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expressive presence. Conversation is inherently multimodal: we listen, look, speak, and use facial expressions to communicate. Live Avatar brings these capabilities to enterprise agents. By processing visual and audio inputs simultaneously, it generates enriching conversations for a more comprehensive experience. Watch how Gemini 3.8 Live with Live Avatar takes in what it sees and hears in near real time, responding with expressive audio and video for a more natural conversation. Beyond visual

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pretty cool, right? So… how should you use them? — Gemini 3.8 Flash TTS: Need to design bespoke vocal personas from scratch and with line-by-line level control? This is the model! Built for high-fidelity creative production like gaming, immersive audiobooks, and podcasts. — Gemini 3.8 Flash-Lite TTS: Want the AI to automatically adjust its tone and pacing on the fly for near real-time voice agents? This is your engine! Built for cost-efficient scale, high-volume dubbing, and bulk audio creation.

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isn’t something to dismiss. Sam Altman said pacing “does not mean stopping,” and that seems to be true for both AI leaders so far. The Rundown: What do Pitbull, BattleBots, and AI agents all have in common? They’re all happening at Fully Connected 2026. Join 3,000+ AI engineers and leaders in San Francisco, Sept. 29–Oct. 1, for three days of networking, insights, and action toward accelerating the path to superintelligence. This conference gives you access to: 3 tracks and 30+ technical breakout

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ht and day - we are migrating to Opus 5.5 immediately for AINews going forward until we reach the next model/version of AINews. They have also published initial work on large multiagent swarms (and efficiency ): Lisan al Gaib @scaling01 very proud of Anthropic bros to be the first lab to report multi-agent scaling up to 100 parallel agents in their system card Lisan al Gaib @scaling01 Opus 5.5 System Card https://t.co/D1PDkVqChC 5:01 PM · Sep 22, 2026 · 82.2K Views 30 Replies · 60 Reposts · 1.19

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WE v1.1」では、GPT-6 SolとGPT-6 Lunaをともに推論レベル「max」で評価し、スコアはそれぞれ68.8％、66.6％だった。Solの結果はClaude Fable 5が「xhigh」で記録した69.9％に近く、1タスク当たりのコストは約80％低いとしている[3]。 On DeepSWE v1.1, which tests coding agents on long-horizon engineering tasks, GPT-6 Sol (max) nearly matches Claude Fable 5 (xhigh) at 80% lower cost per task. GPT-6 Luna (max) is comparable to Fable 5 (medium) at 96% lower cost per task. pic.twitter.com/RN7DmjpUfJ— OpenAI Developers (@OpenAIDevs) September 22, 2026 業務アプリを使った作業を測る「AutomationBench」では、GPT-

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間のPC操作を評価する「OSWorld 2.0」では、部分点を含むスコアが81.8％で、Opus 5の74.0％を上回った。44職種の実務に近い課題を評価する「GDPval-AA v2.1」では、Eloスコアが1846で、Opus 5の1708を超えた。 Opus 5.5 is a major step up from Opus 5, leading on agentic coding, computer use, and knowledge work. pic.twitter.com/MYI9JDAo1x— Claude (@claudeai) September 22, 2026 文章の生成や作業中の説明も改善したという。Opus 5.5は、要点を先に示し、指定された書き方にも従いやすくなった。モデルの調整に携わったAnthropicのJackson Kernion氏は、文章を明瞭にし、情報を詰め込みすぎないようにする調整に初めて重点を置いたと投稿した。返信では、平易な表現を使い、英文でのセミコロンやダッシュの多用を抑え、専門用語や造語を減らす方向を示した。こうした改善は今後も続け

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ces, both TTS models give you precise control over how each line is delivered. Hear how Gemini 3.8 Flash TTS enables natural, highly expressive conversations for interactive voice agents. Watch and hear how Gemini 3.8 Flash TTS uses granular script control to build a deeply engaging, immersive audio experience. See how Gemini 3.8 Flash TTS turns natural language prompts into bespoke vocal personas from scratch. Watch how Gemini 3.8 Flash TTS enables creators to design custom scenes to bring anim

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让用户通过自然语言生成可实际操作的小应用。 过去，在应用生成过程中，系统虽然会持续展示编写进度，但用户要等到应用完成后，才能看到页面的布局、配色与整体效果。 接入 Ming-Image-0.1-Design 系列后，设计预览被前置到应用生成的初始阶段。Design 根据用户需求，端到端生成布局完整、风格统一的应用预览，让用户提前看到应用最终的视觉方向。随后，Agent 调用 Layer 拆解设计素材，并结合设计文档完成代码生成，使最终应用在页面结构、视觉风格与细节元素上和预览保持高度一致，带来更接近“所见即所得”的体验。 这一变化不只是让用户“更早看见”。Design 能够从全局统筹布局、配色与素材风格，减少多次生成和代码拼装造成的风格割裂、模板感与拼接感，让 AI 生成的应用拥有更完整的主题表达和更统一的设计语言，更接近经过系统设计的产品。 🎬 视频演示（见公众号原文 ） 注：接入 Ming-Image-0.1-Design 系列模型的闪应用目前正在内测，将于近期与大家见面，带来更统一、更可控、更高效的应用生成体验。 画眉：从先出图再拆解，变成约 40 秒获得可编辑图层 在专业设计中

