Mamba: New Selective State Space Model vs Transformer

Mamba: a selective SSM state-space model that generalizes S4 to enable linear long-context scaling, million-token sequences, and improved language modeling.

Sora: OpenAI's Video Model Architecture and Use Cases

Overview of Sora, OpenAI's video diffusion model using a spatiotemporal autoencoder and DiT Transformer to generate high-resolution, minute-long videos.

Nvidia AI Chip Roadmap Explained

Technical analysis of Nvidia's GPU roadmap: annual cadence with H200/B100/X100, One Architecture, SuperChip design and NVLink interconnect evolution.

Why A100/H100 Beat RTX 4090 for Large-Model Training

Technical analysis comparing RTX 4090 and H100 GPUs: why the 4090 is impractical for large-model training but viable for inference with optimized batching and KV cache.

Global and China AI Server Market Size

Analysis of AI server market dynamics, NVIDIA dominance, and Huawei Ascend's role in domestic substitution of AI chips amid export controls and foundry capacity gains.

Inside Amazon's AI Chip Architecture

Analysis of AWS Trainium2 architecture and its relationship to Inferentia, with performance projections, core/memory scaling, NeuronLink bandwidth and instance implications.

AI Server PCB Hardware Breakdown

Teardown analysis of NVIDIA DGX A100 AI server PCBs: PCB types, area and per-system value breakdown for GPU board assembly, CPU motherboard, substrates and accessories.

ORB-SLAM3 Architecture and Workflow Analysis

Overview of ORB-SLAM3 architecture and visual-inertial SLAM: tracking, local mapping, loop/map merging, Atlas and IMU-camera fusion for pose estimation and optimization.

Nvidia AI Chip Roadmap Analysis

Technical overview of Nvidia roadmap: annual GPU cadence, One Architecture and SuperChip strategy, NVLink interconnects and switch roadmap for 2024–2025.

NVIDIA's Three Core Technologies: CUDA, NVLink, InfiniBand

Technical overview of CUDA and NVLink for GPU-accelerated AI: architecture, interconnect bandwidth, and scalable multi-GPU networking.

Precision Comparison: FP64 FP32 FP16 TF32 BF16 INT8

Concise overview of numeric precision formats, FP64, FP32, FP16, TF32, BF16 and int8, comparing bit widths, accuracy trade-offs and use cases for AI training and inference.

Fixing ChatGPT API Timeout Issues

Mitigating ChatGPT API streaming timeouts: shorten retry intervals and monkey-patch APIRequestor.arequest_raw to set aiohttp.ClientTimeout (connect, total, sock_read).

Common GPU System Architectures Explained

Overview of GPU topology and interconnects, comparing 8?GPU A100/A800 configurations with NVLink/NVSwitch, storage NIC roles, and bandwidth bottlenecks.

FlashAttention 1/2/3: Transformer attention optimizations

Technical overview of FlashAttention v1–v3: memory-aware tiling, recomputation, and FP8 GPU optimizations that reduce HBM I/O and accelerate Transformer attention.

Build a Raspberry Pi Voice Assistant

Raspberry Pi AI guide: hardware, compatible frameworks (TensorFlow, OpenCV), and step-by-step instructions to build a voice assistant using SpeechRecognition and gTTS.

10 Key Moments in AI History

A concise technical review of AI history covering 10 pivotal milestones—from Dartmouth and perceptron to deep learning breakthroughs and the rise of large models.

OpenAI Sora: a new AI model for video generation

Technical overview of OpenAI's Sora and its video generation capabilities, core machine learning foundations, and potential impacts on production workflows and society.

12 Practical GPU Optimization Tips for AI Training

12 strategies to improve GPU utilization and compute efficiency in AI/ML workloads, covering mixed precision, data pipelines, profiling and distributed training.

Embodied Intelligence: A Beginner's Guide

Concise overview of embodied intelligence: definitions, categories (humanoid, wheeled, legged) and core technologies such as motion control and decision-making.

Popular LLM Inference Stacks and Setups

Survey of LLM inference stacks covering throughput, latency and cost; explains hardware constraints, KV cache, quantization, paged/grouped attention, and practical optimizations.