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ImaginAIry imagines & edits images from text inputs

This is a Pythonic wrapper around stable diffusion with image editing by InstructPix2Pix. The four images featured below (top) are generated by the following command:

imagine "a scenic landscape" "a photo of a dog" "photo of a fruit bowl" "portrait photo of a freckled woman"

Then they are edited (bottom) with the following commands:

>> aimg edit scenic_landscape.jpg "make it winter" --prompt-strength 20
>> aimg edit dog.jpg "make the dog red" --prompt-strength 5
>> aimg edit bowl_of_fruit.jpg "replace the fruit with strawberries"
>> aimg edit freckled_woman.jpg "make her a cyborg" --prompt-strength 13
ImaginAIry imagines & edits images from text inputs


A library for building apps with LLMs through composability

Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. But using these LLMs in isolation is often not enough to create a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.

This library is aimed at assisting in the development of those types of applications.

LangChain is designed to help with prompts, chains (sequences of calls), data augmented generation, agents, memory & evaluation tasks.

Martin Heinz

Python magic methods you haven’t heard about

Python’s magic methods - also known as dunder (double underscore) methods - can be used to implement a lot of cool things. Most of the time we use them for simple stuff, such as constructors (__init__), string representation (__str__, __repr__) or arithmetic operators (__add__/__mul__). There are however many more magic methods which you probably haven’t heard about and in this article we will explore all of them (even the hidden and undocumented).

Bill Prin

Why I ditched Django for NextJS

If you’re feeling the FOMO of JavaScript or you’re writing “spaghetti code” just to do something a NextJS component would do out of the box, then read this post from Bill Prin on why he moved from Django to NextJS.

The summary is that using a language like Python or Ruby for a significant web project has increasingly gotten less reasonable over time to the point where now, in 2022, it’s getting hard to justify. By not keeping your web stack in pure Javascript, you are making your life unnecessarily difficult (as usual, we’ll include languages like TypeScript as part of the JavaScript ecosystem). You will almost certainly invest a bunch of time-solving problems that would be automatically solved for you if you just stuck with JavaScript.

I will provide specific examples of solving problems using Django that would have been trivially solved in NextJS.

He goes on to share two reasons why you should use Python or Ruby for web projects in 2022.

You’re working on an existing project that hasn’t been migrated yet or is not worth migrating.
You are already a master of a Python or Ruby web stack, and you need to implement a new project as soon as possible, and you don’t have time to learn a better stack.

The Changelog The Changelog #511

The terminal as a platform

This week we’re talking with Will McGugan about using the terminal to not just build software, but also to deliver software. Will is a few months into his journey of building Textualize, a company he started around his open source projects Textual and Rich. When combined Textual and Rich give you a Python framework to build beautiful full-featured TUIs for the Terminal. We talk with Will about his big idea of the terminal as a platform, how he got here from first principles, what it takes to build Textual apps and whether or not they can replace not so good web admins, building, launching, and distributing Textual apps, why Python was his choiice of language, the big picture and business model behind Textualize, and why he’s building this as open source and in public.

Martin Heinz

Python CLI tricks that don't require any code whatsoever

Out-of-the-box, the Python standard library ships with many great libraries, some of which provide CLIs, allowing us to do many cool things directly from terminal without needing to even open a .py file.

This includes things like starting a webserver, opening a browser, parsing JSON files, benchmarking programs and many more, all of which we will explore in this article.


A tool for refurbishing and modernizing Python codebases

Point Refurb at your Python code to see how bad good it is. Here’s the author’s motivation:

I love doing code reviews: I like taking something and making it better, faster, more elegant, and so on. Lots of static analysis tools already exist, but none of them seem to be focused on making code more elegant, more readable, or more modern. That is where Refurb comes in.


A statement-based scheduling framework for Python

Unlike the alternatives, Rocketry’s scheduler is statement-based. Rocketry natively supports the same scheduling strategies as the other options, including cron and task pipelining, but it can also be arbitrarily extended using custom scheduling statements.

That’s pretty useful! I used to struggle to shove conditionals in to my cron jobs. Example time:

from rocketry.conds import daily, time_of_week
from pathlib import Path

def file_exists(file):
    return Path(file).exists()

@app.task(daily.after("08:00") & file_exists("myfile.csv"))
def do_work():


Create rich Python apps in the browser with HTML

PyScript is a Pythonic alternative to Scratch, JSFiddle, and other “easy to use” programming frameworks, with the goal of making the web a friendly, hackable place where anyone can author interesting and interactive applications.

Lots of code examples of various apps (clock, repl, todos, etc) here. I love the why behind this effort:

As an industry, we have focussed on making the impossible possible, rather than focussing on making the possible accessible to all.

They want to bring programming to the 99%. Somebody’s gotta do it…

Martin Heinz

Here's why you should be using Python's walrus operator

The assignment operator - or walrus operator as we all know it - is a feature that’s been in Python for a while now (since 3.8), yet it’s still somewhat controversial and many people have unfounded hate for it.

In this article I will try to convince you that the walrus operator really is a good addition to the language and that if you use it properly, then it can help you make your code more concise and readable.

Sean Moriarity

Elixir versus Python for data science

Sean Moriarity:

A common argument against using Nx for a new machine learning project is its perceived lack of a library/support for some common task that is available in Python. In this post, I’ll do my best to highlight areas where this is not the case, and compare and contrast Elixir projects with their Python equivalents. Additionally, I’ll discuss areas where the Elixir ecosystem still comes up short, and using Nx for a new project might not be the best idea.

Sean is a prominent member of the Elixir community, so that’s the perspective on display here, but it’s a thorough and well-reasoned comparison. He concludes:

While there are still many gaps in the Elixir ecosystem, the progress over the last year has been rapid. Almost every library I’ve mentioned in this post is less than two years old, and I suspect there will be many more projects to fill some of the gaps I’ve mentioned in the coming months.


Python 3.11 is up to 10-60% faster than Python 3.10

One beautiful thing about open source software: how hundreds of thousands (millions?) of people’s Python apps got faster while they were sound asleep. From 3.11’s release notes:

CPython 3.11 is on average 25% faster than CPython 3.10 when measured with the pyperformance benchmark suite, and compiled with GCC on Ubuntu Linux. Depending on your workload, the speedup could be up to 10-60% faster.

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