Topics
16
Organized around core technical directions.
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A personal knowledge hub organized around technical topic knowledge .
This site distills my long-term practice in enterprise R&D and platform architecture, covering backend engineering, distributed systems, cloud-native systems, telecom core OSS/NMS, quantitative trading systems, and AI engineering delivery. You can start by searching for a specific problem, or enter through the content map, topics, series, and guides along a structured path of problem definition, solution design, engineering implementation, and retrospective evaluation.
Map
Content map summary
Start with the topic distribution and content scale. To follow the knowledge structure, open the topic page or the content map.
Series
7
Connect staged themes and chapter order.
Articles
76
Capture concrete problems, engineering reviews, and technical notes.
Guides
3
Provide entry points for continuous topic reading.
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Curated starters · Representative paths
Representative paths
Use these to quickly understand the site's main directions: quant engineering, microservice governance, and AI programming evaluation.
Quant Trading Practice
Quantitative trading system development record
From Micang Trader's system boundaries, data flow, trading periods, backtesting consistency, performance defense lines, test defense lines to architecture evolution, the system reviews how a real-level quantitative trading system is designed, verified and reconstructed.
Quantitative system development practice Quant TradingAI Engineering
Microservices Governance
From enterprise-level CF platform to cloud native: more than ten years of evolution of enterprise-level microservice governance
Taking the architectural evolution from enterprise-level Cloud Foundry to cloud-native governance from 2015 to 2026 (to date) as the main line, we systematically review the observations, traffic, resilience, releases, and technical decisions of enterprise-level microservice governance.
Microservice governance MicroservicesCloud Foundry
AI Coding Mentor
AI Coding Mentor Series
Systematic interpretation around AI programming assessment, problem design, collaboration models, case studies, and SFT data generation.
AI programming assessment Ai Coding MentorProgramming Evaluation
Reading Paths
Structured reading paths
Move through complete series and practical guides without hunting through the article archive.
Latest
Latest articles
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- #01AI engineering practiceRAG Quality Evaluation and Safety Controls: From Rule-Based Evaluation to Release Gates
A release-quality guide for retrieval, citation, and answer evaluation, six safety layers, privacy-aware telemetry, and public RAG launch gates.
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A release-quality guide for retrieval, citation, and answer evaluation, six safety layers, privacy-aware telemetry, and public RAG launch gates.
RAG Quality Evaluation Safety Controls - #02AI engineering practiceRAG Retrieval Implementation Deep Dive: Chunking, Hybrid Retrieval, and Intent Routing
An implementation guide for chunking, stable chunk IDs, hybrid retrieval, rule-based intent routing, current-page summaries, rerank, fallback, and verification.
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An implementation guide for chunking, stable chunk IDs, hybrid retrieval, rule-based intent routing, current-page summaries, rerank, fallback, and verification.
RAG Chunking Hybrid Retrieval - #03AI engineering practiceRAG System Architecture: Edge Runtime, Hybrid Retrieval, and Incremental Indexing
A practical architecture guide for a knowledge hub RAG assistant using Cloudflare Workers, Vectorize, D1 FTS5, KV, hybrid retrieval, and incremental indexing.
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A practical architecture guide for a knowledge hub RAG assistant using Cloudflare Workers, Vectorize, D1 FTS5, KV, hybrid retrieval, and incremental indexing.
RAG Edge Computing Cloudflare - #04AI engineering practiceAI-TDD: Requirement Contracts and Multi-Dimensional Evidence Acceptance
AI-TDD turns human intent into a Manifest requirement contract, then accepts AI-generated work through evidence chains and Gate verdicts.
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AI-TDD turns human intent into a Manifest requirement contract, then accepts AI-generated work through evidence chains and Gate verdicts.
AI TDD AI Engineering Ai Coding Mentor - #05JavaJava Ecosystem Outlook: JDK 25 LTS, JDK 26 GA, and JDK 27 EA
An enterprise architecture view of Java's next decade: version strategy, roadmap status, ecosystem boundaries, cloud-native operations, AI governance, and performance evolution.
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An enterprise architecture view of Java's next decade: version strategy, roadmap status, ecosystem boundaries, cloud-native operations, AI governance, and performance evolution.
Java Jdk Ecosystem
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About this site
A personal knowledge hub organized around technical topics, focused on original interpretations, engineering retrospectives, and structured technical writing.