Tech learn by Shalu

Flight Delayed, but the Learning Never Stops! ✈️💻
While waiting for my Indigo flight, I’ve been diving deep into the next big shift in tech: AI Orchestration. 🤖⚙️
Most people think 2026 is just about better Chatbots. They are wrong. The future isn't about "asking" AI questions; it’s about Orchestrating multiple AI agents to execute entire business workflows autonomously.
Imagine one AI agent researching a market trend, another analyzing the data, and a third writing the report—all managed by one "Orchestrator." That is the skill that will make you UNBEATABLE in the job market. 🛡️
I’m curious—where are you in your AI journey?
1. Just started with ChatGPT 🙋‍♂️
2. Learning Prompt Engineering ✍️
3. Moving towards AI Agents/Orchestration 🤖
4. Just here for the Tech vibes! ✨
Drop a comment below! Let’s turn this delay into a masterclass. 👇
#ScienceAndTechnology #AIOrchestration #CloudComputing #TechLearnByShalu #FutureOfTech #IndigoDiaries #WomenInTech

1 week ago | [YT] | 0

Tech learn by Shalu

1 week ago | [YT] | 1

Tech learn by Shalu

Is your hard work in office place enough for your success???

1 month ago | [YT] | 0

Tech learn by Shalu

❤️❤️🧿🙏

4 months ago | [YT] | 0

Tech learn by Shalu

Will miss this place

4 months ago | [YT] | 0

Tech learn by Shalu

Yes AI bubble is real #aibubble

4 months ago | [YT] | 0

Tech learn by Shalu

NVIDIA DGX GB300 next video

4 months ago | [YT] | 0

Tech learn by Shalu

NVIDIA GPU & AI Quick Reference Guide
1. NVIDIA GPU Architecture Basics
CUDA Cores: Parallel processors for general-purpose GPU computing.
Tensor Cores: Specialized for deep learning matrix operations.
Memory Types: Global, shared, and texture memory optimize performance.
Streaming Multiprocessors (SMs): Core units containing CUDA cores.
2. CUDA Programming Intro
CUDA (Compute Unified Device Architecture) is NVIDIA's parallel computing platform.
Kernel functions run on the GPU, launched with grid and block dimensions.
Memory hierarchy is crucial for performance optimization.
Common languages: CUDA C/C++, Python with Numba/CuPy, PyCUDA.
3. AI Frameworks Optimized by NVIDIA
TensorFlow with TensorRT integration for inference acceleration.
PyTorch with cuDNN for deep learning training performance.
ONNX Runtime with CUDA execution provider for model deployment.
RAPIDS for GPU-accelerated data science workflows.
4. Common Interview Questions
Q: What is the difference between CUDA cores and Tensor cores?
A: CUDA cores handle general parallel tasks; Tensor cores accelerate matrix math used in deep
learning.
Q: How does GPU memory hierarchy affect performance?
A: Proper use of shared and global memory reduces latency and improves throughput.
Q: What is CUDA kernel launch configuration?
A: It defines how threads are organized into blocks and grids for parallel execution.
5. Key Tools & SDKs
CUDA Toolkit – Core libraries and compiler for GPU programming.
cuDNN – GPU-accelerated primitives for deep learning.
TensorRT – Inference optimizer and runtime engine.
Nsight Systems – Profiling tool for GPU applications.

4 months ago | [YT] | 0

Tech learn by Shalu

How is nityo infotech???

4 months ago | [YT] | 0

Tech learn by Shalu

Is it worth to learn databricks please let me know #databricks

4 months ago (edited) | [YT] | 0