Alan Wang

Contents

Table of Contents

Posts grouped by section, domain, and topic.

Knowledge 34
AI 13
  • Agents
    Inside a RAG Agent: Runtime, Memory, Tools, and Retrieval

    Five design boundaries inside a LangGraph RAG agent: control flow, safety and tools, retrieval compatibility, checkpointed memory, and session isolation.

  • Agent System Architecture

    A simple overview of agent system architecture, the orchestrator and its four core components, LLM, tools, memory, and context engineering.

  • Agent Harness Overview

    A sketch of how agent systems split responsibilities between the model and the harness around it.

  • Agentic Workflow: Reason, Act, Observe

    A compact sketch of a tool-first agentic workflow, showing how reasoning, tool use, observation, memory, and human review fit together in one loop.

  • Evals
    Five Ways to Evaluate an AI System

    Deterministic checks, reference-based metrics, LLM-as-judge, human review, and online feedback: what each one compares, and when to reach for which.

  • LLM
    Three Types of Transformers

    Encoder, decoder, and seq2seq transformers compared by what they output, an example model, typical use cases, and typical size.

  • LLM Basics: Transformers, Tools, and Agents

    Notes on the three transformer architectures, chat templates and special tokens, tool calling and MCP, and the agent Thought/Action/Observation loop.

  • ML
    PPO vs DPO

    A short note on PPO and DPO, two ways to align a model's responses with human preferences.

  • Attention Is All You Need

    A sketch of the Transformer paper, with the main architecture pieces and a few key questions to keep in mind while reading it.

  • ReLU, Neural Nets, and Backpropagation

    A compact sketch of ReLU activations, neural network structure, and backpropagation flow.

  • RAG
    Agent and RAG Tradeoffs: Cost, Latency, Quality, and Evaluation

    A practical note on cost-latency-quality tradeoffs in agent and RAG systems, how to evaluate RAG by separating retrieval quality from answer quality, and how to structure a project narrative.

  • RAG Overview

    A visual overview of retrieval, ranking, and generation flow in a RAG pipeline.

  • VLM
    VLM Basics: What a Model Actually Sees

    A frame never reaches a vision-language model as pixels. Seven facts about images, JPEG, base64, vision encoders, and where VLMs still fall short, checked against a real evaluation pipeline.

Backend 6
  • Concurrency
    Python GIL: Why Multiple Threads Do Not Mean Parallel Python

    The GIL means one CPython process can run only one Python-bytecode thread at a time. This is a threading issue, not an async/await issue.

  • Python Backend Concurrency Model

    A Python backend concurrency cheat sheet: processes, threads, coroutines, sync versus async, the GIL, and how to choose between threads, async, and processes.

  • FastAPI
    FastAPI Concurrency Implementation

    How FastAPI actually runs sync and async routes: Uvicorn worker processes, one event-loop thread, AnyIO's thread pool, database pool sizing, and deployment traps.

  • WebSocket
    WebSocket Connection Manager

    A WebSocket connection manager lets the backend remember who is connected, push to a specific user, broadcast to everyone, and clean up disconnected sockets.

  • FastAPI WebSocket Handler Lifecycle

    Understand why a FastAPI WebSocket handler lives as long as the connection lives, from accept to receive loop to disconnect cleanup.

  • WebSocket Protocol Fundamentals

    WebSocket starts as an HTTP Upgrade request, switches to a frame-based protocol, and keeps one TCP connection open for full-duplex realtime communication.

Cloud 1
Data Systems 5
HPC 2
  • GPU / PyTorch
    Distributed Data Parallel

    A compact visual guide to DDP: full model copies, per-GPU batches, gradient all-reduce, NCCL, and when Forge should use DDP versus FSDP.

  • HPC + GPU + PyTorch Practical Guide

    A practical note for the full HPC to GPU to CUDA to PyTorch to backpropagation workflow on Northeastern Explorer.

Messaging 3
Networking 2
  • Foundations
    Network Layering Model

    A compact poster explaining the TCP/IP five-layer model, where common protocols belong, how HTTPS travels through the stack, and how to debug network issues by layer.

  • Protocols
    TCP Handshake Basics

    A compact note on how TCP opens with SYN, SYN-ACK, ACK and closes with FIN, ACK, FIN, ACK.

Tools 2
  • Command Line
    Git Command Cheatsheet

    A quick Git command cheatsheet covering repository setup, branching, status checks, commits, sync, and rollback basics.

  • Python
    Python Command Cheatsheet

    A quick command cheatsheet for checking Python, managing virtual environments, handling packages, and running files or modules.

Projects 7
AI 4
Cloud 3
System Design 6
Case Studies 3
  • Database Basics
    Designing a Like Feature

    A practical system design note for a post like feature, from database modeling to large-scale asynchronous like-count aggregation.

  • System Design Questions
    URL Shortener

    Requirements, short code generation strategies, caching, and scaling reads and writes with separate read/write services.

  • Malicious IP Detection

    Requirements, QPS estimation, a hot/cold path split, and three deep dives: Bloom filter sizing, blacklist propagation, and layered detection.

Fundamentals 1
  • Components
    System Design Components

    Nine components that show up in nearly every system design interview, what each one actually solves, and the one trade-off worth knowing for each.

OOD 2
  • Interview Prep
    OOD Interview Answer Flow

    A compact interview reference for structuring object-oriented design answers and explaining the four core OOP principles.

  • Parking Lot
    Parking Lot OOD Basics

    An entry-level object-oriented design note for a parking lot system, focused on vehicles, spots, and simple spot assignment.

Thanks for reading.

© 2026 Alan Wang