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The Drop Times conducted an in-depth analysis of Drupal users across the world's largest cities, focusing on locations with populations over one million. Using LinkedIn data, this study identifies key urban centers with significant Drupal communities, highlighting 81 cities with more than 500 "Drupal People." This analysis aims to support local community growth and enhance global Drupal engagement. Discover the cities where Drupal is thriving and learn how to get involved in organizing and promoting local events.

The Drop Times aims to enhance and track the global growth of the Drupal community. By utilizing LinkedIn's public search capabilities, we can estimate the geographical spread of Drupal users. Despite certain limitations, this method provides a practical approach to gauge community growth and plan targeted events. Learn how we navigate data challenges and leverage LinkedIn for meaningful insights.

Hi, it's week 2 and my last scheduled week for research. I spent this week looking over more of Krita's code base and pixel-perfect algorithm. There was a large focus on looking at examples of how pixel-perfect is achieved (specifically in Aseprite)....

Today I’d like to share a new set of Human Interface Guidelines (HIG) for KDE’s software that I’ve written, replacing the old one. This work was done over the past several months in consultation with many KDE designers and developers; the merge request says 42 people were CCd, and almost 500 comments were posted during the 2+ month review process.

You can read the document at https://develop.kde.org/hig.

Wait, why does anyone need this at all?

Strictly speaking, we don’t need a HIG. Developers with an excellent eye for design can usually produce good results even without one. But for most of us, it’s useful to have guidelines to follow so you don’t have to think about the design side too much. Even for design-oriented developers, it’s useful to be able to have a quick reference for common patterns and rules.

And having a HIG is a good idea for any organization that wants for its software to share a similar look-and-feel and mode of operation, or any small individual developer who wants their software to match fit in well with a specific target platform (ours, in this case). When software fits into the visual and functional conventions of the platform with which it wants to integrate most closely, people already familiar with that platform learn to use it faster and like using it more. It feels at home to them. Comfortable, familiar, appealing.

Having and following a HIG makes all of this possible. And besides, we already had one anyway! But it needed major work.

Ok, so what was wrong with the old one?

To be honest, it wasn’t very good. I say this as a contributor to the old one! But it suffered from multiple problems that demanded a total rewrite:

• It was hard to find anything because the basic structure was confused, with excessive segmentation.
• Content was severely outdated and reflected design paradigms that KDE developers haven’t used in years, and that the industry in general has moved away from. It was also missing a lot of information relevant to how we write apps today.
• Far too wordy; hard to find the actionable recommendations amid all the filler text!
• Full of old mockups and screenshots that weren’t even needed to illustrate the point, and which would become out of date again quickly if we took the time to update them.
• Many basic style errors: misspellings, incorrect English grammar, awkwardly phrased sentences, etc.

For these reasons, very few KDE developers or designers still paid any attention to the old HIG, besides of a small number of pages that did still have a reasonably high threshold of information density. And for the same reason, the HIG got very few contributions. I think when something is in a sufficiently poor state, it can even discourage people from contributing, because any small improvement seems like a raindrop in the ocean of urgent need. It’s demoralizing.

How is this new one any better?

The new one was informed by research into how KDE apps look and work today. Some inspiration for structure and organization was taken from the ElementaryOS, GNOME, Apple, and Google HIGs — but not content! The content is all KDE.

The new HIG had the following design goals:

• Make 100% of the content actionable. No filler text, no rambling philosophy, no redundancy — just actionable recommendations for how to design your app. Short and sweet, and to the point!
• Reflect how KDE designs software today. For example we’ve seen a huge growth in new Kirigami-based apps using QtQuick as their UI technology (including some older KDE apps getting their UIs ported to Kirigami), and Kirigami is the UI platform that’s improving most quickly in KDE. So I made the decision to focus the recommendations on Kirigami as a platform. There’s still some content about QtWidgets, but it’s a Kirigami-focused document.
• Have flat navigation, so it’s easy to find everything. No deeply nested sub-pages and awkward categorization that makes you wonder why a page is in this category and not that one.
• Get the new content into good enough shape that people feel comfortable contributing. Hopefully people will start to submit tweaks, improvements, bug-fixes, and maintenance!

Is it 100% finished?

No, absolutely not! The HIG is intended to be a living document that evolves over time to better describe KDE’s design goals and software development paradigms.

Like any rewrite, there are bound to be rough edges and omissions compared to the old version. Maybe I missed a piece of useful information in the old HIG that had been buried somewhere but retained some value. Maybe there’s low-hanging fruit for improvement. Help out by contributing! It’s just some text with markdown styling; contributing to the HIG is waaaaaaay easier then contributing code.

In particular, there are some known omissions and limitations that you can help with:

• It needs more images. Multiple pages have embedded TODO comments where it would be nice to add a relevant image to illustrate a point.
• There’s nothing at all about visual style in general, simply delegating those decisions to the system’s active theme. To a certain extent this is intentional because we support visual theming, but it would also be good to add content about KDE’s default Breeze theme. I deliberately omitted this for now because a bunch of KDE’s designers are working on a fancy new style, and developers are working on a whole new theming engine to apply it. So the world around the HIG is in a state of flux. But if anyone wanted to add more about the current Breeze style anyway, that would be nice. Just know that it may be replaced once the new style is released.
• …With one exception: the entire section on Breeze icon design. This was kept from the old HIG content because it remains useful. Still, the presentation could use an overhaul to improve information density and collapse needless sub-pages into one parent page.
• The recommendation to use Kirigami is awkward for powerful apps that use features not currently available in Kirigami-based apps, such as dockable sidebars/panes, customizable toolbars, customizable keyboard shortcuts, and more. If you’re a developer, help add these features!

Ok cool, how do I contribute?

That’s great, it’s super duper appreciated! See these two links to learn how to contribute changes:

• https://develop.kde.org/hig/#contributing
• https://invent.kde.org/documentation/develop-kde-org/-/blob/master/README.md

If that’s too scary, we can help you get set up! Contact folks and we’ll see what we can do.

Still too scary? Then you can donate to KDE. Our budget is tiny, so your money genuinely does have an impact!

Today marks the release of  KDE Stopmotion 0.8.7!

About Stopmotion

Stopmotion is a Free Open Source application to create stop-motion animations. It helps you capture and edit the frames of your animation and export them as a single file.

Direct capture from webcams, MiniDV cameras, and DSLR cameras. It offers onion-skinning, import images from disk, and time lapse photography. Stopmotion supports multiple scenes, frame editing, basic sound track, animation playback at different frame rates, and GIMP integration for image. Movies can be exported to a file and to Cinelerra frame lists.

Technically, it is a C++ / Qt application with optional dependencies to camera capture libraries.

Changes in release 0.8.7

This release comes with no new features, but improvements to the project itself.

Changes

• The project is now officially called to KDE Stopmotion. The former name Linux Stopmotion is no longer used.
• Support for qmake has been removed. Use CMake instead.

