Recent submissions

Comment · 1 month ago
I don't know if the splat is exactly the same, but the playcanvas engine has no false transparency on this one: https://superspl.at/scene/7e4e9bcb that splat is actually of fairly high quality, so unless this is a materially different splat (with some of the gaussians dropped out maybe?) I'm inclined to blame the engine.
Comment · 2 months ago
Both use LPDDR5x, they're not shipping LPDDR6 (yet).
Comment · 2 months ago
Looks neat! I'd love for it to feed the MIDI notes back into my player piano instead of playing out of my iPhone's (comparatively tinny) speakers though.
Comment · 6 months ago
Hi, security professional here! A lot of the time, we don't need it.
Comment · 6 months ago
The central point of this app is to determine proximity of two devices. That's not possible today in a cross-platform way using web apps.
Comment · 6 months ago
The Quick 861DW has been a hobbyist favorite for a long time, and comes in at ~$300 (USD)
Comment · 10 months ago
For modern systems, stack buffer overflow bugs haven't been great to exploit for a while. You need at least a stack cookie leak and on Apple Silicon the return addresses are MACed so overwriting them is a fools errand (2^-16 chance of success).

Most exploitable memory corruption bugs are heap buffer overflows.

Comment · 10 months ago
Yep! 60t/s on the 8 bit MLX on an M4 Pro with 64GB of RAM.
Comment · 10 months ago
There's MLX versions of the model, so yes. LM Studio hasn't updated their mlx-lm runtime yet though, you'll get an exception.

But if you're OK running it without a UI wrapper, mlx_lm==0.30.0 will serve you fine.

Comment · 1 year ago
Comment · 1 year ago
I do vulnerability research. Those things would do the exact opposite of what you're aiming for. They'd be received with glee by mercenary spyware companies, _especially_ being able to load things into higher levels of privilege.
Comment · 1 year ago
Comment · 1 year ago
It'd run on a 5090 with 32GB of VRAM at fp8 quantization which is generally a very acceptable size/quality trade-off. (I run GLM-4.5-Air at 3b quantization!) The transformer architecture also lends itself quite well to having different layers of the model running in different places, so you can 'shard' the model across different compute nodes.
Comment · 1 year ago
From what I've been reading the inference workload tends to ebb and flow throughout the day with much lower loads overnight than at for example 10AM PT/1PM ET. I understand companies fill that gap with training (because an idle GPU costs the most).

So for data centers, training is just as important as inference.

Comment · 1 year ago
I believe they mean the source region's tag, rather than the destination.

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