{"authors":[{"name":"熊鑫伟 (Xinwei Xiong)"}],"description":"Tech blog by Xinwei Xiong — AI Builder, open source contributor and digital nomad sharing Kubernetes, Go, AI projects and travel.","feed_url":"https://cubxxw.com/ai-agent/feed.json","home_page_url":"https://cubxxw.com/ai-agent/","icon":"https://cubxxw.com/assets/og-image.png","items":[{"authors":[{"name":"熊鑫伟 (Xinwei Xiong)"}],"content_text":"Classic \"rank + click\" fails in the GEO era because most value happens where the user never visits you. This final chapter gives you a workable measurement system: prompt testing, AI referral traffic, GSC cross-check, dedicated tools (Profound/Peec), and a low-cost DIY monitor built on this repo's own scripts. Chapter 6 (finale) of the GEO series.\n","date_modified":"2026-07-11T18:28:03+08:00","date_published":"2026-07-11T12:00:00+08:00","id":"https://cubxxw.com/ai-agent/posts/geo-measurement-and-tools/","image":"https://cubxxw.com/images/columns/geo/en-06-measurement.svg","language":"en-us","summary":"Classic \"rank + click\" fails in the GEO era because most value happens where the user never visits you. This final chapter gives you a workable measurement system: prompt testing, AI referral traffic, GSC cross-check, dedicated tools (Profound/Peec), and a low-cost DIY monitor built on this repo's own scripts. Chapter 6 (finale) of the GEO series.\n","tags":["GEO","Measurement","Analytics","AI Search","Tools","Content Strategy","SEO"],"title":"GEO Measurement \u0026 Tools: How to Know If AI Actually Cites You (with a DIY Monitor)","url":"https://cubxxw.com/ai-agent/posts/geo-measurement-and-tools/"},{"authors":[{"name":"熊鑫伟 (Xinwei Xiong)"}],"content_text":"Four chapters of method — now real data. I dug through cubxxw.com's Google Search Console and PageSpeed Insights and diagnosed it layer by layer with the five-layer model: why 878K impressions produced only 852 clicks, which queries are noise and which are gold, how to protect a domain migration, and a priority-ranked rebuild checklist. Chapter 5 of the GEO series.\n","date_modified":"2026-07-11T18:28:03+08:00","date_published":"2026-07-11T11:30:00+08:00","id":"https://cubxxw.com/ai-agent/posts/geo-blog-rebuild-case-study/","image":"https://cubxxw.com/images/columns/geo/en-05-case-study.svg","language":"en-us","summary":"Four chapters of method — now real data. I dug through cubxxw.com's Google Search Console and PageSpeed Insights and diagnosed it layer by layer with the five-layer model: why 878K impressions produced only 852 clicks, which queries are noise and which are gold, how to protect a domain migration, and a priority-ranked rebuild checklist. Chapter 5 of the GEO series.\n","tags":["GEO","Case Study","SEO Audit","Analytics","Content Strategy","AI Search","Hugo"],"title":"GEO Blog Rebuild Case Study: Running the Five-Layer Model on Real Data","url":"https://cubxxw.com/ai-agent/posts/geo-blog-rebuild-case-study/"},{"authors":[{"name":"熊鑫伟 (Xinwei Xiong)"}],"content_text":"Your technical base and structure are right — so why still no AI citations? Because the final gate is trust, and most trust comes from off-site. This chapter covers operationalizing E-E-A-T, building entity consistency, why Reddit + Wikipedia are 66% of AI citations, and how a personal blog builds off-site endorsement pragmatically. Chapter 4 of the GEO series.\n","date_modified":"2026-07-11T18:28:03+08:00","date_published":"2026-07-11T11:00:00+08:00","id":"https://cubxxw.com/ai-agent/posts/geo-trust-and-endorsement/","image":"https://cubxxw.com/images/columns/geo/en-04-trust.svg","language":"en-us","summary":"Your technical base and structure are right — so why still no AI citations? Because the final gate is trust, and most trust comes from off-site. This chapter covers operationalizing E-E-A-T, building entity consistency, why Reddit + Wikipedia are 66% of AI citations, and how a personal blog builds off-site endorsement pragmatically. Chapter 4 of the GEO series.