Features

• Port serialization to libarchive. libtar is abandoned. (thanks to Bastian Germann)

Bugfixes

• The .sto files miss the tar trailer. (#16, thanks to Bastian Germann for providing a fix)

Improvements

• Use pkg-config to find dependencies vorbisfile and xml2 (thanks to Barak Pearlmutter)
• Remove code that relies on deprecations in Qt 5; this is a preparation to move to Qt 6.
  

Future plans

• Transition from Qt 5 to version 6. I am stuck with my port as QAudioDeviceInfo that was dropped in Qt 6. I need some help to port Stopmotion to the new way to handle audio with Qt 6 / Qt Mulimedia.
• We should integrate better to KDE's tech stack: Internationalization, using KDE libraries, update and reformat documentation.
  

Get involved!

If you are interested, give Stopmotion a try. Reach out to our mailing list stopmotion@kde.org or have a look into our project. Share your ideas or get involved!

Drupal's migration system allows the use of a number of different plugins to perform source, processing, and destination functions. 

Process plugins are responsible for copying and sometimes manipulating data into the destination. There are a number of different process plugins that allow you to get data in different ways and and apply it to your destination fields.

Both the core Migrate module and the excellent Migrate Plus module contain a number of different process plugins that you can use to process your data in different ways.

Out of the box, the default process plugin is the get plugin, which can be used like this in your migration scripts.

destination_field: plugin: get source: source_field

This is often shortened to the following, which has exactly the same functionality.

destination_field: source_field

Most of the time you will want to avoid creating custom plugins, but sometimes your migration requirements will necessitate their use. You might find that your source data is very messy and needs to be cleaned up before importing it into the site. Process plugins are a really good way of doing this, but it is essential that you write tests to cover every situation that you might encounter. 

In this article we will look at two custom migrate process plugins that are built in different ways and how to test them. This will dive into some concepts around Drupal plugin management, dependency injection, as well as unit testing and data providers with PHPUnit.

First, let's look at the migration script that we will be using in this article. All of the source code for this migration example is available on GitHub.

Read more

I’m a heavy user of Firefox profiles. Apart from using different profiles for different activities, I also have a few extra profiles that all run in the Default activity.

This means that I need to have different icons shown in Plasma’s panel in order to be able to easily differentiate which profile a window belongs to.

Sure, I use the tasks applet which shows the window title instead of the icon-only one (I prefer usability to minimalism), but still, it isn’t enough as sometimes the active tab in a Firefox window might not have the most informative title.

Plasma seems to rely on the application name and the window class when choosing the icon it will show in the panel. Which means that, by default, all Firefox instances end up having the same icon.

Librewolf with a custom profile icon

Fortunately, Firefox allows you to specify the window class it should use through command line arguments.

firefox -P ProfileName --class WindowClassName

And, to connect a launcher to a specific window class, you just need to add the following line to the .desktop file:

StartupWMClass=WindowClassName

So, in order to have a nicely supported Firefox profile, you can create a launcher with a desktop file similar to the following:

[Desktop entry] Exec=firefox -P SocialSites --class FirefoxSocialSites Icon=user-available-symbolic StartupWMClass=FirefoxSocialSites

It also works with Firefox derivatives such as Librewolf (which can be seen in the screenshot above) and others.

You can support my work on Patreon, or you can get my book Functional Programming in C++ at Manning if you're into that sort of thing. -->

Why write an update?
I wrote a blog post on Software Development with Generative AI last year, which was questioning the approach of the current AI software authoring assistants. I believe the bigger picture holds true that to fully utilize AI to write software, will require an entirely different approach. Changing the job of a software developer in a far more radical manner and perhaps making many of today's software languages redundant.

However I also raised the issue that I found the current generative AI helpers utility questionable for seasoned developers:
"The generative AI can help students and others who are learning to code in a computer language, but can it actually improve productivity for real, full time, developers who are fluent in that language?
I think that question is currently debatable... (but it is improving rapidly) ... We may reach that point within a year or two"

Well it hasn't been a year or two, just 6 months. But I believe the addition of the Chat window to CoPilot and an improvement in the accuracy of its models has already made a significant difference. 

On balance I would now say that even a fluent programmer may get some benefits from its use. Given the speed of improvement it is likely that all commercial programming will use an AI assistant within a few years. 

To delay the inevitable and not embed it in to your work process is like King Canute commanding the sea to retreat. There are increasing numbers of alternatives available too. However as the market leader I believe it is worth going in to slightly more depth as to the current state of play with CoPilot.

Copilot Features

The new Chat window within your IDE gives you a context sensitive version of Copilot ChatGPT that can act as a pair programmer and code reviewer for your work. 

If you have enabled auto-complete then you instigate that usage by writing functional comments, ie prompts then tabbing out to accept the suggestions it responds with.

To override these prompts, you instead can use dot and get real code completion options (as long as your IDE is configured correctly). Since code completion has your whole codebase as context, it complements CoPilot reasonably well. But whilst the code completion is always correct, CoPilot is less so, probably more like 75% now compared to its initial release level of 50%

It takes some time to improve the quality of your prompting. An effort must be made to eradicate any nuance, assumption, implication or subtlety from your English. Precise mechanical instructions are what are required. However its language model will have learnt common usage. So if you ask it to sort out your variables it will understand that you mean replace all hardcoded values in the body of your code with a set of constants defined at the top, explain that is what it thinks you mean and give you the code that does that.

You can ask it anything about the usage of the language you are working in, how something should be coded, alternatives to that etc. So taking a pair programming approach and explaining what you are about to code and why to CoPilot chat as you go,  can be very useful. Given rubber duck programming is useful, having an intelligent duck that can answer back ... is clearly more so. 

It excels as a learning tool, largely replacing Googling and Stack Overflow with an IDE embedded search for learning new languages. But even for a language you know well, there can be details and nuances of usage you have overlooked or changes in syntactic standards with new releases you have missed.

You can also ask it to give your file a code review. Where it will list out a series of suggested refactors that it judges would improve it.

Copilot Limitations

Currently however there are many limitations, understanding them, helps you know how to use CoPilot and not turn it off in frustration at its failings! 

The most important one is that CoPilot's context is extremely limited. There is no RAG enhancement yet, no learning from your usage. It may seem to improve with usage, but that is just you getting better at using it. It does not learn about you and your coding style as you might expect, given a dumb shopping site does that as standard.

It does not create a user context for you and populate it with your codebase. It simply grabs the content of the currently edited file and the Chat prompt text and the language version for the session as a big query. The same for the auto-suggestion. But here the chat text is from the comments or doc strings on the lines preceding. 

Posting the lot to a fixed CoPilot LLM that is some months out of date. Although apparently it has weekly updates from continuous retraining. 

This total lack of context can mean the only way you can get CoPilot to suggest what you actually want is to write very detailed prompts. It is often simpler to just cut and paste example code as comments into the file - please rewrite blah like this ... paste example. Since only if its in the file or latest Chat question will it get posted to inform the response.

