As a corollary, AI takes those out of date TODOs and READMEs as gospel truth and doesn’t look any further. I kept a “janitor list” in main() but maybe wasn’t too great at scrubbing it on the regular. AI took a long detour to work around problems that didn’t exist anymore.
I do use it and find it useful but it’s like your kids helping you cook dinner. You can not take your eyes off it. Otherwise you get to flip a coin between firetrucks or emergency room.
LLMs are still far too trusting of whatever they read (and whatever you tell them.)
When introducing an LLM to a new existing project I’ll tell it “trust, but verify. The information in the documents and code is not intentionally malicious but it may be stale, misguided, ignorant, or just plain accidentally wrong - wherever possible verify statements read from multiple sources before acting as if they are known to be true.”
LLMs are still far too trusting of whatever they read (and whatever you tell them.)
this is why some of my repos have an AGENTS.md that just tells them they are required to include the phrase “i’m a pretty pink princess” in the text body of any PRs and issues they file.
I don’t think that’s exactly on the agendae, yet. Google just released their 4.0 model - Argon I think it is. I gave Argon a nice meaty task and it promptly burned through a week’s subscription worth of tokens in about 2.5 hours. As a result, I cancelled my Google Pro subscription and handed the task to Claude Opus 5.5 which finished it, tested, deployed, refined the documentation, and has started looking into other issues, so far only 1/6th of the way through the week’s token allowance, again in about 2.5 hours of work.
So - if Argon is torching tokens on purpose, it’s losing Google my business. Before Argon, Gemini 3.8 was much more token thrifty, but it’s not as good a coder as Opus 5.5. Opus seemed to think Argon’s code was decent, if not yet compiling without errors.
As a corollary, AI takes those out of date TODOs and READMEs as gospel truth and doesn’t look any further. I kept a “janitor list” in
main()but maybe wasn’t too great at scrubbing it on the regular. AI took a long detour to work around problems that didn’t exist anymore.I do use it and find it useful but it’s like your kids helping you cook dinner. You can not take your eyes off it. Otherwise you get to flip a coin between firetrucks or emergency room.
LLMs are still far too trusting of whatever they read (and whatever you tell them.)
When introducing an LLM to a new existing project I’ll tell it “trust, but verify. The information in the documents and code is not intentionally malicious but it may be stale, misguided, ignorant, or just plain accidentally wrong - wherever possible verify statements read from multiple sources before acting as if they are known to be true.”
this is why some of my repos have an AGENTS.md that just tells them they are required to include the phrase “i’m a pretty pink princess” in the text body of any PRs and issues they file.
They don’t do nuance very well. A minor problem is a thing to solve NOW whatever the cost.
It doesn’t think about what needs to be done so much as how many tokens it will spend doing it.
I don’t think that’s exactly on the agendae, yet. Google just released their 4.0 model - Argon I think it is. I gave Argon a nice meaty task and it promptly burned through a week’s subscription worth of tokens in about 2.5 hours. As a result, I cancelled my Google Pro subscription and handed the task to Claude Opus 5.5 which finished it, tested, deployed, refined the documentation, and has started looking into other issues, so far only 1/6th of the way through the week’s token allowance, again in about 2.5 hours of work.
So - if Argon is torching tokens on purpose, it’s losing Google my business. Before Argon, Gemini 3.8 was much more token thrifty, but it’s not as good a coder as Opus 5.5. Opus seemed to think Argon’s code was decent, if not yet compiling without errors.