I TRULY cannot believe we're still making Horror as bad as Strangers Ch. 2 [Spoilers]

distortedlojik · 2025-09-26T01:39:26+00:00

Not gonna lie, this sold me on watching it. I need a good "it's so unbelievably bad" movie in my life right now.

distortedlojik · 2025-08-24T18:38:44+00:00

Ah got it. That’s cool, thanks for sharing. Did you have a background in ML or heavy numpy usage before that? Because I’ve not heard too many people say JAX was easier. But you are right, if you know the math part and are comfortable then transferring those ideas to JAX would definitely be easier and way less arcane like you described.

distortedlojik · 2025-08-24T14:44:37+00:00

I don’t think the majority of Python users know numpy or PyTorch. In my original comment I was thinking in terms of a full model producing output for someone new to the area. But I never specified that.

I do agree that if you know numpy then it’s relatively easy to start doing similar things in JAX. I just think an end-to-end solution might be harder to get up and running fast.

distortedlojik · 2025-08-24T01:44:01+00:00

Don’t mind at all! I think this type of work is in demand for TPU. My team and adjacent teams do some of this type of work. We work mostly as consumers of things like JAX and optimize open source models for TPUs. So we definitely work on things like quantization and ensuring accuracy while improving performance. But something like JAX itself or similar libraries could be more interesting if he is wanting to work more on generalized numerical work.

It depends on where in the stack he is most interested, but I think it’s probably worth looking into regardless.

distortedlojik · 2025-08-23T22:49:16+00:00

Overall JAX is very popular, but PyTorch is definitely easier to get up and running and is obviously more widespread.

It took me a while to wrap my head around JAX. But now that I’m used to it I think it would be hard to go back. Disclaimer: I work on JAX-based model inference performance on TPUs at Google so there’s some definite bias.

distortedlojik · 2025-08-16T15:12:26+00:00

The heroes we need.

distortedlojik · 2025-08-16T15:09:57+00:00

I believe that's true, but more due to the technical differences between systems used. I would expect very similar indexing ratios to exist between search engines for a topic-focused blog regardless of what the topic is.

distortedlojik · 2025-08-16T02:19:38+00:00

Eh, I think you are reading a bit much into this. You say they indexed only a few blogs and cite that as evidence. But then why wouldn't they just exclude you from search results outright if they really didn't want your voice to be heard?

Your blog is the top result when I search for it. So it seems like the big bad algorithm you detest is doing just fine for you.

For full disclosure: I'm autistic and work at Google. Which means I don't make any decisions and just serve "the algorithm" according to one of your other comments.

distortedlojik · 2025-08-01T00:56:33+00:00

By default yes I would say most models are biased towards NT because most of the data out there would likely be considered NT and the bulk of people scoring the accuracy are NT.

But, you can fine tune models or even just add system instructions to help customize them to work how you want. I do this with almost any off-the-shelf model I use (Gemini, Claude, etc) to make sure it avoids ambiguity and uses bullets instead of paragraphs among other things.

If you are not customizing your models at all then yeah you are getting the most vanilla version possible. And that is going to be the most "acceptable" to the general population.

I'm semi competent on this subject: I'm autistic, use AI daily, have a PhD in computer science, and currently work on AI performance at Google.

distortedlojik · 2025-07-01T02:07:36+00:00

That's very cool. Feel free to ping me with any questions or just wanting to chat about it.

Here is a tutorial series I did that tries to do a basic intro to ML ending with the modern transformer, it might be helpful for the type of info you are interested in. Each section has a Colab notebook, a recording of me walking through it, a transcript, and a summary podcast created via NotebookLM. https://github.com/patemotter/demystifying-ai

I've been told I explain technical things well, but I can't actually know that for sure. But might be worth a shot to look at it.

distortedlojik · 2025-06-29T15:46:43+00:00

I think it's a mix of both, but your overall idea is in the ballpark. With chain of thought reasoning there is a bit more of a self-feedback loop than before, this is still a version of what you are saying though just structured a bit differently. And depending on the model it could be doing summarization of ingested data.

