A simple storage for my dotfiles using stow.

What about it? I like it simple, so don’t worry about large files (maybe)

And also, I want it to be easy to configure 😉

• Stow
• Fish
• A nerd font (Jetbrains)

And the packages that I use

Just use your favorite package manager. Me? I use two

sudo dnf install neovim tmux hyprland rofi waybar dunst git
cargo install fzf eza blight ripgrep

git clone https://github.com/TaliAly/dotfiles.git ~/.dotfiles
cd ~/.dotfiles
stow . # It just because 

As you might tell, I use the theme of Rose Pine a lot which, is great until you consider that I don’t add the apps that I use with it. Why? Because I don’t care about them, maybe in the future.

If you have trouble with Lazy, just move the config folders out and let lazy install itself into the editor, then you can add the files.

Because sometimes my package manager won’t have some utils that I like, and I hate that, so I found out that cargo can install in a self contained enviroment and I’m happy with that.

it’s fast, good and it’s less hassle to add scripts to it (but writing them can be quite challenging)

• Theprimeagen’s dotfiles
• Rose pine theme

Latest Rust compiler and cargo in a linux container (using docker).

The images are available in my Docker Hub repository:

• $ docker pull tomastomecek/rust — this is the most recent stable release
• $ docker pull tomastomecek/rust:nightly — this is the most recent, functional nightly release
• $ docker pull tomastomecek/rust:clippy — latest nightly image with clippy

Every stable image is also tagged with version of Rust compiler, so for example:

$ docker pull tomastomecek/rust:1.17.0

For more info what versions are available, see the section Tags.

You should mount your project inside directory /src within the container. cargo and rustc commands are then available in the container.

Here is a guide how to perform some common actions:

1. Compile a file:

$ ls -lha .
total 4.0K
-rw-rw-r-- 1 me me 37 May 23 13:10 main.rs

$ docker run -ti -v $PWD:/src/ tomastomecek/rust rustc ./main.rs

$ ./main
It works!

$ ls -lha .
total 3.5M
-rwxr-xr-x 1 me me 3.5M May 26 18:11 main
-rw-rw-r-- 1 me me   37 May 23 13:10 main.rs

2. Create a new project using cargo:

$ mkdir the-best-project

$ cd the-best-project

$ docker run -ti -v $PWD:/src/ tomastomecek/rust cargo init --bin
     Created binary (application) project

$ ls -lha .
total 8.0K
drwxrwxr-x 4 me me  61 May 26 18:35 .
drwxrwxr-x 7 me me 143 May 26 18:29 ..
-rw-r--r-- 1 me me  76 May 26 18:35 Cargo.toml
drwxr-xr-x 6 me me  96 May 26 18:17 .git
-rw-r--r-- 1 me me 120 May 26 18:35 .gitignore
drwxr-xr-x 2 me me  19 May 26 18:35 src

3. Compile a cargo project:

$ ls -lh .
total 8.0K
-rw-r--r-- 1 me me  76 May 26 18:35 Cargo.toml
drwxr-xr-x 2 me me  19 May 26 18:35 src

$ docker run -ti -v $PWD:/src/ tomastomecek/rust cargo build
   Compiling src v0.1.0 (file:///src)
    Finished dev [unoptimized + debuginfo] target(s) in 0.34 secs

$ ./target/debug/src
Hello, world!

These images are being created using a very simple CI/CD pipeline. Here’s how it works:

1. Travis CI initiates a build every day using its Cron Jobs feature.

2. This build script is executed.

3. Rust stable docker image is built.

4. Tests for Rust stable docker image are executed. These tests verify that

   • Rust compiler is able to compile Rust code.
   • Cargo is able to create a new project.
   • This cargo project can be built.

5. If the tests passed, push the image to Docker Hub.

6. Do steps 3 and 4 for nightly image.

With this very simple pipeline you can be sure that

• you get functional images
• you get latest Rust compiler
• you can pick a version of Rust

SmartI18N will die Internationalisierung von Webprojekten vereinfachen und die Umsetzung beschleunigen. Es gibt einen Online Editor mit welchem Texte in Echtzeit angepasst und verändert werden können. Für die Einbindung stehen verschiedene SDK’s zur Verfügung die das Arbeiten mit SmartI18N vereinfachen.

Mehr Informationen zu SmartI18N findest du unter www.smarti18n.com

Development has ended, support has vanished, and the code is now living its best life in a quiet repo somewhere, sipping digital margaritas. 🍹

Feel free to fork it, remix it, or just stare at it nostalgically — but don’t expect it to do any new tricks. It’s not dead… it’s just resting. 😴

Für den Start empfehlen wir dir, den SmartI18N Server von www.smarti18n.com zu nutzen. Später kannst du mit Hilfe eines Docker Image SmartI18N selbst hosten. Dazu aber später mehr.

