Seems like this pretty much, though perhaps the more interesting piece is that 'generate a bunch of tokens consistent with human output associated with the environment without more specific direction' influenced the models to have what seemed to be better tokens generated later when they had to now be consistent with specific scenarios.

Though I'm too lazy to read the paper and the 'generally beat' phrasing gives me some wondering about how definitive this observation is versus how much of it might be finding what they wanted to find in the results.

As much as it can apply to online communities in general including slashdot, Reddit and reddit-likes *really* take the cake for creating circle-jerk communities. Creating niche communities with moderators that ban at any comment they didn't want to see. Saw a reply to one of my comments that I thought to be a bit dumb (basically, that we should replace TLS certificates with blockchain), but not horribly unforgiveable or anything, and the comment got removed and the user banned from the community. Mod saw 'blockchain' and while I understand a negative gut reaction, nothing that should have rated a moderator going that far.

Alternatively, some people in that subreddit can't stand that anyone could express valid concerns about Tesla and decides that it must be some irrational, inexplicable blind anti-Tesla rather than informed and real concerns.

Not to worry, recall that Musk *also* has ambitions of X being a finance company, so voila, every child can have an 'X brokerage account' waiting for them with a share in it. Now he gets every child defaulting to his network, no problem.

I don't think a grant of one share of a specific stock will be a particularly educational event. I think generally in school a teach will, at some point, do a more educational exercise where students are actually doing mock trading and, critically, trying to evaluate different companies and make predictions on where things will go.

Yeah, that latter one basically focused on the fact the output looked verbatim like the 'training' material, so the judgement was that AI was more an excuse than a demonstrable transformative thing.

Note in some other case I recall the AI company arguing that the ingest and training of material is 'transformative' but the engines could be used to violate copyright, but then the *operator* would be on the hook for infringement, as the operator supplied the prompt that resulted in infringement.

There was also that OpenAI deal with Disney that was going to let Disney characters be used in OpenAI output but that got dropped. Lots of mixed assumptions in play with the AI companies asserting that they don't need permission, getting permission or forcing permission anyway, and shifting responsibility to the users.

Even if it exactly works as advertised, if you thought a rock kicked up by a tire was bad, just wait....

And thanks to hiring 1,000 new people to patch it, now they have N * 1.5 vulnerabilities!

I meant infested, not invested

*You* would not want this.

However on endpoints vaguely related to your work, your employer *loves* excuses to give Microsoft money to invest as many endpoints as possible.

Nowhere in this do I see how maintaining the history in this manner even benefits AI. I haven't seen evidence that it can benefit from an insanely over-granular change history.

This is not debating the volume of code, it's the granularity of delivering it as tiny 'changesets'. Just because you *can* create a system that can partition changes into 3 second intervals does not mean there's any value for machine nor human in doing so.

To start, the condescension is hardly helpful.

The point stands, you have to consider the audience for the change tracking. How much granularity is really useful even for the AI models themselves to digest? Certainly no human will digest, but I also don't think the AI is going to be prioritizing putting hundreds of thousands of change history entries into the context window on the off chance that it is relevant to the task at hand.

It seems like a solution to pretend around change management while abandoning any actual use of change management, by human or machines.

Though in that scenario, it seems bad or useless for the AI as well, as the bloated change history is a waste of context window. Unless they just ignore it in which case what was the point in bothering.

But perhaps it is just another attempt to normalize "human shouldn't be in the loop" mindset, the new toy is but an excuse to say you *should* be having a codebase that is being changed every couple of seconds without bound.

I just cannot wait for that day. When people have a least a *little* less incentive for certain folks to butt in on other people's jobs and tell them how it should be done, despite never even doing nor using an AI to do said job in a serious capacity.

This story triggered a colleague to tell me that I need to stop using git, despite never having used *any* version control scheme nor even using an AI to generate code, just super armchair software development. I do not understand that mindset of people that invested in the nitty gritty of software development without the will to actually do software development.

A changeset history is only so useful compared to analysis of the code as it is. Even pretty large context windows need prioritization. I would think an overly granular changeset history would be a really low prioriity.

Compared to more aggregated changes into fewer changesets, where the history would be probably more digestable, even for AI consumption.