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I doubt there any reasonable people outside the USA that still think Trump's regime is reasonable. Reasonable people of the USA, why would anyone still think this regime is OK? Please explain as I'm completely dumbfounded

Would "decrease randomness" be acceptable to you?

The problem in Scarf's case wasn't Haskell's type system, but the long compile time for even small changes.

I thought that was part of the appeal of this. There's something spooky and ironic about it.

I hate the marketing-selling-linkedn style as much as anyone, but I don't think it's an LLM thing in particular. It's a style that existed before LLMs and it's very easy to make LLMs avoid it with one or two prompt paragraphs.

For what it's worth, I didn't get that vibe reading this post.

I feel this is a lazy, straw-manning comment.

Maybe it's not something they would "typically miss", but, from proof by existence, it's something they sometimes miss.

It does speak to the benefits of using lean in that you don't need to be clever about the different examples you test.

>The strike by Alibaba is described as a "distillation" effort, which Anthropic has said involves training a less capable model on the outputs of a stronger one.

Claude used TB of content without permission to train their model and it was ok for them. Now someone else uses the output of a Claude model to train model and they cry foul.

Latinamerican here. When you talk about "adversarial country" I think of the USA (they can kidnap a president, kill people on boats without a trial, etc) and not China. YMMV for different regions.

>I'm still a AI skeptic

What does this mean in June 2026 wrt coding?

To me it sounds like being a "rice cooker skeptic". Some people don't like using rice cookers, some do.

Here it's important to take into account the consequences / cost of false positive vs false negatives.

If you're building a dashboard for visualizing something fun (hot dog sales in sport games) then the corner case error has low cost. I'm happy having this vibe coded dashboard that works 99/100 and my world is better with it existing.

Crypto is on the opposite scale (and I'm surprised this blog doesn't realize it): 9999/10000 isn't good enough because the corner cases have dire consequences. So, yeah, bad example for vibe coding

Read parent's post carefully. The post starts by saying that discussing whether they have subjective emotion is a waste of time, so the post is definitely NOT saying that Claude has emotions.

I've had good experiences with Cursor so far and it's my main IDE. I've noticed some UI changes, but I've switched fast and they didn't bug me

I find talking about X psychosis (or generally using mental illness metaphors) unproductive. It sets up the conversation to be "nothing else to do with this person".

Maybe the problem is you, but you won't figure that out if you think the other person has psychosis.

For example, maybe you need to do a better job explaining, changing your language, simplifying things, being more concrete with consequences.

Or maybe you aren't understanding that the other person has different objectives/ loss function that makes them make seemingly weird conclusions.

The USA has the biggest incarceration rate of any developed country.

If you say that it's hard to get the state to put you in jail, then the only way I can reconcile that with facts is that people in the USA commit crimes X10 times more than in other developed countries.

Do you think that's true?

Are you talking about open source or commercial products? I can't speak for the pytorch lighting case, but I wouldn't be surprised if the maintainers didn't get any $ from it. They would be sad if the credibility of the package suffers, but ultimately it wouldn't make a big difference to them

You can evaluate the limits of a spoon by trying to cut meat with it.

The point is what are the typical use cases for the tool / what are the agreed upon areas of application?

Making the LLM do math with large numbers, I would argue, is not in its typical use case, thought it's at the border.

Asking an image generator model to calculate numbers before running an image sounds definitely NOT like a reasonable use case (do people need it? Will people try using it for this purpose?)

"curve-fitting" has a long history (centuries old) and could be regarded more as a numerical method issue.

Rigorous understanding of what is over fitting, techniques to avoid it and select the right complexity of the model, etc, are much newer. This is a statistical issue.

My point is that forecasting isn't curve fitting, even thought curve fitting is one element of it.

>as it seems to be mostly written by AI.

Is there something in particular that made you conclude that or are you going just with how it felt?

For what it's worth, it didn't seem to me.

Not a local, but in my experience this is due to tourists not being able to speak Japanese, which makes the people working in a place very uncomfortable ("will this person follow the rules? How can I do proper service if I can't communicate?"). A 大丈夫、少し日本語をしゃべります (it's ok, I speak a bit of japanese) has been enough to open the doors for me.

That being said, they do have issues with some nationalities. For example, the average American is way too loud for the average japanese place. Even if they think they are being polite, they just talk too loud and too much for japanese sensibilities.

I get your point, but I don't think your nit-pick is useful in this case.

The point is that you can't abstract away the details of back propagation (which involve computing gradients) under some circumstances. For example, when we are using gradient descend. Maybe in other circumstances (global optimization algorithm) it wouldn't be an issue, but the leaky abstraction idea isn't that the abstraction is always an issue.

(Right now, back propagation is virtually the only way to calculate gradients in deep learning)

>But all intelligence, of any sort, is "jagged" when measured against a different set of problems or environments.

On the other hand, research on "common intelligence" AFAIK shows that most measures of different types of intelligence have a very high correlation and some (apologies, I don't know the literature) have posited that we should think about some "general common intelligence" to understand this.

The surprising thing about AI so far is how much more jagged it is wrt to human intelligence

>Things also become very difficult to reason about because their is state in the bandit stats that are being used to optimize things. You can often think of that as a black box, but sometimes you need to look inside and it can be very difficult.

One way to peak into the state is to use bayesian models to represent the "belief" state of the bandits. For example, the arm's "utility" can be a linear function of the features of the arm. At each period, you can inspect the coefficients (and their distribution) for each arm.

See this package:

https://github.com/bayesianbandits/bayesianbandits

>the limits of LLMs will be hit long before we they start to take on human capabilities.

Why do you think this? The rest of the comment is just rephrasing this point ("llms isn't suited for AGI"), but you don't seem to provide any argument.

50% of Gaza destroyed, 100% of the hospitals. It's a good thing they precisely targeted Hamas assets

Not sure what is your point. The Israeli military could throw a few atomic bombs and wipe out the entire population in Gaza. That they don't is a sign of restraint for you?

You're assuming the objective is to lower the civilian casualties. From the statements of prominent Israeli ministers and the actual behavior of the bombardment it's pretty clear that, for the Israeli government, killing civilians is a feature, not a bug

They're usually thought as "decoder only"

Maybe I'm using the wrong definitions, but I think that's backwards.

Say you are receiving records from users and different intervals and you want to eventually store them in a different format on a database.

Streaming to me means you're "pushing" to the database according to some rule. For example, wait and accumulate 10 records to push. This could happen in 1 minute or in 10 hours. You know the size of the dataset (exactly 10 records). (You could also add some max time too and then you'd be combining batching with streaming)

Batching to me means you're pulling from the database. For example, you pull once every hour. In that hour, you get 0 records or 1000 records. You don't know the size and it's potentially infinite

>AI as an excuse lay people off.

Why do you think the managers/business owners need an excuse to lay people off? If it's legal and economically beneficial to them, they'll fire people. Having AI won't help them as an excuse. In fact, I would say it sounds like a much worse excuse than "the economy is on a rough spot" or something like that