\n","tags":["GEO","E-E-A-T","Digital PR","Content Strategy","AI Search","Community","Branding"],"title":"GEO Trust \u0026 Endorsement: Why Reddit and Wikipedia Make Up Half of AI Citations","url":"https://cubxxw.com/ai-agent/posts/geo-trust-and-endorsement/"},{"authors":[{"name":"熊鑫伟 (Xinwei Xiong)"}],"content_text":"Principles done — this chapter is all hands-on: how to write Answer-First paragraphs, turn headings into questions, whether FAQPage/HowTo schema still matters after Google retired the rich results, the right way to do llms.txt and tldr, and how to weave internal links into a topic cluster. With code and before/after. Chapter 3 of the GEO series.\n","date_modified":"2026-07-11T18:28:03+08:00","date_published":"2026-07-11T10:30:00+08:00","id":"https://cubxxw.com/ai-agent/posts/geo-structured-content-tactics/","image":"https://cubxxw.com/images/columns/geo/en-03-structured.svg","language":"en-us","summary":"Principles done — this chapter is all hands-on: how to write Answer-First paragraphs, turn headings into questions, whether FAQPage/HowTo schema still matters after Google retired the rich results, the right way to do llms.txt and tldr, and how to weave internal links into a topic cluster. With code and before/after. Chapter 3 of the GEO series.\n","tags":["GEO","Structured Data","Schema","Content Strategy","SEO","AI Search","Hugo"],"title":"GEO Structured Tactics: Writing \"Worth Citing\" Into Every Paragraph (Answer-First, Schema, llms.txt)","url":"https://cubxxw.com/ai-agent/posts/geo-structured-content-tactics/"},{"authors":[{"name":"熊鑫伟 (Xinwei Xiong)"}],"content_text":"To get cited by AI, first understand how it picks. This chapter takes the RAG pipeline apart to the component level: query fan-out, hybrid retrieval, vector semantics, multi-stage reranking, and citations pre-embedded before generation. The one core takeaway — the retrieval unit is the passage, not the page. Optimize the chunk. Chapter 2 of the GEO series.\n","date_modified":"2026-07-11T18:28:03+08:00","date_published":"2026-07-11T10:00:00+08:00","id":"https://cubxxw.com/ai-agent/posts/geo-how-ai-retrieves-and-cites/","image":"https://cubxxw.com/images/columns/geo/en-02-retrieval.svg","language":"en-us","summary":"To get cited by AI, first understand how it picks. This chapter takes the RAG pipeline apart to the component level: query fan-out, hybrid retrieval, vector semantics, multi-stage reranking, and citations pre-embedded before generation. The one core takeaway — the retrieval unit is the passage, not the page. Optimize the chunk. Chapter 2 of the GEO series.\n","tags":["GEO","RAG","AI Search","Retrieval","LLM","Embeddings","Content Strategy"],"title":"GEO Mechanics: How AI Retrieves, Re-ranks, and Cites You","url":"https://cubxxw.com/ai-agent/posts/geo-how-ai-retrieves-and-cites/"},{"authors":[{"name":"熊鑫伟 (Xinwei Xiong)"}],"content_text":"When 68% of Google searches no longer produce a click and AI hands the answer straight to the user, the \"rankings\" that classic SEO fights for are quietly losing value. GEO (Generative Engine Optimization) fights for something else: getting the AI to understand, trust, and cite you when it writes the answer. A pillar-length guide from first principles to methodology to a real case study on my own blog — and the opening chapter of the GEO series.\n","date_modified":"2026-07-11T17:27:12+08:00","date_published":"2026-07-10T22:00:00+08:00","id":"https://cubxxw.com/ai-agent/posts/geo-generative-engine-optimization-guide/","image":"https://cubxxw.com/images/columns/geo/en-01-guide.svg","language":"en-us","summary":"When 68% of Google searches no longer produce a click and AI hands the answer straight to the user, the \"rankings\" that classic SEO fights for are quietly losing value. GEO (Generative Engine Optimization) fights for something else: getting the AI to understand, trust, and cite you when it writes the answer. A pillar-length guide from first principles to methodology to a real case study on my own blog — and the opening chapter of the GEO series.