SGLang is a framework for efficient inference optimization that supports both text and image generation workloads. This course provides practical training on deploying and optimizing models, which is directly relevant for engineers looking to improve inference performance and reduce latency in production AI applications.

SGLang is a framework for efficient inference optimization that handles both text and image generation workloads. This course provides practical training on reducing inference latency and computational costs, valuable for engineers deploying language and multimodal models in production.

SGLang is an open-source framework for efficient inference that supports both text and image generation with optimized serving capabilities. This course provides practical guidance on using SGLang to accelerate model inference, which is directly applicable for engineers building production AI systems.

New course on SGLang covering efficient inference techniques for both text and image generation. SGLang is a practical tool for optimizing LLM inference performance, making this relevant for engineers building production AI applications.

Practical walkthrough of running local audio transcription using Gemma 4 E2B model with MLX framework on macOS via uv run. Demonstrates real-world inference with a 10GB model and shows actual transcription output with accuracy notes, useful for developers building local AI audio pipelines.

Practical guide on building custom GPTs for workflow automation and maintaining consistent outputs through purpose-built AI assistants. Covers the technical process of creating and deploying specialized GPT configurations for specific use cases.

A guide on using ChatGPT as a writing assistant for content development through drafting, revision, and refinement workflows. While practical for daily writing tasks, it covers general LLM usage patterns rather than novel technical insights or advanced engineering techniques.

A tutorial on leveraging ChatGPT as a research assistant for source gathering, information analysis, and citation management. Covers practical workflows for using LLMs to structure research tasks, though the specific techniques may be familiar to those already working with prompt engineering and RAG patterns.

A practical guide on using ChatGPT for data analysis workflows, covering dataset exploration, insight generation, and visualization creation. While useful for engineers integrating AI into analytics pipelines, it's general-purpose instruction rather than a new tool or technical breakthrough.

Guide on leveraging ChatGPT's search and deep research capabilities to find current information, evaluate source credibility, and organize findings into structured outputs. Practical for engineers building research-heavy applications or integrating search features into AI workflows.

Guide on using ChatGPT's image generation capabilities (DALL-E integration) with practical techniques for prompt engineering and iterative refinement. Covers workflow for creating visuals through the ChatGPT interface, useful for engineers building AI applications that need visual generation features.

ChatGPT's Projects feature enables organizing related conversations, files, and custom instructions in a single workspace, improving workflow management and team collaboration. This is useful for engineers managing multiple AI-assisted tasks, though it's primarily a UI/UX feature rather than a technical capability advancement.

Practical guide to multimodal embedding and reranker models that extend traditional RAG pipelines to handle text, images, and other modalities in a shared embedding space. Covers model loading, encoding mixed-modality inputs, and computing cross-modal similarities with concrete code examples and performance considerations.

A comprehensive Chinese technical guide ("御舆") that deconstructs AI Agent architecture, specifically analyzing Claude Code's design patterns including conversation loops, tool permission pipelines, context compression, and the Agent Harness runtime framework. Provides a transferable mental model for building production-grade agent systems across different frameworks without relying on prompt engineering tutorials.

In-depth technical analysis of Claude Code's source architecture, covering the agent loop, context engineering, tool system, and production-grade error recovery strategies. Includes a companion project (Claude Code From Scratch) with ~4000 lines of TypeScript/Python and 11-chapter tutorial for building your own AI programming agent from scratch.

Comprehensive reference guide organizing 45+ LLM architectures with visual model cards and detailed explanations of attention variants (MHA, GQA, sliding window, etc.) used in modern models. Includes both a web gallery and printable poster, serving as a practical learning resource for understanding contemporary transformer architectures.

A comprehensive AI engineering curriculum spanning 260+ lessons across 20 phases (~290 hours) covering fundamentals from linear algebra to autonomous agent swarms in Python, TypeScript, Rust, and Julia. Each lesson produces reusable artifacts (prompts, skills, agents, MCP servers) that can be immediately integrated into AI coding workflows, with personalized learning paths based on existing ML/DL knowledge.

Comprehensive overview of inference-time scaling techniques for LLMs, covering methods like chain-of-thought prompting, self-consistency, best-of-N ranking, and rejection sampling with verifiers. The author shares practical experimentation results (achieving 15% to 52% accuracy improvement) and categorizes approaches from both academic literature and proprietary LLM implementations, making it directly applicable to deployed systems.