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News & links about Python programming.
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A theme editor for JupyterLab

This post introduces a new theme editor extension for JupyterLab that allows users to customize the look and feel of the JupyterLab interface. The theme editor extension is a simple interface that allows users to select colors, font family, and font size, and to preview the changes in real time.

https://blog.jupyter.org/a-theme-editor-for-jupyterlab-8f08ab62894d
Rift

An AI-native language server for your personal AI software engineer.

https://github.com/morph-labs/rift
Building Search DSLs with Django

The article explains how to build a custom search DSL (Domain Specific Language) for Django projects. The DSL allows users to search for content in a more natural way, using keywords and phrases instead of complex SQL queries.

https://danlamanna.com/posts/building-search-dsls-with-django/
Playing with Streamlit and LLMs.

This post describes how to use Streamlit to build a simple interface for interacting with large language models (LLMs). It also includes code examples that show how to use Streamlit to display text, images, and tables, and to interact with LLMs through prompts and queries.

https://lethain.com/streamlit-llms/
Remote Interactive Debugging of Python Applications Running in Kubernetes

In this tutorial we will create a setup for remote debugging of Python applications running in Kubernetes, which will allow you to set breakpoints, step through code, and interactively debug your applications without any change to your code or deployment.

https://martinheinz.dev/blog/99
The Annotated S4

This post provides an overview of the Structured State Space for Sequence Modeling (S4) architecture which is a new approach to very long-range sequence modeling tasks for vision, language, and audio, showing a capacity to capture dependencies over tens of thousands of steps. It also includes code implementations that allow readers to experiment with the S4 architecture.

https://srush.github.io/annotated-s4
Designing Pythonic library APIs

The article discusses some principles for designing good Python library APIs, including structure, naming, error handling, type annotations, and more. The author argues that Python's flexibility can be a double-edged sword, and that it's important to design APIs that are easy to use and understand.

https://benhoyt.com/writings/python-api-design/
Geospatial Data in your Graph

In this stream we explore some techniques for working with geospatial data in Neo4j. We will cover some basic spatial Cypher functions, spatial search, routing algorithms, and different methods of importing geospatial data into Neo4j.

https://www.youtube.com/watch?v=djMsdSxvd2E