https://cubxxw.com/tags/architecture/ Tech blog by Xinwei Xiong — AI Builder, open source contributor and digital nomad sharing Kubernetes, Go, AI projects and travel. https://cubxxw.com/assets/og-image.png https://cubxxw.com/ Hugo -- gohugo.io en-us Mon, 29 Jun 2026 09:30:00 +0800 https://cubxxw.com/ai-technology/posts/dissecting-open-lovable/ Mon, 29 Jun 2026 09:30:00 +0800 Mon, 29 Jun 2026 23:33:51 +0800 熊鑫伟 (Xinwei Xiong) https://cubxxw.com/ai-technology/posts/dissecting-open-lovable/ 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. AI & Technology AI Agent LLM Architecture Sandbox Harness https://cubxxw.com/ai-technology/posts/relay-agent-architecture-design/ Wed, 24 Jun 2026 10:00:00 +0800 Wed, 24 Jun 2026 14:18:19 +0800 熊鑫伟 (Xinwei Xiong) https://cubxxw.com/ai-technology/posts/relay-agent-architecture-design/ 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. AI & Technology AI Agent LangGraph LLM Architecture TypeScript Python Technical diagram showing the five-layer architecture of the Relay job-search Agent system: UI layer, API orchestration layer, Agent execution layer, shared services layer, and data and integration layer https://cubxxw.com/ai-technology/posts/agent-engineering-the-98-percent-harness/ Wed, 17 Jun 2026 09:30:00 +0800 Wed, 24 Jun 2026 14:18:30 +0800 熊鑫伟 (Xinwei Xiong) https://cubxxw.com/ai-technology/posts/agent-engineering-the-98-percent-harness/ 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%. AI & Technology AI Agent LLM Context Engineering Architecture MCP A technical diagram with a tiny agent loop at the center, surrounded by concentric rings of the eight pillars: orchestration, context, memory, tools, reliability, evaluation, cost, governance