Archive: 2026/03

Masked modeling, next-token prediction, and denoising are the three core pretraining methods behind today's generative AI. Each powers different applications-from chatbots to image generators-and understanding their strengths helps you choose the right model for your needs.

Generative AI must comply with WCAG accessibility standards just like human-created content. Learn how to apply assistive technology requirements, avoid legal risks, and build truly inclusive AI systems.

Tensor parallelism lets you run massive LLMs across multiple GPUs by splitting model layers. Learn how it works, why NVLink matters, which frameworks support it, and how to avoid common pitfalls in deployment.

Combining pruning and quantization cuts LLM inference time by up to 6x while preserving accuracy. Learn how HWPQ's unified approach with FP8 and 2:4 sparsity delivers real-world speedups without hardware changes.

Sandboxing external actions in LLM agents prevents dangerous tool access by isolating processes. Firecracker, gVisor, and Nix offer different trade-offs between security and performance. Learn which method fits your use case.