At the time of writing CoPilot is due to at least retain and learn from Chat window history to extend its context a little. But currently it only knows about the currently open file and latest Chat message. Other providers have tools that do load the whole code base, for example Cody, plus there are open source tools to post more of your code base to ChatGPT or to an open source LLM.

As this blog post update indicates, the whole area is evolving at an extremely rapid pace.

The model it has for a language is fixed and dated. Less so for the core language but for example you may use a newer version of the leading 3rd party Postgres library that came out 2 years ago. But the majority of users are still on the previous one since it is still maintained. Their syntax differs. Copilot may only know the syntax for the old library because that is what it was trained with, even though a later version is being imported in the file, so is in Copilot's limited context. So any chat window or code prompts it suggests will be wrong.

I have yet to find it brings up anything useful that I didn't know about the code when using the code review feature, plus the suggestions can include things that are inapplicable or already applied. But I am sure it would be more useful for learning a new language.

AI prompting and commenting issue

Good practise for software teams around code commenting are that you should NOT stick in functional comments that just explain what the next few lines do.  The team are developers and they can read the code as quickly for its base functionality. Adding lots of functional commenting makes things unclear by excessive verbosity.
It is something that is only done for teaching people how to code in example snippets. It has no place in production code.

Comments should be added to give wider context, caveats, assumptions etc. So commenting is all about explaining the Why, not the How.

Doc strings at the head of methods and packages can contain a summary of what the function does in terms of the codebase. So more functional in orientation, but as a big scale summary. So again they are a What not a How.

It looks like current AI assistants may mess that up. Since they need comments that are basically as close to pseudo code as possible. Adding information about real world issues, roadmap, wider codebase, integration with other services ... ie all the Why is likely to  confuse them and degrade the auto-complete.

Unfortunately code comments are not AI prompts for generating code and vice versa.
Which suggests that you may want to write a temporary prompt as a comment to generate the code, then replace it with a proper comment once it has served its purpose.

Or otherwise introduce a separate form of hideable prompt marked comment that make it clear what is for the AI and what is for the Human!

Alternatively use the chat window for code generation then paste it in.

Copilot Translation

Translation is an area where Copilot can be very beneficial. As a non-native English speaker you can interact with it in your own language for prompting and comments and it will handle that and translate any comments in the file to English if asked to.

Code translation is more problematic, since the whole structure of a program and common libraries can be different. But if the code is doing some very encapsulated common process. For example just maths operations, or file operations. It can extract the comments and prompts and regenerate the code into another language for you.

One can imagine that one day the only language anyone will need will be a very high level, succinct English-like language, eg. Python.
When you want to write in a verbose or low-level language. You just write the simpler prompts in a spoken language, but use Python when it is faster to communicate explicitly than spoken. Since spoken languages are so unsuited to creating machine instructions.
Press a button and Copilot turns the lot into verbose C or Java code with English comments.

No dia 16 agosto é comemorado o aniversário do Projeto Debian, e todos os anos comunidades ao redor do mundo organizam encontros para celebrar esta data.

Chamado de Debian Day (Dia do Debian), o evento sempre conta com uma quantidade expressiva de comunidadades brasileiras organizando atividades nas suas cidades no dia 16 (ou no sábado mais próximo).

Em 2024 o Debian Day celebrará os 31 anos do Projeto Debian e o dia 16 de agosto será numa sexta-feira, por isso provavelmente a maioria das comunidades organizarão suas atividades no sábado, dia 17.

Estamos fazendo uma chamada de organizadores(as) para o Debian Day em 2024. A ideia é reunir, em um grupo no telegram, as pessoas interessadas em coordenar as atividades das suas comunidades locais para trocar experiências, ajudar os(as) novatos(as), e discutir a possibilidade do Projeto Debian ajudar financeiramente as comunidades.

O Debian Day na sua cidade pode ser desde um encontro em uma pizzaria/bar/restaurante para promover a reunião das pessoas, até um evento mais amplo com palestras/oficinas. Então não existe obrigatoriedade sobre como deve ser o encontro, tudo depende do que você e a sua comunidade querem e podem fazer.

Existe a possibilidade de solicitarmos ao líder do projeto Debian para reembolsar algumas despesas. Por exemplo, para produzir adesivos, pagar as pizzas, encomendar um bolo, etc.

Venha fazer parte do grupo Debian Day BR no telegram e discutir as ideias: https://t.me/debian_day_br

Se você topa esse desafio e vai organizar um Debina Day na sua cidade, não deixe de adicionar a sua cidade com as informações necessárias aqui.

No dia 16 agosto é comemorado o aniversário do Projeto Debian, e todos os anos comunidades ao redor do mundo organizam encontros para celebrar esta data.

Chamado de Debian Day (Dia do Debian), o evento sempre conta com uma quantidade expressiva de comunidadades brasileiras organizando atividades nas suas cidades no dia 16 (ou no sábado mais próximo).

Em 2024 o Debian Day celebrará os 31 anos do Projeto Debian e o dia 16 de agosto será numa sexta-feira, por isso provavelmente a maioria das comunidades organizarão suas atividades no sábado, dia 17.

Estamos fazendo uma chamada de organizadores(as) para o Debian Day em 2024. A ideia é reunir, em um grupo no telegram, as pessoas interessadas em coordenar as atividades das suas comunidades locais para trocar experiências, ajudar os(as) novatos(as), e discutir a possibilidade do Projeto Debian ajudar financeiramente as comunidades.

O Debian Day na sua cidade pode ser desde um encontro em uma pizzaria/bar/restaurante para promover a reunião das pessoas, até um evento mais amplo com palestras/oficinas. Então não existe obrigatoriedade sobre como deve ser o encontro, tudo depende do que você e a sua comunidade querem e podem fazer.

Existe a possibilidade de solicitarmos ao líder do projeto Debian para reembolsar algumas despesas. Por exemplo, para produzir adesivos, pagar as pizzas, encomendar um bolo, etc.

Venha fazer parte do grupo Debian Day BR no telegram e discutir as ideias: https://t.me/debian_day_br

Se você topa esse desafio e vai organizar um Debina Day na sua cidade, não deixe de adicionar a sua cidade com as informações necessárias aqui.

The Current State of AI Software GenerationThe user tries to describe what they want generated in terms of a snippet of high level programming language code using standard English. They submit it to the AI tool. So what are they asking the AI to generate and how does it do it?

The high level language

High level programming languages are human languages composed of english and maths symbols designed for the comprehension and composition of precise computer instructions. The language makes no more sense than English to a computer. It has to be compiled or interpreted to computer language for it to run. So it may compile to an intermediate bytecode language and then maybe to human readable assembly language - before final translation into the unreadable machine code that the computer runs.

A programmer learns the high level language and becomes fluent in it. They can read and understand the functionality of that code. With the complexity of the machine specific implementation stripped away.

