explore VS PostgreSQL

GitHub: Many researchers and organizations share datasets on GitHub repositories. You can search for repositories with datasets using specific keywords. GitHub

There isn't a lot going on there, but I like the way he added the little language and tech-stack icons to his GitHub profile using the images served by the GitHub Explore page as well.

Checking the GitHub explore page for fun projects and inspiration

Uh? How is this AI thingie different from Github Explore?

https://github.com/explore

What is the real URL for Github Feed?

The Before I Die Code project’s front end is built with React, JavaScript, HTML, and CSS, and it’s currently deployed on Vercel. However, the technology will change with the deployment as I am planning on applying for this open-source project to be featured on the GitHub explore page. For this, the project will need to be using GitHub pages.

New users finding a project is much more likely on GitHub. I'm not necessarily talking about search. I would expect that experience to be about the same on both, though generally, I see a lot more empty projects showing up in results on GitLab for some reason, at least for things I've searched for there. Github seems to do reasonably well with search ranking. I'm more concerned about the poor experience with https://gitlab.com/explore compared to https://github.com/explore where people are going to be discovering new libraries when they don't know what they are looking for and are either browsing topically or just browsing for fun and learning. GitLab seems to do particularly poorly in their curation and selection of what they show you. GitHub on the other hand, has connected me with countless extremely high quality projects through this feature. Finally, the discoverability advantage of GitHub over gitlab is also simply because more people use GitHub. You don't need to primarily use GitHub to use it to point to GitLab If you want to work there, but you're certainly going to have more users finding your project if you have presence on GitHub.

You can also star projects you find interesting and github will use that for the EXPLORE tab to show you other cool projects.

Subscribe to atom/rss feed of https://github.com/explore (you prolly want to have a gihub account) or https://github.com/trending and be sure to at least 'follow' any projects that may interest you. No need to install everything.

Otherwise search for "good first issue" or similar, there are some sites that curate them. Or see GitHub Explore

Data Handling: Utilizes Windmill for data pipelines, with a primary database powered by PostgreSQL. Auxiliary data storage is handled by MongoDB, with Redis for caching to optimize performance

PostgreSQL

The LMS Moodle Operator serves as a meta-operator, orchestrating the deployment and management of Moodle instances in Kubernetes. It handles the entire stack required to run Moodle, including components like Postgres, Keydb, NFS-Ganesha, and Moodle itself. Each of these components has its own Kubernetes Operator, ensuring seamless integration and management.

Another key feature of txtai is being able to quickly move from prototyping to production. This article will demonstrate how txtai can integrate with Postgres, a powerful, production-ready and open source object-relational database system. After txtai persists content to Postgres, we'll show it can be directly queried with SQL from any Postgres client

SQL (Structured Query Language) databases are relational databases. They organize data into tables with rows and columns, and they use SQL for querying and managing data. Examples include MySQL, PostgreSQL, and SQLite.

Node.js, MySQL and PostgreSQL servers installed on your machine

Each app’s front end is built with Qwik and uses Tailwind for styling. The server-side is powered by Qwik City (Qwik’s official meta-framework) and runs on Node.js hosted on a shared Linode VPS. The apps also use PM2 for process management and Caddy as a reverse proxy and SSL provisioner. The data is stored in a PostgreSQL database that also runs on a shared Linode VPS. The apps interact with the database using Drizzle, an Object-Relational Mapper (ORM) for JavaScript. The entire infrastructure for both apps is managed with Terraform using the Terraform Linode provider, which was new to me, but made provisioning and destroying infrastructure really fast and easy (once I learned how it all worked).

I've used MySQL for years. But recently, I found myself working PostgreSQL and simple things like dumping and restoring a database are different enough that I decided to document the process. It's straightforward enough once I knew how.