodrai's implementation was unnecessarily complex, however.
Microsoft Bets on Influencers To Close the Gap With ChatGPT
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Pytest 9.0.0 released
Security updates for Monday
Visa and Mastercard Near Deal With Merchants That Would Change Rewards Landscape
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What's the Best Ways for Humans to Explore Space?
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NVIDIA Connects AI GPUs to Early Quantum Processors
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Rust Foundation Announces 'Maintainers Fund' to Ensure Continuity and Support Long-Term Roles
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Nonprofit Releases Thousands of Rare American Music Recordings Online
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What Happens When Humans Start Writing for AI?
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Apple Explores New Satellite Features for Future iPhones
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Kernel prepatch 6.18-rc5
About KeePassXC's code quality control (KeePassXC blog)
The KeePassXC project has recently updated its contribution policy and README to note its policy around contributions created with generative AI tools. The project's use of those tools, such as GitHub Copilot, have raised a number of questions and concerns, which the project has responded to:
There are no AI features inside KeePassXC and there never will be!
The use of Copilot for drafting pull requests is reserved for very simple and focused tasks with a small handful of changes, such as simple bugfixes or UI changes. We use it sparingly (mostly because it's not very good at complex tasks) and only where we think it offers a benefit. Copilot is good at helping developers plan complex changes by reviewing the code base and writing suggestions in markdown, as well as boilerplate tasks such as test development. Copilot can mess up, and we catch that in our standard review process (e.g., by committing a full directory of rubbish, which we identified and fixed). You can review our copilot instructions. Would we ever let AI rewrite our crypto stack? No. Would we let it refactor and rewrite large parts of the application? No. Would we ask it to fix a regression or add more test cases? Yes, sometimes.
Emphasis in the original. See the full post to learn more about the project's processes and pull requests that have been created with AI assistance.
A proposed kernel policy for LLM-generated contributions
Genetically Engineered Babies Are Banned in the US. But Tech Titans Are Trying to Make One Anyway
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Python Foundation Donations Surge After Rejecting Grant - But Sponsorships Still Needed
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Blue Origin Postpones Attempt to Launch Unique ''EscaPADE' Orbiters to Mars
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Blue Origin Livestreams Attempt to Launch Unique ''EscaPADE' Mission to Mars
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