Development

Unveiling the Unique Essence of Our D8 Linux Terminal Theme

Hello, fellow developers and enthusiasts! Today, I want to share the story behind our D8 Linux Terminal Theme, a project that’s close to our hearts. Recently, we attempted to upload this theme to the WordPress theme directory but faced some unexpected challenges. The review team had concerns, primarily around understanding the theme’s purpose and its unconventional design approach. Why This Theme? Our D8 Linux Terminal Theme is not your typical WordPress theme. It’s designed for those who love the simplicity and nostalgia of the Linux terminal. We wanted to break away from the conventional design norms of WordPress themes – […]

Development

Redmine Time Tracker – Chrome Extension

A Chrome Extension with Vue.js to efficiently track time for Redmine tickets. #Setup Page # Issues Page # Search Results Features Steps to Use the Extension https://github.com/kzorluoglu/kedmine-chrome#steps-to-use-the-extension New Feature – 16.10.2023 New Feature – 16.10.2023 CSV to Redmine Table Converter Views: 59

Development / Linux / Server / Ubuntu

MJPG-streamer installation and run at System Startup using Systemd – Ubuntu Server

mjpg-streamer is a popular tool for streaming MJPEG video from webcams. In this tutorial, we will learn how to configure mjpg-streamer to run automatically at system startup using a systemd service unit file. Installation via Snap Step 1: Create the Service Unit File In the text editor, paste the following content: [Unit] Description=MJPG Streamer After=network.target [Service] ExecStart=ExecStart=mjpg-streamer -i “input_uvc.so” -o “output_http.so -w /home/kzorluoglu/charts/octoprint/www” Restart=always [Install] WantedBy=multi-user.target Step 2: Enable and Start the Service BONUS: if you want to run mjpg-streamer without sudo, you can try the following steps. Add your user to the video group: sudo usermod -aG video <your_username> […]

AI / Development / PHP

AI Model Trainer with EleutherAI/gpt-j-6b for Chameleon Shop Codes

The Trainer use actually the best chatgpt alternative model on huggingface. Here is the Training notice from Original Source: This model was trained for 402 billion tokens over 383,500 steps on TPU v3-256 pod. It was trained as an autoregressive language model, using cross-entropy loss to maximize the likelihood of predicting the next token correctly. https://huggingface.co/EleutherAI/gpt-j-6b#training-procedure Dataset Links: https://d8devs.com/chameleon-base-and-chameleon-shop-datasets-20230530-1918/ Views: 61