Leaving just the precise functional maths and english / symbology that describes the computer functionality. They think in that code, in order to write it.
Even then, the majority of a programmers time is spent debugging the high level language - and fixing what they have written to be bug free. Because it is difficult to think clearly in code, pre-determining  all edge cases etc.

Unlike English language, it can succinctly describe computer functionality in a few lines. 

The AI

A detailed English language description of what functionality is required. Plus the name of a high level programming language, are submitted to the AI tool.

It does a search of the web, eg. stack overflow etc. for results for that code language. For Chatbot use (eg. ChatGPT) it applies an English language Large Language Model, LLM (a numeric encoding of learning of the English language) to generate a well phrased aggregation of the most popular results that match the English prompt. 

For software use (eg. CoPilot) it works just the same, but the LLM learns English to high level software language aggregate translation. From code examples data, eg. github, to generate what the code syntax might be to match the English description of it.

Finally it returns an untested snippet of generated high level code.

The Non-Developer

The non-developer pastes it in place and tries to run the program with it in.

They may be able to puzzle out the high level language - but don't naturally think in it, just as people without mathematics skills can only think as far as basic arithmetic and are dyslexic when it comes to complex equations.

It seems to work around 50% of the time. When it fails they, go back to square one and try to rephrase their English prompt. 

They patch together block after block of prompt created generated code. A crazy paving of a program that likely has a number of bugs and inappropriate features in it. But it kind of works, for the non-developer, that is good enough.

The code gets pushed out there with all its imperfections, and starts to populate the web of code data that is used to generate the next AI code snippet.

Or the Developer
The developer reads the code and understands it, determines if it should do what they want. Or if they just want to use some of it as an example.

They cut paste and rewrite it, using it as a hint tool. Or an extension to their IDE's existing auto-code generation tools that work using templated code and language / import library searches.

Hopefully their IDE is set up to clearer distinguish between real code completions and possible generative code completions. Since otherwise the percentage of nonsense code created by the generative AI pollutes the 100% reliability of IDE code completion, and harms productivity.

Then they run their code and debug as usual.

At least 75% of programming time is not on writing code, but on making sure that the high level instructions are exactly correct for generating bug free machine code. So iteratively refining the lines of code. With code a single comma out of place can break the whole program. When language has to be so carefully groomed, succinct minimal language is essential.

For many developers adding an imprecise, non mathematical language, that is entirely unsuited to defining machine code instructions, such as English, to generate such code is problematic. It introduces a whole layer of imprecision, complexity and bugs to the process. Slowing it right down. Along with requiring developers to write a lot lot more sentences (in English) rather than just quickly typing out the succinct lines of Python (or similar) programming language they have in their head.

The generative AI can help students and others who are learning to code in a computer language, but can it actually improve productivity for real, full time, developers who are fluent in that language?

I think that question is currently debatable. Because I believe the goal of adding yet another language to the stack of languages that need to be interpreted for humans authoring computer code, especially one as unsuited as English, is only useful for people who are far from fluent in the software language.

Once we move beyond error prone early releases of LLMs like ChatGPT-4 then tools such as CoPilot may start to become much more effective at authoring software, and actually produce code that is as likely to work first time and have the same amount of bugs as your average software developer's first cut of the code. We may reach that point within a year or two. At which point professional software developer will need to be adept at using it as part of their toolset.

Even so I believe the whole conception of the application of AI to writing software could benefit from more work engaged in a computer centric alternative approach to the current one focussed on generating plausible human language responses. It only dominates because of all the efforts related to NLP and human interaction. But taking that and sticking on to writing human software languages is more about creating a revenue stream than attempting to have AI do the main work of software development.

Until then, AI will never be able to replace me, as a software developer. Only be another IDE tool I need to learn ... in time when it improves sufficiently to increase productivity.

NOTE - June 2024 Update
Having come back to CoPilot 6 months later. I have come to appreciate some of its new features so have added a new blog post that accepts that it now provides utility even for the seasoned programmer.

Another WayCopilot and the like currently use the ChatGPT approach of a Chatbot front end tied to an English language LLM to generate aggregate search engine results in a human language. But there is no domain specific machine learning knowledge about the semantics of the content. So it doesn't understand, and certainly doesn't pre-check the code. Just as ChatGPT doesn't understand the search engine content. Since currently there are no domain specific trained models for the content in the loop. So if asked a question about pharmacy it doesn't plug in one of the AI models that has learnt pharmacy and is used by that industry to aid in the development of medicines. It understands nothing, it is a chatbot, just a constructor of plausible answers based on search popularity.
Similarly CoPilot has learnt how to predict what code somebody might be trying to write, but it hasn't learnt how to code.

This approach cannot lead to AI generating innovative new coding approaches, full self-coding computers, or remove the need for human readable high level programming languages.

There have been experiments with applying test driven development to AI generated code, but I have not heard of serious attempts to address the bigger picture...

• Move all functional code writing to be AI only.
• Remove the need for any high level computer language for humans to gain fluency in.
• Have AI develop software by hundreds of thousands of iterative composition  TDD cycles.
• Parallel refactoring thousands of solutions to arrive at the optimum one.
• Use AI that understands the machine code it is generating by training it on the results of running that code. 
• The ML training cycle must be running code not matching outputs to pre-ranked static result training sets.
• In addition to the static LLM that encodes the learning of machine code authoring, dynamic training cycles should be run as part of the code composition. Task based ephemeral training models.
• Get rid of the wasted effort training AI to understand English, Python, Java, Go or any other existing human language evolved for other tasks.
• Finally we are left with the job of telling the computer what its software should do.
  We do not want to use English for that, its way too verbose and inaccurate, similarly we don't want a full high level programming language to do it. We need a new half way house. A domain specific language (DSL) for defining functionality only, designed for giving software specification's to AI that it can use to generate automated test suites.

Self-Programming Computers

Exploring the last point in more detail...

Create a higher level pseudo-code language for describing the required functionality that is more English readable than even current high level languages such as Python.

Make that functional DSL focus on defining inputs and outputs - not creating the functionality, but creating the black box functional tests that describe what the working code should do.

Maybe add tools for a slightly no-code approach, with visual generators for the language, eg graphical pipeline builder tools. For people who find thinking visually easier than thinking symbolically.

The software creator uses the DSL to create an extensive set of functional definitions for a project.

The DSL language design and evolution is optimised for LLM interpretation.  So it has very tight grammatical and syntactical usage that promote accurate generative outputs.

A new non-developer friendly high level pseudo code language / rigorous AI prompt writing lingo.

Some basic characteristics of the DSL:

1. auto-formatting (like Go) minimizing syntactical variation
2. To quote Python's creator - 'There should be one-- and preferably only one --obvious way to do it.'
   But strictly applied, rather than as a vague principle as Python does
3. unlike any other high level language, the design needs to be optimized only for specifying functionality, a high level templating language from which test suites are generated.
   
