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In this tutorial, we build and operate a fully local, schema-valid OpenClaw runtime. We configure the OpenClaw gateway with strict loopback binding, set up authenticated model access through environment variables, and define a secure execution environment using the built-in exec tool. We then create a structured custom skill that the OpenClaw agent can discover and […]
Complex prediction problems often lead to ensembles because combining multiple models improves accuracy by reducing variance and capturing diverse patterns. However, these ensembles are impractical in production due to latency constraints and operational complexity. Instead of discarding them, Knowledge Distillation offers a smarter approach: keep the ensemble as a teacher and train a smaller student
In this tutorial, we build and run a complete Pose2Sim pipeline on Colab to understand how markerless 3D kinematics works in practice. We begin with environment setup, configure the project for Colab’s headless runtime, and then walk through calibration, 2D pose estimation, synchronization, person association, triangulation, filtering, marker augmentation, and OpenSim-based kinematics. As we progress,
A Coding Guide to Markerless 3D Human Kinematics with Pose2Sim, RTMPose, and OpenSim Read More »
In this tutorial, we take a detailed, practical approach to exploring NVIDIA’s KVPress and understanding how it can make long-context language model inference more efficient. We begin by setting up the full environment, installing the required libraries, loading a compact Instruct model, and preparing a simple workflow that runs in Colab while still demonstrating the
A deep neural network can be understood as a geometric system, where each layer reshapes the input space to form increasingly complex decision boundaries. For this to work effectively, layers must preserve meaningful spatial information — particularly how far a data point lies from these boundaries — since this distance enables deeper layers to build
Sigmoid vs ReLU Activation Functions: The Inference Cost of Losing Geometric Context Read More »
In this tutorial, we explore how to use Google’s LangExtract library to transform unstructured text into structured, machine-readable information. We begin by installing the required dependencies and securely configuring our OpenAI API key to leverage powerful language models for extraction tasks. Also, we will build a reusable extraction pipeline that enables us to process a
In this tutorial, we explore ModelScope through a practical, end-to-end workflow that runs smoothly on Colab. We begin by setting up the environment, verifying dependencies, and confirming GPU availability so we can work with the framework reliably from the start. From there, we interact with the ModelScope Hub to search for models, download snapshots, load
In this tutorial, we explore the latest Gemini API tooling updates Google announced in March 2026, specifically the ability to combine built-in tools like Google Search and Google Maps with custom function calls in a single API request. We walk through five hands-on demos that progressively build on each other, starting with the core tool
In this tutorial, we build a complete Open WebUI setup in Colab, in a practical, hands-on way, using Python. We begin by installing the required dependencies, then securely provide our OpenAI API key through terminal-based secret input so that sensitive credentials are not exposed directly in the notebook. From there, we configure the environment variables
In this tutorial, we implement an advanced, practical implementation of the NVIDIA Transformer Engine in Python, focusing on how mixed-precision acceleration can be explored in a realistic deep learning workflow. We set up the environment, verify GPU and CUDA readiness, attempt to install the required Transformer Engine components, and handle compatibility issues gracefully so that
