Sharing project experiences, insights into cutting-edge architectures, focusing on cloud computing, microservices, big data, hyper-convergence, storage, data protection, artificial intelligence, industry practices and solutions.
A paper from Yixing Intelligent detailing the Tile‑level Instruction Set Architecture (TISA) dynamic scheduling framework was accepted at ISCA 2026, showing how a new compiler‑hardware co‑design can dramatically improve AI accelerator utilization and outperform static scheduling approaches on major models.
Anthropic’s decision to block OpenClaw and other third‑party harness platforms stems from massive cost overruns, strategic ecosystem lock‑ins, and tightened Terms of Service, marking a pivotal shift in the AI industry from cheap subscription models to strict usage‑based pricing and tighter control.
This guide breaks down why users abandon OpenClaw due to unexpected expenses and security vulnerabilities, then provides step‑by‑step, cross‑platform instructions—including CLI commands, Docker cleanup, residual file removal, registry edits, and permission revocation—to ensure a complete and secure uninstallation.
This guide compares three mainstream OpenClaw deployment methods—local PC installation via WSL, cloud‑server deployment on platforms like Tencent Cloud, and paid one‑click services—detailing their costs, security, stability, setup steps, and suitability for different user needs.
This guide breaks down a step‑by‑step workflow—starting with tool selection, character design, and script drafting, then moving through batch asset generation, automated video creation, transition tricks, and final polishing—to help beginners produce a high‑impact AI‑generated short drama within three days.
The Global Computing Alliance’s 2026 report outlines ten transformative trends—from explosive AI compute growth and the rise of supernodes to embodied intelligence, heterogeneous architectures, network‑centric designs, and the imminent commercialization of quantum computing—showing how compute power is becoming the strategic engine of the digital economy.
Engineers training large AI models often see noticeable FP16/BF16 result differences between GPUs and NPUs, and even between generations of the same chip, due to floating‑point representation limits, hardware design choices, software library implementations, compiler optimizations, and parallel execution nondeterminism.
The article explains Google’s TPU data‑center architecture, detailing the vertical Scale‑Up strategy within a SuperPod, the horizontal Scale‑Out across SuperPods, the 3D Torus topology with Twisted variants, and the multi‑layer network design that enables petabyte‑scale AI training and inference.
The article provides an in‑depth technical analysis of Nvidia’s Blackwell B200 GPU, detailing its multi‑chip architecture, cache hierarchy, memory bandwidth, atomic operation latency, compute throughput, and tensor memory features, and compares these metrics against Nvidia H100, A100 and AMD MI300X to assess its suitability for AI workloads.
The article examines the shifting AI‑chip landscape, explaining how Google’s TPUv7, backed by massive pod architecture and optical circuit switching, challenges Nvidia’s GPU dominance by offering superior system‑level efficiency and lower total cost of ownership for large‑scale model training.
