getnormality

This is a strong benchmark! None of these could be remotely mistaken for human art. Opus 5 comes closest.

Germany trading nuclear for coal in 2022, and the role of the Greens supporting it, was widely reported by credible outlets; see for example [1]:

> With little natural gas supplies of the country’s own, and its heavily supported renewable sector unable to fully make up the shortfall, German leaders faced a dilemma. To maintain enough gas reserves to get the country through the winter, they could try to put off the closure of Germany’s last three remaining nuclear reactors, which were scheduled to shutter by the end of 2022 as part of Germany’s post-Fukushima turn against nuclear power, and even restart already closed reactors.

> Or they could try to reactivate mothballed coal-fired power plants, and make up some of the electricity deficit with Germany’s still-ample coal reserves.

[...]

> Last week, the country’s parliament, with the backing of members of the Green Party in the coalition government, passed emergency legislation to reopen coal-powered plants, as well as further measures to boost the production of renewable energy.

I acknowledge that the politics and history of the nuclear decommission is more complicated than I understood - [2] seems to be a useful walkthrough. But it all happened with Green support and is the fruits of decades of Green ideological advocacy.

[1] https://archive.ph/v3iLS

[2] https://www.cjfp.org/for-germanys-green-party-the-50-year-dr...

Obviously foreseeable consequences of an action are owned by the agents that pushed them and sit on the critical path of that action happening. The Greens fit this criterion. They pushed nuclear decommission first, without renewables ready to replace. To any reasonable observer, it is obvious that this ensures that coal would replace the nuclear. They did it anyway.

> As always with a lot of these: it's not saying causation.

But what they are saying is, it would be valuable if it was causative wiggles eyebrows

Last sentence of the abstract:

> Sleep regularity may be a simple, effective target for improving general health and survival.

We'll know European Greens are serious about climate change when they stop decommissioning nuclear plants and replacing them with coal.

> One command. Everything configured. Nothing to research.

Being talked at by someone's AI copypasta feels like being in the Truman Show.

"Why don't you let me fix you some of this new Mococo drink? All natural cocoa beans from the upper slopes of Mount Nicaragua, no artificial sweeteners!"

"What the hell are you talking about? Who're you talking to?"

Why do your opinions about what a "well-behaved" agent is override the wishes of a site's owner?

Seems like we needed an annual meditation on this topic until 2021, then we took a 5 year hiatus? What happened?

What you're suggesting seems to go implausibly far beyond what the paper says.

RL post-training alters the parameters of the transformer, while your f(manifold) idea seems to suggest that a new layer on top would suffice, no need to alter the transformer itself at all.

It would be extremely handy if that were so, but I'm guessing it isn't, or it would be the prevailing approach.

A while ago a lot of the discussion about overparameterization was about explaining "double descent", the observation that test error doesn't descend monotonically and actually hits a local maximum around the point where the model has just enough parameters to interpolate the data. My favorite article about double descent looks at this in terms of splines [1]. If I can try to summarize that article: when you are designing a parametrized model to fit to data, you have a choice. You can either:

1. Avoid overparameterization by design. Manually create or choose a space of functions that has limited degrees of freedom by construction.

2. Accept overparameterization and regularize.

The latter tends to be more robust, because of the bitter lesson. It's not practical to manually design an ideal, on-demand, just-right limited-parameter model for every dataset we are presented with. The best way to approach that ideal, it turns out, is really to just let the computer figure it out via regularized optimization over an overparameterized space.

Statisticians started moving in favor of overparameterization long before deep learning got off the ground. This trend dates back at least to the machine learning bible, Elements of Statistical Learning (2001).

[1] https://mlu-explain.github.io/double-descent/

So is Paul Krugman

This app is a great example of what AI does to your brain. No one making their own choices in the app design would make each question need three clicks.

It kinda makes sense given that one of their major products is a computer that runs an operating system literally called ChromeOS.

Your hilariously specific hypotheses remind me of how little I know about technology.

This weird trend reached an apex in a Feb 2026 OpenAI blog post [1], recently on the front page [2], which describes the process for building... something... written 100% by agents.

