For what it's worth, the Navy method (https://www.omnicalculator.com/health/navy-body-fat) always matched my DEXA scan results much more closely than any BIA scale I used ever did. Given that, it would make sense that you could estimate it from photos.
A robust open source profile is my single favorite hiring profile indicator. However, with the current state of things, if I get a whiff of AI-driven "contribution" it becomes an instant black mark against the candidate.
So far I've been impressed enough with the HW4 Teslas that I haven't had them do anything that I had to intervene to correct or prevent. It's pretty amazing at how well it handles all kinds of things - construction, weird merges, road debris. This morning, there was a tire in the middle of the road, which caused traffic ahead of me to slam to a halt. Mine had to brake hard enough that ABS engaged, and then navigated around the tire. I was impressed.
This is the only (very small) blemish on an otherwise fantastic game.
I know this game is a satirical sendup of gacha, but in the same way that Universal Paperclips subverted the clicker game genre and made something fantastic, I find this stripped-down gacha utterly charming. Thank you to the dev!
I used a combo of low-dose retatrutide, tesamorelin, and ipamorelin and lost about 15lb over 45 days, including 60% of my visceral fat, and put on 4lb of muscle, per before-and-after DEXA scans. I lifted regularly, ate well, and prioritized protein, and while I definitely under-ate protein, I was very pleased to find that I was able to increase muscle mass while cutting the fat. My visceral fat was the primary target here, since I'd been unable to get it to budge despite consistent training and diet. Very pleased.
It's actually very easy to cool in deserts, because low humidity makes it very easy to move heat into the ambient air. You have to contend against ambient temperatures, but that's what insulation is for. The other big things you need for datacenters are reliable power and a low probability of infrastructure-disrupting natural disasters.
I've done a lot of exploratory work with Stable Diffusion LoRAs, and I actually do buy that there's some juice here, though it's almost certainly not nearly as good as other techniques can be. In particular, this technique will likely avoid the intruder dimension problem which plagues naive LoRA. SVD is expensive, but you only have to do it once at the beginning of training.
I haven't done much research lately, but when I was working on it, I was having substantial success training an adapter of the form U_k @ P @ A, where U_k was the top k left singular vectors of the underlying weight, and then P and A were your typical LoRA projection matrices.
The 13 parameters are kind of misleading here; the real juice is going to be in the P_i fixed random matrices. My suspicion is that they are overfitting to the benchmark, but they almost certainly are observing a real gain in model capacity that is largely due to avoiding the intruder dimension problem.
