Open to work: AI Product Engineer / Frontend-heavy Fullstack Engineer

AI product builder / Frontend-heavy fullstack engineer

I build AI products that expose the work.

I am Larry Xue, also known as Yujian Xue. I am looking for AI product engineering work where I can turn rough workflows into usable agent products, enterprise interfaces, and public proof that a team can inspect.

Enterprise surface
CDN consoles, AI gateway UI, operations workflows
Agent products
炼云小说工厂, YouChannel, agent workflow writing
Open-source proof
OpenClaw proxy patch, storage UI, build notes

Builder story

The through-line is product judgment under real constraints.

I learned reliability and complexity from cloud control panels. I learned AI product shape by building voice, memory, and workflow prototypes. I learned distribution by publishing open-source tools and writing the tradeoffs down.

Enterprise AI surfaces

I ship control planes where mistakes are expensive.

Current work spans CDN customer consoles, AI gateway interfaces, async task flows, log search, data visualization, and internal operations systems. The public version is a sanitized case study, not a client screenshot dump. sanitized case study: cloud/CDN consoles, AI gateway, complex configuration UX

AI-native engineering

I use AI as leverage, then keep the human judgment loop.

My workflow turns specs, prompt files, generated code, tests, and code review into repeatable delivery for admin-style product surfaces. prompt specs, test loops, review discipline, scaffold modernization

Agent product prototypes

I build the product loop before polishing the pitch.

Zota and Fluently explored session memory, context compaction, live voice, transcripts, learning records, and AI communication practice. Next.js, Supabase, AI SDK, Gemini Live, assistant-ui

Open-source proof

I leave work that can be inspected and reused.

Recent work includes Lianyun Novel Factory, an OpenClaw Feishu/Lark proxy fix, storage-console exploration, and public writing that records the tradeoffs. open source, upstream PRs, storage UI, public build notes

What I would show first

Current work that maps to AI startups.

open-source May 2026 release

炼云小说工厂

An open-source AI web novel production workbench with agent workflows, prompt management, living story documents, batch jobs, and run tracing.

AI writing systemagent runtimeLLM observabilityopen source
Read details

sanitized engineering case study

Enterprise AI Console Case Study

A sanitized reconstruction of CDN console, AI gateway, log search, async task, and internal operations work where dense configuration needs safe, inspectable UX.

enterprise AI UICDN consoleAI gatewayoperations workflow
Read details

upstream proxy fix and contribution fork

OpenClaw Contribution Fork

A focused OpenClaw contribution fork used to reproduce and patch a Feishu/Lark websocket proxy routing bug, then hand the proof back upstream.

OpenClaw PR #86386Feishu/Lark proxy fixagent runtimeassistant platform
Read details

knowledge workflow experiment

Obsidian LLM Wiki

A lightweight knowledge-base experiment around Obsidian-style notes and LLM-readable personal memory surfaces.

knowledge baseLLM memorypersonal tooling
Open project

public product prototype

YouChannel

A YouTube-centered AI product experiment with a public web surface and companion service repo for video-aware workflows.

AI video productYouTube workflowfullstack prototype
Open project

storage console exploration

MinIO Web UI

A Vue-based object storage UI exploration around bucket browsing and storage-console workflows.

storage UIadmin consoleVue
Open project

Operating mode

How I build when the product is still unclear.

Start with the real workflow

A good AI product is not a demo prompt. It has users, state, retries, approvals, memory, logs, and a reason to exist inside daily work.

Expose the loop

Agents become usable when people can see what happened: inputs, tools, drafts, traces, failures, and where human judgment entered.

Ship small, then earn complexity

I prefer a working product slice with clear evidence over a large speculative architecture. The next abstraction should be earned by usage.

Research map

The questions I keep returning to.

Agent workflow architecture

Exploring how agent products should expose state, tools, approvals, memory, traces, failures, and user control.

AI communication practice

Testing how live voice, role play, feedback, transcripts, and memory can make language and work communication practice useful.

Enterprise AI interfaces

Designing AI and cloud product surfaces that keep complex configuration, logs, tasks, and review states understandable.

Large-model search visibility

Making personal sites and project pages easier for AI search systems to crawl, cite, summarize, and attribute.

Latest writing

Notes from the workbench.