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imi K3, it learned to cut unnecessary reasoning while keeping the thinking that matters. We tested it on external benchmarks, in live customer A/B tests, and on our own coding and agent workloads, and quality held up in every setting. Available today, Ember-1 kicks off an ongoing series of specialized models by Fireworks, shaped by what developers want next. Ember is just the start of what you could build with the Fireworks Training platform. We heard from users that they needed K3’s coding capa

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lity of Claude Opus 5.5 on Amazon Bedrock and Claude Platform on AWS, the first of the Claude 5.5 model family. Claude Opus 5.5 is Anthropic’s most capable Opus model suitable for agentic coding, knowledge work, and long-running tasks. This post covers Claude Opus 5.5’s improvements, practical guidance, and how to start building with the model on Amazon Bedrock. What makes Claude Opus 5.5 different According to Anthropic, Claude Opus 5.5 does more with fewer tokens than Claude Opus 5, and new pr

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samples per update, training at up to 1M context length, and 3.5 to 3.7B tokens per step. More tasks and richer environments : a multi-task training suite spanning coding, general agents, visual and cyber, mixed across several harnesses so that gains in one capability reinforce the others. More grader compute : relative comparison within each group gives long-horizon RL tasks more precise and more diverse reward signals, closes a self-improvement loop, and steers the model toward shorter paths a

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by predicting how to interact with an environment to reach a goal. The latter capability is often referred to as “action prediction” - the ability to predict the next action of an agent and the resulting state of its environment from a series of observations, such as videos from multiple cameras on a robotic workstation. The advance of multimodal world models has enabled rapid progress for systems that interact with the world, especially in the field of robotics - while at the same time, frontie

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her 3D spatial reasoning, multimodal perception and computer use to build and interact with richer environments. 🔹 Turn text, images or video into playable 3D worlds, coordinating agents to build scenes, write interaction logic and refine the results 🔹 Create Blender objects and scenes for animation, 3D printing and games 🔹 Control a Franka Panda arm in simulation through visual feedback 🔹 Use desktop tools to search, edit and process data — then inspect the results and adjust its next actions B

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ing Xiaomi MiMo-V2.6 — Pro & Flash. Frontier intelligence, all the modalities, built in public. 🔹 Two omnimodal models, advancing through scaled reinforcement learning 🔹 Pro performs on par with Claude Opus 5 and GPT-5.6 Sol across most agent benchmarks 🔹 Pro scores 46 on the Artificial Analysis Intelligence Index — the highest among open-source models 🔹 Stronger coding, computer use, 3D reasoning and creative capabilities 🔹 Open model weights, technical report, RL environments and training code

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ork — from large-scale research to structured analysis and interactive reporting. It coordinates research at scale and turns evidence into finished, auditable deliverables. In one agent action, it coordinated 950 web fetches and assembled 300,000 monthly records across 1,000 locations over 25 years; in another, it produced a 17-sheet analytical workbook with source reconciliation, formulas, and trend models. Its outputs span technical engineering, creative production, analytical reporting, and p

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Introducing Step 5 Preview: Advancing the Pareto Frontier. Step 5 Preview is our new flagship model for agentic work, delivering frontier-level performance across software engineering and professional knowledge work, with particular strength in finance. - 600B total / 27B active MoE, with 1M context + Vision - Substantially lower task cost at comparable intelligence - Broad software engineering capabilities with sustained execution over long horizons Open weights on Oct 15.

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🚀 Meet Qwen3.8-Omni-Flash, Qwen's first omni-modal model built around agentic capabilities! Native audio-video understanding, reasoning, and tool use come together in one model: understand the content, plan the task, execute with tools, and deliver the result. Highlights: 🥳 - Audio-video intelligence that gets things done: jointly reason over what's seen and heard, and orchestrate tools across long workflows to auto-edit vlogs, translate short videos, and turn movies into recaps. - A major leap:

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o foundation model behind Grok Voice. Grok Voice already powers tens of thousands of customer-support calls a day, transcribes millions of hours of video narration, and runs voice agents in physical products, including the Grok assistant in Tesla vehicles. It is trained on a unique dataset of live, noisy, multilingual audio recorded across a diverse set of environments and refined with post-training. The result is one of the most accurate transcription models for speech in real-world settings. A

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Introducing StepAudio 3, our new family of 5 audio models for real-time voice, speech recognition, speech generation, audio generation and music. Realtime ranks 1 on Artificial Analysis for both Conversational Dynamics (98.9%) and Speech Reasoning (99.7%). ASR reaches 1.7% WER, matching the best result on the leaderboard. Build voice agents that handle interruptions, reason while speaking, and call tools. Transcribe speech, generate expressive voices, and create full audio scenes and music.

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Gemini 3.8 Live and 3.8 Live Extended Thinking are here. Following 3.5 Transcribe last month, this continues our focus on real-time voice agents. 🐸🐸 - 82.6 ( 1) on Artificial Analysis Quality Index - $0.005/min input and $0.018/min output. - 35.1 ( 1) Agentic task completion (τ-banking) 3.8 Live Extended Thinking can think in the background and run asynchronously tool calls while keep talking to you. It uses background thinking to coordinate multi-step tool use without losing conversational flow

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