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Architects' Tech Alliance
Architects' Tech Alliance
May 8, 2026 · Artificial Intelligence

Token Fundamentals: A Technical Panorama of AI Language Units

Tokens are the smallest language building blocks that AI models process, representing characters, words, subwords, punctuation or emojis; they determine context window size and generation speed, so tokenization directly impacts model understanding accuracy and efficiency, as explained in the 2026 Token Report.

AI fundamentalsContext Windowlanguage models
0 likes · 4 min read
Token Fundamentals: A Technical Panorama of AI Language Units
PMTalk Product Manager Community
PMTalk Product Manager Community
Apr 25, 2026 · Product Management

3 Pitfalls I Encountered When Transitioning from Traditional to AI Product Management

A former traditional product manager shares how a naive AI feature request exposed his lack of AI knowledge, why learning programming, algorithms, or certificates didn’t help, and the three practical paths—using AI, building an AI feature, and filling essential basics—to successfully become an AI product manager.

AI fundamentalsAI product managementPrompt engineering
0 likes · 11 min read
3 Pitfalls I Encountered When Transitioning from Traditional to AI Product Management
Spring Full-Stack Practical Cases
Spring Full-Stack Practical Cases
Apr 11, 2026 · Artificial Intelligence

Master AI Fundamentals: Tokens, Context Windows, Temperature, Hallucinations & RAG

This article breaks down five essential AI concepts—tokens, context windows, temperature settings, hallucinations, and retrieval‑augmented generation—explaining how they work, why they matter, and how to apply them effectively when building or using large language model applications.

AI fundamentalsContext WindowRetrieval Augmented Generation
0 likes · 12 min read
Master AI Fundamentals: Tokens, Context Windows, Temperature, Hallucinations & RAG
Code Mala Tang
Code Mala Tang
Apr 7, 2026 · Artificial Intelligence

Demystifying LLMs: From Tokens to Agents – An Engineer’s Deep Dive

This article provides a comprehensive, engineering‑focused breakdown of large language models, covering their Transformer roots, tokenization, context windows, prompt engineering, tool integration via MCP, and autonomous agents, while offering practical examples and actionable insights for developers.

AI fundamentalsLLMPrompt engineering
0 likes · 10 min read
Demystifying LLMs: From Tokens to Agents – An Engineer’s Deep Dive
AI Algorithm Path
AI Algorithm Path
Jan 11, 2026 · Artificial Intelligence

How Vector Embeddings Enable AI to Understand Anything

This article explains the principle of vector embeddings, shows how they turn words, images, audio and other data into dense numeric vectors, compares them with one‑hot encoding, describes static and contextual models, training methods, similarity metrics, and a wide range of real‑world AI applications.

AI fundamentalsMultimodalRAG
0 likes · 15 min read
How Vector Embeddings Enable AI to Understand Anything
ShiZhen AI
ShiZhen AI
Dec 1, 2025 · Artificial Intelligence

AI Comic Episode 3: What Exactly Is a Token?

This episode explains that a token is the smallest text chunk an LLM processes—ranging from characters to subwords—covers why subword tokenization avoids vocabulary explosion, compares token counts across languages, describes the computational cost of sequential generation, and introduces visual tokens for multimodal models.

AI fundamentalsLarge Language ModelsMultimodal
0 likes · 7 min read
AI Comic Episode 3: What Exactly Is a Token?
Wuming AI
Wuming AI
Nov 30, 2025 · Artificial Intelligence

What Exactly Is a Large Language Model? A Simple Guide to AI, Transformers, and How They Work

This article explains the relationship between AI, machine learning, deep learning, and large language models, detailing their evolution, training stages, transformer architecture, attention mechanisms, inference APIs, and practical usage examples, while demystifying common misconceptions about LLM capabilities.

