The hottest Python Substack posts right now

And their main takeaways
Category
Top Technology Topics

Issue #59

Infra Weekly Newsletter • 9 implied HN points • 21 Aug 23
🕹 Technology Python
  1. OpenTF Manifesto aims to challenge HashiCorp's recent license change.
  2. Monti Ransomware resurfaces with new Linux variant and evasion tactics.
  3. OpenMandriva Lx 23.08 released featuring Linux Kernel 6.4 and Mesa 23.2.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Once a Maintainer: Ralf Gommers

Once a Maintainer • 5 implied HN points • 15 Mar 24
🕹 Technology Python
  1. NumPy released its first major version upgrade in 16 years - NumPy 2.0.
  2. Open source maintainers often start as volunteers and gradually get more involved over time.
  3. Maintaining a widely-used open source project involves balancing stability with evolving the project for user needs like GPU support.

Segmentation Tools Prizes ($35,000 total)

Vesuvius Challenge • 7 implied HN points • 21 Apr 23
🕹 Technology Python
  1. Segmentation Tools Prizes offer $35,000 total in prizes for tools to help with segmentation of scrolls.
  2. Community members are actively working on projects related to scroll data analysis and segmentation.
  3. Tools like Volume Cartographer and VolumeAnnotate are recommended for segmentation tasks.

Bloom Filters and Beyond: An Illustrated Introduction and Implementation

Confessions of a Code Addict • 5 HN points • 05 Sep 23
🕹 Technology Python
  1. Bloom filters are efficient data structures for quick searches in large datasets and minimize memory usage, with a probabilistic approach to determining membership
  2. Bloom filters use hash functions and bit vectors to store data item membership information while conserving memory by not storing actual items
  3. Counting Bloom Filters are an extension that allow item deletion but come with weaknesses such as handling hash collisions and counter overflow, providing an advanced data handling tool

Week #6 - Python #2

Judson’s Substack • 5 implied HN points • 03 Jul 23
🕹 Technology Python
  1. Object-Oriented Programming (OOP) is like creating blueprints and instances that can be reused.
  2. In Python, you can create classes with attributes like shape and flavor, and then create instances like my_cookie or your_cookie.
  3. The concept is similar to the Aristotelian distinction between universal (class/blueprint) and particulars (instances).

Laziness is a Superpower

!important • 3 HN points • 01 Nov 23
🕹 Technology Python
  1. Laziness can be a superpower, leading to finding clever solutions to avoid tedious tasks.
  2. Using Python and XML extraction, the author automated the process of extracting code snippets from a book.
  3. By leveraging existing elements in the document structure, the author efficiently organized and named thousands of code snippets.

Once a Maintainer: David Wobrock

Once a Maintainer • 2 HN points • 20 Feb 24
🕹 Technology Python
  1. David Wobrock got into programming due to his parents being involved in meteorology and him tinkering with terminal commands from an early age.
  2. Wobrock's journey into open source started during his studies, with his first major contribution being a Python plugin for Visual Studio.
  3. In the Django community, the maintenance work involves a core team, the Django Software Foundation, technical boards, and security boards, showcasing a structured and collaborative approach.

Building the machine learning interface

The Palindrome • 2 implied HN points • 22 Jan 24
🕹 Technology Python
  1. Building a modular interface is crucial as machine learning models complexity increases.
  2. Transitioning from procedural to object-oriented programming can greatly enhance understanding and performance in machine learning.
  3. Good design is essential in setting the framework for machine learning models, drawing inspiration from PyTorch and scikit-learn.

Malloy's Near Term Roadmap

Making Things • 2 implied HN points • 15 Jun 23
🕹 Technology Python
  1. Introducing MalloySQL, a new file type that combines Malloy and SQL for data transformation.
  2. Malloy simplifies Window Functions in SQL, making complex calculations easier to understand.
  3. Malloy offers a standard data function library that operates consistently across different SQL engines.

The Atlantis (Old 9:30 Club) Spotify Playlist Automation

Expand Mapping with Mike Morrow • 0 implied HN points • 05 Apr 23
🕹 Technology Python
  1. The author automated the process of creating a Spotify playlist featuring artists from a new music venue lineup in DC.
  2. They used Spotipy, a Python wrapper for the Spotify Web API, to compile songs from each artist's featured playlist.
  3. The biggest challenge faced during the process was getting the proper access code to work with the Spotify Web API.

Hello! Welcome to Overflow!

