Hands on DSH
A technical guide to DeepSeek Harness architecture, implementation, deployment, usage, and its differences from OpenClaw.
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Cross-Attention in Multimodal AI: When One Stream Needs to Read Another
A practical guide to cross-attention, from Q/K/V to Stable Diffusion’s text-conditioned U-Net and cross-modal dimension design.
Read MoreThe Great Interconnect Debate: NVLink, InfiniBand, UALink, and the Quest for AI Supremacy
A comprehensive panoramic view of AI and HPC high-speed interconnect technologies — NVLink, InfiniBand, UALink, and Ultra Ethernet — covering architecture, performance, competition, and complementarity.
Read MoreOpenClaw Agent Technical Deep Dive
This document outlines the architecture and implementation of a production-ready OpenClaw agent system deployed on edge hardware. Over a four-day sprint, a complete system was engineered, moving from concept to a stable, running instance. The project involved significant hardware and software integration, including a dual-model LLM strategy for cost and latency optimization, multimodal input via OpenAI Whisper, and asynchronous communication channels. This post-mortem serves as a technical deep-dive for engineers and architects working on similar agent-based systems.
Read MoreBuild a Fashion-MNIST CNN with PyTorch
This document details the process of developing and optimizing a Convolutional Neural Network (CNN) for the Fashion-MNIST dataset using PyTorch. Building upon the foundational work of "Let's Build a Fashion-MNIST CNN, PyTorch Style" [1], this project introduces significant enhancements, including a redesigned CNN architecture, a systematic hyperparameter tuning process, and comprehensive testing and visualization. The optimized model achieves a training accuracy of 99.02% and a test accuracy of 91.01%, a substantial improvement over baseline models. This report outlines the methodology, model architecture, optimization techniques, and data analysis that led to these results.
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