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Salesforce CodeGen Tutorial: Generate, Validate, and Rerank Python Functions With Unit Tests and Safety Checks

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

In this tutorial, we implement an end-to-end workflow for Salesforce CodeGen. We load a CodeGen model from Hugging Face, prepare it for code generation, and use it to generate Python functions from natural-language prompts. We then move beyond basic inference by adding function extraction, syntax checking, static safety checks, unit-test-based validation, best-of-N candidate reranking, multi-step […]

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NVIDIA SkillSpector Guide: Scanning AI Skills for Security Risks with Static Analysis and SARIF Reports

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

In this tutorial, we explore how NVIDIA SkillSpector helps us evaluate AI skills for security risks before they are used in real-world workflows. We build a controlled corpus containing both benign and deliberately vulnerable skills, scan them through SkillSpector’s programmatic LangGraph workflow, and organize the resulting risk scores and findings with pandas. We then visualize

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How to Build Memory-Efficient Transformers with xFormers Using Packed Sequences, GQA, ALiBi, SwiGLU, and Causal Attention

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

In this tutorial, we implement xFormers: a practical toolkit for building fast, memory-efficient Transformer models on GPUs. We begin by validating memory-efficient attention against a standard attention implementation, then compare their speed and memory consumption across different sequence lengths. We then examine causal masking, packed variable-length sequences, grouped-query attention, and custom ALiBi positional biases. Finally,

How to Build Memory-Efficient Transformers with xFormers Using Packed Sequences, GQA, ALiBi, SwiGLU, and Causal Attention Read More »

How to Build a Parsing Pipeline with Docling Parse for Layout-Aware Document Intelligence

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

In this tutorial, we build a workflow for using Docling Parse to analyze PDF documents at a detailed structural level. We start by preparing a stable Python environment, handling common Colab dependency issues, and generating a custom multi-page PDF with text, columns, table-like content, vector shapes, and an embedded image. We then use Docling Parse

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A Coding Hands-On on FineWeb for Streaming, Filtering, Deduplication, Tokenization, and Large-Scale Web Corpus Analytics

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

In this tutorial, we explore the FineWeb dataset through an advanced hands-on workflow. We stream a manageable sample of the dataset without downloading the full multi-terabyte corpus, inspect its schema and metadata, and analyze key fields such as URL, language, language score, and token count. We also reproduce simplified versions of FineWeb’s quality-filtering pipeline, apply

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How to Build a QwenPaw Agent Workspace with Custom Skills, Model Providers, Console Access, and Streaming API Testing

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

In this tutorial, we implement a QwenPaw workflow that provides a practical environment for building and testing an agent-powered assistant. We install and initialize QwenPaw, configure its working directory, set up authentication, connect optional model providers via Colab secrets, and create a structured workspace with custom skills and local knowledge files. We also launch the

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A Coding Implementation on Spatial Graph Neural Networks for Urban Function Inference Using city2graph, OSMnx, and PyTorch Geometric

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

In this tutorial, we build an end-to-end spatial graph learning pipeline using city2graph. We start by collecting real urban POI data and street network information from OpenStreetMap, with a synthetic fallback to ensure the workflow remains reliable. We then engineer spatial features, construct multiple proximity graph families, and compare how different graph-building strategies represent the

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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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