Git Finds – Telegram
Git Finds
24 subscribers
212 photos
12 videos
5 files
1.26K links
Download Telegram
Hi 👋

We are happy to announce the second edition of our long-awaited testing course!
The second edition will be even more advanced and feature-full 🙂

We all know that sometimes people struggle with tests, because there are so many things to get right:
- Frameworks
- Mocking
- Data generation
- Flakyness
- Speed
- Different levels and kinds of tests

Sounds hard? We are here to help!

We will start with just one free webinar, where we will cover the most essential part, it is a foundation fore every developer who writes tests.

We even created a rather big project to be tested as a part of the homework for the later parts of this course. Check it out: https://github.com/tough-dev-school/python-testing-homework

Date: 06.09.2023
Time: 19:00 GMT+3
Language: ru

Register via our telegram bot to get a translation link: @tough_dev_bot
And prepare your questions 🙂

The course itself will start on 11.09
https://education.borshev.com/python-testing

See you there! 👍️
Forwarded from PythonDigest
Знакомимся с RepkaPi.GPIO SysFS. Установка и управление GPIO через Python 3. Теоретические основы работы GPIO портов
https://ift.tt/JcbY9jt

Начнем знакомство с подключаемой библиотекой RepkaPi.GPIO, данная библиотека написана на Python 3 и для управления GPIO использует методы, реализованные через SysFS.
Forwarded from PythonDigest
scrapy - 2.10.1
https://ift.tt/QCc9ouL

Гибкий фреймворк для написания web-пауков (парсеров). Скачать можно по ссылке: https://pypi.python.org/pypi/scrapy
Хорошей недели всем 🌝

Open-source engine for Heroes of Might and Magic III
https://github.com/vcmi/vcmi

fheroes2 is a recreation of Heroes of Might and Magic II game engine.
https://github.com/ihhub/fheroes2

PlayStation Vita port of fheroes2 project
https://github.com/Northfear/fheroes2-vita

Ну и HoTA (исходников мы там не увидим, но может быть увидим Фабрику)
https://h3hota.com/ru/faq
https://h3hota.com/ru/download
Forwarded from HN Best Comments
Re: Kagi Small Web

Vlad from Kagi here, thanks for posting. The RSS feed unexpectedly broke (edit: the feed is back up! [3]) just as we published the blog post, in the true spirt of "small web" :) Should be up in 30 minutes which will enable the site to function too (it uses the same feed).

This has been a personal pet project of mine and I spent considerable time getting my hands dirty with the code, as the team was busy with other initiatives. When I said the "feed broke" for the launch I meant I broke it. Software is messy especially for an old school dev. I learned in the process I am not a very good coder anymore (if I ever was one?), constantly going back and fixing stuff I previously thought was solid. Check it out in the linked repo [1].

Most importantly - I found the site replace the need for discovery for me, and getting to know various different humans and their writing felt good! A lot of unexpected stuff surfaced and the web felt close again. I think there is a glimpse of hope in the concept and I hope you see it too. And the improvements to search quality and diversity this brings are real.

You can check the list of included websites here [2]. And all the recent posts already surface in Kagi results (for relevant queries).

[1] https://github.com/kagisearch/smallweb

[2] https://github.com/kagisearch/smallweb/blob/main/smallyt.txt

[3] https://kagi.com/smallweb

freediver, 14 hours ago
Forwarded from Viktor Mikalayeu
🚀 Привет, коллеги IT инженеры! 🖥

У нас отличные новости для всех, кто готовится к экзаменам CKA,CKAD и CKS или просто хочет освоить Kubernetes. Мы создали совершенно бесплатную open-source платформу, которая делает подготовку максимально удобной.

Что мы предлагаем:
Создание всех необходимых ресурсов (VPC, subnets, EC2) автоматически.
Настраивайте кластеры под различные сценарии в несколько кликов.
Эквивалент killer.sh, но абсолютно бесплатно.
Возможность легко добавлять свои собственные сценарии.
Тесты для проверки правильности выполнения заданий.
Контроль времени для максимально реалистичных мок-экзаменов.

Сделайте вашу подготовку к экзаменам эффективной и удобной. Присоединяйтесь к нашей платформе и поделитесь своим опытом!