4. the language will never be used to implement functionality
5. uses simple english vocabulary and ideally minimal mathematical symbology

These DSL prompts are written with a LLM for the DSL it helps create its own prompts and the code creator uses it to refine all the DSL definitions that specify the full functionality. 

The specification DSL auto generates all the required tests in a low level language.

Since the system should also have a generative AI LLM trained for C or assembly language.
This is what creates the actual functional code by iteratively running and rewriting it against the specification encoded into the tests.

The AI tool then generates the tests for that implementation and uses TDD to generate the actual functional code - eventually the system should improve to a level better than most software developers. The code it writes no longer needs to be read by a human - because a human will be unable to debug it at anything like the speed the AI tool can.

So we use generative AI to do the part of the job that actually takes all the time. Debugging, refactoring and maintaining the code, making sure it really does what is required functionally. Rather than the quick job of writing a first cut of it that might run without crashing.

Most importantly we don't introduce the use of the full English language, the language of Shakespeare, the language of puns, double meanings, multiple interpretations, shades of grey, implied feeling and emotions, into a binary world to which it is entirely unsuited.

Also we don't need English or high level computer languages in the stack of mistranslation at all.
Because we are not training the AI to understand human languages. We are training it to write its own machine code language based on defining what behaviour it should implement.
BDD / TDD generative AI if you like.

Human's no longer learn complex mathematical process based languages that can be translated into machine code. They learn a more generic language for specifying functional behaviour.

This gives more freedom to widen the DSL to mature into a general precise AI prompt language.

Whilst allowing computers to evolve more machine learning driven software architectures that are self maintaining and not so constrained into the models imposed by current human intelligence and coding practise based programming languages.

Could AI could take my job?Perhaps if all of the above were in place, then finally we would arrive at a place where AI could replace traditional software development and high level software languages.
With concerted effort it could be in 10 years, if some big companies put some serious investment in trying to replace traditional software development.
Code monkeys will all be automated. Only software architects would be required and they would use a new functional specification AI prompt language, not a programming language.

Of course if politicians are scared that dumb ChatGPT can already write as good a speech as they can. Plus replicate all the prejudices and errors of its training data and trainers.
Then setting AI free to fully write software, and itself ... will be way more scary in its long term implications.
Meanwhile we are currently at a place where it arguably doesn't even improve productivity for an experienced software developer, only allows non-developers, students and other language newbies to have a go at writing one of the many dialects of human languages, known as computer languages. 

Their mix of math, english, symbols, logic and process may appear more like English than Musical notation or pure maths, but sadly they are no more suited to creation by an English language Chatbot approach.

I built a little Python script called the Floyd-Warshall CSV Generator. It takes a CSV of graph edges as input, and generates a CSV of the edges that are the shortest paths between all pairs of vertices.

The script is a simple wrapper of the SciPy floyd_warshall function, which in turn implements the Floyd-Warshall Algorithm. Hope you find it useful for all your directed (or undirected) weighted graph needs.

Given an input CSV of the following graph edges:

point_a,point_b,cost a,b,5 b,c,8 c,d,23 d,e,6

When the script is called as follows:

floyd-warshall-csv-generator &bsol /path/to/input_data.csv &bsol --vertex-i-column-name point_a &bsol --vertex-j-column-name point_b &bsol --weight-column-name cost &bsol --no-directed &bsol --max-weight 35

It generates an output CSV that looks like this:

point_a,point_b,cost a,b,5.0 a,c,13.0 b,c,8.0 b,d,31.0 c,d,23.0 c,e,29.0 d,e,6.0

That is, it generates all the possible (indirect) paths from one point to all other points, based on the (direct) paths that are already known, with duplicate (undirected) paths filtered out, and with paths whose cost is more than max-weight filtered out.

I wrote this script in order to generate the "all edges" data that's shown in the World Locality Transit Graph, which I'll also be blogging about real soon. Let me know if you put this script to any other interesting uses!

Contrarian view on cursor-based pagination.

GraphQL documentation recommends cursor-based pagination, and it has subsequently become a popular standard.

In general, we’ve found that cursor-based pagination is the most powerful of those designed. Especially if the cursors are opaque, either offset or ID-based pagination can be implemented using cursor-based pagination (by making the cursor the offset or the ID), and using cursors gives additional flexibility if the pagination model changes in the future. As a reminder that the cursors are opaque and that their format should not be relied upon, we suggest base64 encoding them. …

{ hero { name friends(first: 2) { totalCount edges { node { name } cursor } pageInfo { endCursor hasNextPage } } } }

There are several oversights with this well-intentioned advice.

Cursors and state

Cursors imply state, at least they used to. A database cursor is used for iterating over a result set. Meaning it has transactional integrity to pick up where it left off.

The vast majority of GraphQL APIs are inherently stateless. The “cursor” is being decoded as input to a new request, and offers no guarantees. From this observation, the advice falls apart.

The problem with stateless pagination is inconsistency; items may shift, appear, or disappear. Which gives the client the perception of missing or duplicate items. This happens regardless of whether the pagination is offset or ID based. Arguably worse in the case of IDs, since the reference can move arbitrarily or be gone.

Cursors don’t solve the consistency problem; they give the client the false impression of solving the problem.

Opaqueness and compatibility

The claim is that an opaque cursor is compatible across changes. Changed to do what exactly, would be the more relevant question.

Taking a step back, what is the problem being solved here? We assume there is a list of items, with an inherent ordering, and too many to return to the client with acceptable performance.

Given those assumptions, the first obvious step is an optional size limit. That is not in dispute; the disagreement if over the “offset”. A simple and versatile solution is a range filter over whatever field(s) is relevant to ordering. This is not even remotely controversial when the field in question has a name like date. In other words, “pagination” is not necessarily the problem that needs solving.

Range filters with a size limit are sufficient to implement pagination, and new optional filters are always backwards compatible. They also offer the flexibility of search, whereas cursors can only be used iteratively. And what if the client does not want visibility into the range filters? That is exactly what offset is for; offset is a range filter over an implied index field.

There is a reason why the recommendation does not offer a useful example of this supposed compatibility; there isn’t one. The advice is equivocating on the ambiguity of an after: $ID filter. Is the ID field relevant to the ordering?

• If yes, then it is just another range filter
• If no, then it is just another placeholder for index

There is no third case. There is no future secret field that relates to ordering, is relevant to the client, but somehow still opaque to the client.

Stateless pagination is a combination of range filters and size limits. No matter what the input fields are called. A true stateful is cursor is opaque precisely because it does not represent any known field.

Next optimization

The “next” piece of advice is that the cursor implementation should indicate whether another request is worthwhile. Again, in a stateless API, the server can make no such guarantee.

If the server can provide a total count, by all means do so. It solves the “next” problem, and is more generally useful.

If it is not feasible for the server to provide a total count, how is it going to implement whether there are more items? At the data layer, it is going to stop processing at N + 1 items instead of the requested N. The client could do that too. Instead of requesting the next 10, it could go to 11.

