Building a simple data science agent that can handle Basic data operations (load, filter, aggregate) Code generation and execution Visualization with auto-display Excel/CSV support Conversation context To make the system more robust, we need to incorporate advanced data handling capabilities — including large file support, database connectivity, multi-DataFrame input, and data versioning to enable undo or rollback functions. It … Continue reading Create a Simple Data Science Assistant Agent

After a successful single-index agent creation, I am pondering about creating a multi-index agent, the benefits are obvious: Scalability: Add new indexes without changing core code Maintainability: Each index config is isolated Flexibility: Easy to modify methodology for one index without affecting others Testability: Test each index independently Reusability: Share common logic (QC, processing rules, … Continue reading Architecture of a Multi-Index Agent

Let's face it: the world of AI agents is moving fast, and they need to call external tools to get anything done. The initial big swing at standardization was the Model Context Protocol (MCP), but many of us quickly hit a wall. It felt too heavy. If you're tired of spinning up complex middle layers … Continue reading UTCP Could Be Better Than MCP

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