QT is a cross platform UI development framework, its goal is to look native to the platform it operates on. This video by a linux maintainer from 2014 explains its benefits over GTK, its a fun video and I don’t think the issues have really changed.

Most GTK advocates will argue QT is developed by Trolltech and isn’t GPL licensed so could go closed source! This argument seems to ignore open source projects use the Open Source releases of QT and if Trolltech did close source then the last open source would be maintained (much like GTK).

Personally I would avoid Flutter on the grounds its a Google owned library and Google have the attention span of a toddler.

Not helping that assessment is Google let go of the Fuschia team (which Flutter was being developed for) and seems to have let go a lot of Flutter developers.

Personally I hate web frontends as local applications. They integrate poorly on the desktop and often the JS engine has weird memory leaks

It was a mixture of factors.

Data was to be dumped into a S3 bucket (minio), this created an event and anouther team had built an orchestrator which would do a couple of things but eventually supply an endpoint a reference to a plain/txt file for analysis.

For the Java devs they had to [modify the example camel docs.](https://camel.apache.org/manual/rest-dsl.html) and use the built in jackson library to convert the incoming object to a class. This used the default AWS S3 api to create a stream handle which fed into the OpenNLP docs. .

The Python project first hit a wall in setting up Flask. They followed the instructions and it didn’t work from setup tools. The Java team had just created a new maven project from the Intelij but the same approach didn’t work for the Python team using pycharm. It lost them a couple days, I helped them overcome it.

Then they hit a wall with Boto3, the team expected to stream data but Boto3 only supports downloading, there was also a complexity issue the AWS SDK in Java waa about 20 lines to setup and a single line to call, it was about 50 lines in Python. On the positive side I got to explain what all the config meant in S3.

This caused the team anouther few days of delay because the team knew I used a 350MiB Samsung TV guide to test the robustness and had to go learn about Docker volume mounting and they thought they needed a stateful kubernetes service and I had to explain why that was wrong.

Basically Python threw up a lot of additional complexity and the docs weren’t as helpful as they could have been.

If you have the freedom try Typescript.

The tsx files are almost identical to jsx except for the need to define the field types your ingesting.

While thats a little extra work, it allows Visual Studio Code to perform deeper analysis and provide much more helpful contextual hints.

I grew to love JSX and tried TSX out of interest and you couldn’t convince to go back to pure JS

You do, but considering the scales they process data I suspect Google would be better building Go tooling (or whatever the dominate internal language is).

A few years back I was trying to teach some graduates the importance of looking at a programming language ecosystem and selecting it based on that.

One of my comparison projects was Apache OpenNLP/Camel vs Flask/Spacy.

Spacy is the go to for NLP, I expected it to be either quicker to develop, easier to use, better results or just less resources.

I assigned Grads with Java experience Spacy and Python experience OpenNLP.

The OpenNLP guys were done first, they raved about being able to stream data into the model and how much simplier it made life.

When compared with the same corpus (Books, Team emails, corporate sharepoint, dev docs, etc…) OpenNLP would complete on 4GiB of RAM in less than a second on 0.5vCPU. Spacy needed 12GiB and was taking ~2 seconds with 2vCPU. They identified the same results…

Me and a few others ended up spending a day reading the python and trying to optimise it, clearly the juniors had done something daft, they had not.

It rather undermined my point.

My expectation is whatever the solution it needs to dockerise and be really easy to deploy via docker compose or Kubernetes so people can quickly and easily set up their own.

The front end is effectively static files so I would probably choose Apache or Express (whichever gives me a smaller docker image)…

For the backend I would choose Java for Spring Boot. An Alpine image with OpenJDK and the app is tiny. Spring has a library for every kind of interface making them trivial to implement but the main reason is hibernate.

Hibernate (now Spring Data) was the first library for being able to switch out databases without having to change code (its all config). A lot of mastodon instances struggle with the resource requirements of elastic search so letting small instances use something like postgres would seem ideal.

