NVIDIA AI ✓
@NVIDIAAI · Santa Clara, CA · joined 07 Jun 2016
Teaching your AI new tricks.
338 522Followers
897Following
13 685Posts total
4.5MViews on collected posts
โพสต์ล่าสุด
กำลังค้นหาโพสต์ใหม่…
And thank you @NVIDIAAI for the #NVIDIAGrant to make this project possible!
5.7K views · 12 likes
· 0 reposts · 7 replies
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Need faster LLM inference without sacrificing accuracy? Speculative decoding can help.
Choosing the right draft length and drafting method depends on your model, workload and hardware. We break down five practical guidelines for balancing throughput and latency. https://t.co/k6y
18.4K views · 248 likes
· 23 reposts · 20 replies
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DGX Spark Live: Perplexity Portable Computer Goes Local https://t.co/jYwLOCtKly
70.6K views · 186 likes
· 21 reposts · 8 replies
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@NVIDIAAI Being a member of @NVIDIA Inception means more to us than simply having another title.
For DATAHEDGE AI, it means having the opportunity to learn, build more, and grow alongside one of the most important ecosystems shaping the future of AI computing.
Now, we want to t
175 views · 8 likes
· 2 reposts · 0 replies
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The Open Secure AI Alliance is beginning its next chapter with the Linux Foundation.
We helped form the alliance to bring organizations together around open, practical ways to secure AI systems. Great to see that work continue with the Linux Foundation.
34.2K views · 411 likes
· 57 reposts · 24 replies
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The Open Secure AI Alliance is now part of the Linux Foundation.
Originally founded by @nvidia, the Open Secure AI Alliance will develop open source tools, shared standards, and defensive practices that help organizations inspect, audit, and secure AI systems.
Learn more: http
39K views · 106 likes
· 21 reposts · 6 replies
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AITX Austin Hackathon Winners Spotlight https://t.co/JHdIq4f7GV
11.2K views · 60 likes
· 5 reposts · 4 replies
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@NVIDIAAI @CrowdStrike Collab with @_redteam_ and @bitsecai
437 views · 8 likes
· 1 reposts · 0 replies
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The detection rules were tested against eight new attacks not used to create them.
Three rules met all quality gates and caught all eight.
The setup paired Nemotron 3 Ultra for orchestration with a fine-tuned Nemotron 3 Super for detection writing and repair.
Read the https://
4.3K views · 27 likes
· 0 reposts · 1 replies
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When tested against the original recorded attack, the optimized open pipeline improved mean backtest detection from 16.5% to 41.9% across independently seeded sessions.
It brought together domain context, specialized models, tools and validation. https://t.co/GHWUZ2ta90
5.3K views · 29 likes
· 2 reposts · 1 replies
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Can a security defense catch an attack it hasn’t seen before?
We teamed with @CrowdStrike to evaluate an offensive-defensive system built on its SafeMind agentic system, where AI agents simulate controlled attacks, turn telemetry into detection rules, then test them against new
62.6K views · 593 likes
· 88 reposts · 36 replies
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As AI agents take on more complex work, where should security live?
The harness guides what an agent tries to do. Infrastructure controls what it can actually do.
Our AI safety and security teams share how they’re thinking about security across the agent stack. 👇 https://t.co/r
76.4K views · 421 likes
· 57 reposts · 54 replies
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@NVIDIAAI Five fckin months https://t.co/sGwIAOh3DD
11.1K views · 234 likes
· 9 reposts · 4 replies
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NVIDIA AVO continuously inspects, plans, implements, and evaluates, using memory, tools, and execution feedback to build on what it learns along the way. This allows the system to sustain progress across long-running tasks rather than starting over with each model context.
Read
150.1K views · 840 likes
· 66 reposts · 34 replies
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Our general-purpose coding agent just scored 100% on the ARC-AGI-3 interactive reasoning benchmark.
NVIDIA AVO completed all 183 levels across all 25 public environments, figuring out what to do with no instructions, explicit rules, or stated goals. https://t.co/UgROuDrMtn
1.1M views · 6.2K likes
· 703 reposts · 339 replies
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GIF
@huggingface And it ships with its data.
Alongside Lightning, we’re releasing Nemotron-RL-Agentic-Terminal-Pivot, the open agentic RL dataset used to post-train its coding agent capabilities.
