kmaitreys

PDS 70, my beloved.

For those who don't know, only PDS 70b and c are the exoplanets directly imaged which are still embedded inside thier nascent protoplanetary disk. For most other planets, the disk gas has mostly been cleared/dissipated/accreted.

For those who are more interested you can refer to the detection papers for both:

PDS 70b: https://www.aanda.org/articles/aa/pdf/2018/09/aa32957-18.pdf

PDS 70c: https://www.nature.com/articles/s41550-019-0780-5

Arxiv link for Nature paywall (PDS 70c): https://arxiv.org/abs/1906.01486

You can't see the directly-imaged exoplanets with amateur telescopes. Best we can do is to use the RA and DEC coordinates from this site and observe the host star/planetary system (you'll still just see the star at best).

Without going into details, basically the reason comes down to the fact they are tiny and the star is too bright in comparison. Without a coronagraph which can block the starlight even the most powerful telescopes (like JWST) we've built can't image them.

Just a heads up: Your classifier thinks Sun (the company) is actually Sun (the star) and hence it[1] shows up among the posts under Astronomy topic.

[1] https://news.ycombinator.com/item?id=49787436

To know what something is about, a natural way is to do it yourself.

This topic has somewhat recently acquired a connotation where it yields a polarized response. People seem "tired" of the language and the episodes like Bun rewrite don't help, especially in current LLM-obsessed era.

Amidst this, just let me try to give my own experience with language while working in a field where it doesn't have much traction (scientific/numerical programming). A couple of years ago, after being tired trying to make Python faster, I was looking for a language to write simulation code in. Fortran (and C/C++) has been the go-to choice in my field for decades but I wanted to try a modern language. I tried Julia but the workflow didn't come naturally to me. Also by default, it also wasn't ahead-of-time compilable. Rust was my second choice but it's type system/design, expressiveness and tooling (rust-analyzer) won me over. I have written so much of it and I really enjoy writing it. Borrow checker isn't really an issue 99% of time when writing scientific code. And when it is (self-referential data structures), you can just use an arena (or something equivalent). The C/Fortran inter-op is great so it's still so easy for me to build my own abstractions on top for the more general stuff like solving ODEs and sparse matrices.

And of course, there is speed, memory safety and ability to parallelize things so effortlessly (rayon is magical), but I feel those were never the things that actually won me over. The language was just a joy to write.

No. The file uses same extension as Jonathan Blow's Jai which is actually a language GitHub supports.

If you can bear his idiosyncrasies, Tsoding did a stream on it recently. It was pretty cool.

https://youtu.be/4vcA7yJMoPg

I have similar thoughts.

To those who feel on the contrary, I would genuinely like to understand why average consumer won't be priced out of hardware? The silicon industry is already quite centralised. Everywhere we already see the concept of ownership disappearing.

It's quite difficult for me to visualise a non-dystopian future where our PCs are just mere screens and every compute happens on a remote cloud, owned by some corporation, charging you subscription fees to even add and multiply numbers.

I would be the happiest if this (perhaps the most) pessimistic scenario doesn't pan out, but I can't deny that it feels like that's where we are heading.

Maybe not?

https://en.wikipedia.org/w/index.php?title=Jacobian_conjectu...

I would also like to know this. Fortran itself is column-major, so I would guess the internal layout isn't same for multi-dimensional arrays when compared to row-major C? I'm not sure how LFortran represents arrays internally though.

I'm not sure if I would ditch Zotero. Using grep over a bib file is nice but I guess you are opening the DOIs in browser every time?

The reason I like Zotero is because I can use it offline, have my all documents synced (the backup feature is one of the best things) when needed and I can organize my papers according to project/paper I'm working on. Actually one thing I do wish Zotero would add is tab groups which will allow me have papers open but grouped by project. It's based on Firefox which does support this so I hope it's a matter of time.

Your understanding of what means value to this world is deeply pitiable.

I'm sorry but this is not a good enough argument for gutting science. You need to justify that capacity and coverage increase is required.

The companies trying to make these plans are just looking for money. It's not for some noble goal for progress. If you fail to see that, I'm sorry for you.

Yes, I can do most of the things now because enough is already up there. Can you elaborate why do you want more? Like a million more as these companies are planning?

When is it enough?

Can you define what progress is being made by creating more space junk?

Sure but it doesn't we can replace ground-based oned with space telescopes. They don't make the same class of observations

Is this thread being astroturfed by bots from big tech? Some absolutely obtuse comments.

That's an insane take. Do you actually understand what is being argued? It's an threat to ground based telescopes in the name of more slop-generating infrastructure.

The issue at hand is with the ground based telescopes. We need both ground-based and space-based telescopes because with ground based telescopes you can create arrays and interferometers enabling a much larger baseline (in layman terms this means much higher angular resolution)

What advancement is being made exactly? SpaceX data centres to create more AI slop?

What exactly is the implication of saying this? This is not about over-regulation at all.

What about ground based telescopes and interferometers which enable observations with much much larger baseline?

Space-based and ground-based telescopes are complementary to each other. You can't replace one out for the other.

It depends on the kind of people. Most normal people don't do that, it's not a reddit-like platform after all.

But most researchers and grad students (like me) often subscribe to daily mailing list of the papers dropping that day from their particular field. Having a cursory read at the paper titles and then opening the papers further relevant to you is a morning ritual for many.

For something more rigorous, I would like to take this opportunity to share rebound[1], something we use for n-body simulations in our field (planet formation). Perhaps few people here are already familiar with it. It has a Python interface but the C interface is very easy to use as well and has plethora of pre-set examples which can be visualized using GLFW. It's very very cool!

[1]https://github.com/hannorein/rebound

I have never understood this way of approaching programming. I feel the best way to learn a language is solving a problem you have in that language.

Figure out an actual real problem you have which you can solve with programming and just implement it in a language you're thinking about learning. This way there's nothing to invest in because you're solving a problem instead of approaching programming as if it was some coursework. It is not.

Why do people still view languages as an investment? If you know programming then it's just a different syntax. Focus on learning programming.

It means that you are impressed by mediocrity.

cf: https://nitter.net/mitchellh/status/2060088112257372610#m

If you're impressed by something which was done by AI, then you're not qualified enough to judge it.

Probably some tokenmaxxing competition between the employees. The whole company seems under some kind of AI psychosis.

> https://gist.github.com/karpathy/442a6bf555914893e9891c11519...

Last thing I saw Karpathy talk about was this, which I find hard to believe that it came from a smart person.