timlod

FWIW, the performance considerations section is a little simplistic, and probably assumes that exact dataset/problem.

For GIN for example, perfomance depends a lot on the size of the search input (the fewer characters, the more rows to compare) as well as the number of rows/size of the index.

It also mentions GiST (another type of index which isn't mentioned anywhere else in the article)..

I've used it before for realtime-uses (not production though where you'd need 100% guarantees for no drop-outs), latency has not been an issue. I think you essentially get the latency of the plugins you're using since this is a JUCE wrapper.

Ultimately it depends on how much work you do and how efficient an audio thread you built. pedalboard is not a library which does audio playback itself, it just effects buffers you give it. I used python-sounddevice, which are bindings for PortAudio - if you don't use much CPU you can comfortably run plugins in realtime.

Obviously you're still beholden to the GIL in Python (until further notice) so if worse comes to worst you might experience the unlucky dropout.

It's not virtual analog tech scientists building a better musical instruments :)

I had no clue what a fog harp is, turns out it's used for harvesting water - interesting tech!

Wow, I didn't see that. Great to see this level of interest early on!

Audio Source Separation I think is the general term used in research. It is often applied to musical audio though, where you want to do stem separation - that's source separation where you want to isolate audio stems, a term referring to audio from related groups of signals, e.g. drums (which can contain multiple individual signals, like one for each drum/cymbal).

The title is a bit confusing as open-source separation of ... reads like source separation, which this is not. Rather, it is a pitch detection algorithm which also classifies the instrument the pitch originated with.

I think it's really neat, but the results look like it could take more time to fix the output than using a manual approach (if really accurate results are required).

I'd say it depends on what your other options are. I don't think the technology will replace me soon, even at the rate I see it improving. At this point it's still a tool we can use to deliver faster, if we use it wisely.

Especially about ChatGPT et al. - I use it daily, but having the proper foundation to discern and verify its output shows me that it's still very far from being a competent programmer for any but the 'easy' tasks which have been solved hundreds of times over.

Like I hinted, I also view all of this hype sceptically. I dislike the 'we need AI in our org now!' types and am not planning on taking on projects if I don't see their viability. But there's obviously still a lot of demand and people offering services like those in TFA who're just looking to cash in, and that seems to work.

If you can find projects you believe you can make a difference in with your background, why not give it a shot?

Time will tell, but I would cautiously say say yes.

Note that I don't usually work in that particular space (I prefer simple solutions and don't follow the hype), didn't sell myself using 'AI' (I was referred), and also would always tell a client if I believe there isn't much sense in a particular ask.

This particular project really uniquely benefits from this technology and would be much harder, if possible at all, otherwise.

I'm a Data Scientist currently consulting for a project in the Real Estate space (utilizing LLMs). I understand the article is hyperbole for perhaps comedic purposes, and actually do perhaps 80% align with a lot of the authors views, but it's a bit much.

There is industry-changing tech which has become available, and many orgs are starting to grasp it. I won't deny that there's probably a large percentage of projects which fall under what the author describes, but these claims are doing a bit of a disservice to the legitimately amazing projects being worked on (and the competent people performing that work).

I do like the Task Energy plot towards the end (second to last plot).

It shows how wasteful these new Intel processors are compared to their direct competition.

I’ve been using Windows throughout my childhood and start of my CS career - now I use Windows for specific software (audio/music) and Linux for developing (about 8 years I guess). I had a 1-year stint with macOS because I was developing an iOS app, and have been the troubleshooter for people with macs at my previous job, so I consider myself somewhat ‘multilingual’ when it concerns OSs.

As a power user, Linux is just so much nicer. I constantly get frustrated, especially with macOS, about stuff that I can’t easily. In Linux my stuff works and if it doesn’t it can be made to work (usually). In Windows/Mac it’ll often take considerable effort to make the system work the way I want, or it’s just not possible.

I think with proprietary software ‘it just works’ is only a thing if you’re happy with the basic experience that is tuned to the average person. If you have more complex needs, you should be using Linux (and if you know your stuff or use the right distro, things will likely also ‘just work’).

I see, I did misread it!

But I don’t think I can agree with what you meant then - why would our brain mostly ignore it in real life but not in video games? Where is the transition from something feeling real (in an immersive way) to us not liking it because it feels awkward, and why does it happen? I’m imagining a “perfect simulation” game which is like real life in ways that matter/don’t get in your way in terms of gameplay - I think everyone would be awed (of course this can be argued though). What would need to degrade in terms of realism for it to seem awkward/not be immersive anymore?

I agree with the movie example, but in a game you don’t have to watch the mundane - it’s just background “noise” to make the world believable.