thread

AI-Powered Jupyter Notebook built using React.

https://github.com/squaredtechnologies/thread

piku

The tiniest PaaS you've ever seen. Piku allows you to do git push deployments to your own servers.

https://github.com/piku/piku

Alga

CLI for remote controlling LG webOS TVs.

https://github.com/Tenzer/alga

React + Django + Ninja: Full-stack app in 15 mins

We'll build a React and Django app fast, using Django Ninja - aiming for under 15 minutes.

https://www.youtube.com/watch?v=X1lDWzvIXRs

A Beautiful and Timely Python Multi-page Streamlit Application

Showcasing historical summer Olympic data through multiple data visualizations.

https://johnloewen.substack.com/p/a-beautiful-and-timely-python-multi

pdf-to-podcast

Convert any PDF into a podcast episode!

https://github.com/knowsuchagency/pdf-to-podcast

Joining Strings in Python: A "Huh" Moment

The article discusses the performance implications of using generators vs list comprehensions when joining strings with str.join() in Python. Contrary to expectations, using a generator expression with str.join() is slower than a list comprehension because the CPython implementation converts the generator to a list internally before joining the strings, negating the memory efficiency ben...

https://berglyd.net/blog/2024/06/joining-strings-in-python/

Ruff: Internals of a Rust-backed Python linter-formatter - Part 1

Ruff is an extremely fast Python linter written in Rust, deriving its speed from parsing Python code and implementing linting rules natively in Rust rather than Python. The article provides insights into Ruff's internals, including its initial implementation using RustPython's parser, the evolution to a hand-written recursive descent parser, and its caching and parallelization mechanisms.

https://compileralchemy.substack.com/p/ruff-internals-of-a-rust-backed-python

Amphi

Open-Source Python ETL. Extract, transform and load data with low-code. Generate native Python code you can deploy anywhere.

https://github.com/amphi-ai/amphi-etl

Introduction to Machine Learning: Why There Are No Programmed Answers

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Introduction to Machine Learning: Why There Are No Programmed ...

https://pyimagesearch.com/2024/05/06/introduction-to-machine-learning-why-there-are-no-programmed-answers/

Boosting AI with Python: Using Click, Jinja2, and GPT Libraries

This talk explores enhancing AI projects using Python with tools like Click CLI for command-line interfaces, Jinja2 for dynamic content generation, and GPT libraries for language model integration. Attendees will see a practical project demonstration and gain skills to build functional AI applications, along with opportunities to connect with other Python developers.

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

A beautiful Python monstrosity

Creating performance tests for Python Morsels exercises is a frequent annoyance

I loathe writing ...

https://treyhunner.com/2024/06/a-beautiful-python-monstrosity/

Semantic search with Django, PostgreSQL, & pgvector

In this talk we will see how to add semantic search functionality to an existing Python-based web project, in particular Django, with data storage on PostgreSQL.

https://www.youtube.com/watch?v=4hl8LpDKRMw

textual-serve

Serve Textual apps locally.

https://github.com/Textualize/textual-serve

AI-Math-Notes

AI Math Notes is an interactive drawing application that allows users to draw mathematical equations on a canvas. Once an equation is drawn, the application uses a multimodal LLM to calculate and display the result next to the equals sign.

https://github.com/ayushpai/AI-Math-Notes

NumPy 2.0.0

NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs.

https://github.com/numpy/numpy/releases/tag/v2.0.0

django-render

Build fast, user-friendly applications with Django and React.

https://github.com/kaedroho/django-render

Parsing Python ASTs 20x Faster with Rust

The post describes how the integration of Rust can significantly speed up the parsing of Python Abstract Syntax Trees (ASTs). By rewriting critical components in Rust, the process can be made up to 20 times faster compared to the original Python implementation, offering substantial performance improvements for developers working with ASTs in Python.

https://www.gauge.sh/blog/parsing-python-asts-20x-faster-with-rust

Do These 5 Things if You Don’t Want to Write Crappy Code

In this video, I'll share 5 key things you should do to avoid writing crappy code. These tips, drawn from my own experience, will help you write clean code.

https://www.youtube.com/watch?v=vhdUyGs_f6c