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I'm surprised nobody here is saying "what if your staging server is on the LAN, or you want to test a website from another machine?" Somehow Safari allows it, and Chrome has to go through this system that prompts you. I try never to miss one of these permissions ("yes"), because having Chrome blocked from local websites is just no good.

First time I've seen AI-authored instructions from Apple. I guess it is hard for them to just post a new .pem because it could be MITM'd.

likely you're getting 90% off the API rate exactly so they can train on your data, even some of it

Great idea - it's your "driving directions" app that decides if you drive by these cameras, or not!

Plaid is the main risk - if they were hacked, that's way worse than a log of my AI queries. What's exciting with LLMs is the possibility to do classification much better.

Dr. Sandra Kooij is a good person to look up if you are interested in adult ADHD and sleep. She has a book about it. From what I remember, there are a few things that are known:

1. a large majority of ADHD sufferers are phase-delayed and struggle with insomnia

2. you can induce ADHD-like symptoms in normal people by restricting sleep

3. Some of the interventions that improve sleep timing and quality affect ADHD (but it isn't a total cure)

looks great - jxl-rs is faster since June 2026 (using just a bit more RAM)

I love the codec and am glad to see it supported again. They overcame the security issues using a rust SIMD port. I wonder what "approximately as fast as the best non-memory-safe alternative" means - the wording suggests they didn't beat the C++ implementation, but how close is it?

You don't want a backup vulnerable to "reading iMessage" either - maybe they should just encrypt these things at rest.

The main outcome of the original papers is RR (relative risk) and the main number discussed in this article is "people living near a landmark" which is obviously nonsensical. You'd need to compute your "Costco risk index" using epidemiological risk compared with a baseline, and there's not one place here where they claim to do it. As written, this does not appear to be a serious effort.

I have two NVIDIA GPUs (16GB+16GB) here, and it detects them each twice (says I have 4 GPUs). But then, it says most models are too big (anything >8GB?) and seems to run only on one GPU (5070ti).

Unfortunately even with my 5070ti, llama.cpp seems to be about 20-30% faster at decode, running as:

set CUDA_VISIBLE_DEVICES=0 build\bin\Release\llama-server -hf google/gemma-4-12B-it-qat-q4_0-gguf -ngl 99 --no-mmproj-offload -mg 0 -c 262144 -fa on --host 0.0.0.0

Total software developer comp (worldwide) is ~$1T

AIs are no better than their training data, and there aren't high-quality cues everywhere - go ask advice for some highly-spammed topic like "mattresses".