Better yet, why stop at the server optimizing for N + 0? If it knows there is just 1 more item, why not go ahead and include that last one too. N + 2 anyone? Obsessing over the last “next” is a pointless micro-optimization, all the more so because it is irrelevant whenever the total count is not coincidentally a multiple of N. If N is arbitrary, then optimizing for a particular residue mod N is clearly arbitrary.

API design

Not only is there no good reason to blindly add opaque cursors, there is also no reason to add range filters before needed. A size limit alone solves the first order of magnitude of performance issues. If a client requests the first 10 items, then needs the next 10, actually pressure test whether it is unreasonable to request the first 20. The advantage is the client then has a consistent snapshot of the first 20 regardless of changes, which could provide a better user experience.

A simple strategy for pagination: start with none. Then proceed to next steps as performance warrants.

1. size limit
2. range filter on known field(s)
3. offset

In the unlikely event your API is stateful, you didn’t need this advice because you already had a cursor. Otherwise, cursors are an overly-complicated useless abstraction.

Note

Open-source efforts around scikit-learn at Inria are spinning off to a new enterprise, Probabl, in charge of sustainable development of a data-science commons.

Contents

• Prelude: funding scikit-learn is hard
• The birth of a new ambition
• Probabl, a mission-driven enterprise
• Probabl is already having an impact
• My position within Probabl, my vested interests
• More to come

Prelude: funding scikit-learn is hard

Scikit-learn is a central software component in today’s machine learning landscape, and it is open source, governed by a community, easy to install, and well documented. It started many years ago as a project that we did on the side, and we were joined by many volunteers, which was key to the success of the project. We soon decided to ensure that scikit-learn was not only a volunteer-based effort. Over more than a decade, I’ve dedicated a lot of energy to this, using a variety of funding mechanisms: first grants (as an academic), then sponsoring and related contracts with various actors.

Digital commons eliminate scarcity and exclusivity

Funding digital commons is really hard. People build fortunes by leveraging competitive advantages, by creating lock-ins, or selling access to data. What makes a great open-source library, as scikit-learn, is exactly what prevents these tricks: we are committed to being independent, easy to use and install, lightweight…

The birth of a new ambition

Scikit-learn is very successful, but it could be more. For instance, it does not facilitate pushing to production as much as tensorflow, which can be served, deployed to android… And scikit-learn is not very visible to top decision makers: it’s not a line on their budget, a brand that they know. As a consequence, it is not reaping the benefit of its success [1].

[1]Many commercial tools are sitting on top of open source software like scikit-learn (splunk, sagemaker, to name only a few), making profits, and not helping in any way the open source world that they build upon. The French government is backing us to push the envelope

3 years ago, the French government challenged us to go further, to consolidate the ecosystem into a consistent data-science commons. The strategic interest of France is to preserve some technological autonomy on data, eg sensitive data. Thus, the government offered us, at Inria, a funding opportunity to go further.

They promised us a lot of money (dozens of millions of Euros), but with a specific mission to develop a sustainable “data-science commons” [2] ecosystem around scikit-learn. I’ll spare you the details of the amount of meetings we had, documents that we wrote, to sketch the outline of the project. I pushed forward a vision of technical components that fit in the broader open-source ecosystem, complementing it.

[2]The letter that we received from the French government specifically defines the objective in these words: “data-science common” (“Communs numériques pour la Science des Données”)

As I moved forward, I faced a difficulty: the French government wanted a sustainability plan, and private investment to back it. To be honest, this is not what I’m good at. François Goupil, the COO of the scikit-learn consortium, was helping me, but we needed more for our ambitions. And this is when we started talking to Yann Lechelle, a tech entrepreneur with an impressive track record interested in the impact of France on the global tech world.

Probabl, a mission-driven enterprise

With Yann, we built a new vision. Our challenge is to be long-term sustainable and virtuous for scikit-learn, its broader ecosystem, and its community. Yann brought in a business point of view, and I tried to bring that of open-source communities beyond probabl [3], for instance avoiding to getting in the way of others building businesses that contribute to scikit-learn. Indeed, we are convinced that having a broad and diverse community around scikit-learn is central to its future.

[3]One of the first things that Probabl did (Guillaume Lemaître, to be specific), was submit a grant application (to the Chang-Zuckenberg Institute), to fund, via NumFocus, a developer employed by Quantsight, with no money transiting via Probabl (one reason being that we have no operations outside of Europe so far).

Our sustainability model is still being finetuned. What I can tell is that it will involve a mix of professional service, support & sponsorship agreement, as well as a product-based offer, where we supplement scikit-learn with enterprise features. Our focus will be on features that are typically not the focus of open-source developers: integration in large structures, such as access control, LDAP connection, regulatory compliance. We will not shoehorn scikit-learn in open core or dual licensing approaches: we want our incentives to be aligned with scikit-learn, and its ecosystem, being as complete as possible.

Foster growth and adoption of our open-source stack

In a sense, our inspiration is that of RedHat, where the growth of the company fosters the growth and adoption of the software (Linux in the case of RedHat), beyond the company, in an ecosystem, and for a wide variety of applications.

Strong growth will mean external capital. To ensure that we do not lose the focus on our mission, building data-science commons, Yann penciled down a specific governance of the company (and then validated it with many people, as we are a spin-off from a governmental organization). The ultimate share structure, and the board, are divided in three electoral colleges: one for outside investors, one for founders and employees, and one for public institutions. This ensures a balance of power that hopefully will keep us aligned to our mission. I think that this structure sends a strong signal that we are not just another for-profit that will go from creating useful tech to dark money-generating patterns.

Probabl is already having an impact

A strong open-source team In February, the whole team developing scikit-learn at Inria moved to Probabl, joined by Adrin Jalali, a Berlin-based core developer of scikit-learn and fairlearn. We’ve been hiring excellent people, and we now have 9 people on open-source (see the Probabl team), spending their time contributing to open source (Jérémie, for instance, has been doing the last releases for scikit-learn).

Fostering an ecosystem Probabl is not only about scikit-learn. We are prioritizing 8 libraries, central to the machine-learning and data science ecosystem: joblib, fairlearn, imbalanced-learn… In general, as we have always done, we will not hesitate contributing to upstream or related projects. Our goal is to have a healthy open-source ecosystem around data-science.

Not only software Not everybody sees the important lines of code. I’ve become increasingly aware of the need to do outreach and communication, to coders, but also to decision makers. At Probabl we dedicate energy to be in business meetings, to participate in the tech narrative, to teach how to best do data science, eg with didactic videos. We’re starting a mentioning program, we’ll be organizing sprints… I am convinced that all this is a useful long-term investment.

My position within Probabl, my vested interests

I am a French civil servant (a researcher at Inria, one of our national research institute). Such a position comes with strong responsibilities to control conflicts of interest. The creation of Probabl underwent strict scrutiny (that took a long long time). I have been recently cleared to take an active role: 10% of my time is allocated to be a scientific and open-source advisor for Probabl.

