Together AI (@togethercompute) NVIDIA Nemotron 3.5 Lightning is now live on Together AI. The fastest open model in its class is built for always-on agents that need to complete high-volume, specialized work quickly. — https://nitter.net/togethercompute/status/2087163477404041345#m

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Fireworks (@FireworksAI_HQ) Looking for a faster specialized model for your Agent Work? NVIDIA Nemotron 3.5 Lightning (30B MoE, 3B active params) is now live on Fireworks. It’s distilled from NVIDIA Nemotron 3 Ultra to be your high-volume agent engine. With strong performance on PinchBench and top-tier scores in AA-Omniscience Non-Hallucination, Nemotron 3.5 Lightning is engineered for reliable, high-productivity agentic workflows. Built for specialization, not generalization. Start building: app.fireworks.ai/models/fire… — https://nitter.net/FireworksAI_HQ/status/2087185731815084100#m

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Baseten (@baseten) NVIDIA Nemotron 3.5 Lightning is live on Baseten day 0! This is the fastest open model in its class, built for long-running agents. Compared to similar-sized open models, it offers: - 4x higher throughput - 50% lower cost - 63.4% fewer output tokens in production testing - 30B MoE architecture (3B active) - 1M token context length — https://nitter.net/baseten/status/2087173719873446192#m

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Applied Compute (@appliedcompute) Nemotron 3.5 Lightning by @NVIDIAAI is now supported for training and inference on the Applied Compute Platform. On our agentic coding benchmark, decode throughput, time to first token, and median user latency remained effectively unchanged as concurrency and total token throughput scaled 16x. Its LatentMoE and Mamba architecture lets us scale sparsity, context length, and batch size with minimal overhead, dramatically increasing iteration throughput across post-training runs. — https://nitter.net/appliedcompute/status/2087165637651239375#m

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Lila Sciences (@LilaSciences) We're building scientific superintelligence with @nvidia 's Nemotron 3.5 Lightning. Here's more on what this means for improving reasoning capabilities: blogs.nvidia.com/blog/nemotr… Link NVIDIA Nemotron 3.5 Lightning and NeMo Switchyard Deliver Faster, Smarter, More Efficient Agentic AI The new lightweight open model and routing library delivers greater control over AI, data and workflows across edge devices, PCs, workstations, data centers and the cloud. blogs.nvidia.com — https://nitter.net/LilaSciences/status/2087186631384051859#m

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2026/08/13 06:12（台北）

Harvey (@harvey) We post-trained @NVIDIAAI Nemotron 3.5 Lightning on Legal Agent Bench with @trajectorylabs . Here's what we found: 1) Post-training improved agent performance from 0% to 8.3% on held-out LAB tasks, beating both Opus 4.6 and the much larger post-trained Nemotron 3 Ultra. 2) Performance improved across nine practice areas with no regressions. 3) Post-training reduced average model output from 90k to 37k tokens, increasing the model's reward-per-token by 2.4x. Through our collaboration with NVIDIA and Trajectory we’re committed to pushing the frontier of legal intelligence and cost efficiency with open weight models. Deep dive: — https://nitter.net/harvey/status/2087166789876945338#m

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AgileRL (@AgileRL_Inc) We are proud to announce a collaboration between AgileRL and @NVIDIAAI to support the post-training of Nemotron models. NVIDIA's Nemotron open-source model family is now available for fine-tuning on Arena, our platform for creating AI agents specialized at any task. NVIDIA gave us early access to their latest 30B3A parameter MoE model, Nemotron 3.5 Lightning. On Arena, we trained it to outperform Claude Sonnet 5 on two different tasks: a long-horizon reasoning challenge and a real customer support workload. Nemotron proved exceptionally easy to post-train: a single six-hour run on four H100 GPUs was enough to master the reasoning task, at context lengths beyond 50,000 tokens. The full results are in the announcement, linked below. With this collaboration, Arena customers get access to NVIDIA Nemotron models as soon as they are released, with the training and evaluation setup already built around them. Post-train Nemotron on your own data, environment and edge cases, to build an agent that masters your task. Across industries including banking, insurance, aerospace and government, production traffic is moving off frontier model APIs and onto infrastructure these companies control. Businesses want agents with genuine expertise in their specific task, trained on their own data. Security, control and data sovereignty demand that model weights remain on their own infrastructure. And they want the fixed cost of hardware they own, rather than per-token pricing that compounds with every request. This collaboration provides that path. Begin with a dataset, an RL environment, or simply a description of the job the agent has to do, and our team will build the rest with you. Arena handles the training and deploys the finished agent in one click, with the weights yours to keep. — https://nitter.net/AgileRL_Inc/status/2087169292982714631#m

