When I ask for their input, it's always for a situation where I'm capable of judging if their input is useful or not.

In all situations I use them, it doesn't matter if they're correct at all. I'm asking for ideas, alternatives, links for blogs or articles. I talk things out with them...

I don't think we should ever "trust" LLMs. This seems like the wrong usecase for them.

There's this nice blog [3] that explains why they chose Shift instead of other keys, and also gives a nice overview of the pattern.

[1] https://design-system.service.gov.uk/patterns/exit-a-page-qu... [2] https://design-system.service.gov.uk/components/exit-this-pa... [3] https://beeps.website/blog/2024-10-09-why-govuk-exit-this-pa...

Comments URL: https://news.ycombinator.com/item?id=48594568

Points: 1

# Comments: 0

https://youtu.be/l6DKRf-fAAM

https://adamsilver.io/blog/form-design-from-zero-to-hero-all...

He has posted many new things since. Probably one of the best UX resources on the web.

Sent some waves to a couple of friends

E.g. Fishmans - Long Season is a 40 minute song, but the website's considers it as divided into 4-5 parts. And you don't have to listen to the full song to get a scrobble.

In the Spotify data you get the exact number of seconds you listened to it. And it is surprisingly complete and easy to use too. With LLMs I bet you can load it into pandas and construct queries for any insight you want in seconds.

- https://lastfmviz.netlify.app/ - shows what you've been listening to as a grid of album covers. You can scroll down as long as you want. It's cool to look back and remember where I was when listening to specific music.

- https://lastfmstats.com/ - generates tons of rankings, line charts, racing bar charts, etc. A couple I like: "Artist streaks" (I listened to Pavement tracks 122 times in a row in August 2023), "Unique artists in a single month" (225 in July 2025) and "Unique weeks per artist/album/track" (good to identify what you're always listening to vs. what you listened to heavily in a specific time)

- https://pmcdonough8133.github.io/last.timer/ - shows your listening rankings by hours, minutes instead of just scrobble count. This really should be a default feature in the site, as some artists have average track length 2-3x times of others.

If you use Spotify, another site I've had loads of fun with is https://explorify.link/.

From the talk on the Gemini subreddit it's severely lower than before. I'm likely canceling my AI Pro.

The update also broke the app for me. Editing a message crashes the app every time. I'm on a Pixel lol

With LLMs, we can't tell anymore if something is a labor of love with hundreds of hours of work behind it, or half a dozen prompts to Claude Code.

Following this logic, you're precisely the age you should be to write a list like this :P

https://webcammictest.com/mic/

The album chart queries are also incredible. The site has a very detailed system of genres and descriptors so you can find exactly what you want.

Note that apps built from the SDK don't have access to the full history, only up to some cutoff. I tried a couple over the years and wrongly concluded Spotify deleted your history after some time.

The data download does contain everything, which was a very pleasant surprise. I didn't think I'd ever see the data from the couple years gap in my last.fm.

If you use Spotify, you can download your full listening history here: https://www.spotify.com/us/account/privacy/. You get it in a pretty convenient JSON format and with a little bit of code it's pretty easy to create some visualizations.

There are also websites for visualizing this data. I'm quite fond of this one: https://explorify.link/. It allows you to do some custom queries.

I came across it when reading Butterick's Practical Typography and it's possibly the lowest effort/clearest improvement guideline in the book.

Now I can't unsee websites that do it wrong.

The Regional Finals and World Finals are in a single venue with a very controlled environment. Just like the IOI and other major competitions.

National High School Olympiads have been dealing with bigger issues because there are too many participants in the first few phases, and usually the schools themselves host the exams. There has been rampant cheating. In my country I believe the organization has resorted to manually reviewing all submissions, but I can only see this getting increasingly less effective.

This year the Canadian Computing Competition didn't officially release the final results, which for me is the best solution:

> Normally, official results from the CCC would be released shortly after the contest. For this year’s contest, however, we will not be releasing official results. The reason for this is the significant number of students who violated the CCC Rules. In particular, it is clear that many students submitted code that they did not write themselves, relying instead on forbidden external help. As such, the reliability of “ranking” students would neither be equitable, fair, or accurate.

Available here: [PDF] https://cemc.uwaterloo.ca/sites/default/files/documents/2025...

Online competitions are just hopeless. AtCoder and Codeforces have rules against AI but no way to enforce them. A minimally competent cheater is impossible to detect. Meta Hacker Cup has a long history and is backed by a large company, but had its leaderboard crowded by cheaters this year.

There are a couple parts at the start and the end where a lady points her phone camera at stuff and asks an AI about what it sees. Must have been mind-blowing stuff when this section was recorded (2023), but now it's just the bare minimum people expect of their phones.

Crazy times we're living in.

In the IEEEXTREME university programming competition there are ~10k participating teams.

Our university has a quite strong Competitive Programming program and the best teams usually rank in the top 100. Last year a team ranked 30 and it's wasn't even our strongest team (which didn't participate)

This year none of our teams was able to get in the top 1000. I would estimate close to 99% of the teams in the Top 1000 were using LLMs.

Last year they didn't seem to help much, but this year they rendered the competition pointless.

I've read blogs/seen videos of people who got in the AOC global leaderboard last year without using LLMs, but I think this year it wouldn't be possible at all.