I've been considering a long-term quadruped project and have been poking around the builds that are out there. There's a ton of cool stuff, but so far I haven't seen anything open-source seems to match the dynamic motion capabilities of the mini-cheetah. "Dynamic motion capabilities" is pretty hard to pin down without benchmarks, but subjectively I mean the speed, rough-terrain capabilities, and performance jumping/falling. (Even more subjectively I mean the backflip). Given the seven year gap that really surprised me. My question for the community is two-fold: Is there an open-source quadruped build that does match the mini-cheetah that I just missed? If not, why? submitted by /u/lellasone [link] [Kommentare]

question - i was just informed that I cannot transfer crypto back to my bank account, AML? Is this true? How are people who are taking payments this way transfer it to bank? submitted by /u/Several_Luck5717 [link] [Kommentare]

I work in firmware adjacent to AI, so not an ML guy exactly, so that's why I've come here. For work we got a bit concerned about Mythos and all the hype made me explore some benchmarking work. I now have this pretty cool benchmark that's about 80% done sitting around and haven't had the time to polish it up and show it off. I was hoping some more AI focused people could check it out, tell me if it's duplicate work, or if it is worth putting some time into and finishing. Also happy for some help too. The rundown of the code is that it is Juliet code that's been "hidden" to look somewhat like a real codebase, removing LLM's natural advantage when viewing known CWEs, while preserving the "ground truth" associated with Juliet. I also used an LLM to inject comments into the code in accurate, misleading, or neutral sentiments, allowing the user to examine how comments and plain English data can manipulate an LLMs ability to identify a CWE. There are a couple hundred CWEs, generally enough code to fill up the input context, the work that needs to be done is around presentation, actual benchmarking of publish LLMs, and possibly pruning of a couple CWEs that might occasionally get caught by certain LLMs as Juliet code still. Here's the project. Hopefully this doesn't break rule 6. I am not a regular here, just looking for advice. submitted by /u/Psychological_Meat_6 [link] [Kommentare]

Aiming for a 10 year life-cycle for smartphones

A popular way to explain how current LLMs work is to say that “all” they do is predict the next most likely word in a sentence. From one perspective, this is correct. Trained on all human language, the LLMs distilled … Continue reading →

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What every line of a PyTorch training loop does, why it belongs where it is, and what breaks if you move it.