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Fastino Labs (@fastinoAI) In collaboration with @nvidia we're releasing two new open weight models: Fastino-Nemotron-3.5-Lightning-Finance and Fastino-Nemotron-3.5-Lightning-Healthcare. Working closely with the Nemotron team, we developed both models on Nemotron 3.5 Lightning using the Fastino Fine-Tuning Agent, which autonomously ran the entire post-training pipeline. The base model was highly responsive, with the agent reaching substantial gains across both domains with compact datasets alone. - Fastino-Nemotron-3.5-Lightning-Finance excels at numerical reasoning, research, and summarization over financial concepts and documents. When evaluated, the fine-tuned model increased FinQA execution accuracy by 43.37 points (15.86% to 59.23%), and increased 7.81 points (from 49.65% to 57.46%) on BizFinBench. - Fastino-Nemotron-3.5-Lightning-Healthcare specializes in clinical conversation quality, summarizing notes and discharge documentation, and extracting medical concepts from unstructured text. In post-training, the Fastino Fine-Tuning Agent improved performance by a margin of 4.28 points on HealthAdminBench (from 25.67% to 29.95%) and 5.09 points on MedCalc-Bench (from 49.09% to 54.18%). It also increased flag accuracy on MEDEC by 11.32 points, beating Opus 4.6 and beating Muse Spark by 0.98 points. Both models are available on @huggingface under the Apache 2.0 license: - huggingface.co/fastino/Fasti… - huggingface.co/fastino/Fasti… Today we are also releasing the Fastino Fine-Tuning Agent in private preview, which you can sign up for access to here: fastino.ai/nvidia-collaborat… — https://nitter.net/fastinoAI/status/2087191704965410910#m

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Dream Security (@DreamGroupAI) Today, @nvidia launched Nemotron 3.5 , its new open model built for fast, high-volume execution in agentic AI systems. Dream had early access to the model. Our researchers, led by Guy Feigenblat, Shai Nahum Gefen and Dmitry Basin, invested extensively in supervised fine-tuning, post-training, domain adaptation, cybersecurity reasoning and agentic tool use to advance Dream’s proprietary agentic cybersecurity conversational model. Pleased to see strong performance across Dream’s internal cybersecurity benchmarks that will help our customers better understand their security posture, investigate risks and turn large volumes of security data into clear, actionable insights. Full launch blog here: developer.nvidia.com/blog/nv… Link NVIDIA Nemotron 3.5 Lightning Delivers Fast, Accurate Specialized Task Execution for Long-Running... Long-running AI agents spend most of their time on high-volume execution: tool calls, result validation, and subagent delegation. Using a frontier reasoning model for every execution step adds cost… developer.nvidia.com — https://nitter.net/DreamGroupAI/status/2087182879004442865#m

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CrowdStrike (@CrowdStrike) We’re excited to see the latest NVIDIA Nemotron model and continue pushing the boundaries of what AI can do for cybersecurity. ⚡️ CrowdStrike customized Nemotron 3.5 Lightning for cybersecurity agent workflows, achieving analyst-grade accuracy with faster training and significantly lower compute requirements. The result: helping auto-close up to 88% of benign detections, so security teams can focus on the threats that matter most. — https://nitter.net/CrowdStrike/status/2087221023456583887#m

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Agentic AI is changing the compute requirements behind AI infrastructure. AMD’s @MadhuR_PDX joins @AustinSemis to discuss why there’s no one-size-fits-all approach, from CPU and GPU workloads to concurrency, tokenomics and an open compute portfolio. 🎥 Watch Bit by Bit: https://www.youtube.com/watch?v=L7NPjx97qo4

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Unsloth AI (@UnslothAI) 2-bit NVIDIA Nemotron 3.5 Lightning ran tool calls nonstop for 10 minutes on just 22GB of VRAM. 🤯 It cited 80+ websites, executed code & searched for 10 real-world locations. Run and train via Unsloth Desktop. GGUF: huggingface.co/unsloth/NVIDI… Guide: unsloth.ai/docs/models/nemot… Video — https://nitter.net/UnslothAI/status/2087598047589196052#m