\n","tags":["GEO","SEO","AI Search","Generative Engine Optimization","Content Strategy","LLM","AEO"],"title":"GEO: The Complete Guide to Generative Engine Optimization (When Search Stops Giving Links and Starts Giving Answers)","url":"https://cubxxw.com/ai-agent/posts/geo-generative-engine-optimization-guide/"},{"authors":[{"name":"熊鑫伟 (Xinwei Xiong)"}],"content_text":"A full dissection of firecrawl/open-lovable (27k★, paste a URL and get a working React app in seconds), from product to code. Its most interesting trait isn't that it generates code — it's that it uses no agent framework, no Claude Agent SDK, no native tool-calling. Instead it hand-rolls an entire harness on top of the raw LLM API: a text DSL protocol, streaming regex parsing, truncation detection and recovery, manual context orchestration, plus a swappable cloud sandbox layer (E2B / Vercel Sandbox). This is a case study in taming the raw API.\n","date_modified":"2026-07-11T08:33:54+08:00","date_published":"2026-06-29T09:30:00+08:00","id":"https://cubxxw.com/ai-agent/posts/dissecting-open-lovable/","language":"en-us","summary":"A full dissection of firecrawl/open-lovable (27k★, paste a URL and get a working React app in seconds), from product to code. Its most interesting trait isn't that it generates code — it's that it uses no agent framework, no Claude Agent SDK, no native tool-calling. Instead it hand-rolls an entire harness on top of the raw LLM API: a text DSL protocol, streaming regex parsing, truncation detection and recovery, manual context orchestration, plus a swappable cloud sandbox layer (E2B / Vercel Sandbox). This is a case study in taming the raw API.\n","tags":["AI","Agent","LLM","Architecture","Sandbox","Harness Engineering"],"title":"Dissecting open-lovable: An App Generator That Tames the Raw API Without an Agent Framework","url":"https://cubxxw.com/ai-agent/posts/dissecting-open-lovable/"},{"authors":[{"name":"熊鑫伟 (Xinwei Xiong)"}],"content_text":"Using the Relay open-source job-search Agent project as a case study, this article fully breaks down every key design decision in a production-grade multi-agent system: why split a single Agent into 5, how to implement HITL checkpoints with LangGraph, how a three-tier LLM router precisely tracks costs, how a fabrication guard validates at runtime, and how a hybrid backend (Hono/Bun + FastAPI/Python) decouples cleanly. Whether you are building your first Agent PoC or pushing toward production, there are design patterns here you can take away.\n","date_modified":"2026-07-11T08:22:51+08:00","date_published":"2026-06-24T10:00:00+08:00","id":"https://cubxxw.com/ai-agent/posts/relay-agent-architecture-design/","image":"https://cubxxw.com/images/blog/relay-agent-architecture.webp","language":"en-us","summary":"Using the Relay open-source job-search Agent project as a case study, this article fully breaks down every key design decision in a production-grade multi-agent system: why split a single Agent into 5, how to implement HITL checkpoints with LangGraph, how a three-tier LLM router precisely tracks costs, how a fabrication guard validates at runtime, and how a hybrid backend (Hono/Bun + FastAPI/Python) decouples cleanly. Whether you are building your first Agent PoC or pushing toward production, there are design patterns here you can take away.