There’s a downside to too much convenience: it harms our bodies
There is a seductive fantasy being floated by AI executives that all the efficiency their products will bring us will lead to humans finally returning to their essential, best selves. Picture it: when this day arrives, we’ll spring from our chairs, push aside our keyboards and, supposedly, do all things we’ve been meaning to do: hike, cook and finally take a pilates class.
It’s true – AI has already taken some workday drudgery, such as reading and writing contracts, presentations and quarterly reports, off some people’s plates. Within a few years, we’re told, a team of invisible digital assistants will take over mundane domestic chores too: making medical appointments, renewing our car insurance and planning. The vision is enticing: finally, the moment when we can stop switching-switching-switching between screens and devices, put our health first and flourish. Unfortunately, if the history of innovation teaches us anythin
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.
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
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.