I have noticed Go/Rust still expect you to write or manage a lot of stuff Spring gives away for free. Python is ok if your backend is really tiny but there is a lot of boilerplate in how Python libraries work so complex projects get hard to manage and I assume interacting with the fediverse will add complexity.

Immutable distributions won’t solve the problem.

You have 3 types of testing unit (descrete part of code), integration (how a software piece works with others) and system testing (e.g. the software running in its environment). Modern software development has build chains to simplify testing all 3 levels.

Debian’s change freeze effectively puts a known state of software through system testing. The downside its effecitvely ‘free play’ testing of the software so it requires a big pool of users and a lot of time to be effective. This means software in debian can use releases up to 3 years old.

Something like Fedora relies on the test packs built into the open source software, the issue here is testing in open source world is really variable in quality. So somethinng like Fedora can pull down broken code that passes its tests and compiles.

The immutable concept is about testing a core set of utilities so you can run the containers of software on top. You haven’t stopped the code in the containers being released with bugs or breaking changes you’ve just given yourself a means to back out of it. It’s a band aid to the actual problem.

The solution is to look at core parts of the software stack and look to improve the test infrastructure, phoronix manages to run the latest Kernel’s on various types of hardware for benchmarking, why hasn’t the Linux foundation set up a computing hall to compile and run system level testing for staged changes?

Similarly website’s are largely developed with all 3 levels of testing, using things like Jest/Mocha/etc… for Unit/Integration testing and Robots/Cypress/Selenium/Storybook/etc… for system testing. While GTK and KDE apps all have unit/integration tests where are the system level test frameworks?

All this is kinda boring while ‘containers!’ is exciting new technology

Docker swarm was an idea worse than kubernetes, that came out after kubernetes, that isn’t really supported by anyone.

Kubernetes has the concept of a storage layer, you create a volume and can then mount the volume into the docker image. The volume is then accessible to the docker image regardless of where it is running.

There is also a difference between a volume for a deployment and a statefulset, since one is supposed to hold the application state and one is supposed to be transient.

The splash screen (boot screen instead of text)used to get me. It provided by an application called ‘Plymouth’.

You used to need to install it and configure grub, however I think if you go into ‘System Settings’ and type ‘Splash’ KDE has an option to install and choose the screen

I wouldn’t use “certified” in this context.

Limiting support of software to specific software configurations makes sense.

Its stuff like Debian might be using Python 3.8 Ubuntu Python 3.9, OpenSuse Python 3.9, etc… Your application might use a Python 3.9 requiring library and act odd on 3.8 but fine on 3.7, etc… so only supporting X distributions let you make the test/QA process sane.

This is also why Docker/Flatpack exist since you can define all of this.

However the normal mix is RHEL/Suse/Ubuntu because those target businesses and your target market will most likely be running one.

I suspect they mean around packaging.

I honestly believe Red Hat has a policy that everything should pull in Gnome. I have had headless RHEL installs and half the CLI tools require Gnome Keyring (even if they don’t deal with secrets or store any). Back in RHEL 7, Kate the KDE based Text Editor pulled in a bunch of GTK dependencies somehow.

Certification is really someone paid to go through a process and so its designed so they pass.

Think about the people you know who are Agile/Cloud/whatever certified and how all it means is they have learnt the basic examples.

Its no different when a business gets certified.

The only reason people care is because they can point to the cert if it all goes wrong

Debian isn’t old == stable, its tested == stable.

Debian has an effective Rolling distribution through testing than can get ahead of Arch.

At some point they freeze the software versions in testing and look for Release Critical and Major bugs. Once they have shaken everything and submitted fixes where possible. It then becomes stable.

The idea is people have tested a set baseline of software and there are no known major bugs.

For the 4-5 releases Debian has released every 2 years (Similar to Ubuntu LTS). Debian tends to align its release with LTS Kernel and Mesa releases so there have been times the latest stable is running newer versions than Ubuntu and the newest software crown switches between Ubuntu LTS and Debian each year.

For some the priority to run software that won’t have major bugs, that is what Debian, Ubuntu LTS and RHEL offer.