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Litmus helps SREs and developers practice chaos engineering in a Cloud-native way. Chaos experiments are published at the ChaosHub (https://hub.litmuschaos.io). Community notes is at https://hackmd.io/a4Zu_sH4TZGeih-xCimi3Q

Claude skills for LinkedIn. 12 Claude Code and Codex skills that write human-sounding LinkedIn posts, craft comments that get noticed, analyze your feed, and build a publishing cadence, all from your terminal. Content engineering by Creative Content Crafts. MIT.

The Rocq Prover is an interactive theorem prover, or proof assistant. It provides a formal language to write mathematical definitions, executable algorithms and theorems together with an environment for semi-interactive development of machine-checked proofs.

TranslatedNext generation Electron build tooling based on Vite. A new generation of Electron development and build tool that supports source code protection

SkiaSharp is a cross-platform 2D graphics API for .NET platforms based on Google's Skia Graphics Library. It provides a comprehensive 2D API that can be used across mobile, server and desktop models to render images.

Fawkes, privacy preserving tool against facial recognition systems. More info at https://sandlab.cs.uchicago.edu/fawkes

  • privacy-protection
  • privacy-enhancing-technologies
  • face-recognition
  • adversarial-machine-learning

Phan is a static analyzer for PHP. Phan prefers to avoid false-positives and attempts to prove incorrectness rather than correctness.

Automatic HTTPS for any Go program: fully-managed TLS certificate issuance and renewal

  • https
  • acme
  • letsencrypt
  • golang

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.