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How to Build a Dynamic Zero-Trust Network Simulation with Graph-Based Micro-Segmentation, Adaptive Policy Engine, and Insider Threat Detection

ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Security, Software engineering, Staff, Tutorials

In this tutorial, we build a realistic Zero-Trust network simulation by modeling a micro-segmented environment as a directed graph and forcing every request to earn access through continuous verification. We implement a dynamic policy engine that blends ABAC-style permissions with device posture, MFA, path reachability, zone sensitivity, and live risk signals such as anomaly and […]

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Build a Hybrid-Memory Autonomous Agent with Modular Architecture and Tool Dispatch Using OpenAI

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

In this tutorial, we begin by exploring the architecture behind a hybrid-memory autonomous agent. This system combines semantic vector search, keyword-based retrieval, and a modular tool-dispatching loop to create an agent capable of reasoning, remembering, and acting autonomously. We walk through each layer of the design from the ground up, starting with abstract interfaces that

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A Coding Implementation to Portfolio Optimization with skfolio for Building Testing, Tuning, and Comparing Modern Investment Strategies

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

In this tutorial, we explore skfolio, a scikit-learn compatible portfolio optimization library that helps us build, compare, and evaluate different investment strategies in a structured Python workflow. We start by loading S&P 500 price data, converting it into returns, and creating a time-based train-test split suitable for financial analysis. From there, we build simple baseline

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How to Build Technical Analysis and Backtesting Workflow with pandas-ta-classic, Strategy Signals, and Performance Metrics

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

In this tutorial, we implement how to use pandas-ta-classic to build a complete technical analysis and trading strategy workflow. We start by installing the required libraries, downloading historical OHLCV stock data with yfinance, cleaning the returned data structure, and inspecting the available indicator categories inside the library. We then calculate popular indicators such as SMA,

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A Coding Implementation to Build Agent-Native Memory Infrastructure with Memori for Persistent Multi-User and Multi-Session LLM Applications

agentic ai, ai, AI (Artificial Intelligence), Artificial Intelligence, Context Engineering, Editors Pick, Software engineering, Staff, Tutorials

In this tutorial, we implement how Memori serves as an agent-native memory infrastructure layer for building more persistent, context-aware LLM applications. We start by setting up Memori in a Google Colab environment and connecting it to both synchronous and asynchronous OpenAI clients, so that every model call can automatically pass through the memory layer. We

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How to Build a Cost-Aware LLM Routing System with NadirClaw Using Local Prompt Classification and Gemini Model Switching

agentic ai, ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Software engineering, Staff, Technology, Tutorials

In this tutorial, we explore NadirClaw as an intelligent routing layer that classifies prompts into simple and complex tiers before sending them to the most suitable model. We start by installing the required packages, setting up an optional Gemini API key, and testing the local classifier through the NadirClaw CLI without making any live LLM

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A Coding Implementation to Recover Hidden Malware IOCs with FLARE-FLOSS Beyond Classic Strings Analysis

ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Security, Staff, Tutorials

In this tutorial, we explore how FLARE-FLOSS helps us recover hidden and obfuscated strings from a Windows PE file. We begin by setting up FLOSS and the MinGW-w64 cross-compiler. We synthesize a small malware-like executable that hides strings using multiple techniques, including static strings, stack-built strings, tight strings, and XOR-decoded strings. After that, we compare

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How to Build a Single-Cell RNA-seq Analysis Pipeline with Scanpy for PBMC Clustering, Annotation, and Trajectory Discovery

ai, AI (Artificial Intelligence), Applications, Artificial Intelligence, Big Data, Data Science, Editors Pick, Software engineering, Staff, Technology, Tutorials

In this tutorial, we perform an advanced single-cell RNA-seq analysis workflow using Scanpy on the PBMC-3k benchmark dataset. We start by loading the dataset, inspecting its structure, and applying quality control checks to evaluate gene counts, total counts, mitochondrial content, and ribosomal gene signals. We then filter low-quality cells and genes, detect potential doublets with

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A Coding Implementation on Document Parsing Benchmarking with LlamaIndex ParseBench Using Python, Hugging Face, and Evaluation Metrics

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

In this tutorial, we explore how to use the ParseBench dataset to evaluate document parsing systems in a structured, practical way. We begin by loading the dataset directly from Hugging Face, inspecting its multiple dimensions, such as text, tables, charts, and layout, and transforming it into a unified dataframe for deeper analysis. As we progress,

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How to Build Traceable and Evaluated LLM Workflows Using Promptflow, Prompty, and OpenAI

ai, AI (Artificial Intelligence), Applications, Artificial Intelligence, Editors Pick, Large Language Model, Staff, Technology, Tutorials

In this tutorial, we build a complete, production-style LLM workflow using Promptflow within a Colab environment. We begin by setting up a reliable keyring backend to avoid OS dependency issues and securely configure our OpenAI connection. From there, we establish a clean workspace and define a structured Prompty file that acts as the core LLM

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