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Evaluating Multimodal Vision Models with Moonshot PerceptionBench Using Robust Data Loading and Automated Judging

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

In this tutorial, we design an end-to-end evaluation workflow for PerceptionBench. This multimodal benchmark measures fine-grained visual perception capabilities across tasks such as OCR, counting, localization, contextual reasoning, comparison, depth understanding, and hallucination detection. We begin by configuring a Colab-compatible environment, installing the required libraries, and loading a balanced subset of the dataset through a […]

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A Tutorial on GeoAI: Designing Footprint Extraction from NAIP Imagery Using U-Net, Grounding DINO, SAM, and Mask R-CNN

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

In this tutorial, we design a complete GeoAI workflow for extracting building footprints from high-resolution NAIP aerial imagery. We begin by configuring the geospatial deep learning environment, downloading raster imagery and vector labels, and inspecting their spatial properties before generating georeferenced image chips and segmentation masks. We then train a U-Net model with a ResNet-34

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End-to-End Forecasting with TimesFM 2.5: Backtesting, Covariates, Anomaly Detection, and Scalable Colab Deployment

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

In this tutorial, we build an advanced end-to-end time-series forecasting workflow with TimesFM 2.5. We begin by configuring the runtime, installing the required dependencies, detecting available hardware, and generating a realistic multi-store retail dataset with trend, seasonality, pricing, promotions, holidays, temperature effects, and random variation. We then load and compile the TimesFM 2.5 model, examine

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Accelerating Transformer Training with NVIDIA Transformer Engine, Fused Kernels, BF16, FP8, and GPU Benchmarking

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

In this tutorial, we explore how NVIDIA Transformer Engine accelerates transformer workloads by combining fused GPU kernels, BF16 computation, and hardware-aware FP8 execution. We begin by installing Transformer Engine and detecting the active GPU architecture so that we can determine whether the runtime supports TE kernels, FP8 tensor cores, or only the pure-PyTorch fallback path.

Accelerating Transformer Training with NVIDIA Transformer Engine, Fused Kernels, BF16, FP8, and GPU Benchmarking Read More »

LingBot-Map Tutorial: GPU-Aware Inference and Point Cloud Export

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

In this tutorial, we implement an end-to-end streaming 3D reconstruction pipeline with LingBot-Map. We begin by configuring the input source, reconstruction settings, checkpoint selection, and output controls, then probe the available GPU and automatically tune frame limits, camera iterations, scale frames, and KV-cache parameters according to the detected VRAM. We install the repository and its

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Building a Policy-Governed Multi-Agent Financial Research Workflow with Omnigent

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

In this tutorial, we build and execute a multi-agent workflow with Omnigent using a reliable, isolated Python environment created with uv. We configure a financial research lead agent that retrieves a live USD-to-EUR exchange rate from an external API, prepares a concise client-ready summary, and delegates its draft to a dedicated text-auditing sub-agent for clarity

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Building Non-Interactive Agentic Coding Workflows with Moonshot AI’s Kimi CLI, JSONL Streaming, Testing, and Session Memory

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

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 isolated Python 3.13 environment, configure Moonshot API authentication through a TOML-based provider and model definition, and build a reusable Python wrapper for executing non-interactive CLI commands. We then apply Kimi to

Building Non-Interactive Agentic Coding Workflows with Moonshot AI’s Kimi CLI, JSONL Streaming, Testing, and Session Memory Read More »

Deploying a 1-Bit Bonsai-27B Model with PrismML llama.cpp and OpenAI-Compatible Local Inference Workflows

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

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. We begin by validating the GPU runtime, installing the required Python dependencies, compiling the CUDA-enabled inference binaries, and downloading the compressed model weights from

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Designing Skill-Driven Financial Analysis Agents with Claude, Python, MCP Connectors, and Automated Deliverables

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

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 the required libraries, cloning the repository, and programmatically mapping its agents, vertical plugins, partner integrations, managed-agent cookbooks, and financial analysis skills. We then parse the repository’s SKILL.md files into a searchable

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FAIRChem v2 UMA for Multidomain Atomistic Simulation across Molecules, Catalysts, Materials, Vibrations, and Molecular Dynamics

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

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, catalysis, and inorganic materials. We configure an environment, authenticate with Hugging Face to access the gated UMA model weights, and initialize task-specific calculators for the omol, oc20, and omat domains. We

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