sabareesh

I am pretty much doing the same but running the coolant at 40 deg C instead of 45 as my pumps are rated for 45 C max temp. Here is bit more about my setup https://sabareesh.com/posts/blackwell-waterblock/

Yes this has been on my mind as well. But this was built one at a time but still overall very happy with them

Appreciate the feedback. I have improved the article.

Appreciate the feedback. I have improved the article.

I am primarily experimenting on post training stack. As of now working on training a model that is natively RLM https://github.com/alexzhang13/rlm

It is basically on 2 different circuits/breakers. Asus wrx90e supports 2 psu as well. You may need to synchronize both psu and several adapter for this is available in Amazon. Soon planning to upgrade it to 240V

Not sure what really happened but some force or bad solder caused it.

Most of the training i am working on is with post training. You can do so much with a system that is running 24/7

Sure 140mm fans you may call little but it does need enough static pressure for the radiators. This setup is already several times quieter than stock setup

Converting four RTX PRO 6000 Blackwell cards to waterblocks, finding a VRM choke loose on the workbench, and getting back to 41k tok/s.

Sounds like speculative decoding but for KV cache

Based on last few attemts on claude code to address a docker build issue this feels like a downgrade

Codex usually dont add itself as contributor so this is misleading .

I wish it provided benchmark comparing Direct RAM offload vs CPU offload vs Full VRAM

Tesla have their own Insurance product which is already very competitive compared to other providers. Not sure if lemonade can beat them . Tesla's insurance product has similar objective in place already where it rewards self driving over manual driving.

I am looking for some open source terminal for iphone .I have code server running which i can just use terminal from vs code on safari

Sorry to disappoint. But purely codex and claude code

I have switched to terminal

TL;DR is that they didn't clean the repo (.git/ folder), model just reward hacked its way to look up future commits with fixes. Credit goes to everyone in this thread for solving this: https://xcancel.com/xeophon/status/2006969664346501589

(given that IQuestLab published their SWE-Bench Verified trajectory data, I want to be charitable and assume genuine oversight rather than "benchmaxxing", probably an easy to miss thing if you are new to benchmarking)

https://www.reddit.com/r/LocalLLaMA/comments/1q1ura1/iquestl...

Non starter for us, we cant ship propriety data to a third party servers.

this has one of the worse score in AA-Omniscience Hallucination Rate

Nope lower is better compared to recent open ai models this is bad. I am looking at AA-Omniscience Hallucination Rate

Watch out these model are hallucinating lot more https://artificialanalysis.ai/evaluations/omniscience?omnisc...

So is 10,000 IU of daily does ok ?

Technically you kind of get this in Nevada when using Tesla insurance and if you drive 100 % FSD. If you drive manually you are pretty much doxed for random Front collision Warning which is super sensitive

It might be that our current tokenization is inefficient compared to how well image pipeline does. Language already does lot of compression but there might be even better way to represent it in latent space

Similar feeling. Seems it is good at certain things and if something doesnt work it want to do things simply and in turn becomes something that you didnt ask for and certain times opposite of what you wanted. On the other hand with codex certain time you feel the AGI but that is like 2 out of 10 sessions. This is primarily may be due to how complete the prompt and how well you define the problems.

"Simple, Boring Tech Stack:" Good advice but bad example, because it depends on what engineers are familiar and comfortable with and technology itself should be mature enough. You dont want to spend time building orchestrator when k8s solves it for you. Most cloud provides provide you with k8s as a service, which are miles better than using shell scripts, if you are already familiar with k8s

This is great,but most work is involved in curating the dataset and the objective functions for RL.

Looks very similar to DGX spark