10+ 年 Java、TypeScript、AWS、数据平台和企业系统经验。现在聚焦 LLM 工作流、评测治理、审批安全、开发效率和可部署的 AI 产品。 10+ years across Java, TypeScript, AWS, data platforms, and enterprise systems. I now focus on LLM workflows, evaluation, governance, approval safety, developer productivity, and deployable AI products.
精选案例 Selected Work
把选品评分、供应商询价、内容草稿、人工审批、审计和本地私有部署串成安全的运营决策中心。 Connects product scoring, supplier quote priorities, content drafts, human approvals, audit evidence, and private local deployment.
用 open-core / private extension 架构、memory packs、codebase index、Evidence Graph、governed memory distillation、Requirement Case、PR review、scorecard 和 provenance/security 边界，把团队上下文变成可引用、可评测、可审计的 AI 工程工作流。 Turns team context into source-backed AI engineering workflows with an open-core/private-extension model, memory packs, codebase indexes, Evidence Graph, governed memory distillation, Requirement Cases, PR review summaries, scorecards, and provenance/security boundaries.
把发布风险扫描、仓库上下文、测试建议、公开 proof artifacts、DynamoDB 证据和人工审批门禁组织成可审计的工程 AI 工作流。 Organizes release-risk scans, repository context, test recommendations, public proof artifacts, DynamoDB evidence, and human approval gates into an auditable AI workflow.
读取 JaCoCo 报告，定位低覆盖 Maven 类，生成 JUnit/Mockito 测试，运行校验并进入修复循环。 Reads JaCoCo reports, targets low-coverage Maven classes, generates tests, validates, and repairs failures.
把研究、生成、质量评分、人工审核、发布证据和部署配置做成可追踪的内容生产系统。 A traceable content workflow for research, generation, scoring, review, release evidence, and deployment.
能力证据 Capability Proof
Golden dataset、groundedness、引用正确性、成本/延迟追踪、运行历史和 CI 质量门禁。 Golden datasets, groundedness, citation correctness, cost/latency tracking, run history, and CI quality gates.
GitHubSchema 检索、SQL 安全校验、成本控制、本地 mock 执行和可追踪结果。 Schema retrieval, SQL safety checks, cost controls, local mock execution, and traceable results.
GitHubOpen-core / private extension 架构，source-backed memory packs、codebase index、Evidence Graph、governed memory distillation、Requirement Case、PR review summary、TestGen stub、scorecard、provenance/security 边界和 SQLite audit trail。 Open-core / private-extension architecture with source-backed memory packs, codebase indexes, Evidence Graph, governed memory distillation, Requirement Cases, PR review summaries, TestGen stub boundaries, scorecards, provenance/security boundaries, and SQLite audit trails.
GitHub发布风险评分、测试推荐、证据收集、提交材料、UiPath Test Manager proof、DynamoDB proof 和人工审批门禁；公开 demo 使用安全模拟状态，不调用客户系统。 Release-risk scoring, test recommendations, evidence collection, submission materials, UiPath Test Manager proof, DynamoDB proof, and human approval gates; the public demo uses safe simulated state and does not call customer systems.
GitHub Proof Index技术写作 Writing
The next AI bottleneck is the team thread
Why the stronger signal this week was agents moving into shared queues, tickets, and channels rather than staying inside private chats.
AI agents need commit boundaries
Why the deeper signal this week was the move toward staged effects, failure lineage, and cost accountability.
Your agent needs operating documents
Why machine-readable instructions, capability catalogs, eval traces, and usage telemetry are becoming part of the real AI system.
A model name is not a stable architecture
Why upstream changes in model access, permissions, and spend controls now shape system behavior as much as model quality.
职业定位 Profile
Java, Spring Boot, TypeScript, Angular, React, Node, APIs, CI/CD, enterprise release workflows.
AWS S3, Lambda, ECS/Fargate, CloudWatch, DynamoDB, SQS/SNS, API Gateway, RDS, Athena-style workflows.
RAG, agents, test generation, evaluation, SQL generation, content automation, review gates, audit trails.
欢迎用中文或英文交流：企业 AI 落地、RAG / Agent / LLMOps、AWS 数据智能、开发者效率和业务系统 AI 化。 Happy to discuss enterprise AI delivery, RAG / agents / LLMOps, AWS data intelligence, developer productivity, and AI-enabled business systems.