Devils advocate: Splatting, dlss, neural codecs to name a few things that will change the way we make games

Looks like the Barbican in London to me, it’s apartments and a public bar/drinking/working area, nice spot to hang out!

Something makes me uneasy about this being a Google sheet, you need to use credentials to view it and someone has a log of who has accessed it…you can probably even see who’s viewing it in realtime

Use an anonymous account! Or someone should host this on a website or something with higher privacy

Relative point to point

• which Blockchain are we talking here? How does it compare to the current banking infrastructure?
• again, which one? How does it compare to the current pricing?
• escrow is a thing, someone can build up a PayPal equivalent on top of a Blockchain, the list goes on
• the current system doesn’t do great here, some Blockchains makes it way more traceable, in fact
• skill issue, but also solvable with a PayPal equivalent
• not a fact, what does this even mean?
• does it?

You could say the Linux kernel is an astronomically terrible idea because it doesn’t do anything…but it is just the platform, the good comes from what people build on top of it that add all these quality of life features you miss

Buy ydy

I don’t really follow your logic, how else would you propose to shape the audio that is not “just an effect”.

Your analogy to real life does not take into account that the audio source itself is moving, so their is an extra variable outside of just stereo signal -which is what spatial audio is modelling

And your muffling example sounds a bit over simplified maybe? My understanding is that the spatial stuff is produced by phase shifting the LR signals slightly

Finally why not go further? “I don’t listen to speaker audio because it’s all just effects and mirages to sound like a real sound, what only 2^16 discrete positions the diaphragm can be in” :p

There’s is a huge difference though.

That being one is making hardware and the other is copying books into your training pipeline though

The copy occurs in the dataset preparation.

Privacy preserving federated learning is a thing - essentially you train a local model and send the weight updates back to Google rather than the data itself…but also it’s early days so who knows what vulnerabilities may exist

You could try dexed, it’s a YamahaDX7 clone https://github.com/asb2m10/dexed/releases

You need rebase instead. Merge just creates useless commits and makes the diffs harder to comprehend (all changes are shown at once, but with rebase you fix the conflicts in the commit where they happened)

Then instead of your branch of branch strat you just rebase daily into main and you’re golden when it comes time to PR