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33 articles
Learn to implement a Recurrent Looped Transformer (RLT) architecture that maintains decoder state across tokens, enabling unbounded temporal depth for long sequence modeling.
Learn how advanced AI systems can now generate custom synthetic voices from simple text prompts, revolutionizing voice agent development and personalization.
This article explains the NeoMME architecture, a new family of multimodal encoders from H Company that processes text and images in a single Transformer without a vision tower or causal decoder.
This explainer explores Google's WeatherNext 3, a revolutionary weather forecasting system that uses deep learning to interpret satellite data directly, bypassing traditional physics-based simulations.
This explainer article explores the LTX-2.5 world model, a breakthrough in AI video generation that enables high-quality video production on local NVIDIA hardware using open weights and advanced transformer architectures.
Explore the technical innovations behind ByteDance's SeedRealtime, a multimodal AI model that processes audio, video, and text in real time for more natural human-AI interaction.
Learn to accelerate transformer training using NVIDIA's Transformer Engine with fused kernels, FP8, and BF16 optimizations in PyTorch.
This article explains the advanced AI technologies behind Siri's improved performance in iOS 27, including transformer architectures, edge computing optimization, and automotive integration techniques.
Learn how Baidu's Unlimited OCR achieves efficient processing of dozens of document pages in a single pass by mimicking human memory and forgetting mechanisms.
Learn to implement key Transformer architecture components including attention mechanisms and multi-head attention using PyTorch, replicating the technology behind OpenAI's successful AI systems.
Learn how to work with transformer architectures using Python and Hugging Face's transformers library. This tutorial teaches you to load models, process text, and understand the core components of modern AI systems like Google's Gemini.
Learn to implement a simplified version of MiniMax's Sparse Attention mechanism that reduces computational complexity in attention operations while maintaining performance.