Erstell dir mit Hilfe des Editors einen Account und erstell anschließend ein Projekt. Mithilfe des Projekt Keys und Secrets kannst du dann SmartI18N in dein Projekt einbinden.

Derzeit gibt es für Spring Message Sources und AngularJS Schnittstellen. In Zukunft wollen wir weiter Schittstellen ergänzen.

Example folgt

Example folgt

SmartI18N kann selbst betrieben werden. Mehr Informationen findest du im Docker HUB.

Für den Betrieb von SmartI18N benötigst du eine MongoDB. Du kannst eine extern gehostete Instance verwenden oder einen Docker Container.

docker run -d --name smarti18n-mongo mongo

docker run -d --name smarti18n-messages --link smarti18n-mongo:mongo -p 30001:8080 -e MONGODB_URL=mongodb://mongo/smarti18n-messages  smarti18n/messages

docker run -d --name smarti18n-editor -p 30002:8080 -e "SMARTI18N_MESSAGES_HOST=http://localhost:30001" smarti18n/editor

Jetzt kannst du das initiale Admin Passwort aus dem smarti18n-messages Container suchen.

docker logs smarti18n-messages

#######################################################################
Initializing Application
Opened connection \[connectionId{localValue:2, serverValue:2}\] to mongo:27017
Create Default User \[default@smarti18n.com\] with Password \[PASSWORD\]
create default project \[default\] with secret \[SECRET\]
Initializing Application finished
#######################################################################

Mit dem Passwort und der E-Mail default@smarti18n.com kannst du dich im Editor unter http://localhost:30002 einloggen.

SmartI18n is released under version 2.0 of the Apache License.

«crnn-ctc» implemented CRNN+CTC

ONLINE DEMO：LICENSE PLATE RECOGNITION

For each sub-dataset, the model performance as follows:

If you want to achieve license plate detection, segmentation, and recognition simultaneously, please refer to zjykzj/LPDet.

• Table of Contents
• News🚀🚀🚀
• Background
• Installation
• Usage
  • Train
  • Eval
  • Predict
• Maintainers
• Thanks
• Contributing
• License

This warehouse aims to better understand and apply CRNN+CTC, and has currently achieved digital recognition and license plate recognition. Meanwhile, LPRNet(+STNet) is a pure convolutional architecture for license plate recognition network. I believe that the implementation of these algorithms can help with the deployment of license plate recognition algorithms, such as on edge devices.

Relevant papers include:

1. Towards End-to-End License Plate Detection and Recognition: A Large Dataset and Baseline
2. An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition
3. Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks
4. LPRNet: License Plate Recognition via Deep Neural Networks

Relevant blogs (Chinese):

1. Towards End-to-End License Plate Detection and Recognition: A Large Dataset and Baseline
2. An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition
3. LPRNet: License Plate Recognition via Deep Neural Networks

$ pip install -r requirements.txt

Or use docker container

$ docker run -it --runtime nvidia --gpus=all --shm-size=16g -v /etc/localtime:/etc/localtime -v $(pwd):/workdir --workdir=/workdir --name crnn-ctc ultralytics/yolov5:latest

• ChineseLicensePlate: Baidu Drive(ad7l)

# EMNIST
$ python3 train_emnist.py ../datasets/emnist/ ./runs/crnn-emnist-b512/ --batch-size 512 --device 0 --not-tiny
# Plate
$ python3 train_plate.py ../datasets/chinese_license_plate/recog/ ./runs/crnn-plate-b512/ --batch-size 512 --device 0 --not-tiny

# EMNIST
$ CUDA_VISIBLE_DEVICES=0 python eval_emnist.py crnn-emnist.pth ../datasets/emnist/ --not-tiny
args: Namespace(not_tiny=True, pretrained='crnn-emnist.pth', use_lstm=False, val_root='../datasets/emnist/')
Loading CRNN pretrained: crnn-emnist.pth
crnn-emnist summary: 29 layers, 7924363 parameters, 7924363 gradients, 2.2 GFLOPs
Batch:49999 ACC:100.000: 100%|████████████████████████████████████████████████████████| 50000/50000 [03:47<00:00, 219.75it/s]
ACC:98.570
# Plate
$ CUDA_VISIBLE_DEVICES=0 python3 eval_plate.py crnn-plate.pth ../datasets/chinese_license_plate/recog/ --not-tiny
args: Namespace(add_stnet=False, not_tiny=True, only_ccpd2019=False, only_ccpd2020=False, only_others=False, pretrained='crnn-plate.pth', use_lprnet=False, use_lstm=False, use_origin_block=False, val_root='../datasets/chinese_license_plate/recog/')
Loading CRNN pretrained: crnn-plate.pth
crnn-plate summary: 29 layers, 15083854 parameters, 15083854 gradients, 4.0 GFLOPs
Load test data: 149002
Batch:4656 ACC:100.000: 100%|████████████████████████████████████████████████████████████| 4657/4657 [00:52<00:00, 89.13it/s]
ACC:82.147

