AI Bootcamp Day 1: Fixing My Own Tech Problem (and Learning a Lot About Agentic AI)
November 01, 2025
Today was Day 1 of South Florida Tech Hub’s Inaugural AI Bootcamp, and it opened with a crash course in how broad “AI” really is. It’s not just about chatbots writing essays or code—it’s about how machines collect, consolidate, and act on data in real time.
Generative vs. Analytical AI
We talked about the two big categories:
- Generative AI (ChatGPT, Gemini, MetaAI) — the creative side that can generate human-like speech, music, and images.
- Analytical AI (Amazon, Tesla) — the decision-maker side that’s trained to process new data quickly and accurately.
It was a good reminder that creativity and precision in AI aren’t opposites; they’re two halves of the same system.
Agentic AI: The Next Leap
The highlight for me was Agentic AI — systems that don’t just assist, but act on their own.
Example: Manus AI can run 50 tasks simultaneously while you sleep. It thinks, plans, and delivers. Tools like cursor.com and anysphere fall in the same space, blending code automation with autonomous reasoning.
It’s wild to think how close we are to AI that doesn’t just answer questions but gets things done.
How AI Is Being Used Right Now
The bootcamp outlined three big areas where AI is being implemented:
- Knowledge democratization – making information accessible and shareable.
- Data and research insights – finding patterns and predictions in huge datasets.
- Content generation – building or creating something new.
“Orchestration” AI combines all three—an intelligent system that can gather, analyze, and then create.
Open vs. Closed Source AI Development
We also looked at three paths for building with AI:
- Consume — use an existing SaaS or ISV product.
- Extend — customize or build features on top of one.
- Build — start from scratch and create your own AI product.
That middle step, “extend,” feels like where most innovation will happen for a while—where people like us plug AI into existing workflows rather than rebuilding everything.
The Human Side Still Matters
We circled back to the human-centered design process—Empathize, Define, Ideate, Prototype, Test—and compared CX (Customer Experience), UX (User Experience), and EX (Employee Experience). It sounds basic, but it’s grounding. AI doesn’t mean much if you forget the human part.
My Breakout: A Detour That Actually Worked
During the breakouts, we were supposed to do some “value mapping” exercises—talk about customer pains, gains, and jobs. But my group went rogue and spent the time installing the GitHub Copilot CLI.
That ended up being a turning point for me. I’d been struggling to fix my GitHub page for weeks. Copilot walked me through installing Node, then configured my blog with the Architect Jekyll theme. It worked perfectly. I now have a working blog running directly from my GitHub page.
Not the assignment, but a practical win.
Day 1 Takeaway
Today wasn’t just about learning frameworks or buzzwords. It was about watching AI go from concept to execution—right there on my laptop. I didn’t build an app, but I got something working with AI that I’d given up on before.
That’s progress. And it’s exactly why I came.
Next Up: Day 2 — diving into agent orchestration, workflow automation, and the ethics of AI design.
Stay tuned for more notes (and probably more unexpected detours).