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A Production-Style NetworKit 11.2.1 Coding Tutorial for Large-Scale Graph Analytics, Communities, Cores, and Sparsification

ai, AI (Artificial Intelligence), Artificial Intelligence, Data Science, Editors Pick, Staff, Technology, Tutorials

In this tutorial, we implement a production-grade, large-scale graph analytics pipeline in NetworKit, focusing on speed, memory efficiency, and version-safe APIs in NetworKit 11.2.1. We generate a large-scale free network, extract the largest connected component, and then compute structural backbone signals via k-core decomposition and centrality ranking. We also detect communities with PLM and quantify

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A Coding Guide to Build a Scalable End-to-End Machine Learning Data Pipeline Using Daft for High-Performance Structured and Image Data Processing

ai, AI (Artificial Intelligence), Artificial Intelligence, Computer vision, Editors Pick, Machine Learning, Staff, Technology, Tutorials

In this tutorial, we explore how we use Daft as a high-performance, Python-native data engine to build an end-to-end analytical pipeline. We start by loading a real-world MNIST dataset, then progressively transform it using UDFs, feature engineering, aggregations, joins, and lazy execution. Also, we demonstrate how to seamlessly combine structured data processing, numerical computation, and

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How to Design an Advanced Tree-of-Thoughts Multi-Branch Reasoning Agent with Beam Search, Heuristic Scoring, and Depth-Limited Pruning

agentic ai, ai, AI (Artificial Intelligence), AI Agents, Artificial Intelligence, Editors Pick, Staff, Tutorials

In this tutorial, we build an advanced Tree-of-Thoughts (ToT) multi-branch reasoning agent from scratch. Instead of relying on linear chain-of-thought reasoning, we design a system that generates multiple reasoning branches, scores each branch using a heuristic evaluation function, prunes weak candidates, and continues expanding only the strongest paths. We combine an instruction-tuned transformer model with

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How to Build an EverMem-Style Persistent AI Agent OS with Hierarchical Memory, FAISS Vector Retrieval, SQLite Storage, and Automated Memory Consolidation

agentic ai, ai, AI (Artificial Intelligence), AI Agents, Artificial Intelligence, Editors Pick, Staff, Tutorials

In this tutorial, we build an EverMem-style persistent agent OS. We combine short-term conversational context (STM) with long-term vector memory using FAISS so the agent can recall relevant past information before generating each response. Alongside semantic memory, we also store structured records in SQLite to persist metadata like timestamps, importance scores, and memory signals (preference,

How to Build an EverMem-Style Persistent AI Agent OS with Hierarchical Memory, FAISS Vector Retrieval, SQLite Storage, and Automated Memory Consolidation Read More »

A Coding Guide to Build a Scalable End-to-End Analytics and Machine Learning Pipeline on Millions of Rows Using Vaex

ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Machine Learning, Technology, Tutorials

In this tutorial, we design an end-to-end, production-style analytics and modeling pipeline using Vaex to operate efficiently on millions of rows without materializing data in memory. We generate a realistic, large-scale dataset, engineer rich behavioral and city-level features using lazy expressions and approximate statistics, and aggregate insights at scale. We then integrate Vaex with scikit-learn

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How to Build an Explainable AI Analysis Pipeline Using SHAP-IQ to Understand Feature Importance, Interaction Effects, and Model Decision Breakdown

ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, explainable AI, Machine Learning, Staff, Technology, Tutorials

In this tutorial, we build an advanced explainable AI analysis pipeline using SHAP-IQ to understand both feature importance and interaction effects directly inside our Python environment. We load a real-world dataset, train a high-performance Random Forest model, and then apply the SHAP-IQ interaction index to compute precise, theoretically grounded explanations of model predictions. We extract

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How to Design a Production-Grade Multi-Agent Communication System Using LangGraph Structured Message Bus, ACP Logging, and Persistent Shared State Architecture

agentic ai, ai, AI (Artificial Intelligence), AI Agents, AI Infrastructure, Artificial Intelligence, Editors Pick, Technology, Tutorials

In this tutorial, we build an advanced multi-agent communication system using a structured message bus architecture powered by LangGraph and Pydantic. We define a strict ACP-style message schema that allows agents to communicate via a shared state rather than calling each other directly, enabling modularity, traceability, and production-grade orchestration. We implement three specialized agents, a

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A Complete End-to-End Coding Guide to MLflow Experiment Tracking, Hyperparameter Optimization, Model Evaluation, and Live Model Deployment

ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Large Language Model, Machine Learning, MLOps, Staff, Tech News, Technology, Tutorials

In this tutorial, we build a complete, production-grade ML experimentation and deployment workflow using MLflow. We start by launching a dedicated MLflow Tracking Server with a structured backend and artifact store, enabling us to track experiments in a scalable, reproducible manner. We then train multiple machine learning models using a nested hyperparameter sweep while automatically

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A Coding Implementation to Build a Hierarchical Planner AI Agent Using Open-Source LLMs with Tool Execution and Structured Multi-Agent Reasoning

agentic ai, ai, AI (Artificial Intelligence), AI Agents, Artificial Intelligence, Editors Pick, Large Language Model, Open Source, Staff, Technology, Tutorials

In this tutorial, we build a hierarchical planner agent using an open-source instruct model. We design a structured multi-agent architecture comprising a planner agent, an executor agent, and an aggregator agent, where each component plays a specialized role in solving complex tasks. We use the planner agent to decompose high-level goals into actionable steps, the

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How to Build Interactive Geospatial Dashboards Using Folium with Heatmaps, Choropleths, Time Animation, Marker Clustering, and Advanced Interactive Plugins

ai, AI (Artificial Intelligence), Artificial Intelligence, Data Sets, Editors Pick, Machine Learning, Staff, Technology, Tutorials

In this Folium tutorial, we build a complete set of interactive maps that run in Colab or any local Python setup. We explore multiple basemap styles, design rich markers with HTML popups, and visualize spatial density using heatmaps. We also create region-level choropleth maps from GeoJSON, scale to thousands of points using marker clustering, and

How to Build Interactive Geospatial Dashboards Using Folium with Heatmaps, Choropleths, Time Animation, Marker Clustering, and Advanced Interactive Plugins Read More »