AI

You know that feeling when you solve a complex problem and everything just works? That’s exactly how I felt completing my latest knitting marathon. As someone who spends her days wrangling cloud infrastructure, I’ve discovered that knitting scratches the same problem-solving itch in surprisingly similar ways. Over the past few years, I’ve been treating knitting patterns like code repositories - each project a new challenge to debug, optimize, and deploy. And just like my home lab projects, some turn out better than others. Let me walk you through my recent “releases” and what I learned along the way. ...

I’ve recently started using Claude — specifically the Claude Code CLI — and it’s already changed how I manage this blog. Instead of manually writing posts and running git commands, I can now just describe what I want and Claude handles the rest: writing the post, building the site, committing, and pushing to GitHub. It’s pretty wild. To get started I picked up a Claude Pro subscription, which is required to use Claude Code — the free tier doesn’t include it. Totally worth it. Here’s how to get it set up on a MacBook Air. ...

After running a clean, minimal Ghostwriter theme for some time, I recently migrated this site to Hugo-PaperMod. The goal was better performance, built-in dark mode, improved mobile responsiveness, and easier customization while keeping the lightweight blogging feel. What is Hugo? Hugo is a fast, open-source static site generator written in Go. Unlike traditional CMS platforms (WordPress, Ghost, etc.), Hugo compiles Markdown content, templates, and assets into plain HTML, CSS, and JS files at build time—no database, no server-side runtime. This results in blazing-fast page loads, strong security (no attack surface), low hosting costs, and easy version control via Git. ...

Over the next year, I will be completing a research project exploring how biased training data effects the machine learning algorithm of self-driving cars. This project, which I’ll detail in a later post, involves deep neural networks (DNNs), computer vision, linear algebra, and more. Is this project a massive undertaking? Yes. Will that stop me? Absolutely not. Let’s get started. Neural Network Basics First, what is artificial intelligence (AI)? AI is a field of computer science pertaining to programming computers so that they demonstrate human-like intelligence. ...

In my first Capstone post I gave a rundown of basic AI terms and how to use Tensorflow to create your own machine learning (ML) script. I’m using Tensorflow to write a script that can process images of roads and determine whether there is an obstacle in the road or not. Since I’m studying self-driving cars, I’d like to see if using biased data to train an object detection script affects the model’s performance. To do this, I’m using a training dataset of clear, bright road images to train a neural network and then testing that script with corrupted images of roads. If the model can’t recognize obstacles in images that are corrupted, then the biased training dataset did have an affect on the model. ...

In addition to text analysis Microsoft’s cognitive services allow you to convert speech to text, text to speech, and even translate between spoken languages. This powerful AI service is surprisingly easy to use. I was able to speak into my computers microphone and receive a text translation in one language or a spoken translation in multiple languages –all with the code provided on Microsoft Learn. You have the option to work with the speech SDK (software development kit) in C# or python, I choose python. ...