There is no description of what the thing is, no indication of what value it provides its users. The closest it gets is "the product has been used by hundreds of users internally, including daily internal power users".

But the fact that the thing has a million lines of code is repeated twice in the first few hundred words.

[1] https://openai.com/index/harness-engineering/

[2] https://news.ycombinator.com/item?id=48416264

No detail about this in the article or your comment here, but the voluminous lines of code get a big call-out. Very interesting!

Could we hop on a quick call to get a quick status on that quick fix?

So, did this internal prototype ultimately end up being used to create/influence a real product, e.g. Codex app?

We've known for decades that output metrics like LOC/day are very bad measures of real productivity in software. But they seem to be back in vogue in the age of AI, because AI is so good at maxing these useless metrics, and we need to show how impressive our AI is and how impressive our usage of AI is.

The social media posts are from accounts for the franchise store. There is no reason to think those posts were approved by B&M corporate.

Clear enough that my questions can be answered in writing? Someone should do that.

I read the counterclaims from Bricks and Minifigs here:

https://bricksandminifigs.com/blog/blog/2026/05/28/bricks-mi...

This post and TFA have a common issue: no one seems to have a clear, compellingly evidenced account of basic questions about the collection and its history under consignment:

1. What exactly was in the collection?

2. What happened to the collection after it was consigned: which sets were sold, which were stolen or lost, which were moved to off-site storage, etc.?

3. How much money did the original franchise owner owe the consigner for the sets sold?

The peripheral claims about e.g. police malfeasance are disturbing, but without this basic evidence about the substance of the matter, I don't know if it's a great idea for an online mob to take sides.

What I keep wondering is, what would have happened without the AI? Would they have just ignored your request?

In my neck of the woods it's fairly common that when a person doesn't know how to help you they just don't reply, instead of saying "I don't know how to help, sorry". AI-generated responses seem like the evolution of this attitude that one must either ignore or respond in a (superficially) helpful manner.

Yes, this and every internet forum will still be doing this two years hence. Your life will be better if you take to heart this famous passage from Nietzsche:

I do not want to wage war against what is ugly. I do not want to accuse; I do not even want to accuse those who accuse. Looking away shall be my only negation.

I think they're saying it has qualitatively different capabilities that make certain kinds of security work more worth pursuing with the model, not that the model of human-AI interaction has changed.

You're right that they're using a harness like everyone else. The general idea of giving the model a harness is not going to change. I mean even humans need harnesses to accomplish some things.

Wait, so, there's silent reading time that you hate, AND somehow seniors still manage to claim that they didn't read the thing?

> Now most of the friction comes from alignment and coordination with other teams.

Then I see a solution! Why don't we simply put the entire company on one big team?

I think "stochastic parrot" misses the mark as a characterization of LLMs, but so does "artificial intelligence." They're both somewhat helpful and somewhat misleading in complementary ways.

Maybe that's the best one can do when describing something very new and strange. A series of vivid, incompatible metaphors might be the best guide for a while. "Intelligence" as we normally understand it is a significant overstatement, while "parrot" is a massive understatement.

A lot of people seem to lean too much on Conway's Law. It takes social organization as primary instead of itself shaped by the nature of a problem. Maybe the reason Sales and Engineering are different departments is because they are different things.

From one of the answers:

> mathematics only exists in a living community of mathematicians that spreads understanding and breaths life into ideas both old and new. The real satisfaction from mathematics is in learning from others and sharing with others. All of us have clear understanding of a few things and murky concepts of many more. There is no way to run out of ideas in need of clarification.

Yes! And this applies to all human culture, not just math. Everything people have figured out needs to be in living form to carried on. The more people the better. If math, or any product of human skill, is only recorded in papers or videos, that isn't the same as having millions of people understanding it in their own ways.

Modern culture often emphasizes innovation and fails to value mere maintenance, tradition, and upkeep. This can lead to people like the OP feeling that they have nothing to contribute, when actually, just learning math, being able to do it, being able to help others learn it - all of these are contributions.

We are all needed to keep civilization afloat, in ways we cannot anticipate. We all need to pursue some kind of excellence just to keep human culture alive.