@Graffitinights1 For the zillionth time, dumb systems that are empowered can dangerous. like this monstrosity, as an example, or a automatic weapons with poor accuracy. why is that so hard to understand? goodbye. https://t.co/ZR0cQZe54N

@VinceZcrikl @shangdu2005 https://t.co/oS8T98ESjb

Spark 2.0 is here! 🚀 We’re redefining what’s possible on the web with a streamable LoD system for 3D Gaussian Splatting. Built on Three.js, you can now stream massive 100M+ splat worlds to any device from mobile to VR using WebGL2. All open-source. Dive into the tech 👇 https://t.co/VOd6V0Wz1s

For anyone running @NousResearch Hermes Agent locally and wishing it just stayed online: there's now a one-click deployment template on Tencent Lighthouse. Cloud-hosted, sandboxed from your local env, online around the clock, reach it through WhatsApp, Telegram, WeCom, QQ, or other messaging channels. Also: the QQBot plugin is now merged into Hermes Agent's official repo. Just pick QQBot under Messaging Platforms config and you're set.

多Agent团队协作才是Hermes Agent的正确打开方式。 所有需求都塞给单个Agent，上下文和记忆难免杂乱。而且一次只能处理一个任务，让Hermes做研究的时候想问别的就得等着。 如果你已经有一个跑通的Hermes，四步就能搭起一个结构化Agent团队： 1、克隆profile 终端执行：hermes profile create "xxx" --clone 继承你调好的基础配置，但记忆和会话全独立. 2、给每个profile写SOUL.md 确立各自的定位和边界。SOUL.md定义的是这个agent是谁、擅长什么、不该碰什么。 3、项目根目录设置AGENTS.md AGENTS.md放项目结构、协作规则、当前进度，让所有Agent共享同一个任务背景。 4、配置完毕后单独调用，各司其职 终端输入：hermes -p "xxx" 每个agent只处理自己该处理的事，上下文干净，记忆不串一起。 下面是我的配置目录，供参考。

@karatademada https://t.co/9j7ifVqVDi

@Shockwave_App https://t.co/FlFWjBcqgp

@wandb v0.26.0 adds profiling metrics for H100+. What a nice surprise https://t.co/JBeI08Sfvb

@wandb v0.26.0 adds profiling metrics for H100+. What a nice surprise https://t.co/JBeI08Sfvb

@Spectromachina https://t.co/ZfEerEWexX

@WAPSHOP_ETH @Dalos https://t.co/Bx7vaXIfj8

Inflation was coming down in the United States—and then Trump’s tariffs happened. https://t.co/qDh8p6tuzy

Inflation was coming down in the United States—and then Trump’s tariffs happened. https://t.co/qDh8p6tuzy

We will read histories of today’s US in a few decades and wonder how something like this could happen. Slowly allowing Polio and Measles to celebrate comebacks is pure stupidity. https://t.co/ht1if5CD9l

Time to follow https://t.co/K4Rf9nWzU8! https://t.co/8sET4B20ZB

Time to follow https://t.co/K4Rf9nWzU8! https://t.co/8sET4B20ZB

✨ERNIE-Image is here — Baidu's open 8B text-to-image model that punches way above its weight. ✅ Only 8B DiT params, #1 open weights model on GenEval, OneIG, & LongTextBench ✅ Precise text rendering in English, Chinese, and more ✅ Complex instruction following & multi-object control ✅ Posters, manga, multi-panel layouts with structural coherence ✅ Broad styles: photography, cinematic tones, graphic design, and more ✅ Easy to deploy: Runs on 24GB VRAM Two versions: ERNIE-Image — SFT model, stronger general quality in 50 inference steps ERNIE-Image-Turbo — Optimized for speed and aesthetics in just 8 steps 🤗 Model: https://t.co/QCZkSFZEIz https://t.co/Eutm0gurmp 📝 Blog: https://t.co/Gb5uQX4mAU 🖥️ Demo: https://t.co/vikUfdp6KO 🔧 Github: https://t.co/SgFNyxzZKZ 👾 Discord: https://t.co/05DviJtHU0 Join our Discord for free bot access, weekly challenges, and prizes!

