Qualcomm still sucks, but there’s not much Qualcomm in this.

Robotic pick and place board assembly means through-hole components are going the way of the dodo. Sorry.

Adafruit sells breakout boards for various SMT packages so that with a little solder reflow work you can adapt that shiny new chip to your breadboard. https://www.adafruit.com/category/475

>pick and place board assembly means through-hole components are going the way of the dodo

The factories willing to do SMD mixed with through-hole high-power electronics is shrinking. However, package constrained power dissipation limits on SMD technology is real.

Depends on what your firm builds. =3

No, it’s not just QWIC connectors.

It is just the time-honored maker tradition of finding a cool IC and building a dev board around it for the hobbyist. There’s a i2c multiplexer chip, an i2c buffer, and a nicer motor driver.

Article title is nonsensical.

for lack of frequency tuning

“Moving the goalposts” is a useless criticism. Do I need to pick now a single, definitive goalpost where the massive redirection of resources will have become worth it? Or can I just have a laugh at the dumb state of the present?

BEEP BEEP MOTHERFUCKER!

Very interesting technology. Would be exciting to see a hardware startup build a product around this.

Hmm not sure about that. It seems like a lot of complicated effort to produce locomotion. I’ll stick with my reliable horse, thanks.

Where do you even start finding sources for all this?

Websites like Finnrick provide testing for gray market research peptides (including tirzepatide and retatrutide). There are no direct links to shops, etc., but you can at least see the current state of things, both good and bad.

Cremieux is a popular biohacker on Twitter who has a paid Substack describing the process.

That's enough to get anyone started.

Very cool product! And to think, in one of the many prior gaussian splatting threads someone declared there was no way anyone could build a business around the technique.

To be fair, if they first vocalize it they could just use Sparse Voxels which is my favorite differentiable rendering technique https://github.com/NVlabs/svraster

I find the workflow of fitting Gaussian splats and then immediately turning them into voxels to be rather surprising, too. I can imagine some performance benefits at large scale, or maybe a reduced tendency to generate certain types of artifacts, but going straight to voxels seems more straightforward.

> Present-day models tend to produce code that is too defensive, too complex, too local in its reasoning. They avoid strong invariants. They add fallbacks instead of making bad states impossible. They duplicate code, invent bad abstractions, and paper over unclear design with more machinery. Worse though: I so far see very little progress of this improving.

It’s almost as though these models were trained on a vast corpus of largely mediocre code. They will never outperform the median Github user - it is all they know, it is all they can do.