\n","tags":["AI","Agent","LangGraph","LLM","Architecture","TypeScript","Python"],"title":"Building a Production-Grade AI Agent System from Scratch: A Full Architecture Breakdown of Relay","url":"https://cubxxw.com/ai-agent/posts/relay-agent-architecture-design/"},{"authors":[{"name":"熊鑫伟 (Xinwei Xiong)"}],"content_text":"Why context engineering supersedes prompt engineering — a systematic look at context assembly, retrieval, compression, and eviction patterns, drawing from Anthropic, Karpathy, LangChain, and Manus.","date_modified":"2026-07-11T08:22:51+08:00","date_published":"2026-06-22T03:30:00+08:00","id":"https://cubxxw.com/ai-agent/posts/context-engineering-the-new-foundation/","image":"https://cubxxw.com/images/blog/context-engineering-worldline.webp","language":"en-us","summary":"Why context engineering supersedes prompt engineering — a systematic look at context assembly, retrieval, compression, and eviction patterns, drawing from Anthropic, Karpathy, LangChain, and Manus.","tags":["Context Engineering","AI","LLM","Agent","MCP"],"title":"Context Is Not Prompt: Why Context Engineering Is Becoming AI's New Foundation","url":"https://cubxxw.com/ai-agent/posts/context-engineering-the-new-foundation/"},{"authors":[{"name":"熊鑫伟 (Xinwei Xiong)"}],"content_text":"A panoramic map that treats Agent Engineering as a discipline. Starting from the widely cited claim that only 1.6% of Claude Code is AI decision logic while 98.4% is infrastructure, it walks the eight pillars one by one — orchestration, context, memory, tools, reliability, evaluation, cost, governance — explaining the gap each fills, its minimal implementation, and its failure boundary. It fuses 2025 to 2026 frontline engineering from Anthropic, OpenAI, Cognition, Manus, and Temporal, and lands on one line: the model is bought, the harness is built, and your entire engineering leverage lives in that 98.4%.\n","date_modified":"2026-07-11T08:22:51+08:00","date_published":"2026-06-17T09:30:00+08:00","id":"https://cubxxw.com/ai-agent/posts/agent-engineering-the-98-percent-harness/","image":"https://cubxxw.com/images/blog/agent-engineering-harness.webp","language":"en-us","summary":"A panoramic map that treats Agent Engineering as a discipline. Starting from the widely cited claim that only 1.6% of Claude Code is AI decision logic while 98.4% is infrastructure, it walks the eight pillars one by one — orchestration, context, memory, tools, reliability, evaluation, cost, governance — explaining the gap each fills, its minimal implementation, and its failure boundary. It fuses 2025 to 2026 frontline engineering from Anthropic, OpenAI, Cognition, Manus, and Temporal, and lands on one line: the model is bought, the harness is built, and your entire engineering leverage lives in that 98.4%.\n","tags":["AI","Agent","LLM","Context Engineering","Architecture","MCP"],"title":"The Agent Engineering Map: Where Does That 98.4% of the Work Actually Live?","url":"https://cubxxw.com/ai-agent/posts/agent-engineering-the-98-percent-harness/"},{"authors":[{"name":"熊鑫伟 (Xinwei Xiong)"}],"content_text":"AI agent amnesia isn't a functional defect—it's a fundamental gap in the trust account. Starting from Locke's 1689 theory of identity, this article dissects the complete engineering stack for agent identity continuity in 2026: file-as-identity (SOUL.md paradigm), Harness as environmental condition, four-layer memory architecture and Gene Capsule protocol, self-positioning in multi-agent topology, and evaluation as the ultimate identity verification challenge. For practitioners building or designing AI agent systems, and researchers deeply thinking about the boundaries of AI autonomy.\n","date_modified":"2026-07-11T08:22:51+08:00","date_published":"2026-04-05T20:00:00+08:00","id":"https://cubxxw.com/ai-agent/posts/agent-identity-from-locke-to-openclaw/","image":"https://cubxxw.com/images/agent-identity/01-locke-spec.svg","language":"en-us","summary":"AI agent amnesia isn't a functional defect—it's a fundamental gap in the trust account. Starting from Locke's 1689 theory of identity, this article dissects the complete engineering stack for agent identity continuity in 2026: file-as-identity (SOUL.md paradigm), Harness as environmental condition, four-layer memory architecture and Gene Capsule protocol, self-positioning in multi-agent topology, and evaluation as the ultimate identity verification challenge. For practitioners building or designing AI agent systems, and researchers deeply thinking about the boundaries of AI autonomy.