car

Paper: https://arxiv.org/abs/2608.16157

yt: https://www.youtube.com/watch?v=8n4Uo47CadY

Thanks, I’ve changed the submission title from the author’s original.

Thanks, I concur and have changed the author’s original title to better represent the article.

Buying German submarines as one example, joint with Norway to defend the Arctic.

https://team212cd.ca

I should add that Llama.app and Unsloth Studio provide OpenAI-compatible API endpoints (both use Llama.cpp as their backend).

https://llama.app/docs/api

https://unsloth.ai/docs/new/studio#unsloth-as-an-api-endpoin...

Please make this more generic, using the OpenAI API standard with model discovery. That way it works against a bare Llama.cpp instance out of the box, the local inference server that many others wrap around (like Ollama and LM Studio).

This uses the new onboard TTS model from Apple, which is amazing, even with background noise.

The choice of model could be up to the user, depending on the available RAM on their M class Mac. Newer and bigger usually being smarter.

Llama.app/.cpp and Unsloth Studio on Mac already provide guidance to pick the optimal model for the available memory, all model wheights hosted by huggingface. Both apps can use the local huggingface model cache locally.

If you make it configurable, the user could also run a separate machine with an NVidia card and Linux to serve tokens on the LAN. Or use OpenRouter or any other cloud hosted model offering (DeepSeek, Z.ai, etc.).

A transparent proxy that strips IPs, credentials, hostnames, and PII from every request before it reaches the AI — and restores them on the way back.

This looks and works great, super impressed. This is what Siri/Apple AI should be like.

One concern I have is about sending private info to Anthropic. So it would be great to have the option to use local AI.

Local options should support the OpenAI API that seemingly is the standard for these connections, as provided by Llama.app and Unsloth Studio for example. That way one could run against a local model and keep personal information private.

That of course begs the question if a local model is smart enough for the tasks at hand.

I'm also trying out OOMU, a similar but closed source personal assistant app for MacOS. It emphasizes privacy with local models, and even has an 'Airplane' mode. The app has a built in llama.cpp server and uses Gemma-4-4B, their own router model plus a vision model.

Someone rebuilt the Falcon from scratch, seen recently at the VCF West. Pretty insane.

https://re-falcon.com

I posted about Theranos 2013. Not entirely sure, but I think it was when they launched publicly.

Crazy how my gut feeling was more than vindicated.

https://news.ycombinator.com/item?id=6349349

Claire maybe?

Marzipan

How about Verscheisserung / Verschisserung?

I do like the sound of Verscheissification.

As Picard said: Enshitify! (Apologies to my Trekkie friends.)

We've misunderestimated you.

If you are on Mac, have a look at the Llama-macOS app. They claim sensible settings for the linked model downloads. I'd expect the authors of Llama.cpp and the Huggingface folks to know this stuff.

https://news.ycombinator.com/item?id=49328008

If you have llama.cpp installed, Llama uses it. Otherwise, it installs a prebuilt binary for your Mac. Models you've already installed via llama.cpp show up in the app automatically. You can install any GGUF model from Hugging Face, and Llama also recommends models that fit your Mac's hardware.

You can chat with any model in the built-in WebUI, connect other apps (coding agents, chat UIs, editors), or use the API directly. Models load when requested and unload when idle, so they don't take up memory when not in use.

> Features

- 100% local — Models run on your Mac; no data ever leaves it

- Small footprint — 4 MB native macOS app

- Zero configuration — models are auto-configured with optimal settings for your Mac

- Model recommendations — a built-in list of models your Mac can run, installable in one click

- Standard storage — models live in the Hugging Face cache, shared with llama.cpp and other tools

- Built on llama.cpp — from the GGML org, developed alongside llama.cpp

> Installation

To install llama.cpp , run:

  brew install llama.cpp 

To install llama-app, run:

  brew install --cask llama-app