Zikang Wu

wzk.icu · Gleam Lab

AI Agent EngineeringMultimodal InteractionAI Application InfraBackend Platform

Zikang Wu | AI Agent × Multimodal Interaction × Backend Platform

He specializes in integrating LLMs, voice pipelines, business APIs, realtime communication, model services, and edge devices into deployable, measurable, and maintainable AI application systems.

Zikang Wu works across Java backend platforms, microservices, data links, high-concurrency systems, performance optimization, and engineering delivery, and applies that foundation to AI Agent systems, model services, realtime voice interaction, business intelligence, edge-cloud collaboration, and embodied-AI application systems.

655 Technical Notes Bilingual engineering archive
7 Project Stories AI, indie tools, automation
4 Focus Tracks Backend, AI, tools, writing
SSG Search Ready Sitemap, RSS, llms.txt and schema

Start Here

Focus Areas

Backend Platform & Data Links

Java / Spring Cloud, microservice governance, Kafka / Redis / ES, Kubernetes / DevOps, reliability, and high-throughput systems

AI

AI Agent & Model Services

LLM / ReAct / Function Call, MCP, vLLM, local model services, tool-call evaluation, and business API orchestration

Multimodal & Edge-Cloud

Wake word, VAD, ASR, LLM, TTS, WebSocket / gRPC, robots, patrol vehicles, and edge terminal integration

Writing & Knowledge Base

Long-term public writing on Java, big data, DevOps, AI Agent systems, model services, and engineering practice

Now Building

Currently Building

A compact view of the active workbench: public tools, AI engineering, knowledge systems, writing rules, and the site itself.

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Reading Paths

Pick a track first, then follow the most relevant articles and projects without getting lost in the full archive.

Open Start Here →
backend-engineering Backend & Platform Path

Build the engineering foundation around Java, Spring Cloud, microservices, Kubernetes, DevOps, messaging, and data pipelines.

For backend developers, platform engineers, and builders moving from business features to system design.

ai-writing-quality Chinese AI Writing Quality Path

Use stop-slop-zh, anti-slop rules, technical-blog quality checks, review workflows, and prompt engineering to govern Chinese AI writing.

For writers, reviewers, prompt builders, and anyone reducing the templated feel of AI-generated Chinese content.

digital-life Long-term Records Path

Follow annual reviews, project write-ups, training notes, digital-life experiments, and personal reflections as long-term practice.

For readers interested in long-term growth, public notes, review habits, and practice beyond pure technical topics.

Topics

Topic Matrix

Six long-term topic assets connect Start Here, Series, Projects, Workflow, and the content roadmap.

Open all topics →

Who This Helps

This site is built for practical readers

It is not a generic resume page. It is a growing archive of systems, tools, experiments, and writing paths.

Backend engineers moving into AI Engineering

You can follow how service design, observability, deployment, and tool calling connect with LLM applications.

Builders of indie tools and public products

You can inspect how small tools, project pages, SEO/GEO, and long-term writing form a public work system.

Readers who want practical technical archives

You can use the series, tags, project map, RSS, and llms.txt as structured entry points rather than a linear blog feed.

About

AI Agent, multimodal interaction, and backend platform engineer focused on complex systems integration and engineering delivery.

I integrate LLMs, realtime voice pipelines, business APIs, model services, and edge devices into deployable, measurable, and maintainable AI application systems.

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