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News & links about Python programming.
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LangGraph Complete Course for Beginners – Complex AI Agents with Python

This video course introduces LangGraph, a Python library for building advanced conversational AI workflows using a graph-based approach. It guides viewers through designing, implementing, and managing scalable dialogue systems, covering both theoretical concepts and hands-on coding exercises.

https://www.youtube.com/watch?v=jGg_1h0qzaM
Template Strings in Python 3.14: Structured Interpolation

Python 3.14 introduces template strings (t""), which return structured Template objects instead of plain strings, enabling full inspection and control of interpolated expressions. This allows safer, customizable rendering for use cases like shell commands, HTML output, logging, and config generation—offering a powerful alternative to f-strings when you need pre-render control.

https://blog.abhimanyu-saharan.com/posts/template-strings-in-python-3-14-structured-interpolation
Dagster - Data Orchestration and Pipelines with Python & DAGs

This video is a practical introduction to using Dagster for Python-based data orchestration, covering core concepts like assets, definitions, scheduling, and the Dagster UI. Through hands-on examples—including building a pipeline with Polars and DuckDB—the tutorial demonstrates how to define, manage, and automate complex data workflows in modern data engineering.

https://www.youtube.com/watch?v=sKqDq4TFbmY
sre-bot

A Google Agent Development Kit (ADK) powered assistant designed to help Site Reliability Engineers (SREs) with operational tasks and monitoring, particularly focused on Kubernetes interactions.

https://github.com/serkanh/sre-bot
I don't like NumPy

The author, once a fan of NumPy, now criticizes its complexity and opacity when working with high-dimensional arrays, arguing that common operations often become unreadable and error-prone due to confusing broadcasting, indexing, and function conventions. While NumPy excels at simple cases, the post contends that its design choices—especially around implicit broadcasting and lack of expl...

https://dynomight.net/numpy/
Turning Data into Insight

The article demonstrates how to build a flexible, modern data lakehouse architecture using open-source tools like MinIO, Apache Iceberg, Airflow, dbt, Spark, Pandera, and Superset. By integrating these technologies with Docker for easy deployment, it shows how to orchestrate robust data pipelines, ensure data quality, and enable scalable analytics from raw ingestion to interactive dashboards.

https://towardsdev.com/turning-data-into-insight-flexible-lakehouse-with-minio-iceberg-airflow-dbt-spark-pandera-409d036e5542
Machine Learning Prototyping with DuckDB and scikit-learn

In this post, we prototype a machine learning workflow using DuckDB for data handling and scikit-learn for modeling.

https://duckdb.org/2025/05/16/scikit-learn-duckdb.html
nlweb

Building conversational interfaces for websites is hard. NLWeb seeks to make it easy for websites to do this. And since NLWeb natively speaks MCP, the same natural language APIs can be used both by humans and agents.

https://github.com/microsoft/nlweb
Flowfile

Flowfile is a visual ETL tool combining drag-and-drop workflows with the speed of Polars dataframes. Build and analyze data pipelines without code. Perfect for analysts and engineers needing fast, intuitive data processing. Designed to run locally or deploy to production environments.

https://github.com/Edwardvaneechoud/Flowfile/