Not sure it’s a blog post really but I saw a video of Rutger Bregman talking about moral ambition and it directly led to me applying at a non-profit that was better aligned with my own ideals and leaving my job at a pretty large and morally reprehensible corporation.
In case it saves anyone some time (from the article):
"The AMD Ryzen AI Max+ 395(Strix Halo) processor has been available since Spring 2025 and the Halo doesn’t offer anything new on that front."
It has the same 256 GB/s memory bandwidth limit as every board previously, not sure why this is even being released right now as if it's some new fangled thing - you can go get a Framework Desktop for roughly the same price or a GMKtec EVO-X2 for a bit cheaper.
thank you for the tip here! would you say tinc can work more or less like tailscale?
I saw this: "As long as one node in the VPN allows incoming connections on a public IP address (even if it is a dynamic IP address), tinc will be able to do NAT traversal, allowing direct communication between peers."
And wondered if tailscale was doing a bit more magic than tinc is here?
How common is it for peer reviewed papers like this to be so far off their claimed findings?
“According to Google's peer-reviewed and published paper, they claim to have a true positive rate (TPR) above 99.97% -- meaning that they will miss their own watermarks less than 1 in 10,000 times. However, my own empirical testing found that is it much closer to 1 in 20.”
This is very cool - I will likely see if I can use it in place of tailscale for my local LLM hosting. I feel like not having that required login would be great. Also the direct connect feature seems pretty cool, since that’s usually all I need for my use case.
I'm really happy this is one of the top comments here, I am fully local as well.
Just wanted to leave a note for folks who might not have the memory to run a big 32gb model - I just found out there are some pruned models that have really good performance and If I had a smaller machine I might try this pruned unsloth Q4 quant of GLM 4.7 flash that sits at 14gb:
https://huggingface.co/unsloth/GLM-4.7-Flash-REAP-23B-A3B-GG...
I usually use LM Studio for this type of thing but unsloth has their own studio type app that might be even better suited for these quants.
I used GLM 4.7 flash as my main model for months and it was an incredibly tenacious model and very very fast - I think on restricted hardware, this could be a great choice.
This is interesting, I haven’t actually heard you suggest that the labs are focusing on this benchmark before. Have you come around to this position as a result of the quality of pelicans you’ve been getting?
The reason I thought this was an interesting benchmark is because it’s a non-image generating model creating an image using SVG code, so it kinda spans capabilities.
If an AI lab trained a model specifically for animals riding bicycles it seem trivial to modify the prompt and determine if it was trained specifically for that or if it’s generalized a skill and can also generate a proper orangutan walking on stilts or an armadillo on a skateboard, this sort of thing?
I completely disagree, it is probably the best platform currently for this - and the way I run it is as a server with tailscale accessible from my coding machine (same as you suggest here) - the difference is that you can stop the server, use it as a video editing rig on a whim, or use it for training instead of inference (yes PyTorch has caught up and Metal is a great platform for this now).
It’s just so flexible, and I even use it in agent mode (ds4) directly on the machine as well sometimes (it’s really not that bad, I’m often running inference for small side projects on my couch), if there is another machine that can do all of this and still function as one of the more ergonomic, well built, and compact laptops out there, I’d love to hear what it is cause I’d likely be interested!
Really wish this author would blog more, this piece is incredible and includes the code.
Also ModernBERT is amazing if you haven’t used it before, worth spending time with - have used it myself for classification tasks and it’s very impressive.
I think the 9b and 31b dense are Gemma models and the 35B-MoE, and 397B-MoE are Qwen models since these are model sizes covered by each of them respectively