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12 articles
This explainer explores SpaceXAI's Grok 4.5, a Cursor-trained model optimized for coding, agentic tasks, and knowledge work, examining its advanced architecture, training methodologies, and implications for AI deployment.
Learn how to implement a simplified version of Deepseek's DSpark technique that boosts AI performance by using a small model to generate candidates and a larger model for validation.
Learn about the In the Weights score, a novel AI evaluation metric that analyzes neural network parameters to predict model performance and optimize training.
This article explains how AI startups scale their infrastructure on AWS, covering GPU computing, model optimization, and orchestration techniques.
Learn how to improve model performance on rare tasks by adjusting training data frequency, using practical Python examples.
Learn how to create a basic Self-Improving Agent (SIA) that can update both its problem-solving framework and model weights, inspired by Hexo Labs' open-source SIA system.
This article explains how Alibaba's Qwen3.6-27B model outperforms its much larger predecessor on coding benchmarks, highlighting advancements in parameter efficiency and model optimization techniques.
This article explains how Xiaomi's MiMo-V2.5 models achieve frontier-level AI performance with significantly lower token costs, focusing on agentic AI, token efficiency, and advanced optimization techniques.
Learn how advanced AI optimization techniques enable repurposing old tablets as smart home control panels through edge computing and model compression.
Learn to build a complete model optimization pipeline using NVIDIA Model Optimizer with FastNAS pruning and fine-tuning techniques. This beginner-friendly tutorial walks you through training, pruning, and fine-tuning a ResNet model on CIFAR-10 dataset.
Learn how to work with compact language models like Liquid AI's LFM2.5-350M by setting up environments, loading models, performing inference, and understanding reinforcement learning integration.
A new tutorial shows how to run Qwen3.5 reasoning models with Claude-style thinking using GGUF and 4-bit quantization, enabling flexible deployment across different hardware setups.
