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How to Design a Production-Ready AI Agent That Automates Google Colab Workflows Using Colab-MCP, MCP Tools, FastMCP, and Kernel Execution

agentic ai, ai, AI (Artificial Intelligence), AI Agents, Artificial Intelligence, Editors Pick, Model Context Protocol (MCP), Tutorials

In this tutorial, we build an advanced, hands-on tutorial around Google’s newly released colab-mcp, an open-source MCP (Model Context Protocol) server that lets any AI agent programmatically control Google Colab notebooks and runtimes. Across five self-contained snippets, we go from first principles to production-ready patterns. We start by constructing a minimal MCP tool registry from […]

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How BM25 and RAG Retrieve Information Differently?

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

When you type a query into a search engine, something has to decide which documents are actually relevant — and how to rank them. BM25 (Best Matching 25), the algorithm powering search engines like Elasticsearch and Lucene, has been the dominant answer to that question for decades.  It scores documents by looking at three things:

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Implementing Deep Q-Learning (DQN) from Scratch Using RLax JAX Haiku and Optax to Train a CartPole Reinforcement Learning Agent

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

In this tutorial, we implement a reinforcement learning agent using RLax, a research-oriented library developed by Google DeepMind for building reinforcement learning algorithms with JAX. We combine RLax with JAX, Haiku, and Optax to construct a Deep Q-Learning (DQN) agent that learns to solve the CartPole environment. Instead of using a fully packaged RL framework,

Implementing Deep Q-Learning (DQN) from Scratch Using RLax JAX Haiku and Optax to Train a CartPole Reinforcement Learning Agent Read More »

A Coding Implementation for Building and Analyzing Crystal Structures Using Pymatgen for Symmetry Analysis, Phase Diagrams, Surface Generation, and Materials Project Integration

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

In this tutorial, we explore the capabilities of the pymatgen library for computational materials science using Python. We begin by constructing crystal structures such as silicon, sodium chloride, and a LiFePO₄-like material, and then investigate their lattice properties, densities, and compositions. Also, we analyze symmetry using space-group detection, examine atomic coordination environments, and apply oxidation-state

A Coding Implementation for Building and Analyzing Crystal Structures Using Pymatgen for Symmetry Analysis, Phase Diagrams, Surface Generation, and Materials Project Integration Read More »

Safely Deploying ML Models to Production: Four Controlled Strategies (A/B, Canary, Interleaved, Shadow Testing)

ai, AI (Artificial Intelligence), AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine Learning, Staff, Tech News, Technology, Tutorials

Deploying a new machine learning model to production is one of the most critical stages of the ML lifecycle. Even if a model performs well on validation and test datasets, directly replacing the existing production model can be risky. Offline evaluation rarely captures the full complexity of real-world environments—data distributions may shift, user behavior can

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A Coding Implementation to Build an Uncertainty-Aware LLM System with Confidence Estimation, Self-Evaluation, and Automatic Web Research

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

In this tutorial, we build an uncertainty-aware large language model system that not only generates answers but also estimates the confidence in those answers. We implement a three-stage reasoning pipeline in which the model first produces an answer along with a self-reported confidence score and a justification. We then introduce a self-evaluation step that allows

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A Coding Implementation Showcasing ClawTeam’s Multi-Agent Swarm Orchestration with OpenAI Function Calling

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

In this comprehensive tutorial, we present the core architecture of ClawTeam, an open-source Agent Swarm Intelligence framework developed by HKUDS. We implement the fundamental concepts that make ClawTeam powerful: a leader agent that decomposes complex goals into sub-tasks, specialized worker agents that execute those tasks autonomously, a shared task board with automatic dependency resolution, and

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A Coding Guide to Implement Advanced Differential Equation Solvers, Stochastic Simulations, and Neural Ordinary Differential Equations Using Diffrax and JAX

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

In this tutorial, we explore how to solve differential equations and build neural differential equation models using the Diffrax library. We begin by setting up a clean computational environment and installing the required scientific computing libraries such as JAX, Diffrax, Equinox, and Optax. We then demonstrate how to solve ordinary differential equations using adaptive solvers

A Coding Guide to Implement Advanced Differential Equation Solvers, Stochastic Simulations, and Neural Ordinary Differential Equations Using Diffrax and JAX Read More »

How to Build High-Performance GPU-Accelerated Simulations and Differentiable Physics Workflows Using NVIDIA Warp Kernels

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

In this tutorial, we explore how to use NVIDIA Warp to build high-performance GPU and CPU simulations directly from Python. We begin by setting up a Colab-compatible environment and initializing Warp so that our kernels can run on either CUDA GPUs or CPUs, depending on availability. We then implement several custom Warp kernels that demonstrate

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A Coding Implementation to Design an Enterprise AI Governance System Using OpenClaw Gateway Policy Engines, Approval Workflows and Auditable Agent Execution

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

In this tutorial, we build an enterprise-grade AI governance system using OpenClaw and Python. We start by setting up the OpenClaw runtime and launching the OpenClaw Gateway so that our Python environment can interact with a real agent through the OpenClaw API. We then design a governance layer that classifies requests based on risk, enforces

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