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Cambridge Mofang Notes
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Cambridge Mofang Notes

Upholding classic programming, focusing on AI human‑machine collaboration, technology implementation and practice sharing.

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Recent Articles

Latest from Cambridge Mofang Notes

15 recent articles
Cambridge Mofang Notes
Cambridge Mofang Notes
Oct 10, 2026 · R&D Management

AI Accelerates Architecture Design; Architects Must Validate Boundaries, References, and Decisions

While AI can rapidly generate architecture diagrams and specifications, architects must rigorously define system boundaries, verify reference materials, document decision rationale with trade-offs, and adapt their skills to orchestrate AI workflows and evaluate outcomes to prevent unverified assumptions from entering production.

AI orchestrationAI-assisted developmentArchitecture Review
0 likes · 15 min read
AI Accelerates Architecture Design; Architects Must Validate Boundaries, References, and Decisions
Cambridge Mofang Notes
Cambridge Mofang Notes
Sep 25, 2026 · Artificial Intelligence

From Prompt Engineering to Graph Engineering: The Evolution of AI App Development Paradigms

The article outlines five engineering disciplines—Prompt, Context, Harness, Loop, and Graph Engineering—that progressively address the challenges of building reliable LLM applications, from crafting effective prompts to managing complex task execution graphs with state, tools, and human-in-the-loop controls.

AI AgentContext EngineeringGraph Engineering
0 likes · 17 min read
From Prompt Engineering to Graph Engineering: The Evolution of AI App Development Paradigms
Cambridge Mofang Notes
Cambridge Mofang Notes
Sep 9, 2026 · Artificial Intelligence

Clearing AI Confusion: Function Calling, MCP, Tools, Skills, Vectors, Tensors, Tokens, Embeddings

This article clarifies commonly confused AI concepts by grouping them into four categories: model-tool interaction (Function Calling vs MCP), task execution (Tools vs Skills), internal data representation (vectors vs tensors), and text processing (Tokens vs Embeddings), explaining their distinct roles and relationships.

EmbeddingFunction CallingMCP
0 likes · 27 min read
Clearing AI Confusion: Function Calling, MCP, Tools, Skills, Vectors, Tensors, Tokens, Embeddings
Cambridge Mofang Notes
Cambridge Mofang Notes
Sep 7, 2026 · Backend Development

Points System Architecture: Concurrency, Batch Expiration & Idempotent Reconciliation

This article walks through designing a points system architecture, starting from a concrete scenario of consuming 80 points across two batches with retries and refunds, covering data models, idempotency keys, local transactions, cache-aside, event-driven reconciliation, scaling strategies, and evolutionary deployment phases.

Cache AsideIdempotencySystem architecture
0 likes · 27 min read
Points System Architecture: Concurrency, Batch Expiration & Idempotent Reconciliation
Cambridge Mofang Notes
Cambridge Mofang Notes
Sep 4, 2026 · Artificial Intelligence

Model Distillation: Teaching Small Models to Learn from Large Ones

This article explains model distillation through a question rewriting example, detailing how teacher models provide demonstrations to train smaller student models, the differences between distillation, fine-tuning, and quantization, and practical pitfalls like data quality and student capacity limits.

Data Qualityfine-tuningknowledge distillation
0 likes · 19 min read
Model Distillation: Teaching Small Models to Learn from Large Ones
Cambridge Mofang Notes
Cambridge Mofang Notes
Sep 3, 2026 · Artificial Intelligence

AI Model Types, Quantization & File Formats: A Complete Guide

This article explains the four key dimensions of AI models—purpose, modality, quantization, and file format—covering model categories like LLMs, vision, audio, embedding, and reranker models, multimodal concepts, quantization trade-offs (FP16, Q4, Q8), and formats such as Safetensors, GGUF, and ONNX, with a practical checklist for model selection.

Embedding ModelsGGUFSafetensors
0 likes · 27 min read
AI Model Types, Quantization & File Formats: A Complete Guide
Cambridge Mofang Notes
Cambridge Mofang Notes
Sep 2, 2026 · Artificial Intelligence

How LLM Applications Are Built: Workflows, Agents, MCP & Skills

This article explains the architecture of large language model applications, distinguishing between simple chat, fixed workflows, autonomous agents, the Model Context Protocol (MCP) for tool integration, and reusable skills, providing a decision framework for choosing the right approach based on task complexity and stability requirements.

AI architectureAgentLLM applications
0 likes · 18 min read
How LLM Applications Are Built: Workflows, Agents, MCP & Skills
Cambridge Mofang Notes
Cambridge Mofang Notes
Sep 2, 2026 · Artificial Intelligence

Inference Frameworks vs Platforms: How LLMs Actually Run on Your Hardware

This article distinguishes between inference frameworks (llama.cpp, vLLM, SGLang) that execute model computations and inference platforms (Ollama, LM Studio, Xinference) that manage deployment, explaining their roles, interactions, and how to choose tools for local or server-side LLM inference.

AI inferenceLLM servingLM Studio
0 likes · 16 min read
Inference Frameworks vs Platforms: How LLMs Actually Run on Your Hardware
Cambridge Mofang Notes
Cambridge Mofang Notes
Sep 1, 2026 · Artificial Intelligence

From Dense to MoE: Decoding Total vs. Activated Parameters

This article explains the distinction between total and activated parameters in Mixture-of-Experts (MoE) models, contrasting dense and sparse architectures, detailing expert routing mechanisms, and analyzing memory and compute implications across model loading, prefill, and decode stages.

Mixture of ExpertsMoEactivated parameters
0 likes · 15 min read
From Dense to MoE: Decoding Total vs. Activated Parameters