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Locai Labs (@locai_labs) x.com/i/article/208718413558… Article Introducing Juno-N-Coder-25B We’re excited to announce 𝗝𝘂𝗻𝗼-𝗡-𝗖𝗼𝗱𝗲𝗿-𝟮𝟱𝗕, our new agentic coding model. In the spirit of openness, we’re also releasing the model weights to support developers and strengthen the — https://nitter.net/locai_labs/status/2087184904010256874#m

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Your AI coding agent comes with defaults: which model runs, what you pay, and what leaves your machine. You can turn those defaults into choices. In our new short course, AI Coding Workflows: From Cloud to Local, built in partnership with @JetBrains and taught by @paulweveritt, Developer Advocate at JetBrains, you'll rebuild the same app across cloud, hybrid, and fully local setups. Along the way, you'll split work across subagents, put cheaper models on the routine tasks, and finish with models running on your own machine. Enroll for free: https://hubs.la/Q04sM4rQ0

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Uniphore (@uniphore) We tested @NVIDIAAI 's Nemotron 3.5 Lightning against real enterprise agentic workloads & found: 5x throughput vs Gemma 4 31B IT at matched parameter count. Accuracy gains moved Nemotron 3.5 Lightning into evaluation for high-volume paths in the Uniphore's Business AI Cloud — https://nitter.net/uniphore/status/2087163069306917249#m

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Deep Cogito (@DeepCogito) Excited to see @NVIDIAAI continue pushing the open model ecosystem forward with Nemotron 3.5 Lightning. At @DeepCogito , we are big believers in open weight, customizable models and the role they’ll play in making frontier intelligence broadly accessible and useful. NVIDIA has been a fantastic partner to startups like us building at the frontier, and we’re excited to be part of the NVIDIA post-training partner ecosystem. Congrats to the @NVIDIAAI team on the launch! — https://nitter.net/DeepCogito/status/2087214450352992685#m

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distil labs (@distil_labs) Nemotron 3.5 Lightning from @NVIDIAAI is out. As one of a handful of European launch partners, we fine-tuned it against 4 comparable MoE models on 6 tasks. Prompted, third. Fine-tuned, first. Full numbers, and where it loses, from @j_golebiowski : distillabs.ai/blog/the-best-… Video — https://nitter.net/distil_labs/status/2087165952194453734#m

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Trajectory (@trajectorylabs) Continual learning is a bet that the retraining loop will get cheaper over time. With larger models, you can maybe run this loop once every few weeks. But with smaller models, you can run it nightly, per customer. And it keeps recursing: a model per company, then a model per client that company serves, then per matter. We’re getting closer to intelligence cheap enough to meter. On the path to this, we received early access to, and post-trained @nvidia 's Nemotron 3.5 Lightning on @harvey LAB. One click on the Trajectory platform, no new engineering. 0% to 8.3%, above Opus 4.6 at 6.6%. — https://nitter.net/trajectorylabs/status/2087165247023092104#m

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Reasonable (@ReasonableIO) Smaller, faster, capable: writing machine-checked proofs with a 30B open-weight model We fine-tuned NVIDIA’s latest open model Nemotron 3.5 Lightning on 4B tokens of synthetic Verus data. The result: it beats a model ~50x its size on per-attempt pass rate while nearly matching it on pass@3, with faster token generation than any similarly sized open-weight model we tested. Explore how the models we tested fail at formal proofs, how they cheat, and the effects of fine-tuning: reasonable.io/blog/verificat… Link Verification at Lightning Speed | Reasonable Can mid-size open-weight models be fine-tuned to write correct Verus proofs for Rust, fast? A three-part study of capability, speed, and the anatomy of failed proofs. reasonable.io — https://nitter.net/ReasonableIO/status/2087176963043868688#m

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CodeRabbit (@coderabbitai) Easy to train. Smart at routing. Efficient at scale. In collaboration with Baseten, we fine-tuned NVIDIA Nemotron 3.5 Lightning on CodeRabbit's routing decisions in under three hours and for less than $100. The result? > ~4% higher accuracy than our previous GPT-class model. > ~50% lower inference cost. Full breakdown: coderabbit.ai/blog/teaching-… — https://nitter.net/coderabbitai/status/2087175469963858146#m

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2026/08/13 06:12（台北）

Here’s your first peek at Pixel Watch 5. Designed for Gemini Intelligence and @GoogleHealth, it helps you stay on top of your day with proactive assistance, advanced fitness tracking, and breakthrough health features.

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2026/08/13 06:12（台北）

Your Claude in Chrome sessions now carry over to desktop, web, and mobile. Conversations are saved, and your skills and connectors work in the browser. Available on Max and Team today, rolling out to Pro in the coming weeks.

