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How to Build an Advanced, Interactive Exploratory Data Analysis Workflow Using PyGWalker and Feature-Engineered Data
In this tutorial, we demonstrate how to move beyond static, code-heavy charts and build a genuinely interactive exploratory data analysis workflow directly using PyGWalker. We start by preparing the Titanic dataset for large-scale interactive querying. These analysis-ready engineered features reveal the underlying structure of the data while enabling both detailed row-level exploration and high-level…

Cloudflare Releases Agents SDK v0.5.0 with Rewritten @cloudflare/ai-chat and New Rust-Powered Infire Engine for Optimized Edge Inference Performance
Cloudflare has released the Agents SDK v0.5.0 to address the limitations of stateless serverless functions in AI development. In standard serverless architectures, every LLM call requires rebuilding the session context from scratch, which increases latency and token consumption. The Agents SDK’s latest version (Agents SDK v0.5.0) provides a vertically integrated execution layer where compute,…

Agoda Open Sources APIAgent to Convert Any REST pr GraphQL API into an MCP Server with Zero Code
Building AI agents is the new gold rush. But every developer knows the biggest bottleneck: getting the AI to actually communicate to your data. Today, travel giant Agoda is tackling this problem head-on. They have officially launched APIAgent, an open-source tool designed to turn any REST or GraphQL API into a Model Context Protocol…

How to Build Human-in-the-Loop Plan-and-Execute AI Agents with Explicit User Approval Using LangGraph and Streamlit
app_code = r”’ import os, json, uuid import streamlit as st from typing import TypedDict, List, Dict, Any, Optional from pydantic import BaseModel, Field from openai import OpenAI from langgraph.graph import StateGraph, START, END from langgraph.types import Command, interrupt from langgraph.checkpoint.memory import InMemorySaver def tool_search_flights(origin: str, destination: str, depart_date: str, return_date: str, budget_usd: int)…

Alibaba Qwen Team Releases Qwen3.5-397B MoE Model with 17B Active Parameters and 1M Token Context for AI agents
Alibaba Cloud just updated the open-source landscape. Today, the Qwen team released Qwen3.5, the newest generation of their large language model (LLM) family. The most powerful version is Qwen3.5-397B-A17B. This model is a sparse Mixture-of-Experts (MoE) system. It combines massive reasoning power with high efficiency. Qwen3.5 is a native vision-language model. It is designed…

Google DeepMind Proposes New Framework for Intelligent AI Delegation to Secure the Emerging Agentic Web for Future Economies
The AI industry is currently obsessed with ‘agents’—autonomous programs that do more than just chat. However, most current multi-agent systems rely on brittle, hard-coded heuristics that fail when the environment changes. Google DeepMind researchers have proposed a new solution. The research team argued that for the ‘agentic web’ to scale, agents must move beyond…
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