+3 more

I'm excited about voice as a UI layer for existing visual applications — where speech and screen update together. This goes well beyond voice-only use cases like call center automation. The barrier has been a hard technical tradeoff: low-latency voice models lack reliability, while agentic pipelines (speech-to-text → LLM → text-to-speech) are intelligent but too slow for conversation. Ashwyn Sharma and team at Vocal Bridge (an AI Fund portfolio company) address this with a dual-agent architecture: a foreground agent for real-time conversation, a background agent for reasoning, guardrails, and tool calls. I used Vocal Bridge to add voice to a math-quiz app I'd built for my daughter; this took less than an hour with Claude Code. She speaks her answers, the app responds verbally and updates the questions and animations on screen. Only a tiny fraction of developers have ever built a voice app. If you'd like to try building one, check out Vocal Bridge for free: https://t.co/nGrFznAMLh

Excited to partner with @Dropbox on bringing agentic analytics to one of the most sophisticated FP&A teams in software. https://t.co/qGkwXS2bvd

@rahulnanda86 https://t.co/dzgrCH8rDQ

@fellowshiptrust https://t.co/hB5x98nPZQ

The AI middle managers rise up. @IrenaCronin and I write this newsletter every week.   As companies deploy more specialized AI agents, they will need AI middle managers to coordinate tasks, monitor performance, enforce rules, and escalate issues to humans. The idea is that the real challenge is no longer just building smart agents, but managing them effectively so they work together safely, efficiently, and at scale. Read for free: https://t.co/HHwYy7NoAl (and please subscribe!)

Attention Sink in Transformers A Survey on Utilization, Interpretation, and Mitigation paper: https://t.co/QyXl9xYaru https://t.co/Q0mTZvpTET

OmniShow Unifying Multimodal Conditions for Human-Object Interaction Video Generation paper: https://t.co/6r4EVKx31z https://t.co/Iz8mA2WDwE

@altryne https://t.co/k2zKmAEjvN

QuanBench+ A Unified Multi-Framework Benchmark for LLM-Based Quantum Code Generation paper: https://t.co/88saBbFUS6 https://t.co/heFgUGpOvY

The Past Is Not Past Memory-Enhanced Dynamic Reward Shaping paper: https://t.co/nicAA4L9un https://t.co/8ujSodNWUQ

@NemPerez https://t.co/4eASzZhHzw

@Dalos 🤷‍♀️ https://t.co/t8LHH3GKml

i made a map to monitor data centers all around the world tracks construction + nearby power plants + local AI legislation, and follows the politicians behind their bans (+ if they're getting paid to do so!) https://t.co/oKVjhXLGzv

// Multi-User LLM Agents // Every agent framework assumes one user giving instructions. But deploy an agent into a team workflow, and suddenly it has multiple bosses with conflicting goals, private information, and different authority levels. This work formalizes multi-user interaction as a multi-principal decision problem and introduces Muses-Bench with three scenarios: instruction following under authority conflicts, cross-user access control, and multi-user meeting coordination. Even the best model, Gemini-3-Pro, only averages 85.6% across tasks. On meeting coordination, no model exceeds 64.8% success rate. Privacy-utility tradeoffs are especially brutal: models that score near-perfect on privacy (Grok-3-Mini at 99.6%) tank on utility (60.1%). Why does it matter? As agents move into organizational tools, Slack bots, and shared workspaces, multi-principal conflicts become the default, not the exception. Current models aren't ready. They leak more privacy over multi-turn interactions and can't maintain stable prioritization under conflicting objectives. Paper: https://t.co/ttoFSlIYxC Learn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX

🤯Topology & UV — the NO.1 headaches in #3D GenAI. 🔥We just move closer to BOTH at the same time. Introducing SATO: Strips as Tokens, a new autoregressive model for topology & UV, has been conditionally accepted to #SIGGRAPH 2026. Will available at #Hyper3D More details👇