Tung T

Software Development & Real Estate Renting

[Tung T @Tran_Tung]

@systemdesignone Playbook

[Neo Kim @systemdesignone]

I created a playbook to help you learn AI engineering.

It gives you:

• Core concepts behind AI systems.
• Condensed notes to learn full-stack AI engineering.
• Powerful techniques to build, deploy & scale AI apps.

(24 HOURS ONLY!)

1 Follow @systemdesignone [MUST]
2 Like & Retweet to get DM
3 Reply "Playbook"

Then I'll DM you the details.

[Tung T @Tran_Tung]

@systemdesignone Playbook

[Neo Kim @systemdesignone]

I created a playbook to help you learn AI engineering.
It'll give you:

• Core concepts behind AI systems.
• Condensed notes to understand full-stack AI engineering.
• Must know techniques to build, deploy & scale AI apps.

(24 HOURS ONLY!!!)

1 Follow @systemdesignone [MUST]
2 Like & Retweet to get DM
3 Reply "Playbook"

Then I'll DM you the details.

[Tung T @Tran_Tung]

@linhnhicte1610 .

[Tung T @Tran_Tung]

@PThuy2302 Hello

[NVIDIA AI @NVIDIAAI]

Teach an agent a workflow once. Have it remember after every rebuild.

This tutorial shows how to deploy @nousresearch Hermes Agent with NVIDIA NemoClaw and OpenShell, connect it to Slack, Outlook, GitHub, and NVIDIA developer forums, then turn a chat correction into a reusable skill.

Private data stays behind runtime policies. Learned skills persist across deployments.

[NVIDIA @nvidia]

We are partnering with @Microsoft to enable secure, user-controlled AI on Windows.

NVIDIA OpenShell runtime for agents will provide governance tools, policy enforcement, and smart local-to-cloud query routing.

Learn more: https://t.co/zPGwz9xQSW

[NVIDIA Robotics @NVIDIARobotics]

Robot development doesn’t stop at one training loop. 🔁

It’s a continuous cycle of collecting data, refining it, training policies, testing, and repeating.

Discover how we’re helping developers accelerate this cycle with new updates across NVIDIA Isaac technologies including Isaac GR00T, an open, end-to-end development platform for humanoid robots built on NVIDIA Jetson Thor. 🤖

Read the blog: https://t.co/cb3vh1wXnB

#NVIDIAGTC

[NVIDIA Asia Pacific @NVIDIAAP]

NVIDIA AI for Media is bringing real-time AI performance into live and on-demand media workflows.

🎥 Synthetic Video Detector delivers up to 92% accuracy with latency as low as 22 ms for AI video authenticity checks.

⚡ RTX Video Super Resolution and Frame Generation upscale from 480p to 8K and boost frame rates by 2–4x for archive refresh and premium sports streams.

🌍 NVIDIA AI for Media scales across on-prem and cloud, helping teams localize content and unlock new value from massive video archives.

Read the blog: https://t.co/aCMPREasS2

[Job Corner @JOBCORNER247]

Learn AI for free directly from top companies.

1 - Anthropic:
https://t.co/Mrjsy7wQNn

2 - Google:
https://t.co/soo8GgZBMw

3 - Meta:
https://t.co/zgDA5rB45c

4 - NVIDIA:
https://t.co/20ojhxkGLA

5 - Microsoft:
https://t.co/tVtUeTOFAR

6 - OpenAI:
https://t.co/RJCWuvHeBw

7 - IBM:
https://t.co/c9wBhNLnIA

8 - AWS:
https://t.co/5pZnRwgdkl

9 - https://t.co/pqL2k2kg8x:
https://t.co/aUAYgFBtgD

10 - Hugging Face:
https://t.co/sAfFUkDE5N

Must bookmark for future reference.

[NVIDIA Robotics @NVIDIARobotics]

During #NVIDIAGTC at #COMPUTEX2026, we connected with @Solomon_3D's CEO, Johnny Chen to discuss their humanoid solutions built on NVIDIA technology.

[The Humanoid Hub @TheHumanoidHub]

DR02 humanoid from DEEP Robotics.

