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The Developer’s Guide to NeMo Guardrails for Enterprise AI Safety

agentic ai, ai, AI (Artificial Intelligence), AI Governance, AI Infrastructure, Applications, Artificial Intelligence, Editors Pick, For Devs, Security, Software engineering, Staff, Technology, Tutorials

In this tutorial, we build an in-depth NeMo Guardrails pipeline that demonstrates how layered guardrails can control an LLM-based financial assistant across the full request lifecycle. We combine deterministic PII detection and redaction, LLM-based input and output self-checks, retrieval filtering, account-number masking, topical restrictions, and policy-based tool gating. We also implement stateful multi-turn interactions, detailed […]

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Building Agentic Document Intelligence Pipelines: Creating Scientific Figures with AutoFigure

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

In this tutorial, we explore AutoFigure as a practical toolkit for generating scientific figures directly from text descriptions, paper-like content, and structured methodological explanations. In this tutorial, we set up the complete AutoFigure environment, fix dependency issues such as Pillow compatibility, and prepare the required rendering tools for SVG and PNG outputs. We then build

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Auditing Preference Biases and Fine-Tuning Language Models with Direct Preference Optimization on Anthropic HH-RLHF Using TRL and LoRA

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

In this tutorial, we design an end-to-end preference-learning workflow using the Anthropic HH-RLHF dataset and Direct Preference Optimization (DPO). We begin by preparing a robust Colab environment, loading and parsing chosen–rejected response pairs, and auditing the dataset for structural and length-based preference biases. We then run lexical shortcut diagnostics to determine whether surface-level linguistic patterns

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Developing an End-to-End Document Intelligence Pipeline with docTR for OCR, Layout Analysis, KIE, Benchmarking, and Searchable PDFs

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

In this tutorial, we develop an end-to-end OCR workflow with docTR and explore how modern document understanding pipelines combine text detection, recognition, geometry, layout analysis, structured extraction, and export. We generate realistic synthetic invoice documents, load images and PDFs through DocumentFile, construct GPU-aware OCR predictors, and benchmark different detection–recognition architecture combinations for speed and accuracy.

Developing an End-to-End Document Intelligence Pipeline with docTR for OCR, Layout Analysis, KIE, Benchmarking, and Searchable PDFs Read More »

Fine-Tuning Tool-Calling LLMs: A Complete Guide Using XYZ-Aquila-SFT and Qwen3

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

In this tutorial, we implement an end-to-end supervised fine-tuning pipeline for the XYZ-Aquila-SFT dataset, Hugging Face Transformers, PyTorch, and PEFT. We stream and inspect the dataset, parse multi-turn tool-use trajectories, extract structured tool calls, analyze corpus characteristics, and preserve embedded reasoning and observation patterns. We then convert tool schemas between message-embedded and structured formats, render

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Create a Reasoning-Focused LLM: A Practical Guide to Streaming, Curating, and Fine-Tuning the SupraLabs Reasoning Corpus

ai, AI (Artificial Intelligence), Applications, Artificial Intelligence, Editors Pick, Language Model, Small Language Model, Technology, Tutorials, Uncategorized, Vision Language Model

In this tutorial, we build an end-to-end workflow for working with the SupraLabs reasoning corpus. We stream a representative subset directly from the Hugging Face Hub, inspect its source distribution, token-length patterns, task composition, and reasoning-to-answer ratios, and then apply a series of quality filters to remove unsuitable training examples. We transform the retained samples

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Building and Validating a Quantitative Trading Strategy with OctoBot, Walk-Forward Backtesting, Parameter Optimization, and Interactive Analysis

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

In this tutorial, we build a complete quantitative backtesting workflow with OctoBot and OctoBot-Script while keeping the environment isolated from Colab’s preinstalled dependencies. We configure a rule-based trading strategy that combines RSI-based oversold signals, EMA trend confirmation, and ATR-driven adaptive stop-loss and take-profit levels, and we execute it through OctoBot’s native market-order and backtesting APIs.

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Implementing a MiniMax-H3 Multimodal Video and Audio Generation Pipeline with ComfyUI APIs

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

In this tutorial, we implement an end-to-end MiniMax-H3 video generation workflow using ComfyUI as a headless inference backend. We configure the environment around GPU memory, disk capacity, model precision, resolution, duration, sampling strategy, and multiple generation modes, while dynamically selecting an appropriate weight profile based on the available hardware. We install and launch ComfyUI programmatically,

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IMDb Sentiment Analysis with DistilBERT LoRA, TF-IDF Baselines, Calibration, Interpretability, Robustness Testing, and Semi-Supervised Learning

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

In this tutorial, we develop an end-to-end sentiment analysis workflow using the Stanford NLP IMDb Large Movie Review Dataset and compare classical machine learning with parameter-efficient transformer fine-tuning. We begin by establishing a reproducible environment and auditing the dataset for class ordering, review-length skew, duplicate leakage, and preprocessing artifacts before training a strong TF-IDF and

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Designing Scalable Interactive Visualizations with Reflex XY: Composition, Million-Point Rendering, Streaming, Custom Marks, and Export

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

In this tutorial, we explore the advanced visualization capabilities of the XY Python library by building interactive, scalable, and extensible charts. We begin with XY’s composition model, where we combine multiple marks, dual axes, annotations, tooltips, legends, themes, and interactive controls within a single chart declaration. We then work with Pandas DataFrames, faceted layouts, linked

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