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How to Build a Single-Cell RNA-seq Analysis Pipeline with Scanpy for PBMC Clustering, Annotation, and Trajectory Discovery

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

In this tutorial, we perform an advanced single-cell RNA-seq analysis workflow using Scanpy on the PBMC-3k benchmark dataset. We start by loading the dataset, inspecting its structure, and applying quality control checks to evaluate gene counts, total counts, mitochondrial content, and ribosomal gene signals. We then filter low-quality cells and genes, detect potential doublets with […]

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A Coding Implementation on Document Parsing Benchmarking with LlamaIndex ParseBench Using Python, Hugging Face, and Evaluation Metrics

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

In this tutorial, we explore how to use the ParseBench dataset to evaluate document parsing systems in a structured, practical way. We begin by loading the dataset directly from Hugging Face, inspecting its multiple dimensions, such as text, tables, charts, and layout, and transforming it into a unified dataframe for deeper analysis. As we progress,

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How to Build Traceable and Evaluated LLM Workflows Using Promptflow, Prompty, and OpenAI

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

In this tutorial, we build a complete, production-style LLM workflow using Promptflow within a Colab environment. We begin by setting up a reliable keyring backend to avoid OS dependency issues and securely configure our OpenAI connection. From there, we establish a clean workspace and define a structured Prompty file that acts as the core LLM

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How to Build a Lightweight Vision-Language-Action-Inspired Embodied Agent with Latent World Modeling and Model Predictive Control

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

In this tutorial, we build an embodied simulation vision agent that learns to perceive, plan, predict, and replan directly from pixel observations. We create a fully NumPy-rendered grid world in which the agent observes RGB frames rather than symbolic state variables, enabling us to simulate a simplified Vision-Language-Action-style pipeline. We train a lightweight world model

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Build a Reinforcement Learning Powered Agent that Learns to Retrieve Relevant Long-Term Memories for Accurate LLM Question Answering

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

In this tutorial, we build a Reinforcement Learning–driven agent that learns how to retrieve relevant memories from a long-term memory bank. We start by constructing a synthetic memory dataset and generating queries that require the agent to recall specific information. Using OpenAI embeddings, we convert both memories and queries into vector representations, enabling similarity signals

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How to Build a Fully Searchable AI Knowledge Base with OpenKB, OpenRouter, and Llama

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

In this tutorial, we explore how to build and query a local knowledge base with OpenKB using a free, open model via OpenRouter. We securely retrieve the API key with getpass, set up the environment without hardcoding secrets, and initialize a structured, wiki-style knowledge base from scratch. As we move through the workflow, we add

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How to Build Smarter Multilingual Text Wrapping with BudouX Through Parsing, HTML Rendering, Model Introspection, and Toy Training

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

In this tutorial, we explore how we use BudouX to bring intelligent, phrase-aware line breaking to languages where whitespace is not naturally present, such as Japanese, Chinese, and Thai. We begin by setting up the library and working with its default parsers to understand how raw text is segmented into meaningful chunks. We then move

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RAG Without Vectors: How PageIndex Retrieves by Reasoning

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

Retrieval is where most RAG systems quietly break. Traditional pipelines rely on vector similarity—embedding queries and document chunks into the same space and fetching the “closest” matches. But similarity is a weak proxy for what we actually need: relevance grounded in reasoning. In long, professional documents—like financial reports, research papers, or legal texts—the right answer

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A Coding Tutorial on Datashader on Rendering Massive Datasets with High-Performance Python Visual Analytics

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

In this tutorial, we explore Datashader, a powerful, high-performance visualization library for rendering massive datasets that quickly overwhelm traditional plotting tools. We work through its full rendering pipeline in Google Colab, starting from dense point clouds and reduction-based aggregations to categorical rendering, line visualizations, raster data, quadmesh grids, compositing, and dashboard-style analytical views. As we

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A Coding Implementation on kvcached for Elastic KV Cache Memory, Bursty LLM Serving, and Multi-Model GPU Sharing

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

In this tutorial, we explore kvcached, a dynamic KV-cache implementation on top of vLLM, to understand how dynamic KV-cache allocation transforms GPU memory usage for large language models. We begin by setting up the environment and deploying lightweight Qwen2.5 models through an OpenAI-compatible API, ensuring a realistic inference workflow. We then design controlled experiments where

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