Three days. One AI assistant. A pile of real results that crossed from home automation into coding, into life admin. None of them required deep specialization — just clear prompting structure.
1. Making the Smart Home Actually Smart
Problem: the downstairs thermostat ignored the upstairs temperature. Using AI as a rapid research and scripting partner, I:
- Mapped compatibility between Aqara sensors, Home Assistant, and Voice Monkey.
- Wrote automation logic so devices not built to communicate could still share temperature data.
- Troubleshot mismatches faster by asking constraint-rich questions.
Outcome: my downstairs thermostat now responds to the upstairs climate using a tiny battery sensor. No manual control needed.
2. Handling Files Too Big for Context Windows
A single text file exceeded my model’s 128 k context. Instead of chopping it by hand, I had the AI co-design a script to:
- Chunk, label, and process large files automatically.
- Keep each batch under GPU memory limits without swapping to CPU.
Outcome: a reusable tool that keeps future long-context jobs clean and safe for local inference.
3. Rapid Prototyping a Red-Team Tool
In a single afternoon we scoped, scaffolded, and partially implemented a PowerShell red-team harness:
- Outlined MVP features and directory structure.
- Generated starter modules that matched my naming conventions.
- Planned test cases up front — saving debug cycles later.
Outcome: a working framework, ready to expand without re-architecture.
4. AI-Assisted Meal Planning
I applied the same prompt discipline to a personal problem — food planning.
- Defined constraints: budget, nutrition, variety, available stores.
- Generated batch-cooking plans and adaptive shopping lists.
- Used mobile AI for quick ingredient clarifications mid-store.
Outcome: a month of prepped meals and a system I can reuse with new constraints anytime.
5. Miscellaneous Wins
- Installed dual-Python environments with Conda, dependency-error free.
- Fact-checked articles for friends with source citations.
- Built reusable prompting and scripting templates for future projects.
The Common Thread
Different domains, same rhythm:
- Set context. Define the goal, what’s known, and what matters.
- State constraints. Format limits, available tools, end conditions.
- Iterate quickly. Test, refine, restart when off-track.
That’s the heart of effective prompt engineering — and once you see it work, it’s hard to go back to solo trial-and-error.
Closing
Three days weren’t enough for mastery, but they were enough to show how fast progress compounds when the AI is guided properly. It’s not magic. It’s method.