Anyone else secure some profit today? submitted by /u/90Skill10Luck [link] [Kommentare]
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Active AUR malicious packages incident 2026-06-12 - Campbell Jones We are currently experiencing a high volume of malicious package adoptions and updates in the Arch User Repository. We are actively working to track down existing malicious commits and attempting to prevent additional malicious commits from being pushed. While this is happening, and while we work to create a more permanent solution, users may see issues with the following: Creating new accounts on the AUR Pushing package updates Adopting or creating new packages We continue to encourage all users of AUR packages to review all PKGBUILD and install script changes when updating, especially during this time. If you notice suspicious commits to a package that you use, please reach out to Arch staff via the aur-general mailing list with more information. Copyright © 2002-2026 Judd Vinet, Aaron Griffin and Levente Polyák. The Arch Linux name and logo are recognized trademarks. Some rights reserved. The registered trademark Linux® is used pursuant to a sublicense from LMI, the exclusive licensee of Linus Torvalds, owner of the mark on a world-wide basis.
A fully featured Markdown renderer for React
A Holistic Benchmark for Software Engineering
A direct optimization test was conducted on a neural network for MNIST image classification. The network features a 784-32-10 architecture with a total of 25,450 continuous parameters (weights and biases). Instead of employing backpropagation or gradient information, the parameters were optimized using MDP, a Derivative-Free Optimization method. The objective was to directly minimize the Cross-Entropy Loss on a subset of 5,000 training images. Final evaluations were performed on independent validation and test sets. In the best run, MDP achieved an objective loss of 0.0004083, a validation accuracy of 93.7%, and a test accuracy of 93.4%. These results outperform the baseline established by Adam, which achieved a final loss of 0.002945, a validation accuracy of 91.8%, and a test accuracy of 91.7% using the same network architecture. Notably, this optimization was successfully performed over a 25,450-dimensional search space, achieving convergence across 1,000,000 function evaluations without relying on gradients or population-based methods. The code for this test, along with other Python implementation examples, is available in the examples folder of the official project repository: https://github.com/misa-hdez/sgo-lab submitted by /u/Mis4318 [link] [Kommentare]