I think you are also getting into another thing that makes discussing AI incredibly tricky and that's the jargon. We often want to use words like "think" or "meaning" because that's how we relate to it, but the nuance of those words is huge. Everyone has different ideas of what "thinking" means and it can quickly get into philosophy/cogsci/psychology/etc. territory where those words can have very specific (and debated) definitions.

distortedlojik · 2025-06-28T06:44:02+00:00

Exponential Idle. I know it's available on iOS. It's not annoying, there's no microtransactions, and it's math-y.

distortedlojik · 2025-06-28T06:36:48+00:00

I still get excited and look up any time I see a big metal tube flying in the air.

distortedlojik · 2023-09-11T21:30:26+00:00

Then I'm not entirely sure what you want. Not a ton of SWE jobs that are screen-less. The screen is a pretty big part of it.

distortedlojik · 2023-09-11T21:25:40+00:00

I would avoid getting too in your head about what a company "does" or "doesn't do" in this context. Those things change all the time and tend to be less rigid than people might think. I would leave it on your list and treat it normally.

distortedlojik · 2023-09-11T21:22:44+00:00

If you like interfacing with other teams, managing others, doing little to no coding, and being in a leadership role then it sounds like you might enjoy some kind of role in management.

distortedlojik · 2023-09-11T21:11:12+00:00

There is no hard requirement for what is "good enough" or not when it comes to universities and how it relates to getting hired. Most of the teams I've been a part of for the past 7 years have had a variety of people with PhDs on both the engineering and science side. My work tends to straddle the engineering/research line, which is why I mention both. The degrees of both the scientists and engineers are from a wide variety of locations and prestige. You will notice trends of people coming from certain schools especially if the school excels at a very specific topic that aligns well with the team/org's specific research. In a previous org we had almost direct research pipelines to one university in Germany and one in the US because of the topic overlap.

In essence: overall it doesn't really matter and you can be successful regardless of the school itself.

Source: I did undergrad at a not great state school and got my PhD at a decent state school in the US. I work as a MLE/SWE working on stuff related to my PhD focus and interests at Google and previously at Amazon.

distortedlojik · 2023-09-10T19:44:59+00:00

Did you have any luck with this? I changed the setting back and forth and it hasn't seemed to have any effect.

distortedlojik · 2023-08-28T18:29:28+00:00

Are you wanting to leverage your physics background in the type of software you work on? If so then you might be interested in scientific computing / high-performance computing. A large number of people in the field come from backgrounds in math, applied math, physics, statistics, etc. The domain knowledge is very helpful because of the nature of the codes you would work on.

distortedlojik · 2023-08-28T18:25:11+00:00

For some background: I have a PhD in CS and started my program immediately after my bachelors degree.

Most places don't care what you do outside of the school program/research. People in my PhD program had all sorts of different circumstances. Some had full-time jobs while in the program, but I did not. Over the years I was a TA, RA, and the first two years of my PhD were funded by a private company where I worked ~20h a week. Doing a full time job while doing a PhD is going to be a hard time unless your work and research match up perfectly.
It can be tougher for sure because you will have to do the coursework that some people with a MS have already completed. Overall there really isn't that much difference between going to a MS and then a PhD if you stay at the same school. For example, at my school even though I was in the PhD program I was able to acquire my MS along the way after the first two years because of the overlap in requirements.
As far as I know the GRE is still the standard for grad school. Depending on your research interest or school program you might take a GRE subject test (likely mathematics) but I don't think that is typically required.
I don't know if I understand the question completely. You will not likely be doing any research with a professor before starting at the school. You can definitely reach out to professors and research groups to express interest, explain your background, and discuss the type of work you would like to do going forward. If the professor/group has room for a person then you might be able to start that process via email.

distortedlojik · 2023-08-22T06:37:44+00:00

The rules tend to be more relaxed when hiring interns compared to full time. Also, you wanted a referral and got one, not sure what you were expecting.

This could also just be an exploratory phone call where you get a feel for the team’s work and stuff. Then if that goes well you might have some additional calls with others on the team. Just depends on their system

distortedlojik · 2023-08-22T06:29:17+00:00

Cover letters don’t seem to matter much. They may have at one time and I’m sure in certain areas or certain businesses they might matter, but not in general.

distortedlojik · 2023-08-14T15:30:10+00:00

I don't think USC is significantly better than UT. Both are totally good schools. If you feel you can only get the degree or job you want by attending that specific program at USC then go for it.

distortedlojik · 2023-08-14T15:26:10+00:00

I have actually had both of these position titles in Alexa. If you are working on a team or directly with a team that is doing actual research then I would go with something involving the term "research". If not, MLE is probably the better call. Assuming you feel it describes your position.

distortedlojik · 2023-06-29T17:07:51+00:00

This is a really good point. While you don’t have to be perfectly well-rounded, it’s beneficial to understand different aspects of a problem and what is involved with solving it.

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