A lot of folks don't seem to understand that it's the harness and its behavior paired with a particular model that produces the result, and you have to focus on both aspects. He barely acknowledges the importance of the harness in his write-up, beyond the two notes above.

Hacker News has long ago decided that making comments on the quality of the writing in the comments is not welcome. Correcting spelling, correcting word usage, changing phrasing, and making other suggestions to improve the grammar of a post is not something that Hacker News readers want to encounter in the comments section.

It bothers me every day because there are gobs of cases where the writing is horrible, but I understand why the rule exists. It would make the comments section insufferable if everybody were just correcting each other's grammar the whole time.

The point GP is making, which I understand seems dismissive, is that AI is a tool that people are not retreating from, because it offers the same sorts of gains as previous tool improvements we've seen in the field: from compilers to IDEs to sites like Stack Overflow. I think adding AI to that group of improvements makes a ton of sense, as it has the capacity to provide similar gains in productivity.

> COO Kumar Galhotra said Ford had been over-relying on automated quality systems without getting results, per Bloomberg. The returning engineers rebuilt the data pipelines feeding Ford's AI training, mentored junior staff, and reprogrammed the automated systems they had originally been brought in to replace.

Similarly with Commonwealth Bank and IBM, which are cited in your second link. None of these companies are saying they're not going to use AI and they're going to go back to manual labor. What they are saying is that they laid off workers prematurely.

The big lesson is highlighted well by IBM's head of HR, which is you can use AI, but you have to continue to invest in humans:

> “If we don’t continue to invest in entry-level hires, what happens in three-five years?,” IBM’s chief human resources officer, Nickle LaMoreaux, said at a Charter AI Summit in New York. “There’s no pipeline; the well simply dries up,” LaMoreaux added.

Their methodology section says they scan for authors and commit messages indicating AI use. This wouldn't work on any of my repos, for example, because I review and commit everything manually.

Overall, I find this sort of thing distasteful, but to each their own.

Here's what they say:

In the tools file, there's a big list of different LLM agents and tooling. Repo-slopscore analyzes every commit in the history, looking at

    Committer name
    Committer email address
    Commit messages

I responded to you above where you mentioned that AI cannot do nuance with some information from the judge in this particular case that claimants in general cannot do nuance because they don't prompt their AI to examine the whole situation. They only prompt for the outcome that they want. So it's not the AI that's lacking nuance in this scenario. It's the human.

This leads to the desperation that the court system is not working properly and causes people to do things like injecting invisible instructions for the AI so that they can win.

There's a long post near the top of this comment section calling the article "garbage" because of this mistake.

https://stencil.so/blog/the-harness-problem

Three GLM models are mentioned, but so is Deepseek, Grok, Minimax, Kimi, and Gemini.