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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

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

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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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Building a Multimodal RAG Pipeline with NVIDIA NeMo Retriever, Hosted NIMs, LanceDB, Reranking, and Grounded Generation

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

In this tutorial, we build an advanced multimodal retrieval-augmented generation pipeline with NVIDIA NeMo Retriever. We begin by configuring a Python 3.12 environment, installing the required packages, and performing offline PDF text extraction without relying on a GPU or external API key. We then extend the workflow with hosted NVIDIA NIM endpoints to detect page

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Adaptive Experimentation with Meta’s Ax: A Practical Coding Guide

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

In this tutorial, we explore adaptive experimentation using Meta’s Ax with the modern Client API. We work through a complete workflow where we tune a RandomForest model on a synthetic classification dataset while balancing predictive accuracy against model footprint. We begin by defining a mixed search space with integer, float, log-scaled, and categorical parameters, then

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End-to-End Bayesian Marketing Mix Modeling with Google Meridian: Media Measurement, ROI Analysis, and Budget Optimization

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

In this tutorial, we build a complete Bayesian marketing mix modeling workflow using Google Meridian. We begin by installing the required libraries, verifying GPU availability, and exploring a geo-level marketing dataset that includes media impressions, spend, controls, promotions, conversions, population, and revenue. We then map the raw columns to Meridian’s data schema, define interpretable ROI-based

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Pixel-Native RAG: A Practical Guide to Visual Document Indexing

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

In this tutorial, we build a complete pixel-native retrieval-augmented generation pipeline from scratch and examine how document retrieval works without relying on conventional HTML parsing, text extraction, or fixed chunking strategies. We render web pages and PDF documents as images, divide them into overlapping tiles, generate multimodal embeddings with SigLIP, CLIP, or an optional Qwen3-VL

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Building an Advanced AI Skill Security Auditing Pipeline with NVIDIA SkillSpector, LangGraph, YARA Rules, SARIF, and CI Policy Gates

agentic ai, ai, AI (Artificial Intelligence), AI Agents, Applications, Artificial Intelligence, Editors Pick, Language Model, New Releases, Software engineering, Staff, Technology, Tutorials

In this tutorial, we build a workflow for evaluating the security posture of AI skills with NVIDIA SkillSpector. We create a synthetic skill marketplace containing clean, risky, malicious, and MCP-based examples, then scan each skill through SkillSpector’s LangGraph inspection pipeline. We examine risk scores, categorized findings, confidence levels, analyzer completeness, and executable-script indicators before organizing

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