mrbungie

It is kind of noisy because the release recency, which is what your "age" column actually represents, is not important data for the comparison you are trying to make.

Also what message we should get from that table is not really obvious.

There is now an Antigravity CLI which will replace Gemini CLI. Gemini CLI is going to be EOLd by June 18th afaik. Antigravity CLI and GUI share the same agent harness, so it might do the same task.

Source: https://developers.googleblog.com/an-important-update-transi...

He is saying that while holding Worldcoin as THE solution behind his back. He hopes to show it when the time comes.

Well, we were overall better for a couple of centuries after abolishing all-powerful kings + some welfare laws here and there (ymmv, maybe serfdom sounds nice to you). So those changes can work for a while, big emphasis on can and for a while.

Greedy accumulators always end up ruining things for societies when it gets into ridiculous extremes (and there is a part of society that notices and gets fed up).

Yeah, I know AI is useful for that, that's why I said after I learn. Hopefully once.

0 tokens per command >>> Hundreds of tokens per command

It is difficult to believe that you can cobra effect yourself into greatness. I'd rather say the most useful perk for companies doing this is the AI-washing adoption metrics they can report, which will hopefully (for them) increase valuations.

Once I learn a command that is both repeatable and useful, I prefer to either keep it in my mind or in my aliases. Thank you.

I still don't know how to reconcile these reports with what other people say about GenAI-agentic assisted engineering being the only way of working nowadays, especially in startups.

Probably there is no dichotomy going on and it depends on multiple factors, but it seems so weird to see reports that are so different between each other.

Ofc, they were practically dying 30 years ago. But I can't really see why that's is relevant here.

Pretty sure Apple can survive a legal war like this. On the other hand, it sounds like OpenAI is punching above its weight.

The article makes it sound like OpenAI is really entitled in this one. Also legal action is obviously going to be a strong signal of OpenAI's friendliness /s.

I'm always wondering who has the time to consume all the new code that is being produced. Like sure, you can produce at 5-10X the speed, but is someone using those features? Not sure if the typical consumer mind can keep up with such speed of changes.

Investors actually want slop-branded AI on everything right now, so it checks out for companies to maximize for it. We are so fucked.

You usually don't get immediate responses from hires which means delayed gratification and avoiding much of the potential dopaminergic effects you get when engaging with LLMs.

You can play overextending the hire analogy all you want but it is simply not the same.

The gambling part is because of the (hopefully emergent and not purposefully designed) intermittent reinforcement due to the limits. You don't get that with regular hires.

The user will be adversarial and probably learn new tricks to trick the machine, this is not solvable (only) via training data.

Is it though? I thought elite engineers needed to consume billions of tokens monthly and/or pay >100 USD subscriptions to AI tools to realize their potential.

Is psychohistory a thing now? They are just another actor, for sure they have relevant usage data but (1) their view are biased in multiple ways (at least due to perverse incentives), knowingly and unknowingly (2) they are experts in their field, but not necessarily in sociology, economy, and politics, which are arguably necessary to make serious, even if ineffective, predictions about what's going to happen with labor.

I'd rather see what institutions such as NBER have to say than to blindly trust frontier AI labs that won't even publish their methodologies most of the time.

That’s on Anthropic, but also on the broader trend. AI companies and the current state of ML research got us into this reproducibility mess. Papers and peer review got replaced by white papers, and clear experimental setups got replaced by “good-faith” assumptions about how things were done, and now I guess third parties like security companies are supposed to respect those assumptions.

They are in a three-way tension between price, quantity/quality and promises of growth to investors (time horizon-sensitive). Nudge one factor too much and the house of cards falls down.

Then it's contained but depending on the user it can be a vector for a (para)-social engineering attack.

PS: It is Gemini based, that's an LLM.

Not defending GH, but that's what tsunamies of slop can do to a system.

Pretty sure simonw's lethal trifecta [1] has not been "fairly well" mitigated.

[1] https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/

At the same time I'd say sloppy code (human or AI generated) is rarely the best choice. I'd say the best is in between.

We can thank our AI overlords like sama and damodei for that.

They need to offer more 9s of availability before this happens though.

> So for a lot of companies, it's worth using AI to create a replacement.

I'll add the nuance that those might be big companies with slack capacity, or at least firms that already are at a point in their effort/performance curve where marginal effort injections in their core business are not worthy enough (a point that, without being big companies, would be actually weird). Even with AI and as processes become more efficient effort is at premium, and depending on your firm situation an man-hour used in your business might be a better use of effort and time that using it on non-core services.

I've seen Antigravity outputting chinese characters in its thinking traces from time-to-time.

I also remember chinese being discussed as a potential orchestrating language but I don't remember the sources, so 100% anecdotical.

His argument is not about LLM tools but rather about which architecture is better suited for coding with LLMs.