Showing off upgraded payload capacity and dynamic locomotion.

[Rohit Kumar Tiwari @_rohit_tiwari_]

AI Engineering from Scratch.

503 lessons. 20 phases. 320 hours.

https://t.co/UuX9N62VCU

Phase 00: Setup & Tooling (12 lessons)
Phase 01: Math Foundations (22 lessons)
Phase 02: ML Fundamentals (18 lessons)
Phase 03: Deep Learning Core (13 lessons)
Phase 04: Computer Vision (28 lessons)
Phase 05: NLP (29 lessons)
Phase 06: Speech & Audio (17 lessons)
Phase 07: Transformers Deep Dive (14 lessons)
Phase 08: Generative AI (14 lessons)
Phase 09: Reinforcement Learning (12 lessons)
Phase 10: LLMs from Scratch (22 lessons)
Phase 11: LLM Engineering (15 lessons)
Phase 12: Multimodal AI (25 lessons)
Phase 13: Tools & Protocols (23 lessons)
Phase 14: Agent Engineering (42 lessons)
Phase 15: Autonomous Systems (22 lessons)
Phase 16: Multi-Agent & Swarms (25 lessons)
Phase 17: Infrastructure & Production (28 lessons)
Phase 18: Ethics, Safety & Alignment (30 lessons)
Phase 19: Capstone Projects (85 lessons)

[NVIDIA @nvidia]

At #MSBuild, @Microsoft and NVIDIA unveiled an end-to-end stack for developers scaling agentic AI, from Windows devices to cloud to local.

One unified stack. Every layer accelerated.

Highlights include:

💻 RTX Spark: Windows PCs purpose-built for personal agents

💡 NVIDIA open models on Microsoft Foundry, with Anthropic’s Claude models running natively on NVIDIA GB300 Blackwell Ultra systems on Azure

🚀 NVIDIA-accelerated Microsoft Fabric Data Warehouse with up to 6x faster SQL execution

🦞 NVIDIA OpenShell in GitHub Copilot: secure-by-design agent runtime

📣 Fairwater Wisconsin AI factory is live and validated for the NVIDIA Vera Rubin platform

🔗 Read the full blog: https://t.co/n2buTyByjD

[NVIDIA Newsroom @nvidianewsroom]

Agentic AI is moving from cloud to PC.

On stage with @Arm CEO Rene Haas at COMPUTEX, NVIDIA CEO Jensen Huang spotlighted NVIDIA RTX Spark, a new class of Windows PCs bringing the full CUDA and RTX ecosystem to creators, AI developers and gamers.

[NVIDIA Robotics @NVIDIARobotics]

Big moment for physical AI. 🤖

Cosmos 3 gives robots a richer world model to perceive, reason, and act in the real world.

[Google AI Developers @googleaidevs]

We’re launching Gemma 4 12B: Our unified, encoder-free model that brings powerful multimodal intelligence straight to your laptop 🚀

The model bridges the gap between our mobile E4B model and larger 26B MoE models, packaging frontier-class reasoning and native audio into a highly optimized footprint, all under a permissive Apache 2.0 license.

Here’s what makes it unique:

+ Encoder-Less Architecture: We removed the multimodal encoders. The vision and audio inputs flow directly into the LLM backbone.
+ Agentic Performance (16GB VRAM): Run complex, multi-step workflows locally, with performance nearing our 26B model.

[NVIDIA AI @NVIDIAAI]

OpenShell v0.0.55

🧩 Google Vertex AI inference provider
🔒 Profile-backed policy visibility
🐳 Better Podman detection in the gateway
🖥️ Restored GPU procfs baseline behavior
🔧 CI and docs fixes

Run agents against Vertex AI, with improved policy visibility and more reliable Podman and GPU sandbox behavior.

https://t.co/TRlhGwfF8D

[Bloomberg @business]

Nvidia's Jensen Huang calls concerns of AI reducing jobs “complete nonsense," saying instead that companies are hiring more software engineers due to the abilities of new agentic AI features.

More from his keynote speech at the Computex conference in Taiwan, Asia’s biggest tech event: https://t.co/F3gJJi6ZhZ