Picture this: you click one file, and within seconds, a folder appears on your desktop with a list of the week’s AI headlines, each with a clickable link and a short summary explaining what the article’s about.
No digging through search engines, no paywalls, no API fees. Just a clean, automated snapshot of what’s happening in artificial intelligence.
That’s what I tried to build this week. It’s not perfect yet. Some sites block the scraper, and summaries need work, but the process of getting there showed me a new way to build: vibe coding.
AI Tools and Courses I Tried This Week
This project started because I hit a wall with n8n and I’ve felt as though regular python learning hasn’t pushed me far enough towards my goals yet. The automation tool looked promising, but its reliance on paid API keys stopped me cold. (For now.)
I needed a way to track AI news without spending money, so I turned back to BeautifulSoup, a Python library that pulls data directly from website HTML.
It’s like a robot reading a webpage and copying the headlines for you, no API required. That became the core of what I’m calling my “Futureproof Scraper.”
Then I sort of stumbled into vibe coding, a term from Andrej Karpathy. Instead of writing everything myself, I described what I wanted and let Claude.ai handle the code: “A scraper that grabs AI headlines, adds summaries, and saves them in a desktop folder.”
Claude took that and it spit out working code after a few tweaks. My job was to clarify the goal and test the results, not to wrestle with syntax errors.
Finally, I used PyInstaller to turn the script into a standalone app. The plan was to create a downloadable tool for my blog readers, something they could run without installing Python or dealing with libraries.
The first attempt crashed because of a dependency issue with python-docx, but after switching to plain text output, I got a working .app file. It’s not ready to show anyone yet, but it’s a real tool.
The real win wasn’t mastering any single tool though. It was learning that describing the problem clearly got me to a functional solution faster than studying Python manually.
Mapping My AI Learning Curve
A few months ago, I was stuck debugging basic Python errors. File paths that didn’t exist, loops that wouldn’t run, etc.
I wasn’t just coding this week; I was strategizing with ChatGPT and Claude about what makes a tool useful. When the scraper pulled headlines but no summaries, I didn’t bury myself in HTML parsing guides.
I told Claude, “Readers need enough context to know if an article’s worth their time. Can we grab the first paragraph?” Claude adjusted the code to fetch and clean up those paragraphs. Fixed in minutes.
At first, vibe coding almost felt dishonest. Like I was skipping steps. Was I really programming if I wasn’t writing every function? Am I cheating my own learning here? But the more I leaned into it, the more it made sense considering what I really want to do.
My focus shifted from code details to the tool’s purpose: deliver AI news in a way that saves time. Claude handled the technical side, letting me think about what readers actually need. That shift, from syntax to outcomes, changed how I’m going to approach building things from now on.
I’m not saying fundamentals don’t matter. Knowing how HTML parsing works or why a site blocks scraping helps me troubleshoot. But vibe coding showed me that obsessing over perfect code might be a trap.
The real skill is articulating what you want so precisely that an AI can execute it. That’s where the magic happens.
AI Terms/Definitions
I’ve been adding new terms to my glossary every week to lock these ideas in place. As always, these aren’t dictionary-perfect. They’re just how I understand them right now, based on what I’ve seen and read so far in my journey.
Vibe Coding
Programming by describing desired outcomes in natural language rather than writing explicit code. You focus on what the software should accomplish while AI handles the technical implementation details.
Web Scraping
Automated data extraction from websites using code that reads HTML structures and pulls out specific information. Like having a robot assistant that visits news sites and copies headlines into an organized list for you.
PyInstaller
Software that converts Python scripts into standalone applications. It bundles your code with all necessary libraries so users can run programs without installing Python or managing dependencies themselves.
HTML Parsing
The process of analyzing webpage code to locate and extract specific content elements. Your script learns to recognize patterns like “headlines always appear in h2 tags with this particular class name.”
Dependency Hell
When software requires multiple external libraries that conflict with each other or create installation headaches. PyInstaller helps avoid this by packaging everything together upfront.
Closing the Loop
This week showed me what the future of building things might actually look like. Humans directing machines toward solving real problems for real people. I finally got a taste of the future I’m grinding towards.
The web scraper isn’t done. Not even close. It still needs a good amount of work before I put it up on the blog as a downloadable. But, the fact that it functions AT ALL I’m counting as a major step in the right direction.
I guess my point this week is that the real discovery was experiencing vibe coding firsthand. Not as a replacement for technical understanding, but as a way for me to focus on what matters most: identifying problems worth solving and communicating solutions clearly enough to make them happen.
I think this is where the world is going to end up going. When people like me, with extremely minimal coding knowledge can do it, the constraint isn’t technical capability anymore.
It’s problem identification and clear communication. Those remain fundamentally human, developable skills. And they become more valuable as AI keeps getting better.
What about you? What’s the longest you’ve stuck with a tutorial that wasn’t leading to something you’d actually use?
And if AI could handle all the technical implementation, what would you actually want to create?
