New here ✌🏼

[Forensic-Byte]

Introduce yourself

Hey everyone, I’m Ellie. I’m currently working towards famliarizing myself with Python, SQL, Linux, and working ona few certifications on line for supplemental/baseline knowledge before I start my masters in cybersecurity & data analytics as well as masters in criminal justice late August.

I’ve been focusing on structured learning, rather than bits and pieces here and there. I have also joined a few cs organizations to try and network/be around people in the field. Those cs organizations have allowed me to be a part of my first hackathon (also in August). I am very excited for all of these learning opportuities.

Links (optional)

No response

Where are you in your GitHub journey?

Brand new to GitHub

And where are you going next on GitHub?

I want to get comfortable with GitHub and post projects I work on. However, I do fear the internet will be way too harsh for someone who doesn't know much but is trying lol 😅 GitHub seems huuuugeee and so complex.

What technical skills or projects are you working on?

I've been trying to learn AI/ML as well as use it to make things 👾//fiind better ways to optimoze proceses. The main websites I use to learn these new systems is coursera, microsoft, google, and IBM.

Got a question for us? (optional)

Experienced & inexperienced coders, software developers, UI & UX devs, do you use AI in your builds? If so, how?

I feel like I've seen both sides of it so far; the disdain for the "cheating AI slop" and the love for optimization. It's extremely similar to my previous career (tattooing). Experienced tattooers hating on newbies for using the wireless machines, ipads, stencil printers. Newer tattooers loving all the tech and being confused as to why you wouldn't want to use the technology that optimizes everything.

[padilhx]

Hi Ellie, how are you? I was happy to read your thoughts; I’ve been asking myself the same questions about AI.
I’m a full-stack developer, and while I use AI in my daily work, I try to keep it in check so that it supports me—rather than the other way around. The bar has been raised in the tech world, and I believe AI is now an integral part of working in technology; it all comes down to how you use it to help you learn. If used wisely, I don’t see it as a bad thing at all—quite the opposite. It saves time—for instance, by helping spot a bug you might have been struggling to find for days; a quick conversation with the AI ​​can identify the issue so you can move forward (just an example from my own routine).
Wishing you success in your career, Ellie—hope everything goes well!

[bsmensah-ctrl]

The weak point with AI-assisted builds is not the tool itself; it is accepting output before you can explain or test it. I use it, but I try to make the evidence trail do the teaching: state the behavior I expect, add a test that could prove me wrong, run it, and write down why the failure happened before changing the code.

A concrete example from a detection project: an outside reviewer suggested adding one unrelated repeated event to a synthetic security-event stream. That single control exposed a real correlation bug. The useful learning was not the generated patch; it was understanding why the detector had grouped events too broadly and then preserving that lesson as a regression test.

For your first GitHub projects, a small repo with a clear README, a few honest tests, and a short "what I learned / what is still weak" section will usually earn better feedback than trying to look expert. Python plus SQL is a good combination for a tiny forensic-log analysis project. What kind of evidence interests you most right now - endpoint events, network traffic, or case timelines?