Meet uv – A Blazingly Fast, All‑in‑One Python Package Manager In my last post I dove into Poetry , one of the best‑loved modern packaging tools. However, Poetry is just one piece of an toolkit: we still reach for pip to install packages, virtualenv to isolate them, pyenv to juggle Python versions, and maybe Pipenv or pip‑tools for lock‑files. Each solves its own niche, yet hopping between them adds friction. uv removes that friction.

If you’re working with Git and Vim, vim-fugitive is an essential plugin that transforms your editor into a full-fledged Git interface. Here’s how I use Fugitive to review, stage, and commit changes—without ever leaving Vim. Browsing Git History and Logs First Before jumping into edits, it’s often useful to understand the file’s history or recent project changes. :Git log — shows the project’s commit history in reverse chronological order :0Gllog — shows the history of the current file To explore who changed what in a file:

DeepSeek ’s latest moves have sent ripples through the AI community. Not only has it marked the beginning of a new era in artificial intelligence, but it has also made significant contributions to the open-source AI landscape. Their engineering techniques behind DeepSeek are truly impressive, and their reports are quite enjoyable. However, understanding their core ideas can be challenging and demands a substantial amount of effort. At the forefront of this innovation is DeepSeek-R1, a model that built upon the foundation established by preceding projects such as DeepSeek Coder, Math, MoE, and notably, the DeepSeek-V3 model.

Introduction When it comes to writing clean, maintainable, and scalable Python code, design matters. As your projects grow, you’ll often find yourself needing to enforce structure, ensure consistency, and promote reusability. This is where Python’s Abstract Base Classes (ABCs) and Protocols come into play—two powerful features that help you design better software. Abstract classes act as blueprints for other classes, allowing you to define methods that must be implemented by any subclass.

Setting Up DL Experiment Environments A Challenge for Arch Linux Users If you’ve ever tried to set up a new experiment environment for deep learning on Arch Linux, you’re probably familiar with the challenges involved. Arch Linux, renowned for its rolling-release model and cutting-edge updates, provides unparalleled flexibility and control over your system. However, this same flexibility can often lead to headaches when setting up complex environments for machine learning or deep learning experiments.

For the past few months, I’ve been working on an exciting internal project at my company: taking users’ documents and running them through LLM APIs to translate and summarize their content, somewhat similar to DeepL . The output is a collection of translated documents, each overlaid with the newly translated text. Our goal is to provide a stable service that can handle large files efficiently for thousands of employees at Samsung—no small task!