\n","tags":["AI","Open Source","Monthly Notes"],"title":"Agent Identity: From Locke to OpenClaw","url":"https://cubxxw.com/ai-agent/posts/agent-identity-from-locke-to-openclaw/"},{"authors":[{"name":"熊鑫伟 (Xinwei Xiong)"}],"content_text":"This guide provides an in-depth look into the integration and application of language models using the LangChain framework, tailored for developers looking to streamline complex implementations.","date_modified":"2026-07-11T08:22:51+08:00","date_published":"2024-05-22T21:37:34+08:00","id":"https://cubxxw.com/ai-agent/posts/harnessing-language-model-applications-with-langchain-a-developer-is-guide/","language":"en-us","summary":"This guide provides an in-depth look into the integration and application of language models using the LangChain framework, tailored for developers looking to streamline complex implementations.","tags":["AI Development","Language Models","LangChain","AI Frameworks","Machine Learning","API Integration","Natural Language Processing (NLP)","Software Development","Programming","Automation","AI Tools","OpenAI","Deep Learning"],"title":"LangChain: Building LLM Applications","url":"https://cubxxw.com/ai-agent/posts/harnessing-language-model-applications-with-langchain-a-developer-is-guide/"},{"authors":[{"name":"熊鑫伟 (Xinwei Xiong)"}],"content_text":"This article explores the transformative capabilities of Large Language Models (LLMs), which are designed to understand and generate human language, demonstrating a pioneering role in AI technology. These models showcase emergent abilities that significantly surpass those of their predecessors by leveraging vast amounts of data and sophisticated machine learning architectures.\n","date_modified":"2026-07-11T08:22:51+08:00","date_published":"2024-05-15T20:12:29+08:00","id":"https://cubxxw.com/ai-agent/posts/exploring-large-language-models-llms-pioneering-ai-understanding-generation-human-language/","language":"en-us","summary":"This article explores the transformative capabilities of Large Language Models (LLMs), which are designed to understand and generate human language, demonstrating a pioneering role in AI technology. These models showcase emergent abilities that significantly surpass those of their predecessors by leveraging vast amounts of data and sophisticated machine learning architectures.\n","tags":["Security","Functional Programming","LLM"],"title":"Large Language Models: How LLMs Work","url":"https://cubxxw.com/ai-agent/posts/exploring-large-language-models-llms-pioneering-ai-understanding-generation-human-language/"},{"authors":[{"name":"熊鑫伟 (Xinwei Xiong)"}],"content_text":"This comprehensive guide introduces Sora AI, offering developers an accessible, automated, and swift path to harnessing its potential. Dive into various scenarios, master commanding Sora, and explore the multi-faceted applications of this AI model trained to understand and simulate the physical world in motion. Whether you're seeking the latest Sora news, development projects, or open-source contributions, this article is your gateway to the expansive world of Sora AI development.\n","date_modified":"2026-07-11T08:22:51+08:00","date_published":"2024-03-14T08:44:13+08:00","id":"https://cubxxw.com/ai-agent/posts/sora-ease-guide-mastering-sora-ai-for-developers/","language":"en-us","summary":"This comprehensive guide introduces Sora AI, offering developers an accessible, automated, and swift path to harnessing its potential. Dive into various scenarios, master commanding Sora, and explore the multi-faceted applications of this AI model trained to understand and simulate the physical world in motion. Whether you're seeking the latest Sora news, development projects, or open-source contributions, this article is your gateway to the expansive world of Sora AI development.