$ CUDA_VISIBLE_DEVICES=0 python predict_emnist.py crnn-emnist.pth ../datasets/emnist/ ./runs/predict/emnist/ --not-tiny
args: Namespace(not_tiny=True, pretrained='crnn-emnist.pth', save_dir='./runs/predict/emnist/', use_lstm=False, val_root='../datasets/emnist/')
Loading CRNN pretrained: crnn-emnist.pth
crnn-emnist summary: 29 layers, 7924363 parameters, 7924363 gradients, 2.2 GFLOPs
Label: [0 4 2 4 7] Pred: [0 4 2 4 7]
Label: [2 0 6 5 4] Pred: [2 0 6 5 4]
Label: [7 3 9 9 5] Pred: [7 3 9 9 5]
Label: [9 6 6 0 9] Pred: [9 6 6 0 9]
Label: [2 3 0 7 6] Pred: [2 3 0 7 6]
Label: [6 5 9 5 2] Pred: [6 5 9 5 2]

$ CUDA_VISIBLE_DEVICES=0 python predict_plate.py crnn-plate.pth ./assets/plate/宁A87J92_0.jpg runs/predict/plate/ --not-tiny
args: Namespace(add_stnet=False, image_path='./assets/plate/宁A87J92_0.jpg', not_tiny=True, pretrained='crnn-plate.pth', save_dir='runs/predict/plate/', use_lprnet=False, use_lstm=False, use_origin_block=False)
Loading CRNN pretrained: crnn-plate.pth
crnn-plate summary: 29 layers, 15083854 parameters, 15083854 gradients, 4.0 GFLOPs
Pred: 宁A·87J92 - Predict time: 5.4 ms
Save to runs/predict/plate/plate_宁A87J92_0.jpg
$ CUDA_VISIBLE_DEVICES=0 python predict_plate.py crnn-plate.pth ./assets/plate/川A3X7J1_0.jpg runs/predict/plate/ --not-tiny
args: Namespace(add_stnet=False, image_path='./assets/plate/川A3X7J1_0.jpg', not_tiny=True, pretrained='crnn-plate.pth', save_dir='runs/predict/plate/', use_lprnet=False, use_lstm=False, use_origin_block=False)
Loading CRNN pretrained: crnn-plate.pth
crnn-plate summary: 29 layers, 15083854 parameters, 15083854 gradients, 4.0 GFLOPs
Pred: 川A·3X7J1 - Predict time: 4.7 ms
Save to runs/predict/plate/plate_川A3X7J1_0.jpg

• zhujian – Initial work – zjykzj

• rinabuoy/crnn-ctc-loss-pytorch
• we0091234/crnn_plate_recognition
• sirius-ai/LPRNet_Pytorch
• CA-USTC/License_Plate_Recognition_pytorch
• zjykzj/LPDet

Anyone’s participation is welcome! Open an issue or submit PRs.

Small note:

• Git submission specifications should be complied with Conventional Commits
• If versioned, please conform to the Semantic Versioning 2.0.0 specification
• If editing the README, please conform to the standard-readme specification.

Apache License 2.0 © 2023 zjykzj

SoundSwitcher is a class to implement a two state sensor reacting on sound levels.
The sound volume is measured by an I2S micophone (SPH0645LM4 or INMP441)
The state toggles when a changeable arbitrary soundvolume level is exceeded
A changeable hysteresis is accomplished

Example:

https://www.hackster.io/RoSchmi/heating-burner-monitoring-using-esp32-and-microphone-80015d

https://github.com/RoSchmi/Esp32_WiFiManager_HeatingSurvey

Constructor: has two parameter, a configuaration struct and the used microphone type

Initialization: .begin method with 4 parameters (has to be called in setup())
* switchThreshold
* hysteresis
* updateIntervalMs
* delayTimeMs

The first two parameters need not be explained.

