Gemini 3.6 Flash 출시·NVL72 전력 효율성·Laguna S 2.1 오픈 가중치 MOE 발표

We’re rolling out three new models to make AI agents faster, smarter, and cheaper at scale: Gemini 3.6 Flash: It uses fewer tokens than 3.5 Flash to deliver higher quality work at the exact same cost. Gemini 3.5 Flash-Lite: A fast, cost-effective option for everyday tasks like processing documents and agentic search. Gemini 3.5 Flash Cyber: A cybersecurity model built to find and patch critical software vulnerabilities.

Josh Woodward

@joshwoodward

1달 전

Today’s launches are all about better performance, lower latency, and a smaller bill. + 3.6 Flash cuts token usage by up to 65% on complex coding + 3.5 Flash-Lite reaches speeds of 350 output tokens/sec Both are live in the Gemini app today! Next up: Gemini 3.5 Pro, which has officially entered partner testing.

The NVIDIA Vera Rubin platform is here, with 10x better performance per watt. The NVIDIA ecosystem, including @CoreWeave , @GoogleCloud , @Microsoft , and @Oracle Cloud, are standing up NVIDIA Vera Rubin NVL72 to deliver the lowest token cost for the agentic era. NVIDIA Vera Rubin NVL72 on CoreWeave demonstrates 10x more tokens per megawatt than Blackwell in their first measured performance. Benchmark results from @DeepInfra show that the NVIDIA Vera CPU is more than 2x as fast and can support more concurrent AI agents compared with other CPUs. Read more: https:// nvda.ws/4ywCiQn

10x more tokens per megawatt. CoreWeave has the first measured performance of NVIDIA Vera Rubin NVL72, showing 10x improvement in tokens per second per megawatt on DeepSeek-R1 compared to Blackwell.

Today we're releasing Laguna S 2.1, our most capable model to date. It's a 118B total parameter Mixture-of-Experts model with 8B activated per token, a context window of up to 1M tokens, and thinking and no-thinking modes. Capable enough to hold its own against models many times its size. Small enough to run on a single @NVIDIAAI DGX Spark. Laguna S 2.1 is fully open under OpenMDW-1.1, with weights available today on @huggingface https:// poolside.ai/blog/introduci ng-laguna-s-2-1 …

Today we’re releasing Poolside Laguna S 2.1 It is a 118B-total, 8B-active open-weight model built for agentic coding and long-horizon work, with context up to 1M tokens https:// poolside.ai/blog/introduci ng-laguna-s-2-1 … Laguna S 2.1 sits at the top of its weight class and competes with open models many times larger, while remaining small enough to run on a single NVIDIA DGX Spark It is available today under OpenMDW-1.1 via Hugging Face, OpenRouter, the Poolside API, and pool This is a remarkable model by any measure. As much as we can gather, it's the best open weight model in the West, regardless of size But the model itself is only part of the story There is a prevailing narrative that building capable models requires ever more capital, compute, and people. We believe the more important question is how efficiently you can turn those resources into intelligence At Poolside, we approach model building as an industrialized process spanning data, pre-training, reinforcement learning, evaluation, and inference. We call that system the Model Factory. We have written about our approach to model building extensively in a 6 part blog series: https:// poolside.ai/blog/introduci ng-the-model-factory … Laguna S 2.1 went from the Model Factory kicking it off to release in 52 days. The model is the output. The ability to keep building better models, faster and more efficiently each time is the actual innovation. The Model Factory is Poolside's compounding asset And so here we are, 52 days after kicking this off, releasing a 118B/8B MOE that tops 70% on TB 2.1, 78% on SWE-Bench Multi, 59% on SWE-Bench Pro, and 40% on DeepSWE. And it's fully open, from an American company I grew up in the Linux and Python communities, then spent much of my career at Canonical (Ubuntu), Heroku and GitHub. Those communities shaped my belief that important technology becomes more useful when people can understand it, challenge it, and build upon it. And even more important than being useful is being trusted. That is what open weights mean to me. They are not a marketing or distribution exercise. They give people control: the ability to inspect the work, reproduce the claims, modify the model, and run it inside their own environment I believe the West needs a credible open path to frontier intelligence. I believe it should be from an American company. We intend to be that company Laguna S 2.1 is another step in that direction and yet more evidence of Poolside's long held, often times contrarian beliefs and views on how intelligence will be built. One last note. We are doing something very different that we hope becomes industry norm going forward. We recognize that releasing a 118B/8B MOE that performs as well as Laguna S does would be met with some degree of skepticism. Models of this size are not supposed to outperform models 4-25x larger. We double and triple checked our benchmark trajectories to be sure. But we wanted to go another step and release those trajectories for you to see and help us quadruple check them. https:// trajectories.poolside.ai If you find something we missed, we genuinely want to hear about Have fun building whatever thing you can think of with the most persistent little model that could

Tesla's Robotaxi/ride-hailing service is now available in 7 areas: • Orlando: Unsupervised Model Y rides only (new) • Miami: Unsupervised Model Y rides only • Tampa: Unsupervised Model Y rides only (new) • Dallas: Unsupervised Model Y rides only • Houston: Unsupervised Model Y rides only • Austin: Mix of Unsupervised & safety monitor Model Y rides • Bay Area: Safety monitor Model Y rides only

Claude

@claudeai

1달 전

New in Claude Cowork: teach Claude a skill. Record your screen while you do a task, talk through it as you go, and Claude turns it into a skill it can run again. Find it under Record a skill in the + menu of the Claude desktop app. Available on Pro, Max, and Team plans.

Today, SkyPilot is out of stealth. Building custom intelligence is now existential. We help frontier AI teams build intelligence faster by removing their biggest bottleneck: AI compute fragmentation. Frontier teams like @appliedcompute , @AbridgeHQ , @hippocraticai , @hcompany_ai , and @nubank already run on SkyPilot, with 10x faster time-to-intelligence and double-digit increase in GPU utilization. AI teams today get compute anywhere they can. They then firefight compute fragmentation across providers. Researchers burn time on workload setup. Infra gets paged when GPUs go down. Frontier teams build slowly even on the fastest compute. @skypilot_org turns your fragmented compute into one AI supercomputer, so you run frontier workloads faster. Many users manage 10,000+ GPUs across providers with SkyPilot. GPU hours consumption has grown 6x in the last 6 months. 1/ We're launching SkyPilot Platform — the AI compute platform for frontier AI teams to manage large GPU fleets and accelerate building custom intelligence. Optimized for fleet management, team governance, and frontier workloads — pretraining, post-training, multi-cluster serving, and sandboxes. SkyPilot open source users can switch to the platform with a server URL change. 2/ We've raised over $20M led by @Lux_Capital ( @breeves08 ), with participation from @AmplifyPartners ( @dauber , @lennypruss ), @coatuemgmt , @FoundationCap ( @ashugarg , @JayaGup10 ), @RaceCapital , @thehousefund , and top operators like @alighodsi (CEO, Databricks), @JeffDean (Chief Scientist, Google), @rauchg (CEO, Vercel), @amasad (CEO, Replit), @ClemDelangue (CEO, @huggingface ) and more. We're hiring across Engineering and GTM to deliver the platform for the next decade of AI. Above all, I'm excited to be building with the incredible team we've assembled, along with my cofounders Zhanghao @Michaelvll1 , Romil @bromil101 , Scott, and Ion @istoica05 . If you firefight AI compute, let's build.