reesul

I was loosely active in tildes a few years back but dropped it since I got too busy / found myself just going to Reddit for mindless content.

Sounds like tildes is still kicking, then? I’m curious if it’s grown or shrunk in that time. There was quite a few highly active users that were posting good content, but I recall thinking the site was a bit short of “critical mass” for many of the communities.

Maybe I’ll go back and check it out. Thanks for the reminder that tildes is still around!

I believe the 64 variant is the newer part. The older one is a 32 bit architecture IIRC.

Jetson has a big edge in software and documentation, no doubt. Poor documentation and SDKs seems to be the norm for a lot of embedded processors, especially for the AI acceleration piece.

I’m curious for this new beagle board if there’s enough documentation / software to use the AI accelerator.

Looks like a really cool app, it reminds me of period tracking apps I’ve seen before. Congrats on learning this in a few months, thanks for pointing to the resources you used!

What can you tell me about data storage and how it gets used within and without the app? I’m interested in giving it a try but I also want to be convinced that this is protecting privacy.

Yeah I’m very surprised to see a single board computer at less than $100 for a processor like that. Hard to tell what their actual 1ku price is, but if the random Alibaba I found for $20 is right, then that price for the overall board is absurd.

By VPU are you talking about stuff like ISP, video encoder/decoder, or something else?

Among embedded processors I’ve seen touting vision acceleration, gstreamer support is fairly widespread. I bit the bullet to learn it because my role requires it. Maybe it’s Stockholm Syndrome talking, but I’ve somehow grown to like gstreamer. The learning curve was awkward. I struggled with documentation and learned more by analyzing some examples and trial-and-error.

Yeah it’s performance vs cost is honestly nuts. 6 TOPS is a pretty solid NPU, but I don’t know what their software is like. Programming those accelerators is often difficult, especially if you’re a small time customer.

Curious if anyone can weigh in on their SW usability. A quick search for their user level tools showed examples/documentation in Chinese(?)

As someone closer to this in the industry (embedded ML.. and trying to compete) I agree with the sentiment. Their software is good, I willingly admit. Porting a model to embedded is hard. With NVIDIA, you basically don’t have to port. This has paid dividends for them, pun not intended.

I don’t really see the Nvidia monopoly on ML training stopping anytime soon.

Title is off yeah, but the abstract does effectively say that the paper was written amongst a cohort of PhD students

I agree with the other comments that this is a fairly useless article. Perhaps I’m looking too much into this specific example, but I fail to see how the entirety of AI’s success (per this title) is misrepresented due to its inability to provide a horoscope (good example by another comment) that’s specific to some person in question.

We see a lot of articles that swing too far towards “AI will change everything!” just as much as “no, AI is not actually effective/meaningful!”. This is the latter. I’m surprised that anyone would genuinely try to use chatGPT/language LLMs this way.

I’m mainly enjoying chatGPT as a way to distill widespread information on the internet into a pseudo conversation. It’s great for learning new topics - my favorite is generating and explaining code snippets for languages/libraries I’m not familiar with.