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Stochastic Gradient Descent (SGD’s) Frequency Bias and How Adam Fixes It

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

Modern language models are trained on data with extremely uneven token distributions. A small number of words appear in almost every sentence, while many rare but meaningful tokens occur only occasionally. This creates a hidden optimization challenge: parameters associated with common tokens receive constant gradient updates, while parameters tied to rare tokens may go hundreds […]

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A Coding Implementation to Compress and Benchmark Instruction-Tuned LLMs with FP8, GPTQ, and SmoothQuant Quantization using llmcompressor

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

In this tutorial, we explore how to apply post-training quantization to an instruction-tuned language model using llmcompressor. We start with an FP16 baseline and then compare multiple compression strategies, including FP8 dynamic quantization, GPTQ W4A16, and SmoothQuant with GPTQ W8A8. Along the way, we benchmark each model variant for disk size, generation latency, throughput, perplexity,

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A Coding Guide Implementing SHAP Explainability Workflows with Explainer Comparisons, Maskers, Interactions, Drift, and Black-Box Models

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

In this tutorial, we implement SHAP workflows as a practical framework for interpreting machine learning models beyond basic feature-importance plots. We start by training tree-based models and then compare different SHAP explainers, including Tree, Exact, Permutation, and Kernel methods, to understand how accuracy and runtime change across model-aware and model-agnostic approaches. We also examine how

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How to Build Repository-Level Code Intelligence with Repowise Using Graph Analysis, Dead-Code Detection, Decisions, and AI Context

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

In this tutorial, we explore how to use Repowise to build repository-level intelligence for the itsdangerous Python project in a practical and reproducible way. We start with an already cloned repository, configure Repowise using the available LLM credentials, and initialize its indexing pipeline. We then inspect the generated .repowise artifacts, analyze the repository graph with

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How to Build an MCP Style Routed AI Agent System with Dynamic Tool Exposure Planning, Execution, and Context Injection

agentic ai, ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Model Context Protocol (MCP), Staff, Tutorials

In this tutorial, we build a fully functional MCP-style routed agent system from scratch, combining tool discovery, intelligent routing, structured planning, and execution into a single cohesive workflow. We start by setting up a modular tool server that exposes capabilities such as web search, local retrieval, dataset loading, and Python execution, all defined through structured

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How to Build a Django-Unfold Admin Dashboard with Custom Models, Filters, Actions, and KPIs

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

In this tutorial, we build an advanced Django-Unfold admin dashboard. We start by installing Django, Django-Unfold, and the required dependencies, then we create a fresh Django project with a shop application. We configure Unfold with a modern admin theme, custom sidebar navigation, dashboard callbacks, product badges, tabs, filters, actions, and a custom admin homepage. We

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A Coding Implementation to Master GPU Computing with CuPy, Custom CUDA Kernels, Streams, Sparse Matrices, and Profiling

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

In this tutorial, we delve into CuPy as a powerful GPU-accelerated alternative to NumPy for high-performance numerical computing in Python. We start by inspecting the available CUDA device, checking the CuPy version, runtime details, GPU memory, and compute capability so that we understand the hardware environment before running heavy computations. Then, we compare NumPy and

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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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