updateIntervalMs : a time interval in ms. Only when the time interval has expired
a new sound level average is calculated

delayTimeMs: this delay time is introduced for reading the analog soundlevel
after the threshold is exceeded. This parameter determins after
which timeinterval the analog value is sampled (to read not just
at the very beginning of the new state)

soundSwitcher.SetActive() : Has to be called right after the soundSwitcher.begin function

soundSwitcher.SetCalibrationParams() : Is optional, determins an offset and a factor

soundSwitcher.feed() : This function is called frequently from the loop() of the main program
It returns a struct Feedresponse.
After the feed command, Feedresponse.isValid has to be checked
If .isValid is true, Feedresponse.hasToggled is checked
If .hasToggled is true, your reaction on the change of the
state has to be performed

A whole new level of editor immersion is here!
Never before has a developer been able to achieve such code zen!

vim-ckb is a hacky little 50-line plugin I made to put nice colors on my keyboard. I mainly made it to learn a little bit more about vimscript, and it’s an incredibly simple plugin, but it may be the missing piece in your development environment that will boost your productivity and make your kitchen sparkle like new.

This plugin depends on ckb-daemon. It’s completely untested on MacOS, and barely tested on Linux, so it may not work perfectly 😛

Once ckb-daemon is up and running, just install this plugin with your favorite plugin manager and have fun!

vim-ckb just caused me great joy and amusement!

• Oliver Burkill, internet person

It certainly is a plugin!

• Katie Wolfe, developer

I don’t have a Corsair keyboard, but if I did, I would definitely install this plugin and experience the few minutes of mild amusement it has to offer before uninstalling it!

• Brendan McCoy, Logitech keyboard owner

Karabük Üniversitesi’nde 27-30 Ocak 2018 tarihleri arasında vereceğim eğitimin içeriğin aşağıdaki gibi olmasını planlamaktayız.

https://kurs.ab.org.tr/2018/gnu-linux-sistem-yonetimi-05/

https://t.me/joinchat/EhBNxBAjgETEe2fP_Sj8TA

• Virtualbox kurulu Bilgisayar

• Tüm bilgisayarlara VirtualBox kurulumu
• Özgür Yazılım tanımı ve felsefesi
• GNU Projesi ve Linux çekirdeğinin tarihçesi
• Dağıtımlar, pencere yöneticileri ve masaüstü ortamları
• Işletim sistemi ve dağıtım kavramı
• Pencere yöneticisi ve masaüstü ortamı farkları
• Temel bilgisayar mimarisi ve katmanları
• Sanallaştırma Teknolojileri, Virtualbox ve KVM giriş
• Sanal cihaza giriş
• Dizin hiyerarşisi
• Kabuk kavramı ve kullanımı
• Komut kavramı ve kullanımı
• Parametre ve argümanlar

• Dizinlerle çalışmak
  • pwd, ls, cd, mkdir, rmdir
• Dosyalarla çalışmak
  • cat, tac, less, more, touch, head, tail
  • cut, expand, sort, split, tr, uniq, wc
• Dosya yönetimi
  • cp, mv, rm, file, dd, find, locate, updatedb, type, ln, diff
• Girdi ve çıktı kavramı
• Uçbirimde çalışmak
  • bash, echo, env, export, set, unset, history, uname
• Kabuk kısayolları
• Uçbirimde metin düzenleme: GNU Nano ve Vi
• İnternetsiz ortamda çalışmak
  • man, apropos, -help, whatis, which
• Donanım bilgilerinin öğrenilmesi
  • lsusb, lspci, dmidecode
• Metin işlemleri
  • grep, egrep, xargs
  • Düzenli ifadeler, sed ve awk
• Sistemi izleme, süreçler ve servisler
  • top, fg, bg, ps, nice, renice, free, nohup, &, kill, uptime, df, du, lsof, netstat, sysctl, systemctl
• Sistem açılışı ve disk yönetimi
• Yeni bir birim ekleme ve mount kavramı

• Kullanıcı yönetimi ve gruplar
  • etc/passwd, etc/group, etc/skel
  • useradd, userdel, usermod, groupadd, groupdel, groupmod, chage, getent
  • id, whoami, last, whp
• Yönetim kullanıcısı
  • su ve sudo arasındaki fark
• İzin işlemleri
  • chmod, chown, chgrp, umask
• Debian tabanlı sistemlerde Paket Yönetimi
  • apt-get, apt ve dpkg farkı
• apt-get komutları
  • install, remove, purge, update, upgrade, clean, –download-only, download, check
• apt-cache komutları
  • show, search, showpkg, stats
• dpkg komutları
  • -i, -r, -l, -L, -S, -s
• apt komutları
  • update, list, search, show, install, remove, upgrade,
• Zaman ve dil yönetimi
• Arşivleme ve sıkıştırma

