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#OPENMIC# 26thシリーズラスト！ OCEANSの前身クレイユーキーズが 鈴木愛理さんと共にリリースした “この手。”を全員でセッション🎼 今年はライブがなかったぶん、 この企画に参加することが叶い しっかり夏を満喫できました🍉 何度でもお楽しみください〜

#OPENMIC# 26thシリーズvol.4 イダテンドリーマー/宮川大聖を みんなでセッションしました🪄 原曲に大胆な変化を加えた OCEANSならではのアレンジ！ 楽しくかっこよく仕上がったので、 ぜひ何度でも見てくださいね〜

#OPENMIC# 26thシリーズ vol.3が公開されました🎧 今回はTikTokでも人気の Baby you/有華をセッション！ 乾杯前のそわそわな私です。

#OPENMIC# vol.2公開されました。 結成25周年を迎えるHYさんの 大ヒットナンバーAM11:00🛏️ イントロのフレーズが とても印象的で好きなので、 弾かせていただき光栄でした！

DeepMind's Andrew Trask on why the scaling laws are pushing AI from one big model toward a protocol: "The zoomed-out picture is that in the end, AI is gonna be a protocol instead of a program. We're seeing that evolution start to really gather steam as the scaling laws constrain how much data, compute, and talent one company can bring together." "When you combine models from multiple different providers, you're implicitly combining the data, compute, and talent that they trained on. So you can get better, faster models for a lower price, which is pretty crazy when you think about it." "If you want the absolute most accurate model, ensembling the top models is always going to win, you'll get higher scores than any single model. And if you want the best accuracy relative to any unit of price, ensembling some open and closed models is likely gonna own that Pareto frontier quietly for a while." "It won't be until there's a leaderboard that widely recognizes ensembles as comparable to individual models that we start to really see it. Then in 12 to 18 months, that saturates a bunch of benchmarks across the space, and the harnesses pick it up, routing you to the best combinations of models on the fly. That's when the market really starts to change in terms of how people buy intelligence." @iamtrask @openminedorg

OpenMinis：运行在手机上的Agent Codex、Claude Code之类的电脑Agent，能使用电脑里的工具，例如Python、浏览器等。 OpenMinis则是运行在手机上，能读取和使用HealthKit、日历、提醒事项、HomeKit、通讯录、蓝牙等，是手机上的Harness环境。 Github：

🧵 Announcing iSH ARM64 — native Linux on iPhone & iPad, open source. A fork of @iSH_app that adds a same-architecture threaded-code interpreter (codename Asbestos) for AArch64 Linux on Apple Silicon. 1/ iSH ARM64 runs AArch64 Linux on ARM64. 3–30× overhead vs native, down from 15–100×. 2/ 48-bit virtual address space. Unlocked V8, Go, and Rust on iSH ARM64 now. 3/ Node.js 22 now runs on iOS. With --jitless injection. npm install. npx. create-next-app. All working. 4/ Built for AI agent orchestration on iOS. iSH ARM64 shipped as the Agent Shell Sandbox inside OpenMinis — stably running Linux workloads for 10,000+ users. 5/ Compatibility: 205/205 tests pass. 18 categories — Core OS, Python, Node.js, Go, Rust, Perl, Ruby, SQLite, Git, SSH, Editors, Build tools, Media, Crypto, Signals, and more. The code is open. The 10,000 users are production. ⭐ 🙋 PRs welcome. Issues welcome. Stars appreciated. #OpenSource# #iOS# #Linux# #ARM64# #iSH# #SystemsProgramming#

前两天清华团队开源了个好东西，一个 AI 课堂，名字叫 OpenMAIC。 简单说，它是一个 AI 教学系统，有点像一个高级版的 NotebookLM。 你给它一个话题或者丢给它任何学习材料，它就能自动生成一堂完整的 AI 互动课 。 有 AI 老师给你讲，有 AI

(1/2) Glad to announce our OpenMAIC! 🎉 Open-sourcing MAIC (Multi-Agent Interactive Classroom) from Tsinghua University — LLM-driven multi-agent classroom for scalable & adaptive online education. 🏗️ Core Architecture: ✅ MAIC-Craft: Read (multimodal extraction) → Plan (course components + agent generation) ✅ Adaptive Engine: Cognitive student modeling + Token-level personalization (RAG + Bloom's/ZPD/UDL) ✅ Multi-Agent Classroom: 1 Student + N Agents (Teacher, Assistant, 4 Peer Archetypes) ✅ Manager Agent: Class state receptor for turn-taking orchestration 🔗 Give it a try 👉🏻 GitHub: #AI# #EdTech# #MultiAgent# #LLM# #Research# #OpenSource# #Tsinghua#