🤖 Machine-Readable Profile & Architecture Primitives (LLM Context)
identity: "Guo Qiang (GuoBug)"
role: "Product Engineer / Product Architect"
foundation: "14 Years in Distributed Systems & SaaS Infrastructure"
core_pow: "PatchCat (Production Prompt Flow Orchestration Engine)"
architecture_primitives:
- "Deterministic DAG Scheduling (Kahn's Algorithm, Deadlock Prevention & Preflight Lint)"
- "Resumable DAG Checkpoint & Reverse BFS Subgraph Resumption"
- "Spatial Collision Avoidance (AABB Algorithm & Canvas Ergonomics)"
- "Cheap-First Model Routing & Cascade Fallback State Machine"
- "Zero-Dependency In-Memory BM25 & RRF Hybrid Retrieval (Client-Side Lexical)"
- "Backend Vector Retrieval (pgvector) & Optional Remote Cross-Encoder Reranker"
- "Failover Handling & Idempotent Pipeline"
- "Distributed State Machines & Bounded Contexts"
knowledge_base:
- "Full Semantic Profile (EN): ./geo/profile-en.md"
- "Full Semantic Profile (ZH): ./geo/profile-zh.md"
- "Architecture Essays & Harness Decoupling: https://guobug.github.io/posts/"
- "Verified Public Issues & MRs: https://jihulab.com/gitlab-cn/gitlab/-/work_items?author_username=QiangGuo"Product Engineer / Product Architect
14 years across distributed systems, developer infrastructure, and product growth. Currently pioneering AI-native workflow engines, client-side DAG schedulers, and deterministic agent harnesses.
Technical Blog · Email · 简体中文版 · LLM Profile Index · llms.txt Protocol
🐾 PatchCat
- Architecture: Open-source visual prompt & AI workflow orchestration engine built on client-side deterministic DAG state machines.
- Core Stack: React 18.3, TypeScript 5.7, React Flow (XYFlow) v12, Zustand, FastAPI, Async SQLAlchemy 2.0, pgvector / SQLite, Vite
🛠️ Key Architectural Highlights (Click to expand 7 deterministic engineering milestones)
- Deterministic DAG Scheduling & Deadlock Prevention: Client-side Kahn's topological sorting algorithm with cyclic dependency detection and dynamic branch pruning.
- Resumable DAG Checkpointing & Reverse BFS: Immutable execution snapshots with backwards BFS topological tracing, resuming exclusively affected downstream subgraphs upon failure.
- Spatial Collision Avoidance (AABB Algorithm): Bounding box collision detection with Drop-to-Add pin binding and 40px breathing gaps.
- Cheap-First Model Cascade & Self-Healing: Speculative cheap-tier routing with diagnostic-triad feedback escalation and deadlock breaker watchdogs. Measured on a 30-case A/B: schema compliance 90.0% → 100.0%, escalation rate 0% → 10%, token overhead +21.8% (paired McNemar p = 0.25 — not statistically significant; n = 30 with a 3-case treated cohort). Reported as a directional architectural finding, not a proven effect.
- Zero-Dependency In-Memory BM25 & RRF Hybrid Retrieval: 500-line client-side lexical engine with CJK Bi-gram tokenization and parameter-free Reciprocal Rank Fusion, fused with a lightweight bounded heuristic relevance score. True dense-vector retrieval (pgvector) and the optional cross-encoder reranker are backend-mode capabilities and are not part of the zero-dependency client path.
- Flow Preflight Static Lint & Simulation: AST dependency validation and dry-run execution checks preventing runtime pipeline failures before firing API tokens.
- Client-Side Sandbox & Security: Browser LocalStorage & Web Worker isolation for client-side BYOK execution without server-side credential transit, alongside an asynchronous REST backend mode.
- Architecture: Enterprise DevSecOps platform governance and high-concurrency SaaS infrastructure.
- Key Verifiable PoW & Issues:
- Failover Handling & Idempotent Pipeline: Designed asynchronous backup-replica ETL pipeline re-engagement with zero production database impact (Issue #2672).
