GUIDES · 19
AI topic guides
One evergreen reference per topic: what it is, why it matters, how to get started, and the questions everyone asks. News for the pulse, guides for the picture.
Agents
Agents: A Practical Guide to Selection, Architecture, and Production DeploymentAgents represent one of the most transformative paradigms in AI, upgrading LLMs from chatbots to autonomous entities tha
AI coding
How to Choose an AI Coding Tool: A Practical Guide for EngineersAI coding tools are transforming software development workflows. From autocomplete to autonomous agents, they boost prod
AI funding
A practical guide to AI funding: How engineers evaluate investment signalsAI funding is a critical signal for technology adoption. When an AI company secures significant investment or achieves a
AI hardware
AI Hardware Selection Guide: From Chips to Systems, How to Decide?AI hardware is the physical foundation for training and inference of large models. From GPUs to TPUs and custom ASICs, t
Research & papers
A Practical Guide to Following AI Research: Papers, Benchmarks, and BreakthroughsThe pace of AI paper releases is staggering, with dozens of new preprints each week. For engineers and team leads, filte
AI safety
AI Safety & Alignment: A Practical Guide for EngineersAI safety and alignment focus on ensuring AI systems act as intended and avoid unintended harm. With the surge of large-
API & pricing
A practical guide to API pricing: how to choose and avoid pitfallsLLM API pricing is evolving from simple per-token charges to more nuanced structures: input/output separation, caching d
Chinese models
A Practical Guide to Choosing Chinese LLMs: From Qwen to DeepSeekChinese large language models (LLMs) are transitioning from the '100-model war' to real-world deployment. Apple Intellig
Enterprise AI
A Practical Guide to Enterprise AI: Selection, Deployment, and Common PitfallsEnterprise AI deployment is no longer experimental—it's a competitive necessity. From customer support to code generatio
Infra & cost
A Practical Guide to AI Inference Cost & Infrastructure SelectionAs large language models move from experimentation to production, inference cost and infrastructure selection have becom
MCP & Skills
MCP & Skills: A Practical Guide for EngineersMCP (Model Context Protocol) is an open protocol that lets AI models securely access external tools and data sources. As
Model releases
How to evaluate new model releases and decide whether to adopt themNew models are released every few weeks: GPT-5, Claude 4, Gemini 2.5, DeepSeek-V3... each claiming to be 'the best'. For
Multimodal & image
Multimodal & Image: A Practical Guide for EngineersMultimodal models that process text, images, and video simultaneously are among the hottest areas in AI. From GPT-5.6 fa
Open source
A practical guide to open-source models: selection, deployment & pitfallsOpen-source and open-weight models have become a mainstream choice for AI engineering. They allow you to run, fine-tune,
Policy & regulation
A Practical Guide to AI Policy & Regulation for EngineersAI policy and regulation are shifting from vague to concrete. Many jurisdictions have enacted laws (e.g., EU AI Act, Chi
Robotics & embodied
A practical guide to Robotics & Embodied AI for engineersRobotics and embodied intelligence are moving from labs to industry. Traditional robots rely on precise programming and
AI video
A Practical Guide to AI Video Generation & EditingAI video generation and editing uses deep learning to create or modify video content from text or images. With diffusion
Voice AI
Voice AI: A Practical Guide to TTS, ASR, and Voice AgentsVoice AI is evolving from 'hear and speak' to 'understand and interact'. Text-to-speech (TTS) now rivals human naturalne
Worth trying
How to Choose AI Coding Tools: A Practical Guide for EngineersAI coding tools have evolved from autocomplete to autonomous task execution. Tools like Claude Code, Codex, and OpenCode