> are the following parenthesis balanced? ((())))

> No, the parentheses are not balanced.

> Here is the breakdown:

    Opening parentheses (: 3
    Closing parentheses ): 4

> what about these? ((((())))

> Yes, the parentheses are balanced.

> Here is the breakdown:

     Opening parentheses (: 5
     Closing parentheses ): 5

Comments URL: https://news.ycombinator.com/item?id=45440526

Points: 3

# Comments: 0

I suspected my work was in the dataset and it looks like it is! I reached out via the form.

It'll let the AI platforms get around any other platform blocks by hijacking the consumer's browser.

And it makes total sense, but hopefully everyone else has done the game theory at least a step or two beyond that.

It’s a tough bar if LLMs have to be post antirez level intelligence :)

https://www.reuters.com/business/environment/musks-xai-opera...

https://www.politico.com/news/2025/05/06/elon-musk-xai-memph...

> In just 11 months since the company arrived in Memphis, xAI has become one of Shelby County’s largest emitters of smog-producing nitrogen oxides, according to calculations by environmental groups whose data has been reviewed by POLITICO’s E&E News. The plant is in an area whose air is already considered unhealthy due to smog.

Had this set the precedent of working with the community, and _not_ breaking the law, I think we'd be in a better place all around.

Similarly, Amazon tried to take the excess nuclear power, without paying back into the electrical grid infrastructure, and got denied in 2024:

https://www.utilitydive.com/news/ferc-interconnection-isa-ta...

and again in April of 2025:

https://www.ans.org/news/2025-04-16/article-6937/ferc-denies...

https://www.anthropic.com/pricing#api

Opus 4 is $15 / m tokens in, $75 / MTok out Sonnet 4 is the same $3 / MTok in, $15 / MTok out

Comments URL: https://news.ycombinator.com/item?id=43999192

Points: 1

# Comments: 0

But let's look at what has happened with Grok, for example:

From May 6, 2025

https://www.yahoo.com/news/elon-musk-xai-memphis-35-14321739...

>The company has no Clean Air Act permits.

> In just 11 months since the company arrived in Memphis, xAI has become one of Shelby County's largest emitters of smog-producing nitrogen oxides, according to calculations by environmental groups whose data has been reviewed by POLITICO's E&E News. The plant is in an area whose air is already considered unhealthy due to smog.

> The turbines spew nitrogen oxides, also known as NOx, at an estimated rate of 1,200 to 2,000 tons a year — far more than the gas-fired power plant across the street or the oil refinery down the road.

The details are in the specifics here. People are _already_ feeling the effects of the AI race, the consequences just aren't evenly distributed.

And if we look at the "clean" nuclear deals to power these data centers:

https://www.reuters.com/business/energy/us-regulators-reject...

> The Talen agreement, however, would divert large amounts of power currently supplying the regional grid, which FERC said raised concerns about how that loss of supply would affect power bills and reliability. It was also unclear how transmission and distribution upgrades would be paid for.

The scale of environmental / social impacts comes down to how aggressive the AI race gets.

It's completely open, with active engagement and direction from the community:

https://github.com/modelcontextprotocol/modelcontextprotocol

I don't think it would have such wide adoption so rapidly if it were an "over-engineered implementation".

They've since raised more funding recently, and have larger use cases in mind for robotics: https://rerun.io/blog/physical-ai-data

I've spoken with members of the team, and they're all great. Wouldn't hesitate to use the product / work with them anywhere.

I use it in projects to stream internet / connectivity from my phone to the NVIDIA Jetson line, making my robotics projects easily accessible / debuggable:

https://github.com/burningion/bicyclist-defense-jetson?tab=r...

If you look at code being generated by non-programmers (where you would expect to see these results!), you don't see output that is 60-80% of the output of domain experts (programmers) steering the models.

I think we're extremely imprecise when we communicate in natural language, and this is part of the discrepancy between belief systems.

Will an LLM model read a person's mind about what they want to build better than they can communicate?

That's already what recommender systems (like the TikTok algorithm) do.

But will LLMs be able to orchestrate and fill in the blanks of imprecision in our requests on their own, or will they need human steering?

I think that's where there's a gap in (basically) belief systems of the future.

If we truly get post human-level intelligence everywhere, there is no amount of "preparing" or "working with" the LLMs ahead of time that will save you from being rendered economically useless.

This is mostly a question about how long the moat of human judgement lasts. I think there's an opportunity to work together to make things better than before, using these LLMs as tools that work _with_ us.

This is like that, except the government and the type of people on the list are even better targets for their personal devices. The government has strict rules about secrecy and communication for military operations, and strong punishments for not following these protocols, because they can lead to a loss of life.

This is a different sort of "unsecure". The platform itself may be "secure", but the device, being in public where someone could take a picture of military secrets, etc. isn't.

The pyproject.toml in the Model Context Protocol example is just showing the new, "best" way to distribute and install Python projects and dependencies. If you haven't used uv before, it makes working with Python projects substantially better.

The Model Context Protocol server lets the model autonomously use the tool and incorporate its result. It's a much cleaner (imo obviously) separation of tool definition and execution.

Rather than the positive (code compiles), the negative (forgets about a core feature), can be extremely difficult to tell. Worse still, the feature can slightly drift, based upon code that's expected to be outside of the dialogue / context window.

I've had multiple times where the model completely forgot about features in my original piece of code, after it makes a modification. I didn't notice these missing / subtle changes until much later.

The government comes in and takes over. You don’t get to decide, they kill all your chickens and cut you a check.

They created a project named Greyball to identify law enforcement and mislead them.

They created a kill switch for the event of a government raid to gather evidence.

They ordered and then canceled rides on competitor apps.

They tracked journalists and politicians...

The list goes on and on: https://en.wikipedia.org/wiki/Controversies_surrounding_Uber