На данный момент доступны сценарии:
- CKA mock экзамена
- CKS hands-on lab
- CKS mock экзамена

Ссылка на GitHub: https://github.com/ViktorUJ/cks
видео пример запуска CKA mock экзамена: https://www.youtube.com/watch?v=P-YYX4CTWIg
Bare-metal Kubernetes, Part I: Talos on Hetzner
https://datavirke.dk/posts/bare-metal-kubernetes-part-1-talos-on-hetzner/

Bare-metal Kubernetes, Part II: Cilium CNI & Firewalls
https://datavirke.dk/posts/bare-metal-kubernetes-part-2-cilium-and-firewalls/

Bare-metal Kubernetes, Part III: Encrypted GitOps with FluxCD
https://datavirke.dk/posts/bare-metal-kubernetes-part-3-encrypted-gitops-with-fluxcd/

Bare-metal Kubernetes, Part IV: Ingress, DNS, and Certificates
https://datavirke.dk/posts/bare-metal-kubernetes-part-4-ingress-dns-certificates/

Bare-metal Kubernetes, Part V: Scaling Out
https://datavirke.dk/posts/bare-metal-kubernetes-part-5-scaling-out/

Bare-metal Kubernetes, Part VI: Persistent Storage with Rook Ceph
https://datavirke.dk/posts/bare-metal-kubernetes-part-6-persistent-storage-with-rook-ceph/

Bare-metal Kubernetes, Part VII: Private Registry with Harbor
https://datavirke.dk/posts/bare-metal-kubernetes-part-7-private-registry-with-harbor/

Bare-metal Kubernetes, Part VIII: Containerizing our Work Environment
https://datavirke.dk/posts/bare-metal-kubernetes-part-8-containerizing-our-work-environment/

ЗЫ

Чатик по Talos @ru_talos
Чатик по Cilium @ciliumproject
Forwarded from PythonDigest
Визуальное RPG с долговременной памятью, генерируемое из 3 нейросетей и LLamы
https://ift.tt/XNe5nZf

Языковые модели (NLP) сейчас активно развиваются и находят себе всё больше интересных применений. Начиналась же их эпоха с классики жанра — D&D. Это настольная игра, где несколько друзей или просто знакомых синхронно галлюцинируют, представляя себя командой героев в некоем вымышленном мире. Прав же во внутриигровых выборах тот, кто выкинул большее число на игральной кости. Судить сейчас об их мотивации у меня нет никакого желания, да и статья вообще-то не об этом.
Forwarded from HN Best Comments
Re: iNaturalist strikes out on its own

I reverse engineered their stuff a bit. I downloaded their Android APK and found a tensorflow lite model inside. I found that it accepts 299x299px RGB input and spits out probabilities/scores for about 25,000 species. The phylogenetic ranking is performed separately (outside of the model) based on thresholds (if it isn't confident enough about any species, it seems to only provide genus, family, etc.) They just have a CSV file that defines the taxonomic ranks of each species.

I use it to automatically tag pictures that I take. I took up bird photography a few years ago and it's become a very serious hobby. I just run my Python noscript (which wraps their TF model) and it extracts JPG thumbnails from my RAW photos, automatically crops them based on EXIF data (regarding the focus point and the focus distance) and then feeds it into the model. This cropping was critical - I can't just throw the model a downsampled 45 megapixel image straight from the camera, usually the subject is too small in the frame. I store the results in a sqlite database. So now I can quickly pull up all photos of a given species, and even sort them by other EXIF values like focus distance. I pipe the results of arbitrary sqlite queries into my own custom RAW photo viewer and I can quickly browse the photos. (e.g. "Show me all Green Heron photos sorted by focus distance.") The species identification results aren't perfect, but they are very good. And I store the score in sql too, so I can know how confident the model was.

One cool thing was that it revealed that I had photographed a Blackpoll Warbler in 2020 when I was a new and budding birder. I didn't think I had ever seen one. But I saw it listed in the program results, and was able to confirm by revisiting the photo.

I don't know if they've changed anything recently. Judging by some of their code on GitHub, it looked like they were also working on considering location when determining species, but the model I found doesn't seem to do that.

I can't tell you anything about how the model was actually trained, but this information may still be useful in understanding how the app operates.

Of course, I haven't published any of this code because the model isn't my own work.

fogleman, 6 hours ago
Forwarded from Good Shit™️😤😤
https://github.com/forkgram/tdesktop/releases/tag/v4.8.1 so kotatogram was a bust, as it's so old i can't fuckin see spoilers, but here's the last forkgram release before stories got implemented.
Forwarded from Good Shit™️😤😤
https://github.com/fgsfdsfgs/perfect_dark has reached "playable" for windows/linux builds, currently in a somewhat janky state, graphical glitches and the like.

nonetheless, here's the current build with rom file added. no support offered by myself, but if you wanna play around with it go ham.