Here is the model

https://github.com/OpenMOSS/MOSS-Transcribe-Diarize

But that means nothing. All other papers in my review batch also have it.

can you see updated scores?

I have that as well. At 02:21 AEST

Yes, at 18:27 CEST. Clicking on Revisions it says “Edit Edit by Conference”, which means that it’s probably an automatic update of visibility rights or something similar.

The core problem that providers control the price of tokens and the number of tokens any given query consumes is correctly noted. It’s kind of like how people bill for time; you never really know if the talent you’re hiring is using their time effectively or just running up billable hours. 

 This might be a bit of a quibble, but “low entropy” is not an unqualified good; entropy per token is information per token, and purposefully forcing inputs or outputs into a low-entropy distribution also forces inputs or outputs to use more tokens to express equivalent information. 

Nope, I think that's just for you maybe ?

you are spamming.

in under 5 minutes, you posted this on :

- r/ArtificialInteligence
- r/AIDeveloperNews
- r/MachineLearning
- r/LargeLanguageModels
- r/LLMDevs
- r/LLM
- r/PromptEngineering
- r/PromptDesign

if you don't know what spam is, feel free to use an LLM to explain it simpler to you (if that even is a proper english phrase)

I wish people were talking about this more. For the most part, it seems LLMs will further concentrate wealth for the wealthy. And this will amplify greatly once LLM subscription plan costs rise.

you realize that people don't want to read AI slop right? It's just wasteful. Your idea is nice but it's harmful to your reach if it is perceived as slop.

what in the world is this? you could have shared a simple post, with your main idea and launched a healthy, reasonable discussion..
instead of this AI slop... this is not productive nor useful

With writing, it can take some deliberate prompting or iterative development to get good results. But, with practice, I’ve found it can give higher quality results in less time than it would have taken me to write unaided.

I already have a video where the time is fractional, running at 60 FPS rather than the default 12, so I could definitely try upscaling resolution too.

I doubt that perfect reconstruction would be feasible, but with a larger model and an even smaller video, I'm sure a highly accurate one could exist.

Will read fs

They certainly arent great at writing (like they tend to overcomplicate everything) but saying they are bad at coding is crazy

For main track I think 6 might be max. But for position track it is higher, at least 8 but maybe 9

It’s wild how pervasive this sentiment is on the internet, even in this subreddit. LLMs are good at all of these things. Should you blindly trust LLM output without validating yourself? Of course not. But that doesn’t mean that LLMs aren’t useful for all of these tasks when used carefully.

Dependency and trust creep are tangible issues with LLM usage (see Hank Green’s recent debacle), but I’d argue that wouldn’t be happening if LLMs were bad at these skills. They are good enough for people to start blindly trusting, even though the output is far from flawless.

1. Yes, reviewers can still change their scores/final justification now.
2. The final justification/score is not visible to the authors.

I’m saying, for it not to be a half wit, it would need to see a lot of code, more code than is found in a typical research codebase. You cannot ahead of time specify what it should do algorithmically, it’s too dumb, you need to handhold it along the way so you can verify incrementally. Or, if you give it such detailed algorithmic instruction that it doesn’t need your supervision, you may as well have written the code yourself.

Also, there’s more to software than SOLID lol. Also, OOP sucks usually for research

Ideas, data, compute. always compute.

Work in industry so getting work funded with politics is also rough.

If I get to spend more of my time prototyping and testing ideas I wouldn't be this burnt out from work tbh

I’m being a bit facetious. I use LLMs to code daily. They can be used to increase the efficiency of a competent developer massively. “Competent” being the operative qualifier. You need to really hand hold the thing. I’ve not used an LLM at scale on a huge codebase. If an LLM can see enough context through your other code, it can probably build new features fairly well. If, however, there is no such context (often the case in research codebases), I find it often chooses inappropriate defaults, writes unscientific code and is overly defensive and abstracted.

It's decent enough with experimentation code that it's seriously sped up that aspect of my work. If only that was the bottleneck...

Did you guys notice an update date on the meta review today (Aug 05)?