Failing 1/3 of a class if that cohort is genuinely deemed not qualified enough to pass shouldn't be a problem by itself.
But then it raises questions like "are they really unqualified or is the testing methodology inadequate?" and "why was the system unable to provide the necessary growth to such a high slice of the class?". And then the easy way out is to just cherry-pick which students enter the system at all.
My understanding is you're equating `failing a test` to `lacking the relevant skills and knowledge to do a certain task competently`.
The reality is sometimes tests in academia are just not very well made and don't really test what they are supposed to be testing, and that's usually due to multiple reasons like misaligned incentives, staffing shortages and maybe lack of resources / funding.
I don't think the comparison to flight school is relevant enough in this context because it's a too different of a world to traditional academia.
My impression as a driver-in-training is that people are too complacent and forget they are handling a machine that could at any moment kill and maim you, your loved ones and random innocent bystanders. I wish we all were more responsible about it, and I hate the Tesla philosophy of going the opposite way (the touchscreen, the sorta there but not really autopilot, etc)
Tom (the moderator) said he was looking into getting it appended to the HN's CSS. I haven't asked them about it since ages ago, wonder if they dropped the idea.
You have a Minecraft server. You generate money from it (selling VIP packages, et cetera). You could generate more money if you had more players. You can have more players if you consistently DDoS other more popular servers; the experience for these players will be horrible and they might give your server a chance.
> Bézier curves are great, but there are certain things they just can’t do. For example:
It would be nice if we had a better explanation of what is wrong with the Bézier curves in the example. I've put the spring Bézier example side by side with the javascript simulated one, had them both trigger at the same time on a keyboard press and I can barely notice a difference; one doesn't look better than the other to my eyes.
As an unpaid volunteer to a multimillion dollar corporation that has just erased a huge collective volunteer effort, listing in writing the reasons I'm unhappy is already way too much effort.
Asking that same volunteer to hop on a video call is just insensitive. They're the one providing free work; if you care about solving the problem and not losing the volunteer force, you should go where they are (the forums) instead of asking them to come to you (video call). They probably don't want to take time out of their schedule to waste their time talking with a community rep. And they probably don't even want to do a voice/video call.
I've been playing with Suno lately (I'm an amateur/hobby musician, for context) and I've been making some tracks for my own enjoyment. I've shown it to friends and got mixed reactions.
Personally, I think it's still uncanny similar to these AI image gens with multiple hands and nonsense details in the background. You can one-shot a lot of passable stuff but the moment you want to put more effort into it (e.g. correct a word or two, slightly change the style of a section) the track gets really messy very fast.
Even if you could generate real-time 4K 120hz gameplay that reacts to a player's input and the hardware doesn't cost a fortune, you would still need to deal with all the shortcomings of LLMs: hallucinations, limited context/history, prompt injection, no real grasp of logic / space / whatever the game is about.
Maybe if there's a fundamental leap in AI. It's still undecided if larger datasets and larger models will make these problems go away.
As another reader who has no idea what any of this is about, I've coerced my favorite LLM to digest it into ooga-booga format in the style of this essay[1]:
# grug see big sky boom
- sky make ooga FLASH but not light, just invisible whoosh (radio).
- whoosh so strong, like sun work many day, but all squish into blink of eye.
- smart sky-people have big ear rock (CHIME). ear rock say:
"boom come from there, galaxy far, but not too far (only 130 million fire-circles (light years) away)."
- ear rock also have many little ear-brother rock across land, help point finger very good.
- finger point so good, sky-people know spot of boom smaller than tree forest (13 parsec).
- then, magic glass eye (James Webb) look at spot. see old fat star (red giant) glowing soft.
- but fat star not make ooga boom. hmm. maybe fat star have sneaky tiny angry friend (neutron star).
- tiny angry friend go "KRAK!" → make fast radio boom.
# lesson for tree-brain
- boom in sky still big mystery.
- now smart sky-people can say where boom come from.
- if know where, can watch with other eyes, maybe find secret of why.
- grug think: many sky boom = maybe angry tiny stars yelling far away.
# Ooga booga translation:
"Tree no know why sky yell. But now tree know where sky yell. Soon, tree maybe know why sky yell."
But then it raises questions like "are they really unqualified or is the testing methodology inadequate?" and "why was the system unable to provide the necessary growth to such a high slice of the class?". And then the easy way out is to just cherry-pick which students enter the system at all.