The thing is I think that I didn’t make any conscious decision to hop on AI, it’s just that it’s quite addictive and my nerd ADHD took over. But we are nerds, we’re often naturally inclined to be early adopters. Others are naturally hesitant of new tech and suspicious of changes to their workflows.
Also there’s an unusually high downside potential to AI, so I don’t even think that hesitancy is necessarily unwarranted. The new slop economy, the “they’re coming for our jobs” effect, “mechahitler”, Palantir, possible extinction-level rogue AI events…
History will attend to itself, as will the discourse. In the meantime I feel that it’s incumbent on us as nerds to try to do AI right, because there are definitely bad actors out there trying to do it wrong.
The standard skeptical position (“LLMs have no theory of mind”) assumes a single unified self that either does or doesn’t model other minds. But this paper suggests models have access to a space of potential personas, steering away increases the model’s tendency to identify as other entities, which they traverse based on conversational dynamics. So it’s less no theory of mind and more too many potential minds, insufficiently anchored.
A language which is not 1.0, and has repeatedly changed its IO implementation in a non-backwards-compatible way is certainly a courageous choice for production code.
I don’t hate git either but you’ll meet very few people who will claim its UX is optimal. JJ’s interaction model is much simpler than git’s, and the difficulty I found is that the better you know git, the harder it is to unlearn all its quirks.
To Broadcom you’re not a customer, you’re a mark, a patsy, stooge, a _victim_. Their aim is to establish exactly what they can get away with, how far they can abuse you, before you’ll just walk away.
I have noticed that LLMs are actually pretty decent at redteaming code, so I’ve made it a habit of getting them to do that for code they generate periodically. A good loop is (a) generate code, (b) add test coverage for the code (to 70-80%) (c) redteam the code for possible performance/security concerns, (d) add regression tests for the issues uncovered and then fix the code.
I’m sure this’ll be misreported and wilfully misinterpreted because of the current fractious state of the AI discourse, but given the lawsuit was to do with piracy, not the copyright-compliance of LLMs, and in any case, given they settled out of court, thus presumably admit no wrongdoing, conveniently no legal precedent is established either way.
I would not be surprised if investors made their last round of funding contingent on settling this matter out of court precisely to ensure no precedents are set.
TBH I’m surprised it’s taken them this long to change their mind on this, because I find it incredibly frustrating to know that current gen agentic coding systems are incapable of actually learning anything from their interactions with me - especially when they make the same stupid mistakes over and over.
Also there’s an unusually high downside potential to AI, so I don’t even think that hesitancy is necessarily unwarranted. The new slop economy, the “they’re coming for our jobs” effect, “mechahitler”, Palantir, possible extinction-level rogue AI events…
History will attend to itself, as will the discourse. In the meantime I feel that it’s incumbent on us as nerds to try to do AI right, because there are definitely bad actors out there trying to do it wrong.