> generating DTMF tones yourself and injecting them into the audio stream?
When I was an undergrad I had an audio file for each digit and a winamp playlist for each of my frequently dialed numbers. I'd hold my (landline) phone against the computer speakers and double-click the playlist to dial. I'm sure I spent more time setting this up than it ever saved, but it was somehow pleasing that this ridiculously over-powered speed dialer worked.
Back in the days of phone line modems, there used to be a program that you could type in a number, the modem would emit the corresponding tones, and then you could pick up a phone and it'd already be connected. I can't remember what the one I saw was, but I'm sure there were multiple different ones, and I also vaguely recall there were dedicated physical machines that could before that.
You could do this with any terminal program. Your modem won't emit any noise until it hears a carrier tone, so you could just dial, pick up the extension, hang up the modem.
I'm sceptical about the energy motivation, but there are multiple reasons why making invertible deep learning architectures can be interesting or useful. Cf, a series of workshops from 2019-2021: https://invertibleworkshop.github.io/
Since then diffusion models have been popular. Generating from these can be seen as a special case of a continuous time normalizing flow, and so (in theory) is a reversible computation. Although the distilled/fast generation that's run in production is probably not that!
Simulating differential equations is not usually actually reversible in practice due to round-off errors. But when done carefully, simulations performed in a computer can actually be exactly bit-for-bit reversible: https://arxiv.org/abs/1704.07715
I was asked why there are two tides a day in an interview for my undergraduate University place. I blundered through to the classic answer. This stackexchange discussion made me realize I was even more of an imposter than I thought :-).
If it makes you feel better, the crust of the earth does bulge more in line with the classic answer due to the flow of the underlying magma being effectively uninterrupted by solid obstructions. Which then means the classic tidal answer is technically correct, except what we observe as tides is a delta between land and ocean.
8 isn't large enough to overcome randomness, alas!
Edit: I actually looked through https://news.ycombinator.com/submitted?id=imurray yesterday to see if there was anything we could invite a repost of. (I do this sometimes when looking at which users were first to submit an interesting article.) I didn't see anything, but on a second look https://news.ycombinator.com/item?id=7984992 could be interesting, so you'll get an email inviting you to repost that one, if you want to :)
A photo taken on my street (no exif) "only" gives the correct town in chatgpt and gemini, and then incorrectly guesses the precise neighbourhood/street when pushed. Gemini claimed to have done a reverse image search, but I'm not convinced it did. An actual Google reverse image search found similar photos, taken a bit further along the same street or in a different direction, labelled with the correct street (no LLM required).
Looks neat. It would be useful to compare to other implementations: https://ann-benchmarks.com/ -- potentially not just speed, but implementation details that might change recall.
i think with small codebases like this is less about speed and more about education of essentials - i actually often encourage juniors to do small clones like this, feel proud, and then study the diffs with the at-scale repros and either feel humbled or feel like they have a contribution to make.
I see they are still using GloVe word embeddings for the first benchmark. Ah good ol' days! Nothing wrong with it, should still yield a realistic distribution of vectors. Just brings a lot of memories :)
That nice benchmark shows that multiple implementations of HNSW perform differently (my experience also). It would be helpful therefore if HANN benchmarked its implementation against the others, and tried to get the details the same as the best version.
Every product has its hate, but everyone is rarely true. Personally (no longer at Amazon) I was impressed by Chime. It was simple, but rock solid, handling large calls well. Teams is still worse for me (>9 people display is bad, even in MS Edge, when on Linux). Zoom has a finicky interface.
Early in the pandemic I had to use many different systems as an academic, when lots of different contacts pivoted online in different ways. Chime was the least of my problems; it just worked when many other systems struggled.
I liked the Chime meeting/calendar integration at Amazon that could ring everyone at the start of the meeting, meaning that most meetings started promptly.
When I was an undergrad I had an audio file for each digit and a winamp playlist for each of my frequently dialed numbers. I'd hold my (landline) phone against the computer speakers and double-click the playlist to dial. I'm sure I spent more time setting this up than it ever saved, but it was somehow pleasing that this ridiculously over-powered speed dialer worked.
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