Dataset on @huggingface 🤗→ https://t.co/YeiHu0dte7
15.5K views · 89 likes
· 10 reposts · 4 replies
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As always, NVIDIA Nemotron 3.5 Lightning is open and customizable.
This includes weights, data and recipes.
Available now on @huggingface 🤗 → https://t.co/2ADZTwH7CZ
36.2K views · 304 likes
· 23 reposts · 19 replies
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Not every step in an agent workflow needs the same model.
That’s why we’re also releasing NVIDIA NeMo Switchyard, a new open source library for model routing.
Use frontier models for complex reasoning and planning, and Lightning for high-volume, specialized execution.
Learn ht
23.4K views · 164 likes
· 12 reposts · 5 replies
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Long-running agents spend most of their time executing: calling tools, validating results and delegating work.
Nemotron 3.5 Lightning is built for this high-volume execution, at a size that can run anywhere from an NVIDIA DGX Spark to the data center.
See it running agentic htt
32.8K views · 186 likes
· 10 reposts · 5 replies
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Lightning is built to specialize.
Post-train Nemotron 3.5 Lightning with NVIDIA NeMo for your domain data, tools, workflows and policies.
Across cybersecurity, coding, legal and energy tasks, post-training improves accuracy for specialized work. https://t.co/0HBIxtUp6G
52.2K views · 289 likes
· 18 reposts · 6 replies
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Lightning pairs strong accuracy with speed.
On PinchBench, it reaches 86% accuracy while completing 10,000 tasks 35% faster than Qwen3.6 35B at similar accuracy. https://t.co/kzM76u35N3
46.8K views · 333 likes
· 17 reposts · 5 replies
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Introducing NVIDIA Nemotron 3.5 Lightning⚡
An open 30B MoE model with 3B active parameters, built for always-on agents to complete high-volume, specialized tasks faster.
It delivers up to 4x the output speed of similar-sized models. https://t.co/ENWrZe76pU
2.6M views · 5.8K likes
· 669 reposts · 360 replies
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@huggingface Explore NVIDIA Cosmos.
https://t.co/XaNTPzFBXv
5.2K views · 20 likes
· 2 reposts · 2 replies
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10 million downloads and counting for NVIDIA Cosmos models on @huggingface. 🤗
This milestone belongs to the developers and researchers using open world foundation models to build robots, autonomous vehicles and other physical AI systems.
Thank you for downloading, https://t.co/
56.8K views · 458 likes
· 58 reposts · 47 replies
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เทียบกับบัญชีขนาดเดียวกัน
25 โพสต์จาก 90 วันที่ผ่านมา เทียบกับช่วง 100K–1M ผู้ติดตาม แสดงในวงกว้าง แต่มีผู้ชมตอบสนองน้อย.
ยอดดูมัธยฐาน32 809บัญชีนี้5 983ค่ามัธยฐานของ 100K–1M
การเข้าถึง, %9.69%บัญชีนี้1.49%ค่ามัธยฐานของ 100K–1M
การมีส่วนร่วม, %0.68%บัญชีนี้0.93%ค่ามัธยฐานของ 100K–1M
| ตัวชี้วัด | บัญชีนี้ | ค่ามัธยฐานของ 100K–1M | อัตราส่วน |
|---|---|---|---|
| ยอดดูมัธยฐานต่อโพสต์ | 32 809 | 5 983 | 5.48× |
| การเข้าถึง (ยอดดู ÷ ผู้ติดตาม) | 9.69% | 1.49% | 6.50× |
| อัตราการมีส่วนร่วม | 0.68% | 0.93% | 0.73× |
บัญชีอื่นในช่วงนี้ → เปรียบเทียบกับบัญชีอื่น → ค่าอ้างอิงเหล่านี้คำนวณอย่างไร →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
76.4K21 Aug
27.3K
62.6K1 Sep
5.3K
4.3K
437
11.2K2 Sep
39K
34.2K
175
70.6K
18.4K4 Sep
5.7K6 Sep
46.3K
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.71%21 Aug
0.40%
1.18%1 Sep
0.60%
0.68%
2.06%
0.62%2 Sep
0.36%
1.45%
5.71%
0.31%
1.61%4 Sep
0.34%6 Sep
0.15%
Reactions — likes, reposts, replies and quotes — divided by views. Median for 100K–1M accounts is 0.93%.
What the audience does
Likes66.2%17 142 in total
Reposts7.3%1 887 in total
Replies3.9%998 in total
Quotes3.6%931 in total
Bookmarks19.1%4 948 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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