In this tutorial, we demonstrate how to build and execute a multi-agent workflow with Omnigent in a secure, isolated Python environment. Learn to integrate live exchange-rate data, implement hierarchical agent delegation for financial text auditing, and apply hard governance policies—such as cost budgets and tool call limits—to your research pipeline directly from Google Colab.

In this tutorial, we configure and operate Kimi CLI as a fully non-interactive AI coding agent. We install the CLI through uv with an...

In this tutorial, we deploy the 1-bit Bonsai-27B language model using the PrismML fork of llama.cpp, which provides the specialized CUDA kernels required to decode the model’s Q1_0_g128 GGUF quantization format

In this tutorial, we build an advanced workflow around Anthropic’s financial-services repository and reproduce its skill-driven architecture in pure Python. We begin by installing...

In this tutorial, we explore FAIRChem v2 and the UMA universal machine-learning interatomic potential as a unified framework for atomistic simulation across molecular chemistry,...

Explore TileLang, a high-level Python domain-specific language that simplifies the design of high-performance GPU kernels. This tutorial provides a step-by-step approach to implementing complex workloads—including tiled tensor-core GEMM, fused softmax, and FlashAttention—while letting the compiler handle intricate thread mapping, memory layouts, and low-level CUDA instruction generation.

Discover how to create self-evolving AI agents using the OpenSpace framework. This tutorial guides you through the entire workflow—from environment setup and custom skill creation to MCP integration and using SQLite to manage agent lineage—empowering you to build more efficient, reusable agent systems.

In this tutorial, we build a complete workflow for running Baidu’s Unlimited-OCR model on document images and multi-page PDFs. From configuring the GPU environment to comparing high-detail tiled Gundam inference and faster Base modes, you'll learn how to process dense layouts, tables, and cross-page content in a reproducible, end-to-end pipeline.

In this tutorial, we explore EdgeBench as a practical benchmark for evaluating advanced AI agents across diverse task categories, runtime environments, and interaction-time budgets....

In this tutorial, we explore NVIDIA’s srt-slurm framework and learn how we use srtctl to convert declarative YAML configurations into reproducible SLURM benchmark workflows...

We build an end-to-end NVIDIA NeMo AutoModel workflow in Google Colab using a single GPU. We verify CUDA hardware and precision support, install NeMo AutoModel from source, and load an official Qwen3-0.6B LoRA recipe. We then adapt its precision, batch size, checkpointing, and scheduler settings for a constrained runtime. We launch fine-tuning through the automodel CLI, reload the LoRA checkpoint, and compare base versus fine-tuned outputs. We finish with the NeMoAutoModelForCausalLM Python API.

In this tutorial, we build a Google Colab-native plasmid workbench that recreates the core ideas of SpliceCraft inside an interactive notebook environment. Instead of...