EAGLE-4 is here. From EAGLE-1 to EAGLE-3, we’ve kept making inference faster. EAGLE-4 is our fastest yet. 961.6 tokens/s on @Alibaba_Qwen’s Qwen3.8-27B with TokenSpeed (BF16, 4× @NVIDIA B300, TP4, one request, T=0). Github: https://t.co/GwocZk1UTU https://t.co/GawgFNXKWj
#Model releases#Open-source ecosystem#Reasoning
#English text is being prepared.#Video#Open-source ecosystem
FastH3 OmniRef by @haoailab's is now supported in ComfyUI. This distilled version of Minimax H3 allows multi-modal reference generation of videos. It runs locally on your hardware and provide great balance between quality and speed. Click the links below for open weights and cloud ⬇️
#Product updates#Video#Open-source ecosystem
#Product updates#OpenAI
Introducing 𝚍𝚞𝚛𝚊𝚋𝚕𝚎-𝚑𝚊𝚛𝚗𝚎𝚜𝚜. Run Pi, OpenCode, Codex, Claude Code, eve, fx, or your own harness durably on Vercel. With 𝚍𝚞𝚛𝚊𝚋𝚕𝚎-𝚑𝚊𝚛𝚗𝚎𝚜𝚜, agents automatically resume work after failures and run for hours, days, or months. https://t.co/XkqmULA2ng
#Product updates#Agents#Open-source ecosystem
#English text is being prepared.#English text is being prepared.#Google
I launched the first image and video generation models at Google, and kept asking why all AI designs look the same. AI has brought us the WORST era of design. It made content creation effortless, but it also made 90% of it look painfully generic. My co-founder and I spent 2 years building in stealth to change that. Meet Vyb, the first AI design model that understands your taste like a designer would. RT + comment your website URL and we’ll make your first design.
#English text is being prepared.#Image generation#Product updates
EAGLE-4 is here. From EAGLE-1 to EAGLE-3, we’ve kept making inference faster. EAGLE-4 is our fastest yet. 961.6 tokens/s on @Alibaba_Qwen’s Qwen3.8-27B with TokenSpeed (BF16, 4× @NVIDIA B300, TP4, one request, T=0). Github: https://t.co/GwocZk1UTU https://t.co/GawgFNXKWj
#Product updates#Reasoning#Open-source ecosystem
Today we are releasing JST-2, the world's first full-stack physics-native world foundation model. Many world models learn in pixel/video space. We went back to first principles and built the entire model stack in 3D. Generate a 3D scene in seconds and expand it in real time. Create characters with motion built in, all through one general architecture.
#Product updates#Video#Multimodal
Perplexity Computer can now run fully local on your Mac. Work with your native Mac apps and local files, and run tasks without sending sensitive data to the cloud. The model and the agent both run on your Mac. Available now in the Perplexity Mac app. https://t.co/tnvCgCXD7v
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#Product updates#Agents#RAG
#Product updates#English text is being prepared.#Agents
Google announced that Gemini is now available globally on Android Auto and in cars with Google built in, and has begun rolling out Personal Intelligence, which, with authorization, can connect to Workspace (including Gmail and Google Calendar), Google Photos, Google Maps, YouTube, and Search to answer questions such as flight gate numbers, restaurants along the route, and locations in photos. Personal Intelligence begins rolling out on Android Auto today, with cars with Google built in to follow over the coming months. It is currently available to eligible personal Google accounts for users aged 18 and over, excludes Workspace business, enterprise, and education users, and is not available in the European Economic Area, Switzerland, the United Kingdom, or Nigeria.
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#Product updates#OpenAI
#English text is being prepared.#Open-source ecosystem#Google
#Product updates#Google#Speech
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The SGLang team, in collaboration with Google and Meta, has open-sourced SGLang-TorchTPU, a PyTorch-based native backend that brings the full SGLang serving stack to Google Cloud TPU and is already in large-scale production use at Meta. Initial support covers models including Muse Spark Open, GLM-5.3, Kimi K3, DeepSeek V4, Qwen3.8, and Gemma 4, with features such as speculative decoding, KV cache offloading, PD disaggregation, and FP8 KV cache. The team also used TorchTitan and Miles to complete GRPO training of Qwen3-4B on a single 4-chip TPU v7x, with the reward curve matching a 6× H200 baseline within random fluctuation.
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#Industry news#Agents#Safety / alignment
DeepSeek has open-sourced its internal large-scale cloud service inference framework DeepInfer and the production-grade token codec component deepseek-recipe. The former supports distributed inference, speculative decoding, SWA Bounded Replay, and structured output, and can run on NVIDIA and Huawei Ascend platforms. DeepSeek says the high-throughput, low-latency inference service of DeepSeek V4.1 Flash is based on this framework, with a single cluster serving 155 trillion Tokens/day in throughput, at 38 US dollars per card per hour calculated at off-peak prices. The processing logic of deepseek-recipe is fully aligned with the official DeepSeek API. The team also explained that the KVCache management and load balancing components are deeply coupled with internal infrastructure, so the relevant code has not yet been fully open-sourced.
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