AirSOTA
Air School of Thoughts AtoZAirSOTA 知识矩阵：聚合大模型算法架构、科学育儿情境成长、加州地产考牌实战与全球数字化商业出海的权威专栏。
TalentMe · AI 学习与系统架构
工业级 AI 算法核心 69 题、前沿大模型系统架构演进与北美技术面试全流程备考深度长文。
AIE Agent Systems in Production: Orchestration Patterns, Context Budgeting, Reliability & Observability
> **Core Executive Summary**: An agent is a loop — goal, observation, policy, action, memory — but a production agent is a *guarded* loop. This guide…
MLE Coding & Algo Prep: Zero-to-One ML Operators in Pure Numpy
> **核心摘要**：Exhaustive technical deep dive into Pure Numpy handwritten ML operators for live coding interviews.
RS Paper Deep Dive Framework: Articulating Novelty & Research Vision
> **Executive Summary**: In Research Scientist (RS) interviews, interviewers evaluate a candidate’s **independent research taste, long-term technical vision, an
Cluster Scheduling & Ray: K8s Scheduling Pipeline, Raylet Architecture, Object Store, Autoscaling & GPU Scheduling Full Guide
> **Core Executive Summary**: Every ML platform asks one question: given $N$ nodes of CPU/GPU/memory, how do we place $M$ tasks so resources are utilized, users
Deep Learning Foundations: Activations Evolution (GELU/SwiGLU), Loss Function Taxonomy (CE/KL/Huber/InfoNCE/ArcFace) & Autograd Backprop Guide
> **Summary**: Non-linear activation functions, loss functions, and backpropagation form the mathematical pillars of deep learning. This exhaustive guide covers
Preference Alignment: RLHF 3-Stage, PPO Clipped Loss, DPO Math Derivation, GRPO & PRM/ORPO
> **Core Executive Summary**: While Pre-training and Supervised Fine-Tuning (SFT) instill strong language modeling and instruction following in LLMs, models may
Tokenizer & Decoding Strategies: BPE, WordPiece, SentencePiece, Temperature, Top-k/p, Min-p, Gumbel-Max, Repetition Penalty & Sequence Packing
> **Core Executive Summary**: The text processing pipeline of Large Language Models (LLMs) spans three stages: **Front-end Tokenization**, **Mid-end Autoregress
Generalization Theory: Inductive Bias, Double Descent & PAC Learning Paradigms
> **Core Executive Summary**: Why do 70B+ parameter LLMs generalize exceptionally without severe overfitting? **Generalization Theory** explains this modern AI
Diffusion Models: DDPM Derivation, Latent Diffusion, DiT & GPT-4o Native Generation
> **Core Executive Summary**: Generative AI rests on two pillars: Autoregressive LLMs and **Diffusion Models**. Inspired by non-equilibrium thermodynamics, diff
Industry System Case Studies: Pinterest Visual Search & Netflix Recommendation
> **Core Executive Summary**: Real-world system design mastery comes from studying architectures that actually run at scale. This guide dissects **Pinterest** (
AIE Agent 生产系统:编排模式、上下文预算、可靠性工程与可观测性
> **核心摘要**：Agent 本质是一个循环——目标、观测、策略、行动、记忆——但生产级 Agent 是一个**带护栏的循环**。本指南覆盖 LLM Agent 上线的完整工程栈：编排模式（ReAct / Plan-and-Execute / Reflexion / 多 Agent）、具备硬终止保证的循环与图式编排
MLE 算法手写实战：零基础 Pure Numpy 手写 LR、K-Means、Self-Attention 与 NMS
> **核心摘要**：全量拆解 MLE 面试高频机器学习算法手写 (Whiteboard / Live Coding) 实战。深入剖析零依赖 Pure Numpy 实现逻辑回归 (Logistic Regression)、K-Means 迭代聚类、Softmax 数值防溢出技巧与 Self-Attention / NM
RS 论文拆解与研究 Vision：如何向面试官复述 SOTA 论文创新点
> **核心摘要**：在算法研究科学家（Research Scientist, RS）面试中，面试官最看重的是候选人的**独立科研品味（Research Taste）、前沿技术视野（Research Vision）以及对 SOTA 论文的批判性深度解构能力**。面试绝不是简单复述论文摘要，而是展现“从第一性原理审视问题
集群调度与 Ray：K8s 调度管线、Raylet 架构、分布式对象存储、弹性扩缩容与 GPU 调度全景
> **核心摘要**：任何 ML 平台都要回答同一个核心问题：给定 $N$ 台节点（CPU/GPU/内存），如何为 $M$ 个任务分配资源，使利用率最高、用户之间公平、且故障不拖垮整个作业？本指南从第一性原理拆解集群调度基本问题（资源分配/优先级/公平性），深入 Kubernetes 调度管线（过滤/打分两阶段、污点/
深度学习基础全景：激活函数族全演进(GELU/SwiGLU)、损失函数大一统 (CE/KL/Huber/InfoNCE/ArcFace) 与计算图反向传播极客指南
> **核心摘要**：非线性激活函数、损失函数与计算图反向传播共同构成了现代神经网络训练的三大数理基石。激活函数为网络注入非线性表征能力，使得多层神经元能够逼近任意复杂的连续函数（通用近似定理）；损失函数定义了优化目标的几何曲面与物理约束；而基于自动微分 (Autograd) 的计算图与反向传播算法则是高效率求解参数梯