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2026/08/13 06:12（台北）

Get your first look at the Pixel 11 family. Refined hardware, made to last. Fresh colors. New cameras. And designed for Gemini Intelligence. #MadeByGoogle

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There's been a week of hype for DeepSeek Flash So good time to announce that the newest DeepSeek V4 Pro is now available in OpenCode Go

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Thoughtworks (@thoughtworks) Congrats to @nvidia on the launch of Nemotron 3.5 Lightning. We got early access to test it and found it to be fast, highly customizable and controllable. Read Gustavo A. Lujan, Allen Roush and Andy Nolan's findings 👉 thoughtworks.com/insights/bl… — https://nitter.net/thoughtworks/status/2087210505072968059#m

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Tinker (@tinkerapi) Nemotron 3.5 Lightning from @NVIDIAAI is out today and available on Tinker. With just 3B active parameters and optimized for throughput speed, 3.5 Lightning is designed for work where latency and cost matter. — https://nitter.net/tinkerapi/status/2087236122707739040#m

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2026-08-13

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2026/08/13 06:12（台北）

SL2T is our breakthrough sign language-to-text model powering new features for Deaf and hard of hearing users on @Android. Starting with American Sign Language-to-English on Pixel 11, people can sign directly into Gboard and Live Transcribe instead of typing.

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2026/08/13 06:12（台北）

We’re honored that Glassdoor has named our founder and CEO, @JensenHuang , No. 1 on its 2026 Best CEOs list. 🎉 A very meaningful recognition, grounded in feedback from the people who make NVIDIA what it is.

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2026/08/13 06:12（台北）

☁️Mistral is bringing together the inference infrastructure, open models, and long-term commitments Europe needs to control its AI future, and setting a roadmap for the world. 🧵: https://mistral.ai/news/regional-inference-open-models-new-compute/

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2026/08/13 06:12（台北）

Introducing Grok 4.6. It delivers frontier intelligence and is a significant improvement over Grok 4.5 at the same price.

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2026/08/13 06:12（台北）

R to @claudeai: Browser agents can be tricked by instructions hidden in a page. We build defenses against this, and we still recommend a few habits of your own: http://support.claude.com/en/articles/12902428-use-claude-in-chrome-safely

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R to @claudeai: The side panel now runs the same Claude Cowork session as the desktop, web, and mobile apps. Sessions live with your account, not on any single device, so you can start in a tab and pick it up later somewhere else. Give it a try: http://claude.com/chrome

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2026/08/13 06:12（台北）

Grok is the most efficient high intelligence AI

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2026/08/13 06:12（台北）

Grok 4.6 is objectively #1 when considering intelligence, speed & cost

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2026/08/13 06:12（台北）

this is already one of the most important papers of this year. https://www.latent.space/p/ainews-how-to-steal-a-reasoning-trace the methodology doesnt seem clearly explained so here are some notes with a further distillation

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2026/08/13 06:12（台北）

Typical conversation with @ajambrosino A: core alignment has been reached T: core alignment you say A: i say T: say more A: more And yet I’ve never been more excited about what him and the team are building.

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2026/08/13 06:12（台北）

What’s the magic behind the new Pixel 11 family? We asked Shakil Barkat, VP of Devices and Services, to give us the rundown of the big #MadeByGoogle news in less than 60 seconds.

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2026/08/13 06:12（台北）

RT by @elonmusk: Congrats to the @SpaceXAI team on the release of Grok 4.6. Grok 4.6 brings frontier intelligence, running and trained on NVIDIA GB300 NVL72 with NVLink to deliver exceptional performance, reliability and lowest token cost.

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2026/08/13 06:12（台北）

grok 4.6 is now available in OpenCode Zen also if you're a SuperGrok subscriber you can use it right in OpenCode

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2026/08/13 06:12（台北）

R to @nvidia: Read the story: https://nvda.ws/4gt2Ta0

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2026/08/13 06:12（台北）

R to @Google: Learn more about Pixel Tag and our other new devices at #MadeByGoogle, live tonight at 6pm ET → https://x.com/i/events/2081773622465556480

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2026/08/13 06:12（台北）

Introducing Google Pixel Tag, our first-ever finder tag. Attach it to your keys or slip it into your luggage to easily find the things you need, whether they’re near or far, right in Find Hub.