\n","tags":["Blog","sora","AI","github"],"title":"Sora Ease Guide: Mastering Sora AI for Developers","url":"https://cubxxw.com/ai-agent/posts/sora-ease-guide-mastering-sora-ai-for-developers/"},{"authors":[{"name":"熊鑫伟 (Xinwei Xiong)"}],"content_text":"Dive into the world of Sora Technology, a groundbreaking platform for AI-driven video generation. This post is designed for both tech enthusiasts and developers eager to unlock the potential of Sora. Discover how you can leverage Sora to create stunning, AI-generated videos with ease, and join a community of innovators transforming the digital landscape.\n","date_modified":"2026-07-11T08:22:51+08:00","date_published":"2024-02-24T13:30:15+08:00","id":"https://cubxxw.com/ai-agent/posts/exploring-sora-technology-for-enthusiasts-and-developers/","language":"en-us","summary":"Dive into the world of Sora Technology, a groundbreaking platform for AI-driven video generation. This post is designed for both tech enthusiasts and developers eager to unlock the potential of Sora. Discover how you can leverage Sora to create stunning, AI-generated videos with ease, and join a community of innovators transforming the digital landscape.\n","tags":["Blog","sora","AI","chatgpt"],"title":"Exploring Sora Technology for Enthusiasts and Developers","url":"https://cubxxw.com/ai-agent/posts/exploring-sora-technology-for-enthusiasts-and-developers/"},{"authors":[{"name":"熊鑫伟 (Xinwei Xiong)"}],"content_text":"An introduction to vector databases for AI applications. Starts from the prerequisites, the basics of vectors, similarity measures such as cosine similarity, and database indexing, then explains how vector databases differ from traditional databases and where they fit in AI.\n","date_modified":"2026-07-11T08:22:51+08:00","date_published":"2024-01-20T12:57:15+08:00","id":"https://cubxxw.com/ai-agent/posts/vector-database-learning/","image":"https://cubxxw.com/images/blog/vector-database.png","language":"en-us","summary":"An introduction to vector databases for AI applications. Starts from the prerequisites, the basics of vectors, similarity measures such as cosine similarity, and database indexing, then explains how vector databases differ from traditional databases and where they fit in AI.\n","tags":["Blog","AI","Database"],"title":"Vector Database Learning","url":"https://cubxxw.com/ai-agent/posts/vector-database-learning/"},{"authors":[{"name":"熊鑫伟 (Xinwei Xiong)"}],"content_text":"Explore the latest trends and challenges in 2024 in the world of technology and development.\n","date_modified":"2026-07-11T08:22:51+08:00","date_published":"2024-01-14T22:52:24+08:00","id":"https://cubxxw.com/ai-agent/posts/emerging-challenges-and-trends-in-2024/","language":"en-us","summary":"Explore the latest trends and challenges in 2024 in the world of technology and development.\n","tags":["Blog"],"title":"Emerging Challenges and Trends in 2024","url":"https://cubxxw.com/ai-agent/posts/emerging-challenges-and-trends-in-2024/"},{"authors":[{"name":"熊鑫伟 (Xinwei Xiong)"}],"content_text":"Learn how to install and use Auto-GPT for autonomous AI task automation, including setup, configuration, and practical use cases.","date_modified":"2026-07-11T08:22:51+08:00","date_published":"2023-03-18T16:28:30+08:00","id":"https://cubxxw.com/ai-agent/posts/use-auto-gpt/","language":"en-us","summary":"Learn how to install and use Auto-GPT for autonomous AI task automation, including setup, configuration, and practical use cases.","tags":["Blog","AI","autogpt"],"title":"Use Auto Gpt","url":"https://cubxxw.com/ai-agent/posts/use-auto-gpt/"}],"language":"en-us","title":"AI Agent on Xinwei Xiong (cubxxw) - AI, Open Source \u0026 Nomad Blog","version":"https://jsonfeed.org/version/1.1"}