Threat actors are continuing their onslaught against software supply chains, now with malware named after death itself.
The newly-discovered Hades Campaign is a “highly sophisticated” supply chain compromise that targets Python developer environments and runs as soon as infected packages are imported. It uses the popular Bun toolkit to silently execute multi-layer payloads that can extract sensitive data, move laterally across compromised systems, exploit common security frameworks, and even hijack AI gatekeeper analyzer systems via adversarial prompt injection.
Notably, the campaign exploited the popular C++ library ensmallen, as well as packages in the computational biology, bioinformatics, and genotype-phenotype analysis ecosystems.
The most novel thing about this malware is its combination of advanced tactics, noted David Shipley of Beauceron Security. He noted that we’ve seen memory-focused malware, we’ve seen attacks that attempt to defuse large language model (LLM) powered analy
Of all the reasons Python is a hit with developers, one of the biggest is its broad and ever-expanding selection of third-party packages. Convenient toolkits for everything from ingesting and formatting data to high-speed math and machine learning are just an import or pip install away.
But what happens when those packages don’t play nice with each other? What do you do when different Python projects need competing or incompatible versions of the same add-ons? That’s where Python virtual environments come into play.
What are Python virtual environments?
A virtual environment is a way to have multiple, parallel instances of the Python interpreter, each with different sets of packages and different configurations. Each virtual environment contains a discrete copy of the Python interpreter, including copies of its support utilities (such as the package manager pip).
The packages installed in each virtual environment are seen only in that virtual environment and no other. Even large, compl
Explore the best Python web development repositories for building APIs, full-stack web apps, dashboards, machine learning demos, internal tools, and interactive Python-based user interfaces.
In this tutorial, we implement a hands-on workflow for NVIDIA cuTile Python, a tile-based GPU programming interface for CUDA-style kernels in Python. We prepare a Colab-friendly environment and check GPU, driver, CUDA, and cuTile availability before running kernels. We then build tiled vector addition, matrix addition, and matrix multiplication, keeping a PyTorch fallback so the notebook stays executable. We validate correctness against PyTorch and benchmark median runtimes at every stage.
The post NVIDIA cuTile Python Tutorial: Building Tiled GPU Kernels for Vector Addition, Matrix Addition, and Matrix Multiplication in Colab appeared first on MarkTechPost.
This is how LLMs are used today to increase precision in recommendation systems
The post Increase Recommendation Systems’ Precision with LLMs, Using Python appeared first on Towards Data Science.
Google released the Colab CLI, letting developers and AI agents run local code on remote Colab GPU and TPU runtime
The post Google’s New Colab CLI Lets Developers and AI Agents Run Python on Remote Colab GPUs and TPUs From the Terminal appeared first on MarkTechPost.
I got tired of copying files into an AI chat just to get feedback. So I built a pure Python MCP server that gives AI tools direct access to my local project—no frameworks, no dependencies. It runs over stdio for local use and switches to HTTP/SSE for concurrent clients with a single flag. The result: 5 clients, under 50ms, and a design that stays simple without sacrificing capability.
The post My AI Couldn’t See My Files — I Built a Zero-Dependency MCP Server appeared first on Towards Data Science.