I am not paid by Probabl. 100% of my salary comes from Inria (and I was not given a raise because of my involvement in Probabl). I do have financial interests as a founder, but given that I have a small active part, I have one of the smallest amount of shares among founders.

My main interest in Probabl is really the success of its mission: the long-term growth of an open-source data-science ecosystem. Spinning-off from Inria actually continues my efforts in this direction, but with more agility and breadth. And having on top of open source a variety of complementary commercial activities makes it stronger, by answering better the needs of some actors.

More to come

There are many things that we are still ironing. Clearing out specific details takes time (for instance, clearing my role took a while). We are still to announce the future of the sponsorship program that we had set up at the Inria foundation. Its mission has been transferred to Probabl. Currently, Probabl’s open source team is ensuring continuity of our work with the existing sponsors. But we will set up broader partnership opportunities, with a similar governance, that enable third-parties to invest in open source on a roadmap decided jointly with the open-source community.

I believe that we need a lot of transparency in how we decide upon priorities in our open source team. Our 2024 priorities for scikit-learn are visible here.

I look forward to when Probabl will start adding value to scikit-learn for enterprises with an offer enriching scikit-learn and the broader open-source ecosystem.

I am acutely aware that good open source is made of communities, and that communities need trust and understanding of big players such as Probabl (well, so far we are not that big). I hope that with time our actions will become easy to read and speak of themselves.

Creating performance tests for Python Morsels exercises is a frequent annoyance

I loathe writing automated tests for performance-related exercises because they’re always flaky. How flaky depends on the exercise, what I’m testing, and the time variability inherent in the particular Python features that a learner might use.

I came up with a solution for flaky tests recently, but it also makes my tests less readable. I then came up with a tool to improve the readability, but that has its own trade-offs.

The code I eventually came up with is a beautiful Python monstrosity.

1 2 3 4 5 6 @attempt_n_times(10) def _(): nonlocal micro_time, tiny_time micro_time = time(micro_numbers) tiny_time = time(tiny_numbers) self.assertLess(tiny_time, micro_time*n)

I’ll explain what that code does, but first let’s talk about why it’s needed.

The flaky performance tests

My flaky performance tests initially looked like this:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 def test_some_test(self): n, m = 2.45, 2.04 micro_time = time(micro_numbers) tiny_time = time(tiny_numbers) self.assertLess(tiny_time, micro_time*n) small_time = time(small_numbers) self.assertLess(small_time, tiny_time*n) self.assertLess(small_time, micro_time*n*m) medium_time = time(medium_numbers) self.assertLess(medium_time, micro_time*n*m*m) self.assertLess(medium_time, tiny_time*n*m) self.assertLess(medium_time, small_time*n)

The first block runs a performance test for the user’s function on a very small list and on a slightly larger list and then asserting that the slightly larger list didn’t take too much longer to run. The next two blocks run the same code on even larger lists and make further assertions about the relative times that the code took to run.

This roughly approximates the time complexity of this code.

Running performance checks in a loop

These performance checks need to:

1. Predictably fail for inefficient solutions
2. Predictably pass for efficient solutions
3. Run fast (within just a few seconds) even when the code is inefficient
4. Avoid the use of threading because they’ll be running on WebAssembly in the browser
5. Run consistently on pretty much any computer

These 5 requirements together have caused me countless headaches. I get the tests passing well, but they don’t always fail when they should. I get the tests failing and passing when they should, but then they’re too slow. And so on…

Notice the n and m factors in the above assertions:

1 self.assertLess(small_time, micro_time*n*m)

If n and m are too big, we’ll get false positives (tests passing when they should fail). If n and m are too small, we’ll get false negatives (tests failing when they should pass).

To avoid both Type I and Type II errors, I decided to keep n and m small but attempt the assertion block multiple times.

Here’s the (far less flaky) revised code:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def test_some_test(self): n, m = 2.45, 2.04 for attempts_left in reversed(range(10)): try: micro_time = time(micro_numbers) tiny_time = time(tiny_numbers) self.assertLess(tiny_time, micro_time*n) break except AssertionError: if attempts_left == 0: raise for attempts_left in reversed(range(5)): try: small_time = time(small_numbers) self.assertLess(small_time, tiny_time*n) self.assertLess(small_time, micro_time*n*m) break except AssertionError: if attempts_left == 0: raise for attempts_left in reversed(range(3)): try: medium_time = time(medium_numbers) self.assertLess(medium_time, micro_time*n*m*m) self.assertLess(medium_time, tiny_time*n*m) self.assertLess(medium_time, small_time*n) break except AssertionError: if attempts_left == 0: raise

The for loop runs the code multiple times, the break statement stops the code as soon as the assertions all pass, and the except and if ensure that any assertion errors are suppressed until/unless we’re on the final iteration of the loop.

Let’s call this a for-try-break-except-if-raise pattern. It’s an absurdly verbose name fitting of absurdly verbose code.

This for-try-break-except-if-raise pattern works pretty well! But it’s not pretty.

Like many programmers, I believe that Don’t Repeat Yourself (DRY) need not apply to tests. Tests are allowed to be repetitive if the verbosity improves readability.

But there is so much noise in that code! I decided that removing some noise might improve readability. So I devised a helper utility to reduce the repetition.

In search of a solution

While pondering the repetitive noise in this code, I wondered what Python features I could use to abstract away this for-try-break-except-if-raise pattern.

Could I make a context manager and use a with block? That might help with the try-except, but context managers can’t run their code block multiple times, so that wouldn’t help with the for and the break. So a context manager is out.

Could I abstract this away into a looping helper by implementing a generator function? We are looping and generator functions can break early. But, a generator function can’t catch an exception that’s raised within the body of a loop. So a generator function wouldn’t work either.

What about a decorator? 🤔

Context managers and decorators both sandwich a block of code. But decorators sandwich functions and they have the power to run the same function repeatedly. A decorator might work!

Here’s a decorator that will run a given function up to 10 times (until no AssertionError is raised):

1 2 3 4 5 6 7 8 9 def try_10_times(function): def wrapper(): for attempts_left in reversed(range(10)): try: return function() except AssertionError: if attempts_left == 0: raise return wrapper

To use this decorator, we would need to define a function and then call that function:

1 2 3 4 5 6 7 @try_10_times def assertions(): micro_time = time(micro_numbers) tiny_time = time(tiny_numbers) self.assertLess(tiny_time, micro_time*n) assertions()

This isn’t quite good enough though…

1. We need a pattern to run code N times (not necessarily exactly 10)
2. We reference the variables defined in each block in later blocks, so micro_time and tiny_time will need to be available outside that function
3. We need this function to run just one time right after it’s defined… could we do that automatically?