• Virtualbox ile ağ ayarlarının anlatılması
• Ağ yönetimine giriş ve temel komutlar
  • hostname, /etc/hosts, ifconfig, ip, ping, route, ifup, ifdown
  • netstat, dig, tracetoute, tracepath, netcat, lsof -i
• Uzak bilgisayara bağlanma ve dosya transferi
  • ssh, scp
• Kütük takibi ve kontrol
• Temel düzeyde betik yazma ( Bash ve Python )
• Python ile http sunucusu oluşturma
• Apache ile http sunucusuna erişim ve log kayıtlarını inceleme

2/20/2018 14:14 -5:00

• Do no harm
• Do not annoy
• Give users control
  • Closeable
  • Session based
• Tell stories & be compelling

• Responsive Design Principles
  • Mobile First approach
  • Do not consume more than 15% of the user’s viewport
    • Browser chrome must be taken into account, especially on the smallest of devices (ex: iPhone SE)
  • Progressive enhancement if Javascript is disabled, basic content should be rendered.
  • Viewports should be considered as small / medium / large rather than a fixed pixel width or height

• Module(s) should be written in semantically correct HTML
• Progressively Enhanced
  • Should function if Javascript is disabled
• Should be keyboard operable
• Should close on ESC key press
• Alt text should be written in accordance with our policies

• Should be unique to the module itself and not be repeat of other buttons found in the global header
  • This should reduce button “fatigue”
  • This will allow for more perceived targeting towards a specific campaign or cause within the library

• A11Y combinations should come from the Design Tool Kit’s Color Accessibility Table

• Should behave as expected
• Close buttons should close for that session
  • session cookie set to remember action
• Links are visually treated as buttons

• Should be used sparingly and only as an enhancement
• Compressed, in-line SVGs for vector graphics

• System Type should be used
• Keivit only if absolutely compelled by a senior manager
• Type should never be an image
• Type will be set in rems and be based on a default of 16px.
  • If type must be smaller 0.75rem or 12px is smallest acceptable size

• Should be used sparingly
• Depending on context should only run once
• Only when modules load to the screen or are closed by the user

A simple muslim prayertime reminder app for desktop. Made using Electron with electron-react-boilerplate. UI are made using Material UI.

• Table of Contents
• FAQ
• Features
• Showcase
• Download
• Installation
• Uninstallation
• Customization
• Developing
  • Starting Development
  • Packaging for Production
  • Further Instructions
  • Electron React Boilerplate Docs
  • License
• Attribution
• Further help

1. Q: Compatible platform?
   A: This app can be used on Windows and Linux. It should also work on MacOS but unfortunately, I haven’t tested it.
2. Q: What is this app for?
   A: This app works just like any other prayer time/reminder app, it shows prayer time schedule for a specific location/date and the reminder for it.
3. Q: How do i get help for the user settings?
   A: You can check out the wiki on this section.
4. Q: My location is incorrect help
   A: You can set the location manually in the settings. For more info check on the options section of the wiki.
5. Q: Does this work offline?
   A: Yes, the app works offline, internet connection is only needed for location detecting (there is also an offline method if an internet connection is not present) and version checking.

• Prayer times with customizable reminder/notification
• Prayer times schedule (calendar)
• Export prayer times schedule
• Adhan
• Qibla direction

This app is quite modern looking because of Material UI, it also have dark and light theme.

• Latest release
• Sourceforge

1. Download the latest release from GitHub
2. Extract/Install
3. Run the program

Run the uninstaller if you are using the installer version. If using the portable version, you can just delete the folder. On windows, there might be a registry left behind that you can delete if want to by going to regedit and delete the Simple PrayerTime Reminder registry on \HKEY_CURRENT_USER\Software\Microsoft\Windows\CurrentVersion\Run.

Check the wiki for more information.

This app is developed using Electron React Boilerplate. It uses Electron, React, React Router, Webpack and React Fast Refresh.

Install all the packages:

npm install

Start the app in the dev environment:

npm start

To package apps for the local platform:

npm run package

This will compile the app into the release folder.

To compile the app into a certain architecture/platform, you can add extra options to the package command, ex:

npm run package --win --ia32

# If npm does not work, try to use yarn
yarn package --win --ia32

For commands list you can check in the official electron builder website

You can check this repo’s wiki or You can ask in discussions

See Electron React Boilerplate docs and guides here

MIT © Dadangdut33 and MIT © Electron React Boilerplate

Adhan uploaded by Ayat Via Youtube

Mosque icons created by Freepik – Flaticon

• Check the Wiki
• Submit an Issue
• Ask in Discussion