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So Pieter is Dutch and you pronounce it like Peter in English, not Pie-Eater or Pya-ter, just Peter There's also literally Peter in Dutch, but you pronounce that as Pay-ter (???) Of course like usually the English pronunciation is kinda the wrong one here, and most other languages also say Payter if you write Peter, because the Es and As are completely wrongly pronounced in English There's a few famous Pieters in history: - Pieter Stuyvesant, the last Dutch director of New Netherlands and thus New Amsterdam, current day NYC (later Anglicized his name to Peter) - Pieter Rubens, a Belgian painter - Pieter Breugel, also a Belgian painter - Pieter Nuyts, explorer who charted Australia's coasts in 1600s THANK YOU FOR YOUR ATTENTION TO THIS MATTER @pieter
🇪🇺https://t.co/NdorAWqJC3 📸https://t.co/lAyoqmSBRX $138K/m 🏡https://t.co/1oqUgfD6CZ $38K/m 🛰https://t.co/ZHSvI2wjyW $35K/m 🌍https://t.co/UXK5AFqCaQ $17K/m 👙https://t.co/RyXpqGuFM3 $14K/m 💾https://t.co/M1hEUBAynC $6K/m
A 22-year-old woman who is paralyzed from the waist down can walk again thanks to a first-of-its-kind wearable device.
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Cursor 带着他们的 Agent 编程模型 Composer 杀回来了 之前又快又好又不废话的 cheetah 就是 Composer 模型的预览版 全新 2.0 版本，带来了更好的模型、更专业的界面 还在担心 Cursor 的护城河？ 它也是个模型公司了，卷就是了
聊硅基 AI，看有机 Orange。
I keep seeing cool methods in which LLM agents "condense" or summarize context across steps. We worked on this in Fall 2020, somehow coining the same term used now "condensed" retrieval. It shows a very early instance of self-training language models for chains of reasoning.
This preceded autoregressive chain of thought, so reasoning here is a long chain of search queries and extracted snippets from a 5M-document corpus. The scores on HoVer are still extremely hard to match to this day via prompting or RL on modern LLMs!
Classy New Zealand news
CPO at https://t.co/BNZzlkTfVp. Founder of https://t.co/hOAmca8qLm and https://t.co/dRwgbZCSOw. Coffee-making, parenting, building, exploring: RU → CN → NZ → CL → UK → NZ → PL → UK?
RT @jxmnop: this post is complete misinformation LLMs are lossy compressors! of *training data*. LLMs losslessly compress *prompts*, int…
Root node of the web of threads: https://t.co/ifH80GcLpo