Overflow • 0 implied HN points • 28 Apr 23
🕹 Technology Python
  1. Rupesh Mishra is a software engineer with 8 years of experience working with Python, Django, PostgreSQL, MongoDB, AWS, Docker, and Kubernetes.
  2. Rupesh writes articles explaining complex technical concepts like memory management and object instantiation in Python in a simple and easy-to-understand manner.
  3. Rupesh's newsletter covers topics like Python, Docker, Kubernetes, MongoDB, PostgreSQL, Architecture Design, and Microservices, offering a course on Microservices, Docker, and Kubernetes.

Web Scraping from 0 to hero: our first scraper with Microsoft Playwright

The Web Scraping Club • 0 implied HN points • 04 Feb 24
🕹 Technology Python
  1. The web scraping course provided by The Web Scraping Club is always free and appreciated if you want to subscribe with a paid plan.
  2. The choice between using Scrapy and Playwright for web scraping depends on factors like anti-bot protection and content loading.
  3. Setting up the environment for building a web scraper with Playwright involves installing Python, Playwright, and browser binaries.

[Zero To Python] Day 4 - Faking The Screens

Brian Knapp’s Newsletter • 0 implied HN points • 08 Mar 23
🕹 Technology Python
  1. Faking screens in software development involves creating realistic-looking interfaces without complex functionality.
  2. Starting ugly in programming means quickly putting together something intentionally messy to learn and iterate faster.
  3. Use print statements and simple design elements to fake screens in your programming projects.

Redis-driven Dataflow for Clickstream Aggregation

Bytewax • 0 implied HN points • 19 Oct 23
🕹 Technology Python
  1. Bytewax framework strikes a balance between being user-friendly without hiding underlying mechanisms.
  2. When writing custom connectors with Bytewax, focus on transforming messages in the `next_batch` method and delegate other processing to the dataflow.
  3. Consider the partitioned nature of inputs and utilize `list_parts` and `build_part` methods for handling multiple data streams in Bytewax.

Once a Maintainer: Will McGugan

Once a Maintainer • 0 implied HN points • 09 Feb 24
🕹 Technology Python
  1. Will McGugan started programming with a ZX Spectrum in the 80s and later worked on games.
  2. He learned graphics programming from books and built his skills without a strong local community for support.
  3. Creating the popular rich library led to the development of the Textualize application platform for building terminal and web apps.

Code vs. Cells

Reflective Software Engineering • 0 implied HN points • 08 Jun 23
🕹 Technology Python
  1. Modeling everyday problems using test-driven development with a Python tool instead of spreadsheets can lead to better results and easier adaptability.
  2. Creating simple Python tools with scripting languages can automate mundane tasks, improve problem-solving skills, and potentially lead to open-source contributions.
  3. Writing code can be enjoyable and effective in automating repetitive tasks, enhancing problem-solving abilities, and potentially growing into valuable tools for others.

So you want to code a convolution in Python - a bite-sized image processing with kernel

Curiosity-driven AI/ML Research Engineering • 0 implied HN points • 16 Feb 24
🕹 Technology Python
  1. Images are represented as pixels, each containing information about red, green, and blue colors (RGB) within the range of 0 to 255.
  2. Implementing a convolution in Python involves using NumPy arrays and Pillow to manipulate images effectively.
  3. Convolution implementation requires traversing the image pixel by pixel, extracting image slices, computing new pixel values using kernels, and ensuring to handle all three color channels in the output.

Switching Gears To AI Agents

Eddie's startup voyage • 0 implied HN points • 15 Apr 24
🕹 Technology Python
  1. Eddie is shifting focus to AI Agents after leaving his previous company, EnVsion, and finding a passion for AI technology.
  2. AI Agents in agentic systems are AI models tasked with specific roles and responsibilities, contributing to the overall goal.
  3. Eddie is excited about building products with AI Agents, currently developing a Python AI Agent library for further learning and exploration.

Data Doc: How GPT-4 Aced My First-Year Python Coding Assignment

Data at Depth • 0 implied HN points • 13 Nov 23
🕹 Technology Python
  1. GPT-4 has revolutionized the way Computer Science professors deliver and evaluate quality curriculum.
  2. There is a need for a re-design in Computer Science curriculum and evaluation based on advancements like GPT-4.
  3. The integration of advanced technologies like GPT-4 can significantly impact the educational landscape, requiring adaptations in teaching methods.

GPT-4 Code Interpreter: Your Magic Wand for Instant Python Data Visuals

Data at Depth • 0 implied HN points • 24 Jul 23
🕹 Technology Python
  1. GPT-4 Code Interpreter generates data visualization code instantly, allowing users to upload a data file, clean it, load it into a data frame, and display the results.
  2. Users can try GPT-4 Code Interpreter with a 7-day free trial by subscribing to Data at Depth.
  3. John Loewen's post explores a case study example using UN population projection data, showcasing the features and capabilities of the GPT-4 Code Interpreter.