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My biggest gripe with Apple Music is shuffling. When I shuffle my entire library of thousands of songs, I'm hearing the same ~50 songs over and over UNTIL I add a new song to my library. Then suddenly I'm hearing songs I haven't heard in years. How does one screw up randomization that bad, and how has it not been fixed over the past several years?

Is this launched? Looks cool, but you should add a privacy policy.

It's -kind of- launched, still have couple of things to tight.

And will add a privacy policy by the end of the day, thank you for point that one out


Well that's. Just. Great. I bought a 64GB M4 Max MBP last month. I'm past the 14-day return window. I figured the M5 was near, but assumed M5 Max would come a bit later. Not sure where I came up with that.


You can console yourself with the fact that your laptop, unlike one of the new ones if you'd bought that instead, can run macOS Sequoia (without "Liquid Glass") rather than Tahoe.


This is always the gamble with buying a Mac. Either purchase right when the new is released, or be on the fence of your new becoming old a couple of weeks after purchase.


Not sure either since M5 base has been available for months now


Ah yes, that's right. I was looking at the M5 model last month wondering why there was no 64GB option.


Apple released the M5 MBP more than half a year ago...


M5 has been out since last year, no?


Coincidentally, I was asking Claude today if something existed that could identify the key, chord progression, tempo, etc from a playlist of my favorite songs to see if there was any pattern that stood out so I could find similar songs with that vibe. Like a more music theory approach to discovering new songs versus the "people who liked this song, also liked these songs" way.

Even more coincidental, earlier today my wife was saying we should take our "Skylight Calendar" screen device that is hardwired into our wall with us when we move. I said I could just make a DIY one... and then I open HN and see the top post: https://news.ycombinator.com/item?id=47113728

Spooky.

Oh by the way, all of the "Open on Bandcamp" links I clicked were 404 pages.


Did you just "it's not x, it's y" me?


See: Rick Rubin.

"Rick Rubin says he barely plays any instruments and has no technical ability. He just knows what he likes and dislikes and is decisive about it."

https://www.cbsnews.com/news/rick-rubin-anderson-cooper-60-m...

https://en.wikipedia.org/wiki/Rick_Rubin_production_discogra...


How many pelican riding bicycle SVGs were there before this test existed? What if the training data is being polluted with all these wonky results...


I'd argue that a models ability to ignore/manage/sift through the noise added to the training set from other LLMs increases in importance and value as time goes on.


You're correct. It's not as useful as it (ever?) was as a measure of performance...but it's fun and brings me joy.


My "actual job" is a designer, not a career engineer, so for me code has always been how I ship. AI makes that separation clearer now. I just recently wrote about this.[0]

But I think the cognitive debt framing is useful: reading and approving code is not the same as building the mental model you get from writing, probing, and breaking things yourself. So the win (more time on problem solving) only holds if you're still intentionally doing enough of the concrete work to stay anchored in the system.

That said, if you're someone like me, I don't always need to fully master everything, but I do need to stay close enough to reality that I'm not shipping guesses.

[0] https://alisor.substack.com/p/i-never-really-wrote-code-now-...


1 frame of Bob Ross = 1,800s


So with 108,000 (60 X 1,800) Bob Ross PPUs (parallel painting units) we should be able to achieve a stable 60FPS!


Once you set up a pipeline, sure. They'd need a lot of bandwidth to ensure the combined output makes any kind of sense, not unlike the GPU I guess.

Otherwise it's similar to the way nine women can make a baby in a month. :)


The food/housing/etc bill for 108k Bob Ross er... PPU's seems like it would be fairly substantial too.


For those that have homebrewed a base model, does your output have the same AI-isms like overusing em dashes? If so/not, what dataset did you use?


Does yours also use the oxford comma and generally more commas?


AFAIK, those are mostly a consequence of posttraining.


that is a post-training artifact


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