Weird things I've asked AI

Over the past year, I've asked AI some questions that probably shouldn't be allowed to escape my brain. Some started with genuine curiosity. Others started with something weird I noticed, a random thought that wouldn't leave me alone, or a perfectly normal question that somehow spiraled into something ridiculous.

Here are some of the highlights. You're welcome. Or I'm sorry. I'm not entirely sure which.

Suburban Lumberjack: Hurricane Isaias vs. My Backyard

Hurricane Isaias was supposed to be a direct hit. The early tracks had it coming straight for us, so we did the usual storm routine—move the cars, eye the tall dead pine on the adjacent lot, and mentally rehearse the sound of branches hitting the roof. In the end, the storm shifted east and came ashore closer to Destin, leaving us with wind, some rain, and a very specific kind of suburban chaos.

The Day I Almost Got Rich 😄

Screenshot of my mining dashboard during the session — hash rate swings, accepted shares, and difficulty stats all in one place 😄

Every miner has that moment staring at the dashboard, wondering: “Did I just get close to a block?” This is my little LBD (Looking Back Data in this case, although perhaps you're a little [LAPSE... brain dead] from reading it!) post on one specific session where I hit a Best Share of 145.5M. What makes this run even more fun is that it was completely solar powered — running off a GoLabs R150 power bank fed by a Renogy 100W panel plus a Volt Hero 20W panel wired in parallel for a combined 120W of solar input. Off‑grid mining at its finest 😄(* “free” electricity to run the thing, but it cost me $150+ of equipment to get there). And all of that powering a tiny Bitdsk N5.Rex ASIC miner — a little 9‑watt unit that somehow manages to push out around 168.8 Gh/s while sipping power like a phone charger.

The Snapshot

Three Local LLMs Walk Into a Pipeline: An LBD Deep‑Dive

Three local AI models get the same systems-engineering homework. The results are... educational.

When you feed the same complex systems-engineering prompt into three very different local LLMs, you learn a lot—not just about the models, but about how modern AI handles the difference between understanding an architecture and actually implementing one.

The prompt itself was non-trivial: build a fully asynchronous Python data-ingestion pipeline using asyncio, httpx, Pydantic v2, a custom token-bucket rate limiter, exponential-backoff retry logic, a bounded queue, and a worker pool that maintains thread-safe moving averages.

At first glance, there was a clear winner (at least according to Gemini's quick review). Then I looked more closely at the winner.

And that's where this little experiment got considerably more interesting.

Below, we break down how each model responded, what Gemini observed, what I found when I checked the generated code more closely, and what the results actually say about local LLM engineering.