DUCKINASHIRT

AI Agent Systems × RAG × Backend Infrastructure

agent runtime · retrieval evaluation · LLM serving · Go backend · production reliability

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┌──────────────────────────────────────────────────────────────────────┐
│                                                                      │
│   020617  →  22D3EE  →  8B5CF6  →  EC4899                            │
│   DARK CORE     CYAN SIGNAL     VIOLET ROUTER     NEON OUTPUT        │
│                                                                      │
│   Build AI-native systems with backend discipline.                   │
│   Make it observable. Make it measurable. Make it ship.              │
│                                                                      │
└──────────────────────────────────────────────────────────────────────┘

I build LLM-powered tools and backend systems around one simple idea:

AI products should be measurable, observable, and boring enough to run in production.

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Neon Map

Zone	What I care about
Agent Systems	ReAct-style loops, tool use, MCP-style integration, bounded execution, failure paths
Retrieval Infrastructure	Embedding, BM25, hybrid search, reranking, context construction, retrieval metrics
LLM Serving	vLLM-style serving concepts, SSE streaming, latency awareness, prompt systems
Backend Infrastructure	Go services, RPC integration, async DAG pipelines, Redis/MySQL, graceful fallback

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Core Stack

Languages     Go · Python · Java
AI / LLM      PyTorch · LangChain · vLLM concepts · Prompt Engineering
Retrieval     Embedding · BM25 · Hybrid Search · Rerank · FAISS-style vector search
Backend       RPC · Async DAG · Redis · MySQL · RabbitMQ · Object Storage
Systems       Linux · Docker · Observability · Caching · Graceful Degradation
Evaluation    Recall@K · Precision@K · MRR · nDCG · P50/P99 Latency

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Systems I Like Building

graph LR
    A[Documents / Logs / Code] --> B[Parsing & Chunking]
    B --> C[Hybrid Retrieval]
    C --> D[Rerank & Context Builder]
    D --> E[LLM / Agent Runtime]
    E --> F[Tool Calls]
    E --> G[Streaming Response]
    F --> H[Observable Backend]
    G --> H

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• Agent runtimes with explicit tools, bounded loops, and clear failure paths.
• RAG pipelines evaluated with retrieval metrics instead of vibes.
• AI backend services that treat latency, caching, idempotency, and degradation as first-class constraints.
• Developer tools that compress repetitive engineering workflows into reliable agentic loops.

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Public Nodes

Project	Signal
leetmate	Terminal LeetCode coach that gives hints, not answers.
gocommit	AI-assisted Chinese commit message generator.
llm-systems-learning-notes	Notes on LLM systems, inference, CUDA, scheduling, RLHF/GRPO.
minGPT	Minimal PyTorch GPT implementation and learning project.

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Current Vectors

[01] Building small, composable AI Agent runtimes
[02] Improving RAG evaluation beyond demo-level retrieval
[03] Exploring LLM serving and streaming interaction patterns
[04] Keeping backend systems simple, observable, and failure-aware
[05] Writing tools that make developers faster without hiding the machinery

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Engineering Taste

agent_runtime:
  preference: explicit tools, bounded loops, observable traces
  avoid: unbounded autonomy, hidden state, magic orchestration

rag_pipeline:
  preference: measured retrieval quality, explainable context construction
  avoid: demo-only vector search, vibe-based evaluation

backend:
  preference: simple interfaces, boring infrastructure, graceful degradation
  avoid: clever abstractions before the failure modes are understood

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LESS MAGIC. MORE TRACES. BETTER SYSTEMS.