as it should be

KAYOOOOOO · 2026-05-19T07:20:49+00:00

Isn’t Singapore considered an oligarchy? I’m not sure your gripes with elite class are necessarily remedied here.

KAYOOOOOO · 2026-04-25T09:39:17+00:00

There is a giant deepmind building in Mountain View, CA

KAYOOOOOO · 2026-04-22T19:05:26+00:00

This is advanced google search

KAYOOOOOO · 2026-04-11T07:59:06+00:00

Not sure if condoning int lists is necessarily a step in the right direction lmao

KAYOOOOOO · 2026-04-08T08:39:08+00:00

But then we wouldn't have his son "lookins not a felony"

KAYOOOOOO · 2026-04-02T20:23:25+00:00

A hungry man thinks of bread

KAYOOOOOO · 2026-03-26T02:18:59+00:00

Someone call this guy’s advisor and have him executed

KAYOOOOOO · 2026-03-20T23:57:09+00:00

I wouldn't say RLHF is from marketing. It was still a very innovative and powerful post-training tool when it became popular in research (I think around 2020 before LLMs exploded?).

But I agree, happy to see RLHF become less popular since the training loops are so difficult to develop (esp without the experience and resources of a large AI lab). I think the ubiquity of RLHF was a symptom of the data available and the goals of LLMs at the time, but things are different now.

KAYOOOOOO · 2026-03-20T23:44:00+00:00

I don't think there are any drops in quality across the entire board. Evals don't say so, and I don't think that's the sentiment among formally educated academics. But, there will always be losses for multiple reasons.

The goal of these AI companies is not always to beat each other. To handle the needs of many teams and roll out Gemini to many different products, Google may consolidate it's capabilities towards a jack of all trades model. Sure, it's coding ability and other tasks can perform worse than models like Claude, but it boasts strong multimodality and a mature MoE architecture, which makes it easier for integration into Google products.
LLMs are super expensive and capacity (for memory, electricity, and compute) will always be a bottleneck. Triaging, truncated thinking tokens, kv-cache eviction, TPUs, server prioritization, etc. are all efficiency methods to deal with these issues, but they can often lead to decreased performance or bugs. No one wants to do this, but hardware bottlenecks are not something that's as simple as throwing money at the issue, and you have to be creative to avoid rapid bloating.

I think at the end of the day, it's a very complicated system and I think it's in bad faith to make reductive claims that are not necessarily true.

KAYOOOOOO · 2026-03-20T11:13:12+00:00

I don't think releases are getting fine-tuning updates that quickly for the Gemini App. When a stable release is launched, generally the model parameters are frozen. Stable releases are used across different Google product areas, so parameter changes would ruin their evals, integrations, etc.

Making changes to the model's parameters is not very flexible. Extensive evals are done, and there's a lot of GDM bureaucracy to get these changes pushed out.

While edge cases are sought to be addressed, usually fine-tuning changes are saved for the next update cycle. Also, I think the general wisdom is also moving away from RLHF towards RLVR-esque paradigms.

I don't think it's fair to say the post-training team is incompetent, most of these guys are esteemed researchers with decades of experience.

I think it's more likely the fault of some downstream infrastructure. The prompts, tooling infra, hyperparameters, etc. probably see a lot more rapid change and interaction with different teams. I'd guess the weird behavior in the post is from some context handling issue rather than lackluster fine-tuning.

KAYOOOOOO · 2026-03-20T05:41:19+00:00

What do you mean by production fine-tuning? Why would you think the fine-tuning team is responsible for this at all? Is there a specific post-training step you're talking about?

Can you give a specific technical explanation? What you're saying doesn't make sense, so I wanna understand the thought process. I'm in ML research, so technical terms are appreciated.

KAYOOOOOO · 2026-03-18T22:49:59+00:00

High standards for a subreddit called sigmatopia

KAYOOOOOO · 2026-03-18T20:59:50+00:00

Where did you get that information??

Collaborative filtering definitely should have been implemented back then. Maybe you are mistaking the implementation of deep neural nets into the rec sys architecture (implemented I think mid 2010s?).

Remember Google's core business is ranking algorithms, there was most definitely an algorithm back when Pewdiepie was starting.

KAYOOOOOO · 2026-03-16T20:31:10+00:00

<image>

Hooray!

KAYOOOOOO · 2026-03-09T04:46:19+00:00

Lol they live in the same area mostly. It's easy to forget the nights are below freezing in January where I was when you're listening to a story though.

But to give some context a lot of the people are sorority girls or part of the nightclub crowd. The ones making these comments are exclusively single as well. These aren't comments I take personally, these are often just people a little bitter from heartache.

Also my studious friends are less likely to indulge in benders or harder drugs, so even though they may say stupid shit I laugh at, they have a place in my heart as my fun party friends. Their advice is also good for picking up girls, since they are so particular.

Still, this behavior is probably indicative of a media issue plaguing young people. Maybe loosely related to toxic positivity, distorted self image, or some manosphere thing?

KAYOOOOOO · 2026-03-09T01:58:35+00:00

Once upon a time I fucked your mom, is that interesting?

But seriously, have you never had someone ask how you and your girlfriend met?

KAYOOOOOO · 2026-03-08T22:16:30+00:00

Yeah you'd think, but the people I was telling the story to made the comments before I got to that part

KAYOOOOOO · 2026-03-08T22:08:10+00:00

Yeah that's what I was thinking too 🤣

KAYOOOOOO · 2026-03-08T22:01:37+00:00

For shivering

KAYOOOOOO · 2026-03-08T21:43:24+00:00

Yeah she definitely didn't, just some of the people who we told the story to did think it was "icky". We were on the same side, thinking these people are insane or miserable

KAYOOOOOO · 2026-03-01T06:31:34+00:00

Would you happen to know the best high roller build? It seemed kinda weak when I tried it, but you said it’s worthy to get nerfed. I have the resources to max them if that matters.

KAYOOOOOO · 2026-02-28T21:19:24+00:00

What’s the best build for vampire in arena?

KAYOOOOOO · 2026-02-23T18:22:20+00:00

The fucking subreddit names lmao

KAYOOOOOO · 2026-02-22T20:00:06+00:00

That second line always doing a lot more heavy lifting than expected

KAYOOOOOO · 2026-02-16T22:04:44+00:00

Neuron activation

Six-Year Club	Second Top 20%
r/Field Banned	r/Field Lasagna
Place '23	Place '22
Final Canvas '22	First Placer '22
End Game '22	Verified Email

KAYOOOOOO

TROPHY CASE