mhink

Yeah, it's not accounting for metropolitan areas very well. I found it kind of hilarious that Federal Way, WA (a minor part of the Seattle metro area) got an icon but Olympia (the state capitol!) didn't- because Federal Way has a population of 100k in the city proper but Olympia only has 55k (but its metro area has 300k).

> My main issue with Southern-style hot sauce like Tabasco is that it tends to be far too vinegar-forward for my taste. I strongly prefer Mexican-style sauces.

For what it's worth, this is sort of the point of this sauce- like malt vinegar, it makes more sense as a condiment than an ingredient. A lot of Southern and Cajun dishes don't have much acidity in and of themselves, so a vinegar-forward sauce helps balance them out while adding a little bit of heat as well.

From what it sounds like, unlike domains under other TLDs, when you purchase a domain under .name you always purchase specifically the three-segment domain.

i.e. I own john.doe.name, you own george.joe.name. Once this change goes through, only "doe.name" can be owned, so who gets it?

Oh man, that's a blast from the past, I think I had that book! Was that the one with an aluminum cover?

I haven't read much of Shakespeare, but the lightbulb moment for me personally was the 1996 "Romeo + Juliet" movie with DiCaprio. The modern contextualization makes it so much easier to parse the period dialogue.

In fact, a combination of the two is likely to be even more effective. As another commenter mentioned, heuristic-based analysis can generate false positives, but that's less of a problem if it's possible to analyze these in an additional AI step.

Also worth pointing out that the N isn't too terribly large: the article says that the ecosystem has about 4000 plugins and themes? With that volume, you could almost reasonably just use static analysis to flag suspicious plugins (saving tokens), have an AI do a pre-analysis and pass to a human for final decision-making.

I was gonna post this! I actually kept it bookmarked front and center, and have checked in for awhile. It seems that the agent has been blocked this whole time, waiting for its creator to put it in touch with someone it needs to talk to. The creator, in the meantime, seems too preoccupied with being an AI thought leader on Twitter to actually follow up on the "project". Got a lot of attention, though, which was obviously the point.

The entire thing is actually kind of irritating to me, because it's kind of an insult to small farmers- an influential techie comes in and generates all kinds of hype about an AI running a farm, sets the project up as if it's going to be this revolutionary experiment, then apparently completely forgets about it the next time something new and shiny pops up. Meanwhile the project completely fails to fulfill the hype.

Not to mention, I feel a little bad for the agent- admittedly in the same way I'd feel "bad" for a robot repeatedly bumping into a wall. I wish he'd shut it all down, honestly.

One nitpick I noticed:

> String Slicing > You can extract a substring using bracket syntax with a range: s[start:end]. Both start and end are byte offsets. The slice includes start and excludes end.

Given that all strings are UTF-8, I note that there's not a great way to iterate over strings by _code point_. Using byte offsets is certainly more performant, but I could see this being a common request if you're expecting a lot of string manipulation to happen in these programs.

Other than that, this looks pretty cool. Unlike other commenters, I kinda like the lack of operator precedence. I wouldn't be surprised if it turns out to be not a huge problem, since LLMs generating code with this language would be pattern-matching on existing code, which will always have explicit parentheses.

And NTP, if I recall correctly.

> basterized

And yet, it's still somewhat better than the Hacker News comment using bastardized English words.

> how do you safely let your company ship AI-generated code at scale without causing catastrophic failures? Nobody has solved this yet.

Ultimately, it's the same way you ship human-generated code at scale without causing catastrophic failure: by only investing trust in critical systems to people who are trustworthy and have skin in the game.

There are two possibilities right now: either AI continues to get better, to the point where AI tools become so capable that completely non-technical stakeholders can trust them with truly business-critical decision making, or the industry develops a full understanding of their capabilities and is able to dial in a correct amount of responsibility to engineers (accounting for whatever additional capability AI can provide). Personally, I think (hope?) we're going to land in the latter situation, where individual engineers can comfortably ship and maintain about as much as an entire team could in years past.

As you said, part of the difficulty is years of technical debt and bureaucracy. At larger companies, there is a *lot* of knowledge about how and why things work that doesn't get explicitly encoded anywhere. There could be a service processing batch jobs against a database whose URL is only accessible via service discovery, and the service's runtime config lives in a database somewhere, and the only person who knows about it left the company five years ago, and their former manager knows about it but transferred to a different team in the meantime, but if it falls over, it's going to cause a high-severity issue affecting seven teams, and the new manager barely knows it exists. This is a contrived example, but it goes to what you're saying: just being able to write code faster doesn't solve these kinds of problems.

To be fair, the phrase "begging the question" makes almost no sense from a modern English perspective- according to Wikipedia, it's already a bad translation of a Latin phrase that's tied pretty closely to a specific debate format.

By contrast, the colloquial use feels like an abbreviation of the implicit phrase "it begs for the question to be asked", which makes so much more sense than the "correct" meaning that if I'm being perfectly honest, I'd rather use it.

I like Wikipedia's alternate name for the fallacy: "assuming the conclusion", because it explains what's actually happening.

For what it's worth, the first two steps in your lookup would come naturally to a native speaker- it's a suffix formation similar to e.g. "cleanliness" and "friendliness".

"Curmudgeon" itself is interesting, because while it's not particularly common, I actually think a lot of native English speakers would recognize it because it's got a lot of character- for some reason, the way it feels to say and the way it sounds almost has some of the character of the meaning.

TFA mentions that they added personality presets earlier this year, and just added a few more in this update:

> Earlier this year, we added preset options to tailor the tone of how ChatGPT responds. Today, we’re refining those options to better reflect the most common ways people use ChatGPT. Default, Friendly (formerly Listener), and Efficient (formerly Robot) remain (with updates), and we’re adding Professional, Candid, and Quirky. [...] The original Cynical (formerly Cynic) and Nerdy (formerly Nerd) options we introduced earlier this year will remain available unchanged under the same dropdown in personalization settings.

as well as:

> Additionally, the updated GPT‑5.1 models are also better at adhering to custom instructions, giving you even more precise control over tone and behavior.

So perhaps it'd be worth giving that a shot?