19 related articles
Hardware-Software Co-Design: A Guide t…
Explore AI Model Co-Design principles and how hardware-friendly LLM architecture design — covering MoE, GQA, and FP8 quantization — optimizes the accuracy, throughput, and latency trade-off.

Deep dive into Baileys, a WebSocket-based WhatsApp Web API requiring no browser. Supports multi-device, chatbots, and group automation with low resource usage.

Deep dive into running OpenAI GPT-5.6 inside Claude Code: comparing Codex vs Claude Code on subagent orchestration, workflow design, and system prompt quality, revealing how harness engineering determines model output.
Human-Centered AI: Real-World Implemen…
An MSR workshop reveals the truth about AI deployment: from a $20 corneal diagnostic device to expert-in-the-loop chatbots, researchers share real-world experiences of AI in healthcare and design within resource-scarce environments.

An indie dev attempts to train a CPU-native LLM on $0 budget using ternary quantization, sparsity, and fine-grained MoE — with pre-registered success criteria and full public reporting.

This article synthesizes two MSR India Summit talks, exploring two key paths to better AI reasoning: test-time scaling with variable granularity search, and a formal verification framework for trustworthy agent execution.
AI Co-Design in Practice: How One Week…
A developer redesigned their entire website in one weekend through AI co-design. Explore how AI is evolving from a code tool into a true creative partner — and what it means for indie developers.

OpenAI's GPT-5.6 launches with Sawa, Terra, and Luna sub-models the same day as Musk's Grok 4.5, while Anthropic, Meta, and NVIDIA make their moves. A packed week of flagship AI launches.

SiliconLLM builds a CPU-native LLM architecture from scratch, combining selective SSM, ternary (1.58-bit) LUT MLP, and granular MoE, co-designed around the L3 cache bandwidth cliff. Ternary kernels show 4-5x speedup over fp32.

OpenAI GPT-5.6 expands its preview, xAI Grok 4.5 opens access at the same time, Meta releases the Agent-based Muse Image model and Muse Video model, while Apple and DeepSeek launch in-house AI inference chip programs. A quick look at the week's five biggest AI stories.

Beyond the GPU arms race, software is becoming AI infrastructure's true compounding engine. A deep dive into optimization stacking, the CUDA ecosystem moat, and how open source scales individual breakthroughs into community wealth.

AMD officially unveils the Ryzen AI Halo local AI dev kit, priced around $4,000 with 128GB unified memory, capable of running 70B LLMs locally. An in-depth look at its specs, pricing, and market competition.

Deep dive into NVFP4 quantization: using NVIDIA Model Optimizer to compress Nemotron 3 Ultra to FP4 checkpoints, reducing memory by 75% and boosting inference throughput on Blackwell GPUs.
OpenAI's First Custom AI Chip Jalapeño…
OpenAI unveils Jalapeño, its first custom AI chip built with Broadcom, optimized for LLM inference. A deep dive into its architecture, strategy, and impact on NVIDIA and the AI chip landscape.

Deep dive into Sakana AI and NVIDIA's latest research using TwELL sparse packing format and custom CUDA kernels to convert LLM sparsity into real GPU speedups, achieving 20%+ faster inference/training and significantly lower memory usage.
TutorialsComplete guide to setting up a VSCode + Claude Code + DeepSeek AI programming environment from scratch, covering Git installation, Claude Code configuration, CC Switch multi-model management, and VSCode plugin integration for embedded developers.
ResearchDeep dive into how the Humanize framework transforms LLM tokens into engineering productivity via Agent Loops. Covers KDA winning CUDA kernel contests, virtual hardware optimization, and 50% research cost reduction.
Tech FrontiersGo turns 16, evolving from a 2009 open-source experiment to the backbone of cloud-native infrastructure. From Docker and Kubernetes to generics and AI-era opportunities.