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LI; X; FB Today we’re announcing MAI-Cyber-1-Flash inside of MDASH, our multi-agent vulnerability identification and remediation harness. Together they deliver world-class performance at 50% of the cost of leading models. Progress in AI has been startling and so has the new generation of cyber threats it’s unleashing. Attackers now wield increasingly powerful capabilities, probing an ever-growing mountain of code for just a single weakness that lets them in. As the cost of finding a flaw collaps

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As our most intelligent workhorse model yet for coding and agents, Gemini 3.7 Flash brings higher instruction adherence, first-pass code accuracy, and high-quality execution for agentic tasks. See how the model handles building a complex 3D web game with 3.7 Flash in @antigravity: 1️⃣ 3D Engine Logic: Generates Three.js boilerplate, camera movement, and collision detection. 2️⃣ Asset Orchestration: Creates PBR-textured materials, custom sprite sheets with Nano Banana, and particle effects. 3️⃣ P

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Aug 13, 2026 Our most intelligent workhorse model yet for coding and agents. Tulsee Doshi Senior Director, Product Management, on behalf of the Gemini team Your browser does not support the audio element. Today, we’re building on the progress of our widely used Flash series by introducing Gemini 3.7 Flash, our most intelligent workhorse model yet for coding and agents. This release comes just three weeks after Gemini 3.6 Flash , and is a direct result of developer feedback and algorithmic innova

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ce can be deployed, how often it can be used, and whether it can move from exceptional tasks into daily workflows. We have invested heavily in the training environments needed for agentic coding. Each verified environment is deterministic, executable, and graded by real test suites. This gives the model practice on the kind of multi-step work developers actually do: reading code, editing files, running tests, observing failures, and recovering from intermediate mistakes. Advanced mathematical re

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Table of Contents Today we are releasing Grok 4.6 together with SpaceXAI. Grok 4.6 builds on Grok 4.5 with a particular focus on long-running agents and more ambitious interactive and visual work. It stays with complex tasks across many steps, whether researching a topic, analyzing information, working across a codebase, or turning an idea into a polished application or work artifact. Grok 4.6 achieves frontier intelligence across several agentic coding and knowledge work benchmarks. It matches

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Today we are releasing Grok 4.6 . Grok 4.6 builds on Grok 4.5 with a particular focus on long-running agents and more ambitious interactive and visual work. It stays with complex tasks across many steps, whether researching a topic, analyzing information, working across a codebase, or turning an idea into a polished application or work artifact. 0:00 / 0:00 Grok 4.6 achieves frontier intelligence across several agentic coding and knowledge work benchmarks. It matches GPT-5.6 Sol on the Artificia

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m('facets.search.text-copy'); sessionStorage.removeItem('facets.search.filters'); NVIDIA Nemotron 3.5 Lightning Delivers Fast, Accurate Specialized Task Execution for Long-Running Agents Aug 11, 2026 NVIDIA Nemotron 3.5 Lightning is an open 30B mixture-of-experts model with 3B active parameters optimized for high-volume, low-latency execution in long-running AI agents. The model includes speculative decoding with multi-token prediction baked in during pretraining, plus DSpark and DFlash draft mo

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2026 NVIDIA Nemotron 3.5 Lightning is now available on Ollama, and it runs completely on your own device. It’s a 30 billion parameter (3B active) open model from NVIDIA built for agents that stay running: gathering context, calling tools, and working through multi-step tasks. Nemotron 3.5 Lightning is made for agentic tasks such as reading a file, calling a tool, sorting a result, and retrying something that failed. Most of these steps don’t need a large model. At 3B active parameters per token

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s.search.text-copy'); sessionStorage.removeItem('facets.search.filters'); Aug 10, 2026 Muse Glimmer is a 30B open-weight dense model with a 120K+ context window built for local AI agentic work. The dense architecture activates every parameter for each token, delivering reliable instruction following, long-context coherence, predictable latency, and fewer failure modes for multi-step agentic workloads. NVIDIA Tensor Core architecture accelerates Muse Glimmer to over 20K tokens per second on a sin

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st 10, 2026 Muse Glimmer, Meta’s newest open model and the first released by Meta Superintelligence Labs, is now available on Ollama. It’s a 30B multimodal model purpose-built for agent workloads that run locally with a 128K+ context length, released under the Apache 2.0 license. With Ollama, you can now use Muse Glimmer to power coding agent applications such as Claude Code, Codex, Pi and more, as well as long-running personal assistants such as OpenClaw and Hermes. On Apple Silicon, it is avai

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Join us for our inaugural conference, Forge 2026 PUBLISHED 8/10/2026 This model was built with agents in mind. It is designed for "always-on" agents rather than just simple chat interactions. It’s capable of managing many sequential tool calls over multiple turns. It has the ability to recover from failures and retries rather than halting when a tool call fails or returns something unexpected. Muse Glimmer is a 30B dense model made up of 52 transformer layers, grouped-query attention with 32 que

