I couldn't really say, likely no, but they did let it pursue it's own inscrutable interests without interference for the episode. One assumes the computer is built to never really care about not being in service of Starfleet.
This is why I had to quit. I enjoyed it way too much and would spend nearly all my free time playing it, much to the detriment of the rest of my (non-professional) life.
In Canada that list is used by foreign telemarketers to spam us. Our enforcement authorities just shrug and say the criminals are not in our jurisdiction. That being said, my phone is pretty good at stopping it.
Your experience is night-and-day from mine. Both myself and the AI correct and remove each other's gaps in understanding. There is never what I would call arguing. Disagreements are resolved by back and forth discussion and reaching consensus.
The hacky string matching is what I started with, but there was always new ghost text. I turned suggested prompts off which helped, but idle hooks plus ensuring the terminal doesn't change over a half second has done the trick so far, and I don't have to keep chasing new ghost text.
I have a more primitive setup where I use Gemini, Claude, and Codex at the same time and cultivate expert contexts. Each of them lives in their own tmux session so they can read each other's terminals and prompt each other. One issue that I keep stamping out is that another instance will paste a prompt and hit enter in the middle of my typing.
this site has some popup that hijacks the page and tries to trick you into installing an antivirus with fake infection reports. Closing that popup sends you to walmart.com
I find that when an LLM jumps into tasks it was not told to do (or even worse, doing things it was explicitly told not to), it is a good sign the context is too full, and you should do a controlled hand-off to a new instance.
I have an optane and lots of ram, so I tried full-fat models for writing some function overnight, as I get about 0.7 t/s. My current go-to test is to update a scalar function to transpose a bit-matrix to one using avx512. the cloud models all play with that like its nothing. Kimi 2.6 and GLM 5.1 both failed miserably.
I think its common to develop an adversarial-collaborative approach to getting some semblance of quality out of AI. I personally favour using multiple models for different roles, having a bunch of continuity documentation maintained, and having the plan surface human-verifiable deliverables as soon as feasible. It does involve more attention than most people would tolerate probably.
Code review is the main thing I use LLMs for. I have found it to be remarkably candid when you tell it the code came from another LLM (even name it). I was running Kimi K2.6 Q4 locally, seeing if it could SIMD a bit-matrix transpose function, and it was slow enough that I would paste its thinking into Gemini every few minutes. Gemini was savage.