At NVIDIA’s GTC 2025, Jensen Huang said it loud and clear: “Everything can be tokenized.”And with the sheer computing power of GPUs, he added, “everything can be decoded and figured out — it’s just a matter of electricity.” He’s right. But most people don’t fully grasp what “everything can be tokenized” really means. Let’s unpack … Continue reading Everything Can Be Tokenized (by Jensen Huang)

Streamlit is an incredibly powerful and versatile tool, making it essential to invest your time in truly grasping its full potential! It is a Python library that converts scripts into reactive web apps — without HTML, CSS, or JS. Each Streamlit run is stateless by default, hence you need to append conversation history yourself. Streamlit … Continue reading Use Streamlit

I built a strong index recon agent, documenting it here for future improvements and reference for other agents. Index-Solutions/agent_134 The key points are 1. use streamlit for a nice UI; 2. in the agent.py, apply anthropic SDK to take care of conversation history, ReAct chain and especially the ad-hoc data manipulation codes realized by writing … Continue reading Building a Robust Index Recon Agent

Traditional RAG (Retrieval-Augmented Generation) works by embedding texts into high-dimensional vectors and then retrieving the most similar ones when a user asks a question. It’s effective for small and isolated chunks of knowledge. But as the knowledge base grows complex — especially when the content is interconnected like custom SDK codes or component dependencies — … Continue reading Graph RAG — From Scattered Retrieval to Connected Understanding

Wealth and social are essentially the same thing. At the core, both come down to the value you can create for others. You don’t need to pile up gold or dollars or bitcoins, and you don’t need to please people just to be liked. Those are surface-level measures. What matters is whether you can make, … Continue reading Wealth and Social Are the Same Thing

What NVIDIA provides is the cuFFT library one can directly use, #include <iostream> #include <cufft.h> int main() { const int N = 8; // Host input (complex numbers: float2) cufftComplex h_signal[N]; for (int i = 0; i < N; ++i) { h_signal[i].x = i; // real part h_signal[i].y = 0.0f; // imaginary part } // … Continue reading Explore cuFFT

What HTTP 402 Is? When Tim Berners-Lee designed HTTP in the 1990s, he reserved status code 402: “Payment Required” for a future where websites could charge users automatically for access or data. It was never implemented widely because: No native internet payment system existed; Credit cards required human input; Micropayments (fractions of a cent) weren’t … Continue reading HTTP 402 and Stablecoin

There are about 900 estimated CUDA libraries from NVIDIA. I will select the ones relevant to my needs. LibraryPurposeRobotics Use cuBLASGPU-accelerated linear algebra (matrix mult, LU, QR)Rigid-body dynamics, transforms cuSOLVERLinear system & eigen decompositionInverse kinematics, least squares cuSPARSE / cuDSSSparse matrices & solversLarge Jacobian systems, graph optimization cuRANDRandom number generationMonte Carlo simulations, sensor noise cuFFTFast … Continue reading CUDA Libraries I Will Explore

NVIDIA Omniverse is a GPU-powered platform for building, simulating, and collaborating inside realistic, physics-based 3D worlds — used for robotics, digital twins, and AI-driven system design. NVIDIA Omniverse is built directly on top of CUDA and related NVIDIA SDKs. It’s essentially a “metaverse operating system” powered by accelerated computing. It's realized by foundational CUDA packages: … Continue reading NVIDIA Omniverse and AI Factory

NV-Tesseract is a family of deep‐learning models designed specifically for time‐series data (i.e., sequences of values over time) rather than just static tabular or image data. presentation below is by Weiji Chen, AI/ML Engineer, NVIDIA Evolving to Tesseract 2.0 Then Tesseract 2.0 integrate rest of NVIDIA technologies, such as RAPIDs(a python c++ libraries leveraging CUDA-X, cuDF, cuPy, RMM … Continue reading NV-Tesseract Time Series Models