Latest Posts

By Wei Ming T. on Apr 17, 2025

Understand the Mixture of Experts (MoE) architecture powering advanced AI models. Learn how MoE works, its components like gating networks and experts, training techniques including load balancing, and its benefits for building large-scale, efficient models.

By Stéphane A. on Apr 17, 2025

Understand the core differences between LIME and SHAP, two leading model explainability techniques. Learn how each method works, their respective strengths and weaknesses, and practical guidance on when to choose one over the other for interpreting your machine learning models.

By Sam G. on Apr 15, 2025

Transformer models can overfit quickly if not properly regularized. This post breaks down practical and effective regularization strategies used in modern transformer architectures, based on research and experience building large-scale models.

By George M. on Apr 15, 2025

Learn the most effective prompt engineering techniques recommended by Google. Includes actionable examples and clear dos and don’ts to improve your prompts.

By Wei Ming T. on Apr 9, 2025

Learn common causes and practical methods to debug and fix frustrating shape mismatch errors in PyTorch matrix multiplication and linear layers. Includes code examples and debugging tips.

By Jacob M. on Apr 6, 2025

Llama 4 introduces smart architectural changes that make it more efficient and scalable than its predecessors. Here are five engineering improvements in Llama 4 that directly impact model performance and VRAM usage.

By Ryan A. on Apr 6, 2025

Llama 4 introduces major improvements in model architecture, context length, and multimodal capabilities. This post covers the estimated system requirements for inference and training of Llama 4 Scout, Maverick, and the anticipated Behemoth model.

By Ryan A. on Mar 25, 2025

DeepSeek V3-0324 is an updated checkpoint with better coding performance and the same setup as previous versions. Here’s how to run it with the latest weights.

By Wei Ming T. on Mar 24, 2025

Performance comparison of TensorFlow, PyTorch, and JAX using a CNN model and synthetic dataset. Benchmarked on NVIDIA L4 GPU with consistent data and architecture to evaluate training time, memory usage, and model compilation behavior.

By Ryan A. on Mar 13, 2025

Learn the recommended GPU specifications for running Google DeepMind's latest Gemma 3 models efficiently, including VRAM requirements for text and image-to-text tasks.