Threat Actors Expand Abuse of Microsoft Visual Studio Code

Jamf Threat Labs uncovers North Korean hackers exploiting VS Code to deploy backdoor malware via malicious Git repositories in the Contagious Interview campaign

Most Technical Problems Are Really People Problems

  I once worked at a company which had an enormous amount of technical debt - millions of lines of code, no unit tests, and products using l...

BSI entdeckt schwere Sicherheitslücken in Mastodon, einige kleinere in Matrix

Im Rahmen einer Open-Source-Codeanalyse hat das BSI den Messenger Matrix und die Social-Media-Anwendung Mastodon auf kritische Schwachstellen untersucht.

Reusing Computation in Text-to-Image Diffusion for Efficient Generation of Image Sets

Text-to-image diffusion models enable high-quality image generation but are computationally expensive. While prior work optimizes per-inference efficiency, we explore an orthogonal approach: reducing redundancy across correlated prompts. Our method leverages the coarse-to-fine nature of diffusion models, where early denoising steps capture shared structures among similar prompts. We propose a training-free approach that clusters prompts based on semantic similarity and shares computation in early diffusion steps. Experiments show that for models trained conditioned on image embeddings, our approach significantly reduces compute cost while improving image quality. By leveraging UnClip's text-to-image prior, we enhance diffusion step allocation for greater efficiency. Our method seamlessly integrates with existing pipelines, scales with prompt sets, and reduces the environmental and financial burden of large-scale text-to-image generation. Project page: https://ddecatur.github.io/hierarchical-diffusion/

The Pitfalls of Copy-Paste Coding – Ashley Walker's Blog