brap

Also ignoring the fact that without AI many of these offerings would be pricier, lower quality, or just nonexistent.

The usual counterargument is “well I would gladly pay more for man-made art!” which is a fair personal preference, but judging by the market most people clearly disagree, and businesses aren't obligated to cater to your niche.

To say that we must have people that know how to X, because otherwise no one will be able to teach how to X, is kind of circular reasoning. Why is X important to begin with? What’s its inherent value to society?

If knowing the answer is not valuable then asking the question wasn’t valuable to begin with.

We should remember that math exists primarily to serve a purpose and that purpose is not being a fun puzzle hobby for a select few mathematicians.

Imagine a programmer telling their boss it’s not about working software it’s about the journey of writing the code. Good luck getting hired.

I think if pro mathematicians don’t change their tune quickly they will soon see their funding approach zero.

It’s interesting how all of these problems are due to misaligned incentives.

Unpopular opinion: cities should be private, for-profit corporations. Leasing land from the government and charging subscription fees. Like a very big hotel.

I think it’s been like 20 years since I had to use a truly public toilet.

Is this really such a common occurrence for people? Because I feel like toilets are being discussed way too much.

The thing that mostly makes MCP more token efficient is that its methods usually have good documentation/examples and smaller surface area, so it’s easier for the agent to navigate. But this can (and should) also be applied to the API itself...

The other thing is discoverability, but I’m not sure a simple web search would be significantly less efficient.

For web services, what is the point of MCP or even a skill when modern agents can just find API docs on their own and use it?

I think in this day and age agents no longer need official 3P integrations, they can just use whatever API is already available (and in some cases even the UI).

Doesn’t this suggest that the results seen in the OP are merely copying strategies it has seen in other bots (code in the training data), and not actually the agent developing its strategy?

What if we let the agents play directly instead of writing bots?

Love seeing Blood mentioned, sometimes I feel like I'm the only one who played it

>we won't know what the next question should be

What if AI knows better than us?

The vast majority of your posts are about Israel.

https://news.ycombinator.com/submitted?id=slowin

You are right to be skeptical, just look at their submission history: https://news.ycombinator.com/submitted?id=slowin

So learn math to teach math? You see the recursion here right? What’s the end value for society? Why would anyone fund this? How is this different from a hobby?

In a future where learning math has about the same economic value as learning to play chess or the flute, you can see why many mathematicians aren’t very excited about their chosen career paths.

Sounds like a pyramid scheme

What economic value is there in a small group of people understanding something, if they provide no added value by proving things?

“It is difficult to get a man to understand something, when his salary depends upon his not understanding it”

Oi you got a loicense for that maffs?

At what point can we say that Polars is basically an in-memory database? (Genuine question)

When I first started working with cloud providers it was shocking to discover this feature doesn’t exist, basically anywhere.

It’s such a basic thing, not having it has to be deliberate to make you accidentally spend more than you’d like.

If you genuinely believe this nonsense you can probably make millions on Polymarket, go ahead.

If you think US will drop a nuke on Iran you are delusional, borderline insane.

I don’t know about the consumer Gemini API, but if you use the GCP API your data isn’t used.

I think the sandboxing was truly incompetent but in their defence something like this was probably seen as very unlikely. Let’s all hope they do better in the future.

You and the other commenters are all absolutely right, but that’s not really what I was pointing at.

So far our solutions were more or less understood thru some models of reality that we’ve constructed (on our own), which may or may not reflect reality perfectly, and even if most of us never bothered thinking about these models, some people did and they understood them on a very deep level.

But we may be getting to a point where the problems we need to tackle become too difficult for humans to model, or even to notice their existence, like asking an ant how a Boeing 747 works.

Maybe this was always the case but now it seems like we might have a shot at making these solutions useful even if we have no idea what they’re even solving.

I feel like “giving up understanding” is inevitable.

There’s some hubris in thinking we can understand everything. For truly difficult problems, it’s entirely possible that humans are simply incapable of comprehending why a solution is true. But ultimately the practical value of applying that solution to the real world is going to eclipse our need to understand it.

Math is just the beginning. I see it happening in other fields too, like physics and biology. Many of us software devs have already given up on understanding parts of our own systems for the exact same reason.

Seems like a losing battle.

What I don’t understand is, why would you not want “reasoning” in a classifier?

Speed and cost are obvious reasons, but isn’t this a tradeoff?

Am I the only one who feels icky about how these 2 companies always try to one-up each other on release day? It’s fair and all but just feels gross.

Sorry to be that guy but:

>Quiet is not proof that nobody is recording, and a detection is not proof that anyone is

Is such claudespeak it’s not even annoying it’s just funny at this point

One thing that sort of worked for me is to write the skeleton myself, i.e. the general ideas and how they fit together, the overall “flow” of what I want to say, then have an LLM fill in the blanks (I either point it at some context.md or have it ask me questions when details are missing).

It’s kind of like designing the high level software architecture yourself and have the LLM write the code for each component.

Not bulletproof, requires some iteration, but miles better than what it would produce on its own.