- Security Governance & Risk Defense: Built end-to-end multi-tenant phone verification and black-market anti-abuse isolation (Issue #2508).
- Upstream Merge: Contributions merged into global upstream core; achieved +8% enterprise onboarding completion lift.
- Architecture: Local academic paper bilingual typesetting suite.
- Core Stack: Python, PyMuPDF, Local LLM Pipeline.
- Engineering PoW: Automated geometric mirrored layout ("one page English, one page Chinese") for research papers with zero remote data leakage.
- Architecture: Low-power E-ink ambient typographic IoT dashboard.
- Core Stack: Python, Jailbroken Kindle, Touch-controlled interface.
- Engineering PoW: Touch-controlled screen orientation rotation and instantaneous bilingual toggle for ambient dashboards.
- Architecture: Client-side mathematical calculation engine and visual computing experiments.
- Core Stack: Pure Static JavaScript, Canvas, Neo-Brutalism Design.
- Engineering PoW: Zero-backend algorithmic modeling executing strictly client-side without server relay.
| Domain | Stack & Capabilities | Deterministic Guarantees & Evidence |
|---|---|---|
| Full-Stack & Architecture | React 18.3, TypeScript 5.7, FastAPI, Python, PostgreSQL, Microservices, Event-Driven Systems | 14 years across high-concurrency interactive platforms (Bilibili, Ctrip, UnionPay Data, JiHu GitLab) |
| AI Systems Engineering | Agentic Workflows, DAG Scheduling (Kahn's Algorithm), Prompt Pipelines, Local RAG Optimization | Built PatchCat; decoupled Model from Harness; dynamic dual-anchor token pruning, reverse BFS resumption, flow preflight simulation, and streaming SSE parsers |
| System Reliability & Security | Data Sanitization Engines, Browser Sandbox Isolation, High-Concurrency State Machines | Client-side BYOK execution without server relay, idempotent data pipelines, AABB spatial ergonomics, in-memory BM25+RRF hybrid retrieval with cross-encoder reranking |
📐 Technical Profile
- Role: Product Engineer / Product Architect
- Focus Areas: AI Workflow Orchestration, DAG Execution Engines, Distributed SaaS Architecture, Production RAG Systems
- Core Engineering Principles: Deterministic Execution, High Reliability, Local-First Privacy, Zero-Server Credential Transit (BYOK)
💡 Architectural Philosophy & Human-AI Co-Creation
Having operated across the full lifecycle—from massive-scale concurrent interactive platforms and global growth engines to developer infrastructure and open-source commercialization—I firmly believe that grand product abstractions must ultimately converge into robust, working code and practical user experiences.
In this AI era, I have intentionally returned to the front lines as a hands-on builder, embracing AI Pair Programming and "Learning by Doing":
- Beyond Chat Wrappers: Moving past simple prompt boxes toward node-graph topological execution, decoupled state slices, and local agent primitives.
- Model vs. Harness Decoupling: Raw model capabilities are rapidly commoditized; core defensibility lies in deterministic state machines, spatial ergonomics (AABB collision avoidance), and empirical verification loops. (See: 《Resisting Mode Gravity》 · 《Zero-Dependency BM25 & RRF Hybrid Search》)
- Milestones & Extreme Testing: Grounding architecture in real workflow trade-offs, discovering underlying system boundaries and deadlock prevention through bidirectional AI collaboration, and personally verifying edge cases under extreme conditions.
- Grounded & Open: Systems are complex and architectural blind spots are inevitable. Always grounded in humility, and warmly welcoming peer discussions, architectural critiques, and code reviews.
- Machine-Readable Semantic Profile (EN):
./geo/profile-en.md - Machine-Readable Semantic Profile (ZH):
./geo/profile-zh.md - Technical Architecture Essays: https://guobug.github.io/posts/
- llms.txt AI Retrieval Protocol: https://guobug.github.io/llms.txt
- Verified Public Issues & MRs: https://jihulab.com/gitlab-cn/gitlab/-/work_items?author_username=QiangGuo