Played with this a bit and from what I gathered it's purely a re-arch of pytorch models to work as .tflite models, at least that's what I was using it for. It worked well with a custom finbert model with negligible size reduction. It converted a quantized version but outputs were not close. From what I remember of the docs it was created for standard pytorch models, like "torchvision.models", so maybe with those you'd have better luck. Granted, this was all ~12 months ago, sounds like I might have dodged a pack of Raptors?
It seems most of the things Netflix produces is optimized by the algo for attention. When I feel it directing me gives me the ick. Looking at you Squid Game.
Homogeneous systems kill innovation, with that in mind, I guess it’s a good thing DeepSeek disregards licenses? Seems like sledding down an icy slope, slippery. and they suck.
DeepSeek is a card trick. They came up with a clever way to do multi-headed attention, the rest is fluff. Janus-Pro-7B is a joke. It would have mattered a year ago but also just a poor imitation of what's already on the market. Especially when they've obfuscated that they're using a discrete encoder to downsample image generation.
This feels more like misery porn disguised as a crusade to 'protect' the vulnerable. I’m not suggesting we make it easier for people to get scammed, but intentionally engaging seems like a recipe for a harsher clap back. Education > Escalation.
This sounded fun and I wanted to try it, so I built a web version. It's open-source built with React Native. The code is here https://github.com/HatmanStack/savorswipe or it's hosted here https://www.savorswipe.fun You can easily add recipes to the swipe list by snapping a photo of ingredients and directions then adding the photo to the app.
Is it just me or is this stream janky? I had a 30+ min lag for it to start and checked it on my phone with the same wait. I guessed it was happening for everybody but didn't see anything else posted.
This is a tool put out by CSET's emerging technology office to collate and track all of the OSS software repos from "Github Archive, ETO’s Merged Academic Corpus, and many other data sources" by some standard metrics to identify trends. I use their Map of Science a bit to see around the corner. This should be a great addition.