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…
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
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
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
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
AIE Core Cheatsheet: SFT, LoRA, RAG & Agent Interview Map
> **Executive Summary**: The AI / LLM Systems Engineer (AIE) role spans model fine-tuning, retrieval engineering, agentic orchestration, and high-throughput ser