dingocat

> Most humans use Meta products regularly; also optional. For all the feigned outrage, people want this, and will work hard every day to pay for the next iteration of the surveillance apparatus.

This is a perfect example of the revealed-preference fallacy. The use of providers like Meta does not reflect a well-informed, freely chosen preference. Meta spends a ton of money to squash competition, remove viable alternatives, and to make their products addictive.

I used to like listening to music and reading books. Now my AI agent does it for me and it's so much faster at it than I used to be.

The issue with sources like these is that they are inherently passive. They don't include exercise sets. While they can be good supplementary material, learning happens by doing, not by passively watching.

I do say this as a fan of both, though.

You're throwing the baby out with the bathwater, though. Waiting a week still prevents a decent amount of attacks. Not every attack can evade detection for two weeks. Even if it did, security scanning tools might still detect them.

Say hypothetically that 20% of attack slip through, which is still worrying, you can mitigate 80% of attacks by just waiting a week. It's a low risk, high reward strategy.

Yet.

> “Blog” stands for “web log”. If it’s on the web, it’s digital, there was never a period when blogs were hand written.

Did you use AI to write this...? Because it does not follow from the post you're replying to.

Which you have to go to the model page to find.

And their documentation makes that distinction clear, having dedicated a section specifically to the distilled models.

A distinction they make clear and write extensively about on the model page, yes?

What do you mean there is no such thing as R1-1.5b? DeepSeek released a distilled version based on a 1.5B Qwen model with the full name DeepSeek-R1-Distill-Qwen-1.5B, see chapter 3.2 on page 14 of their research article [0].

[0] https://arxiv.org/abs/2501.12948

It hasn't been great. Applying for jobs in this market while having the post-MSc burnout has been tough. I finished an MSC in AI back in June, but there are so few AI-relevant jobs where I live it's depressing. I've interview for data science and analyst roles, but barely get in the door.

I'm getting a good amount of interviews for developer and data engineering positions, but the competition is tough. Many positions have seen a 5x increase in candidates since the same time last year, according to my interviewers.

However, I'm hopefully getting an offer as a data platform engineer soon. The department leader has ranked me as their first choice, so unless the higher-ups complain... Knock on wood.

I have multiple questions regarding the methods of this test.

The biggest one is that, well... The test doesn't aim to see what GPT-4 can do and how well it does it, only whether the participant can guess the (possibly cherry-picked) answer the author decided on. In short, we don't know if he sampled answers and decided on the most probable answer (akin to consensus voting/self-consistency[1]), or if he asked a question and chose the first one.

Maybe GPT-4 guesses the correct answer for a question 80% of the time, but he got unlucky? You don't know, the author doesn't tell you. The answers are generated ahead of time and are the same every time you go through the test.

[1] https://doi.org/10.48550/arXiv.2203.11171

A few pages? You barely need a single sentence. "Build lasting habits by recognizing how triggers lead to action."

Ditto, I can do it as well. I can best describe it as looking at an object and shifting it in/out of focus.