标签：#r

I stared at the GitHub page for what felt like forever. The repo had thousands of stars, hundreds of issues, and a long list of contributors who clearly knew what they were doing. Me? I had a few small personal projects, some half-finished tutorials, and a nagging feeling that I wasn’t “ready” to contribute to real open-source software. Especially not an AI project with fancy models, complex pipelines, and people publishing papers off the codebase. But I wanted in. I wanted to learn how real-world AI systems are built, to get feedback on my code, and to be part of something bigger than my local src/ folder. So I made a deal with myself: no more waiting until I feel “ready.” I’d go from zero to my first pull request (PR) in one focused push. Here’s exactly how I did it, what I learned, and what I’d tell anyone hesitant about contributing to an open-source AI or machine learning project for the first time. Step 1: Pick the Right Project (Not the Biggest One) The biggest mistake I almost made was aiming for the most famous AI repo I could find. Big projects are great, but they can be intimidating and slow for a first-timer. Instead, I looked for: Active maintenance : recent commits, issues being closed, maintainers responding. Clear contribution guidelines: a CONTRIBUTING.md or at least a solid README. Beginner-friendly issues: labels like good first issue, beginner, or help wanted. Scope I could understand: I didn’t need to grasp the entire codebase, just enough to fix one small thing. I ended up choosing a mid-sized open-source AI library : not unknown, not legendary. Perfect. If you’re searching now, try queries like: “awesome open source llm” “open source machine learning projects good first issue” “open source AI tools GitHub” Then scan their issues tab for beginner-friendly tasks. Step 2: Set Up the Project Locally (Without Panicking) Once I picked a project, the next hurdle was getting it to run on my machine. The repo had a typical structure: project/ README.md

Prerequisites Let's create a variable. int myNum = 5 ; Now, myNum refers to the value 5 . However, we can get its memory address using the & operator like this: &myNum . Role 1: Creating pointers A pointer holds a memory address. int * pointerToMyNum = & myNum ; Role 2: Modifying values using a pointer In this case, * works as the dereference operator. * pointerToMyNum = 10 ; Now, if we print myNum , the output will be 10 . Understanding that they are different in each context makes things much easier ✨ Note Both int ptr and int ptr are functionally identical in C.

When a form asks for an image under 100KB, the obvious reaction is to search for an online compressor and upload the file. That works, but it also adds an unnecessary privacy decision: does this image need to leave the device at all? A simpler workflow For ID photos, screenshots, receipts, and other personal images, I prefer tools that do the work locally in the browser. The browser reads the file, resizes or recompresses it, and gives the result back without sending the original to a remote server. My practical process is: Start with the original JPG, PNG, or WebP. Set the required maximum size rather than guessing a quality percentage. Keep the aspect ratio unless the destination specifies exact dimensions. Preview the result at normal size, especially around text and faces. Save the new file under a different name so the original remains untouched. Why target size matters A generic “compress” button may produce a smaller file, but not necessarily one that meets a strict upload limit. A target-size workflow is more useful because it can adjust dimensions and quality together. For many document portals, a visually clean 80–95KB result is safer than a 99.9KB result that may fail after metadata is added. PNG is excellent for flat graphics and screenshots, while JPG is often better for photos. WebP can be efficient, but some older upload forms still accept only JPG or PNG. The destination's rules should decide the output format. The tool I use I built Resize Image around this browser-local approach. It is useful when I need a quick image under a specific size and do not want the original uploaded as part of the resizing process. The link is included for context and disclosure: I am the maker. Local processing does not remove every privacy concern—you should still review the downloaded result and the site where you eventually upload it—but it reduces one unnecessary transfer. The larger lesson is simple: for lightweight image work, the browser is already capable enough

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The Home Depot is once again upgrading its 12-foot-tall skeleton to help keep the viral piece of Halloween decor popular as spooky season creeps closer. Skelly is borrowing some of the tech introduced in the smaller 6.5-foot Ultra Skelly last year, including letting you speak through the skeleton's moving mouth using a mobile app. The […]