AI fundamentalsDeep LearningRLHF
0 likes · 10 min read
What Exactly Is a Large Language Model? A Simple Guide to AI, Transformers, and How They Work
Data Party THU
Data Party THU
Oct 22, 2025 · Artificial Intelligence

Demystifying Large‑Model Reinforcement Learning: From MDP Basics to Bellman and Advantage Functions

This article provides a comprehensive introduction to reinforcement learning for large language models, covering the Markov Decision Process formulation, the four core elements of RL, state‑value and action‑value functions, Bellman equations, and the advantage function that underpins modern policy‑gradient algorithms.

AI fundamentalsBellman equationMDP
0 likes · 13 min read
Demystifying Large‑Model Reinforcement Learning: From MDP Basics to Bellman and Advantage Functions
Volcano Engine Developer Services
Volcano Engine Developer Services
Sep 28, 2025 · Artificial Intelligence

Demystifying AI Jargon: A Beginner’s Guide to Large Language Models

This guide breaks down the complex terminology of large language models—explaining tokens, transformers, self‑attention, RAG, scaling laws, dense vs. sparse architectures, and training stages—using clear analogies and step‑by‑step explanations so readers can confidently understand and work with modern AI systems.

AI fundamentalsLarge Language ModelsModel Training
0 likes · 35 min read
Demystifying AI Jargon: A Beginner’s Guide to Large Language Models
Qborfy AI
Qborfy AI
Aug 25, 2025 · Artificial Intelligence

Unlocking AI Understanding: A Deep Dive into Embeddings and Their Real‑World Applications

This article explains how embeddings transform discrete items such as text, images, or user actions into continuous vectors, walks through the step‑by‑step workflow—from tokenization to normalization—highlights core properties, compares popular models, and showcases practical use cases in e‑commerce intent filtering and medical image retrieval, all backed by concrete examples and code.

AI fundamentalsMultimodalembeddings
0 likes · 7 min read
Unlocking AI Understanding: A Deep Dive into Embeddings and Their Real‑World Applications
Qborfy AI
Qborfy AI
Jul 3, 2025 · Artificial Intelligence

Why Loss Functions Matter: From Theory to Real‑World AI Applications

This article explains what loss functions are, outlines their three essential components, categorizes them for regression, classification, and generation tasks, reviews five classic loss functions with their noise resistance and gradient traits, and offers practical guidelines for selecting the right loss for AI models.

AI fundamentalsDeep Learningclassification
0 likes · 4 min read
Why Loss Functions Matter: From Theory to Real‑World AI Applications
Model Perspective
Model Perspective
May 30, 2025 · Artificial Intelligence

Why Large Language Models Are Just Mathematical Functions: A Rational Perspective

The article argues that large language models are fundamentally mathematical functions that model human language, emphasizing their role as simplified representations, explaining their structural nature, sources of errors, the importance of prompts as boundary conditions, and the need for clear usage assumptions to avoid anthropomorphic misconceptions.

AI fundamentalsLarge Language ModelsPrompt engineering
0 likes · 11 min read
Why Large Language Models Are Just Mathematical Functions: A Rational Perspective
Lao Guo's Learning Space
Lao Guo's Learning Space
Feb 14, 2025 · Artificial Intelligence

Key AI Concepts Explained: Definition, Large‑Model Role, and Future Implications

The article defines Artificial Intelligence, explains how large models enable computers to mimic human intelligence for tasks and learning, and presents a personal view that machines may eventually surpass humans and evolve into a silicon‑based intelligent life with autonomous will.

AI fundamentalsArtificial Intelligencefuture of AI
0 likes · 2 min read
Key AI Concepts Explained: Definition, Large‑Model Role, and Future Implications
AI Large Model Application Practice
AI Large Model Application Practice
Dec 11, 2024 · Artificial Intelligence

What Are Vectors and Why They Power Modern AI

This article explains vectors as numeric representations of data, how they enable similarity comparison, the role of embedding models and vector databases, their use in semantic search and RAG applications, and discusses their advantages and limitations in modern AI systems.