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2026/08/13 06:12（台北）

R to @GoogleDeepMind: We built SL2T with the Deaf community - guided by Deaf Googlers and our AI Sign Language Advisory Committee. Bringing ASL input to phones is just the beginning and we’re working to expand this technology to more sign languages and applications. Find out more → https://goo.gle/3RM3kmx

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R to @GoogleDeepMind: SL2T is state-of-the-art on academic benchmarks, plus it's optimized for real-world use like one-handed signing while holding a phone. To protect privacy, it tracks body poses on-device, while our servers translate them into text.

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R to @GoogleDeepMind: For years, building AI for sign language has been slow due to complex technical challenges and misconceptions about how the languages work. We addressed this by training a model to translate simultaneous movements of the hands, body, and face directly into text, using huge amounts of data.

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2026/08/13 06:12（台北）

Try out Grok @Bot

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2026/08/13 06:12（台北）

Try out Grok 4.6! Double your tokens for next 7 days.

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R to @SpaceXAI: Grok 4.6 is available today in Grok Build, Cursor, Grok Bot, and the API. We’re including 2x usage inside Cursor and Grok Build for the first week. https://x.ai/news/grok-4-6

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R to @SpaceXAI: Grok 4.6 is faster than comparable models and can handle much more challenging tasks than Grok 4.5. It's half the price of other frontier models at $2/M input and $6/M output tokens.

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2026/08/13 06:12（台北）

Try Grok 4.6 on tough real-world tasks!

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2026/08/13 06:12（台北）

R to @Google: Stream #MadeByGoogle live at 6pm ET tonight to get all the details ↓ https://x.com/i/events/2081773622465556480

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2026/08/13 06:12（台北）

would be even more popular than @UseCorgi cafe

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2026/08/13 06:12（台北）

Pareto gold for Grok 4.6

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Tesla FSD FTW

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2026-08-13

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2026/08/13 06:12（台北）

Yes

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2026-08-13

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2026/08/13 06:12（台北）

R to @Google: Tune in to #MadeByGoogle tonight at 6pm ET to learn more ↓ https://x.com/i/events/2081773622465556480

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2026-08-13

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2026/08/13 06:12（台北）

Grok 4.6 is a banger

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2026-08-13

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2026/08/13 06:12（台北）

R to @thsottiaux: Also don’t say Linux, we just shipped that.

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2026-08-13

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2026/08/13 06:12（台北）

Why did you switch to Codex? What do you like about it and what could we improve? Don’t say reset.

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2026-08-13

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x.com/i/article/208720555103…

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I previously promised a reset for every 1M in additional active users for Codex, until 10M. We blew past that and have been silent since 10M. Little surprise for you tomorrow.

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2026-08-13

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R to @opencode: opencode.ai/data

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Correction, it was our first 11T token day

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2026/08/13 06:12（台北）

R to @MistralAI: 💡The world needs an open-source platform, and that’s exactly what we’re building to give our customers more choice and the flexibility to choose the right model for the right task. As part of this, we’ll continue to innovate in frontier, efficient open models across modalities.

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R to @MistralAI: ⚙️That is the framework Mistral is building toward: one in which enterprises, governments, and startups can use the best AI available, shape it around their own knowledge, and retain the value it creates.

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2026-08-13

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2026/08/13 06:12（台北）

R to @MistralAI: 🎯Sovereign intelligence through model choice: We're expanding our platform to third-party open models, starting with http://Z.ai’s GLM-5.2, so enterprises match workloads to the right model and keep the intelligence they build.

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2026-08-13

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2026/08/13 06:12（台北）

R to @MistralAI: 🌎Regional Endpoints and Priority Tier: Customers choose where inference runs - Europe or the U.S. - so requests stay in-region, and local capacity holds when demand peaks, aligning with data residency, regulatory, and latency requirements.

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2026-08-13

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2026/08/13 06:12（台北）

R to @MistralAI: ⚡️A coalition that secures long-term AI capacity: We’re aggregating long-term compute demand in Europe to determine what capacity is built, where it’s located, and whom it serves. Through these multi-year commitments, European Compute Units, participants can use the capacity across the range of products available on Mistral Compute as their needs evolve.

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2026-08-13

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2026/08/13 06:12（台北）

R to @MistralAI: ⚡️A coalition that secures long-term AI capacity: We’re aggregating long-term compute demand in Europe to determine what capacity is built, where it’s located, and whom it serves. Through these multi-year commitments, European Compute Units, participants can use the capacity across the range of products available on Mistral Compute as their needs evolve.

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2026-08-13

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