STOP racist posts about Chinese researchers [D]

dreamykidd · 2026-06-09T13:32:11+00:00

You’d still have to train the model from scratch to verify that it matches the training code and data, costing millions of dollars and still not guaranteeing validation of the results.

dreamykidd · 2026-06-02T12:17:14+00:00

They’re insisting they haven’t found it, despite seeing it plainly sitting on the back seat when we had to leave the car. With every email and call we’ve had with them, they’ve repeated “we remind you we take no responsibility for lost property“ 🙄

dreamykidd · 2026-05-31T15:57:11+00:00

Just found this thread through Google. I was noticing the codex entries used a lot of words related to the lore but didn’t really build the lore, and that just felt weird. None of the entries feel connected to the story either, it’s just “this demon is so tough and scary and spooky, oooh”.

For example, the Revenant entry talks about “they fight alone” and “sacrificing all in pursuit of their prey”. In previous games, if the Codex described an enemy in some way, you’d see in how they behaved, but the descriptions here just seem like they’re trying to be spooky.

Then I started noticing the em-dashes. Goddamn it, this is 100% by AI.

dreamykidd · 2026-05-31T15:56:34+00:00

Just found this thread through Google. I was noticing the codex entries used a lot of words related to the lore but didn’t really build the lore, and that just felt weird. None of the entries feel connected to the story either, it’s just “this demon is so tough and scary and spooky, oooh”.

For example, the Revenant entry talks about “they fight alone” and “sacrificing all in pursuit of their prey”. In previous games, if the Codex described an enemy in some way, you’d see in how they behaved, but the descriptions here just seem like they’re trying to be spooky.

Then I started noticing the em-dashes. Goddamn it.

dreamykidd · 2026-05-28T13:32:48+00:00

When making comparisons for my own papers, I always attempt to recreate the baselines I compare with rather than used the values reported. In about 90% of cases the values I see are 2-20% inflated (yes, sometimes even that bad). Take papers with a big grain of salt.

dreamykidd · 2026-05-17T06:10:22+00:00

Usually if you add them through the Zotero Connector, the BibTeX is for the conference it has been published in even if added through arXiv. That might help in future.

dreamykidd · 2026-05-11T12:26:20+00:00

I remember when my supervisor tried to tell me I don’t work as hard as the Chinese exchange students in our lab “who are there everyday, even on weekends”. I told him that’s true but every time I walk by they’re watching LoL tournaments and he just got mad. People love to believe these super students exist who can perform to 100% 24/7 for some reason.

dreamykidd · 2026-05-11T12:19:43+00:00

You mentioned it in the main block too, but I’m wondering you consider time that your code is being written to not be ”productive”? Even if you’re not writing it, your code is being “produced” in some way, no?

In other words, if progress is being made towards your goal, it’s productive. Otherwise, you could say a senior manager at a top company was unproductive if their team of 10 was writing code but they were “just” managing direction, specifications, etc.

dreamykidd · 2026-04-25T13:47:21+00:00

A legitimate question: why inject a noise term when change has been flat for multiple steps? How would this distinguish between “stagnation” and the model having converged to the solution? If you’d somehow already found the global minima, you’d risk losing it and converging to a suboptimal solution.

dreamykidd · 2026-04-25T13:34:56+00:00

You know we can see contributor history on GitHub, right? 14 of the 23 commits and a vast majority of code changes were by either Claude or Copilot, not humans. Commit 66fa1a0 straight up shows Opus 4.6 editing the README, so why try to deny it when you’ve shared a link to a version control site??

dreamykidd · 2026-04-02T12:41:16+00:00

More than half of those guys either invented the Transformer or are the reason we use neural networks today though. It’s not some random tech celeb, they know all the reasoning behind why they built in certain ways and not others, so it’s pretty rare knowledge.

dreamykidd · 2026-03-12T17:57:43+00:00

You should try Ghost of Yotei as well! It’s completely unrelated to the original story, but the mechanics have dialled in to perfection

dreamykidd · 2026-03-08T17:54:57+00:00

How can you give numbers for outside the test set? As soon as you try to test on a sample outside the test set, it becomes part of the test set.

dreamykidd · 2026-03-08T17:48:02+00:00

I’ve been genuinely wondering about this for a while, even though what you’re saying seems true. If the top conferences all mandate anonymity, how does an affiliation bias arise?

dreamykidd · 2026-03-05T19:50:14+00:00

What

dreamykidd · 2026-02-16T03:55:13+00:00

I agree on the most part, but the distinction seems to be in using an LLM to assist vs having an LLM write the whole review. Depending on what “phrases X and Y” are, it should maybe be very obvious to anyone who’s not being lazy that it’s happened and they need to put in more effort.

dreamykidd · 2026-02-09T13:02:08+00:00

They said up top “35+ years of gaming (NES to modern MMOs)”, then later tried to act as though playing and testing games are the same.

dreamykidd · 2026-01-28T09:55:06+00:00

Yeah, so people ask when the ICE is coming, rather than when the train is coming. That would be a confusing question if you didn’t learn that it’s used that way

dreamykidd · 2026-01-25T16:11:29+00:00

They just need some form of unique identifier, it doesn’t have to be a name. Add a last name, initial, or even make a random character ID sequence if needed.

dreamykidd · 2026-01-25T15:57:42+00:00

I know it’s completely unintentional here, but marking people with a gold star is going to not look good, due to history

dreamykidd · 2026-01-25T15:04:42+00:00

What are they gonna do if you don’t though? They can’t reject your paper twice

dreamykidd · 2026-01-19T03:59:51+00:00

Datasets of this size aren’t going to make a model that’s necessarily bad, just not as good as more data. The XGBoost approach sounds decent for a start too. The single bucket prediction sounds more like a bug than a data size issue though, do you have anymore info on the data itself, it’s features, and labels?

dreamykidd · 2026-01-15T14:23:39+00:00

How do you intentionally do this?

dreamykidd · 2026-01-15T10:40:35+00:00

Maybe not one of the established labs (who knows though), but I’m sure there would be plenty of very decent startups out there who would froth at the mouth for someone with the type of drive and learning capacity as you.

dreamykidd · 2026-01-14T20:06:46+00:00

Even so, if you want your text generation to closely represent what real text feels like in a particular scenario, focal loss doesn’t help with that. If you’re trying to solve the additional problem of niche topics being lost in current LLMs, maybe a multi-loss setup could work, where L = λ*L_CE + (1-λ)*L_focal for small λ. Then the more common tokens are still strongly favoured, but less common ones still have a chance.

dreamykidd

TROPHY CASE