All 3 of these problems are solvable:

1. We need a decorator that accepts arguments
2. We need to use rarely seen nonlocal statement
3. We could have the decorator automatically call the decorated function

The final weird decorator

Here’s the decorator I ended up with:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 def attempt_n_times(n): """ Run tests multiple times if assertions are raised. Allows for more forgiving tests when assertions may be a bit flaky. """ def decorator(function): """This looks like a decorator, but it actually runs the function!""" for attempts_left in reversed(range(n)): try: return function() except AssertionError: if attempts_left == 0: raise return decorator

This decorator accepts an n argument which determines the maximum number of times the decorated function should be called. The decorator then calls the function repeatedly in a for loop and a try-except block. As soon as an AssertionError is not raised during one of these function calls, the looping stops.

The weirdest part about this decorator is that it calls the decorated function. Note that the decorator function doesn’t define a wrapper function within itself… it just runs code right away!

The resulting beautiful Python monstrosity

Here’s the final refactored test code:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 def test_some_test(self): n, m = 2.45, 2.04 micro_time = tiny_time = small_time = medium_time = 0 @attempt_n_times(10) def _(): nonlocal micro_time, tiny_time micro_time = time(micro_numbers) tiny_time = time(tiny_numbers) self.assertLess(tiny_time, micro_time*n) @attempt_n_times(5) def _(): nonlocal small_time small_time = time(small_numbers) self.assertLess(small_time, tiny_time*n) self.assertLess(small_time, micro_time*n*m) @attempt_n_times(3) def _(): nonlocal medium_time medium_time = time(medium_numbers) self.assertLess(medium_time, micro_time*n*m*m) self.assertLess(medium_time, tiny_time*n*m) self.assertLess(medium_time, small_time*n)

The attempt_n_times decorator immediately calls the function it decorates. Each function is defined and immediately called one or more times, in a try-except block within a loop.

That’s why we’ve named these functions with the throwaway _ name: we don’t care about the name of a function we’re never going to refer to again.

Also note the use of the nonlocal statement. Each function in Python has its own scope and all assignments assign to the local scope by default. That nonlocal variable pulls those variables to the scope of the outer function instead.

Compare the above code to the code just before this refactor:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def test_some_test(self): n, m = 2.45, 2.04 for attempts_left in reversed(range(10)): try: micro_time = time(micro_numbers) tiny_time = time(tiny_numbers) self.assertLess(tiny_time, micro_time*n) break except AssertionError: if attempts_left == 0: raise for attempts_left in reversed(range(5)): try: small_time = time(small_numbers) self.assertLess(small_time, tiny_time*n) self.assertLess(small_time, micro_time*n*m) break except AssertionError: if attempts_left == 0: raise for attempts_left in reversed(range(3)): try: medium_time = time(medium_numbers) self.assertLess(medium_time, micro_time*n*m*m) self.assertLess(medium_time, tiny_time*n*m) self.assertLess(medium_time, small_time*n) break except AssertionError: if attempts_left == 0: raise

I find the refactored version easier to skim.

But that attempt_n_times decorator does abuse the decorator syntax. Decorators aren’t meant to call the function they’re decorating.

Is this misuse of decorators worth it?

Is this worth it?

Decorators aren’t supposed to immediately call the function they decorate. But there’s nothing stopping them from doing so. I feel that I’ve traded “normal code” for a beautiful monstrosity that’s easier to skim at a glance.

The attempt_n_times decorator is pretending that it’s a block-level tool by using a function because there’s no other way to invent such a tool in Python.

I think abstracting away the for-try-break-except-if-raise pattern was worth it, even though I ended up abusing Python’s decorator syntax in the process.

What do you think? Was that attempt_n_times abstraction worth it?

In this video I introduce Selenium AT-SPI for testing KDE applications. I present the KDE goals of sustainable software, accessibility, and automation in system testing, and how Selenium helps achieve all of them.

Selenium AT-SPI is an amazing piece of software written by KDE developer Harald Sitter. It is a tool used in KDE to automate tests of GUI applications. This enables developers to design applications that are accessible for all and increase their energy efficiency. As part of Season of KDE 2024 I decided to make a video tutorial for KDE developers.

If you find this video helpful, you can reach out to me on Gitter. I would love to hear back from you 😃

This video was made by Pradyot Ranjan (@pradyotranjan:gitter.im).

• Part 2: Setting up Selenium
• Part 3: Identifying Accessibility Issues
• Part 4: Writing Selenium Tests

In this video I deep dive into writing tests with Selenium. I create a simple test for the KCalc calculator application and run it with Selenium AT-SPI. Similar steps can be followed to write GUI tests for any KDE application.

Selenium AT-SPI is an amazing piece of software written by KDE developer Harald Sitter. It is a tool used in KDE to automate tests of GUI applications. This enables developers to design applications that are accessible for all and increase their energy efficiency. As part of Season of KDE 2024 I decided to make a video tutorial for KDE developers.

If you find this video helpful, you can reach out to me on Gitter. I would love to hear back from you 😃

This video was made by Pradyot Ranjan (@pradyotranjan:gitter.im).

• Part 1: An Introduction to Selenium
• Part 2: Setting up Selenium
• Part 3: Identifying Accessibility Issues

In this video I explain how the accerciser utility works. Accerciser is a tool used to identify and test GUI accessibility elements. I also run accerciser on KCalc, the KDE calculator application.

Selenium AT-SPI is an amazing piece of software written by KDE developer Harald Sitter. It is a tool used in KDE to automate tests of GUI applications. This enables developers to design applications that are accessible for all and increase their energy efficiency. As part of Season of KDE 2024 I decided to make a video tutorial for KDE developers.

If you find this video helpful, you can reach out to me on Gitter. I would love to hear back from you 😃

This video was made by Pradyot Ranjan (@pradyotranjan:gitter.im).

• Part 1: An Introduction to Selenium
• Part 2: Setting up Selenium
• Part 4: Writing Selenium Tests

In this video I set up Selenium AT-SPI locally on KDE Neon. This video follows the Selenium setup guide found here: https://community.kde.org/Selenium.

Selenium AT-SPI is an amazing piece of software written by KDE developer Harald Sitter. It is a tool used in KDE to automate tests of GUI applications. This enables developers to design applications that are accessible for all and increase their energy efficiency. As part of Season of KDE 2024 I decided to make a video tutorial for KDE developers.

If you find this video helpful, you can reach out to me on Gitter. I would love to hear back from you 😃

This video was made by Pradyot Ranjan (@pradyotranjan:gitter.im).

• Part 1: An Introduction to Selenium
• Part 3: Identifying Accessibility Issues
• Part 4: Writing Selenium Tests

G2 posted some fun logos for Kate on reddit.

I think they are nice and flashy and well suited if you want to show your appreciation for Kate and like that art style and a good addition to our awesome icon and mascot.

Static Version Animated Version Licensing

G2 licensed these files under the CC BY-NC-SA 4.0. Feel free to share the stuff with this license and credit for G2.

Comments?

A matching thread for this can be found here on r/KDE.