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Ling-3.0-flash 采用 124B 总参数、5.1B 激活参数的 MoE 架构。它保留了千亿参数模型的知识与能力容量，但在生成每个 Token 时，只激活其中约 5.1B 参数，从而降低单次推理的计算开销。 这让 Ling-3.0-flash 不只是一款“跑得快”的模型，也能够真正进入不同规模的业务系统，提供三种不同的落地选择： API 调用，让 Agent 应用快速上线： 面向希望快速接入、无需自建推理服务的开发者，Ling-3.0-flash 可通过云端 API 直接调用； 单机私有化，让数据留在本地： 面向数据不能出域的企业和团队，MXFP4 与 INT4 版本可在单台 NVIDIA DGX Spark 上完成端到端推理； 高性能部署，释放极致单请求速度： 面向单请求时延敏感的高性能服务，在指定 GPU 测试配置下，平均输出速率突破 1100 tokens/s。 开发者既可以直接调用模型，也可以自由验证、部署和定制模型能力，将 Ling-3.0-flash 接入自己的业务系统、开发工具链和 Agent 工作流。 从云端 API 到单台 DGX Spark、AMD 395

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ent , end = "" , flush = True ) answer content += delta . content For more information, please visit the API doc . Coding Assistants Qwen3.8-Max integrates seamlessly with popular agent frameworks and coding assistants: Claude Code Qwen APIs support the Anthropic API protocol, enabling direct use with Claude Code : npm install -g @anthropic-ai/claude-code export ANTHROPIC MODEL = "qwen3.8-max" export ANTHROPIC SMALL FAST MODEL = "qwen3.8-max" export ANTHROPIC BASE URL = https://dashscope-intl.al

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ed to be very efficient with reasoning tokens relative to its predecessor. In production settings, this means that tool calls are snappier, usually executing before the end of the agent's first sentence. Reasoning efficiency Relative reasoning tokens per response (P50). Lower is better. Conversational Capability We've trained Grok Voice Think Fast 2.0 to be a better conversationalist. By using extensive reinforcement learning to push the model towards patterns we see in real human conversation,

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Kimi K3 is now live on @togethercompute! Happy to have Together AI as our day0 launch partner, giving developers immediate access to K3 through high-throughput inference optimized for coding agents and production workloads. A huge thank-you to the Together AI team for moving fast and making this launch possible!

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Releasing the model weights and technical report of Kimi K3. Kimi K3 is our most capable model: a 2.8T MoE model with native visual understanding and a 1M-token context window. New model architecture: 2.5x the intelligence per unit of compute, not just more params. Alongside Kimi K3, we're opening up more of the stack behind it — high-performance attention kernels, MoE communication library, and infrastructure for running agent environments at scale.

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odels. K3 is a mixture-of-experts transformer: 2.8T total parameters, 16 of 896 experts active per token, a 1M token context window, and native vision. It's built for long-horizon agentic work, and Moonshot put that to the test internally, handing an early version most of the team's kernel optimization work during development. Kimi Delta Attention holds down the cost of attention as sequences grow, and Attention Residuals let deeper layers reach back to earlier attention outputs rather than only

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s on track through long multi-step analyses.Alfredo Andere CEO Claude Opus 5 came out ahead of every model in its family on our internal evals. It isn’t just better on our hardest agentic coding tasks, up 22% over Opus 4.7, it’s steadier, with far less variance run to run. For the millions of builders on Lovable, that consistency is the whole game. Reliable results, build after build.Fabian Hedin Co-Founder Claude Opus 5 is the biggest leap in the Opus family since 4.5. On the same full-stack ap

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（约为 Ring-2.6-1T 的 8.1%），却实现了全方位的能力对标甚至超越。这不仅是一次参数量级的重构，更是一场 paradigm shift（范式转移）——模型获取智能的方式，不追求单纯的规模堆砌，而更关注于对每一寸算力与状态的高效压榨，追求”智能密度”的极致提升。 Ling-3.0-flash 的能力跃迁并非来自参数规模扩张，而源于底层架构升级与 Agent 定向优化的双轮驱动：一方面，原生混合线性注意力、细粒度 KDA 状态记忆与 1/64 稀疏 MoE 协同提升了参数向实际能力的转化效率；另一方面，面向生产力场景的定向打磨强化了模型在复杂规划、多轮工具调用与长程任务交付中的核心战力。在两者的共同作用下，模型以极低体量展现出向更大体量模型对标甚至越级挑战的可能，让高性能与高性价比真正兼得。 具体而言，Ling-3.0-flash 的核心改进体现在如下几个方面： 混合线性架构，原生进化： 从预训练伊始即采用原生混合线性注意力架构（5:1 交替堆叠 KDA 与 MLA），并升级全新 KDA 细粒度对角门控与 1/64 稀疏 MoE。在 124B 总参数、5.1B 激活参数规模下

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under medium-utilization production workloads. Voice in the call center. MAI-Voice-2-Flash now powers Dynamics 365 Contact Center, the enterprise platform for building call center agents used by customers like T-Mobile and EasyJet, bringing our most expressive, natural sounding speech to brand defining conversations while reducing GPU costs up to 89%. MAI-Voice-2-Flash integrated in Azure Voice Live. Customers can quickly build voice agents in Voice Live, powered by MAI‑Voice‑2‑Flash’s natural,

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