AI fundamentalsEmbeddingRAG
0 likes · 10 min read
What Are Vectors and Why They Power Modern AI
Alibaba Cloud Developer
Alibaba Cloud Developer
Nov 28, 2024 · Artificial Intelligence

Understanding Tokenizers and Embeddings in Large Language Models

This article introduces the core concepts of tokenizers and embeddings in large language models, explains how they convert text into numeric IDs and dense vectors, compares different tokenization strategies, and provides practical JavaScript and TensorFlow.js code examples for beginners.

AI fundamentalsJavaScriptLLM
0 likes · 10 min read
Understanding Tokenizers and Embeddings in Large Language Models
Alibaba Cloud Developer
Alibaba Cloud Developer
Jun 25, 2024 · Artificial Intelligence

Demystifying Large Language Models: From ChatGPT Basics to Future Impact

This article walks readers through the fundamentals of large language models—explaining ChatGPT's architecture, training pipelines, required GPU hardware, industry deployment models, societal implications, and future industry trends—offering a cohesive framework for both newcomers and professionals.

AI ImpactAI fundamentalsGPU computing
0 likes · 22 min read
Demystifying Large Language Models: From ChatGPT Basics to Future Impact
Baobao Algorithm Notes
Baobao Algorithm Notes
Dec 30, 2021 · Artificial Intelligence

A 3‑Stage Roadmap to Master Machine Learning from Scratch

The article outlines a solid three‑stage learning path—principles, hands‑on coding, and real‑world projects—backed by curated textbook and course resources, emphasizing code‑first understanding, continuous feedback, and practical application to efficiently master machine learning fundamentals.

AI fundamentalsLearning Pathpractical projects
0 likes · 8 min read
A 3‑Stage Roadmap to Master Machine Learning from Scratch
DataFunTalk
DataFunTalk
Jun 12, 2021 · Artificial Intelligence

An Introduction to Machine Learning: Concepts, Learning Path, and Knowledge System

This article provides a comprehensive overview of machine learning, explaining core AI terminology, distinguishing statistics, statistical learning, and machine learning, outlining a three‑part learning roadmap covering mathematical foundations, algorithms, and Python programming practice, and offering curated resources for building a solid knowledge system.

AI fundamentalsDeep Learninglearning roadmap
0 likes · 8 min read
An Introduction to Machine Learning: Concepts, Learning Path, and Knowledge System
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Jun 16, 2019 · Artificial Intelligence

Understanding AI's Four Core Elements: Data, Compute, Algorithms, and Scenarios

The article breaks down artificial intelligence into four essential components—massive data, powerful compute, effective algorithms, and real‑world scenarios—explaining each element with concrete analogies, hardware benchmarks, algorithm classifications, and a list of typical AI applications.

AI fundamentalsAI use casesBig Data
0 likes · 5 min read
Understanding AI's Four Core Elements: Data, Compute, Algorithms, and Scenarios
DataFunTalk
DataFunTalk
May 21, 2019 · Artificial Intelligence

Deep Learning Foundations: Mathematics, Modern Network Practices, and Research Overview

This article provides a comprehensive overview of deep learning, covering essential mathematics and machine learning fundamentals, modern deep network architectures and regularization techniques, advanced research topics such as structured probabilistic models and generative methods, and a curated reading list for practitioners.

AI fundamentalsNeural Networksmachine learning
0 likes · 4 min read
Deep Learning Foundations: Mathematics, Modern Network Practices, and Research Overview
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Dec 27, 2017 · Artificial Intelligence

Why Is Math the Biggest Hurdle in Deep Learning? A Step‑by‑Step Guide

This article breaks down the essential mathematics—linear algebra, probability, calculus, and optimization—required for mastering deep learning, explains how each topic maps to core deep‑learning concepts, and outlines six progressive learning stages with concrete examples and recommended textbooks.

AI fundamentalsDeep Learninglinear algebra
0 likes · 50 min read
Why Is Math the Biggest Hurdle in Deep Learning? A Step‑by‑Step Guide