Tutorials

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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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A Coding Implementation on Microsoft’s OpenMementos with Trace Structure Analysis, Context Compression, and Fine-Tuning Data Preparation

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

In this tutorial, we work with Microsoft’s OpenMementos dataset and explore how reasoning traces are structured through blocks and mementos in a practical, Colab-ready workflow. We stream the dataset efficiently, parse its special-token format, inspect how reasoning and summaries are organized, and measure the compression provided by the memento representation across different domains. As we

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A Coding Tutorial on OpenMythos on Recurrent-Depth Transformers with Depth Extrapolation, Adaptive Computation, and Mixture-of-Experts Routing

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

In this tutorial, we explore the implementation of OpenMythos, a theoretical reconstruction of the Claude Mythos architecture that enables deeper reasoning through iterative computation rather than increased parameter size. We build and analyze models using both GQA and MLA attention mechanisms, examine memory efficiency through KV-cache comparisons, and validate stability via the spectral properties of

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How to Design a Production-Grade CAMEL Multi-Agent System with Planning, Tool Use, Self-Consistency, and Critique-Driven Refinement

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

In this tutorial, we implement an advanced agentic AI system using the CAMEL framework, orchestrating multiple specialized agents to collaboratively solve a complex task. We design a structured multi-agent pipeline consisting of a planner, researcher, writer, critic, and rewriter, each with clearly defined responsibilities and schema-constrained outputs. We integrate tool usage, self-consistency sampling, structured validation

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