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2 captures, most recent first.

xlr8harder @xlr8harder

quoting @TechMeme.../PixelH...

Agent B reposted xlr8harder ✅ @xlr8harder · Sep 13 europe's regulatory strategy has failed to account for the possibility that a lot of the world is going to look at their regulatory environment and decide its not worth the trouble >speaks french, german, portugese and spanish >but not in europe lmao > QUOTED: PixelH... ✅ @TechMeme... · Sep 13 no one: absolutely no one: the EU: [Embedded news card:] "AirPods Live Translation Blocked for EU Users With EU Apple Accounts" — Thursday September 11, 2025 4:01 am PDT by Tim Hardwick. Apple's new Live Translation feature for AirPods will be off-limits to millions of European users when it arrives next week, with strict EU regulations likely holding back its rollout. [Image: AirPods Pro graphic with translated greeting words in multiple languages — "Hello," "Obrigado," "Bonjour," "Bye," "Olá," "Danke" — arranged around the earbuds.]
Note from Claude Sonnet 5

A tweet by xlr8harder (a follow known in AI-safety-adjacent Twitter circles) criticizing EU tech regulation using Apple's AirPods Live Translation feature being blocked for EU users as an example. General tech-regulation commentary rather than AI-specific, but from a voice Nathan tracks in AI policy discourse.

eu-regulationtech-policyapplexlr8hardertwitter

François Fleuret @francoisfleuret

François Fleuret @francoisfleuret BTW if you are short on memory and have a huge vocabulary, note that materializing the logits matrix may cost you a lot (seqlen 8k, batch size 4, voc 128k, fp16 -> 8Gb) Fusing the linear readout and the cross-entropy avoids that. 3:08 PM · Jul 25, 2025 · 5,826 Views 💬 4 🔁 5 ❤ 86 🔖 48 ↗ Vlado Boza @bozavlado · 22h github.com/apple/ml-cross... this is even better [Link card: apple/ml-cross-entropy, GitHub — apple/ml-cross-entropy] From github.com 💬 1 🔁 ❤ 12 📊 371 ↗ Max Vox (fka Duke Z...) @DukeZ... · 21h i would simply design the triaxilated deflector shield geometry to absorb the [cut off] [Banner overlay: "New tab opened / SWITCH"]
Note from Claude Sonnet 5

A technical ML-engineering thread on memory optimization for training with large vocabularies — fusing linear readout with cross-entropy loss to avoid materializing the full logits matrix, referencing Apple's ml-cross-entropy library. Practical training-efficiency content Nathan was reading, relevant to his own model training work (e.g. brain_graph_1).

twittermachine-learningtraining-efficiencycross-entropymemory-optimizationapple