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A Coding Implementation on MONAI for End-to-End 3D Spleen Segmentation Using UNet on Medical CT Volumes

ai, AI (Artificial Intelligence), Applications, Artificial Intelligence, deep learning, Editors Pick, Staff, Technology, Tutorials

In this tutorial, we build an end-to-end 3D medical image segmentation pipeline using MONAI to segment the spleen on the Medical Segmentation Decathlon Task09 dataset. We work with volumetric CT scans, apply medical imaging transformations such as orientation alignment, voxel-spacing normalization, intensity windowing, foreground cropping, and patch-based sampling, and then train a 3D UNet model […]

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A Coding Implementation on Microsoft SkillOpt for Instrumented Prompt Optimization, Skill Evolution Analysis, and Baseline Comparison

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

In this tutorial, we implement an instrumented workflow for Microsoft SkillOpt. We set up the SkillOpt repository, connect it to OpenAI-compatible model access, configure the optimizer and target models, and run the SearchQA optimization pipeline with a controlled sample limit to keep costs manageable. We first evaluate the original seed skill as a baseline, then

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Building a Code Dataset Pipeline from NVIDIA Nemotron-Pretraining-Code-v3 Metadata with Streaming, Pandas, and tiktoken

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

In this tutorial, we work with NVIDIA’s Nemotron-Pretraining-Code-v3 dataset as a large-scale metadata index for code pretraining research. Instead of downloading the full multi-gigabyte dataset, we stream it, inspect its schema, and build a manageable sample for analysis. We then explore the dataset by studying languages, file extensions, repository frequency, and directory depth, which helps

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NVIDIA cuTile Python Tutorial: Building Tiled GPU Kernels for Vector Addition, Matrix Addition, and Matrix Multiplication in Colab

ai, AI (Artificial Intelligence), AI Infrastructure, Applications, Artificial Intelligence, Editors Pick, Staff, Technology, Tutorials

In this tutorial, we implement an advanced hands-on workflow for NVIDIA cuTile Python, a tile-based GPU programming interface for writing efficient CUDA-style kernels directly in Python. We start by preparing a Colab-friendly environment, checking the available GPU, driver, CUDA, and cuTile installations before running any kernel code. We then build tiled examples for vector addition,

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ClawHub Security Signals: A Coding Guide to End-to-End Security Signal Analysis and Verdict Classification on the AI Skills Dataset

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

In this tutorial, we use the ClawHub Security Signals dataset to examine how different security scanners assess AI skills and related files. We load the dataset directly from the Hugging Face Parquet conversion to avoid compatibility issues with newer dataset metadata, then inspect the main columns, verdict distribution, scanner outputs, and severity labels. After exploring

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Building Reflective Prompt Optimization with GEPA: Multi-Component Prompts, Structured Feedback, and Held-Out Validation

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

In this tutorial, we use GEPA as a reflective prompt-evolution framework to improve the way a language model solves arithmetic word problems. We begin with a weak seed prompt, create a small deterministic benchmark, define a structured evaluator, and pass actionable feedback to GEPA so it can understand why a candidate prompt fails. We also

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A Hands-On Coding Tutorial on Qualcomm AI Hub Models for Classification, Object Detection, and Hardware-Aware Deployment

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

In this tutorial, we work through an end-to-end workflow for Qualcomm AI Hub Models. We start by setting up the required package, discovering the available model collection, and loading MobileNet-V2 for local PyTorch inference. We also handle an important input-shape issue by converting NHWC image tensors into the NCHW format expected by the model. From

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Microsoft Fara Tutorial: Run a Browser-Use Agent in Google Colab with a Mock OpenAI-Compatible Endpoint

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

In this tutorial, we set up Microsoft Fara in Google Colab and run a browser-use workflow from start to finish. We begin by cloning the repository, installing the package, preparing Playwright, and verifying that the installed Fara files work even when the package layout changes. Instead of immediately relying on a heavy Fara-7B deployment, we

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Building a Semantic Search Engine and Open-Status Classifier over the ResearchMath-14k Dataset

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

In this tutorial, we work with the amphora/ResearchMath-14k dataset, a collection of research-level mathematics problems mined from arXiv. We load the dataset, inspect its structure, and explore how the problems are distributed across mathematical fields and open-status categories. We then move beyond basic analysis by extracting field-specific keywords, generating semantic embeddings, visualizing the problem landscape,

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How to Build a Document Intelligence Backend with iii Using Workers, Functions, and Cron Triggers

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

In this tutorial, we build a document-intelligence workflow with iii. We begin by installing the iii engine and Python SDK, then start the engine as a background process and connect a Python worker to it. After the setup, we register separate functions for text normalization, tokenization, sentiment analysis, keyword extraction, reporting, and heartbeat tracking. We

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