06 / ENGINEERING NOTES
Engineering Notes
Long-form notes on AI, engineering systems, and organizational evolution.
Building a Recruitment LLM from Zero to One: A BOSS Zhipin–Style Case Study
A practical open-source blueprint for a recruitment-domain language model: product goals, data governance, continued pretraining, SFT and DPO, RAG, candidate–job matching, evaluation, safety, deployment, and a 12-week delivery plan.
The Moat in Large Language Models Is Not Just Code—It Is Data
As Transformer implementations, training frameworks, and open weights become widely available, the scarce advantage shifts toward data quality, data mixtures, verification, governance, and the feedback loop that improves them.
Agent Architecture for SMBs: A 90-Day Path to Production
Do not copy an enterprise AI platform. Build a production agent with deterministic orchestration, narrow tools, least privilege, evaluation, and measurable business outcomes.
Building Docket with Pragmatic DDD
A code-based look at how Docket uses bounded contexts, domain services, and a modular monolith to manage a professional-services workflow—and how the architecture can evolve.
Xinxu Cloud Brain AI: Building a Reliable Multimodal Emotion System
A production architecture for voluntary text, speech, and drawing inputs, combining modality-specific encoders, robust fusion, multi-task learning, calibrated uncertainty, privacy governance, and continuous evaluation.
Modern Concurrency: From Execution Models to Production Reliability
A practical guide to memory models, Java virtual threads, Go channels, Python asyncio, structured concurrency, backpressure, cancellation, and observability.
Do Large Language Models Understand? From Next-Token Prediction to the Boundary of Intelligence
Large language models are built from next-token prediction and matrix operations. Does that mean they understand nothing? A careful distinction between representation, functional understanding, consciousness, and human experience.
LangChain vs. LlamaIndex: Choosing a Framework and Building Production RAG
A practical comparison of LangChain, LangGraph, and LlamaIndex, followed by an end-to-end production RAG architecture covering ingestion, hybrid retrieval, reranking, citations, evaluation, and access control.
How to Build an AI-Native Team
AI-native transformation is not a tool rollout. It is a redesign of goals, workflows, human-agent responsibilities, organizational context, evaluation, and governance.
From Query Understanding to Knowledge-Driven Recommendations
A production-minded architecture for tax and finance search, combining structured query understanding, hybrid retrieval, knowledge governance, reranking, and continuous evaluation.