Engineering lead · Building AI-powered dev tools & products
📍 Changsha, China
I lead engineering teams building AI-native products and developer tooling. My current focus:
- 🔭 AI coding pipelines — end-to-end workflows that bring real engineering rigor to AI-assisted development: requirement clarification → tech-stack enforcement → TDD → DevOps automation.
- 🛠️ LLM-powered product systems — currently rewriting VOC Insight (a customer-feedback intelligence platform) on NestJS, with a stronger architectural foundation.
- 🧪 Dev workflow augmentation — exploring slash-command toolkits, skill frameworks, and agent patterns that actually hold up in production.
- 📈 Observability for AI systems — designing adaptive crawler & data-collection pipelines instrumented with InfluxDB + Grafana.
const stack = {
languages: ['TypeScript', 'Python', 'Java'],
backend: ['NestJS', 'Node.js', 'FastAPI'],
ai: ['LLM orchestration', 'Agent workflows', 'Prompt engineering'],
data: ['MySQL', 'Redis', 'InfluxDB'],
philosophy: 'Design-first. Pseudocode before code. Ship with rigor.',
};The interesting question isn't "can the model write this code?" — it's "what scaffolding turns a model into a reliable engineer?"
I care about the boring parts: spec clarity, test coverage, deployment hygiene, observability. AI assistance only compounds when the surrounding pipeline is disciplined.
- Skill / slash-command frameworks for AI coding agents
- TDD-driven AI workflows (tests as the spec the model writes against)
- Adaptive strategy systems for long-running data pipelines
- ✉️ Email: jogh1020@126.com
- ✉️ Email: guo487621@gmail.com
🧘 Deep focus mode: ON · ⚡ Vibe coding with discipline