#AI large language models
4584 related articles
·3 min
Introduction to AI Large Language Models: A Must-Read Guide for Test Engineers
A thorough explanation of the essence of AI large language models: from conceptual hierarchy and Transformer mechanics to probabilistic nature, helping test engineers understand LLM strengths and weaknesses.
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From Word Vectors to Large Language Models: The Complete Evolution of AI 2.0 Technology
From word vectors and embeddings to RNNs, BERT, Transformers, and ChatGPT — a complete guide to the technical evolution of large language models and the AI 2.0 era.
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Understanding Large Language Models from Scratch: The Complete AI Evolution from Deep Blue to DeepSeek
A beginner-friendly guide clarifying AI, machine learning, deep learning, and LLMs — tracing the evolution from Deep Blue to AlphaGo, ChatGPT, and DeepSeek.
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·4 min
Getting Started with AI Test Development: Core Principles and Underlying Logic of Large Language Models
New to AI test development? This article breaks down the differences between machine learning and traditional programming, the origins of AI hallucinations, and the core principles of NLP/NLU/NLG to help test engineers build a solid AI knowledge framework.
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GODMODE Project Deep Dive: AI Jailbrea…
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GODMODE Project Deep Dive: AI Jailbreaking Techniques and the Security Arms Race in Large Language Models
GODMODE (G0DM0D3) has 9,300+ GitHub stars fueling debate on AI jailbreaking vs. safety alignment. A deep technical dive into LLM guardrails, prompt injection, and AI security governance.
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AI Large Language Models for Beginners: Core Concepts and Chinese Model Landscape
New to AI? This guide clarifies AI, machine learning, deep learning, and LLMs, traces milestones from Deep Blue to DeepSeek, and maps out China's LLM landscape.
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AI Large Language Models Explained: Principles, Strengths, Weaknesses, and a Testing Guide
A complete beginner's guide to AI large language models: principles, the Transformer architecture, strengths, weaknesses, and practical tips for testers.
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·4 min
AI Large Language Models for Beginners: Core Concepts, History, and China's Model Landscape
A beginner's guide to AI large models: clarify the relationships between AI, ML, deep learning, and LLMs, trace the journey from Deep Blue to ChatGPT and DeepSeek, and explore China's model landscape.
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·2 min
Learning AI Large Language Models from Scratch: A Complete Roadmap That Avoids the Three Biggest Mistakes
90% of AI beginners struggle with large language models due to misdirection, poor Prompt logic, and lack of real-world deployment skills. This guide covers the complete learning path from zero to practice.
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Can AI Prove Mathematical Conjectures?…
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Can AI Prove Mathematical Conjectures? The Truth Behind the GPT-5.6 Incident and the Limits of Large Language Models
A PDF claiming GPT-5.6 Sol Ultra proved the Cycle Double Cover Conjecture sparked debate on Hacker News. We unpack the truth and the limits of LLMs in math proofs.
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Spring AI + Ollama: Run Large Language Models Locally at Zero Cost
Learn how to integrate Spring AI with Ollama to run open-source LLMs like Llama and Gemma locally for free. Covers setup, configuration, and code — switch from OpenAI by just changing dependencies.
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AI Large Language Models Explained: Transformer Architecture & Practical Testing Guide
Deep dive into AI large model principles, from Transformer architecture to probabilistic inference, with practical guidance on LLM applications in testing and AI testing strategies.
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·1 min
Why Are AI Product Waitlists Getting Longer? The Waiting Dilemma from Siri to Large Language Models
From Siri AI waitlists to LLM API queues, long waits have become the norm. Analyzing the compute bottlenecks, marketing strategies, and UX impacts behind AI waitlists.
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Six Core Concepts of AI Large Language Models: The Complete Evolution from Chatbot to Autonomous Worker
Explore six core AI concepts — Agent, RAG, Function Calling, MCP, Skill, and Harness — and how they form a clear evolution from basic chatbots to autonomous AI workers.
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AI Large Language Models for Reverse Engineering: A Workflow That Boosts Freelance Efficiency by 10x
Learn how AI LLMs revolutionize JS reverse engineering—automating encryption cracking, signature reconstruction, and parameter analysis to boost freelance scraping efficiency by 10x.
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Deep Dives·4 min
AI Hallucinations: Why Large Language Models Inevitably "Make Things Up" and How to Deal With It
Deep dive into AI hallucination's three root causes: training objective flaws, exposure bias, and probabilistic generation. Covers classification and practical mitigation strategies including RAG.
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Industry Insights·2 min
Two Major Career Paths in AI Large Language Models: Engineering Implementation vs. Algorithm Research
In-depth analysis of two core AI LLM career paths: engineering implementation vs. algorithm research. Compare education requirements, skills, and job prospects for programmers transitioning to AI.
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Tutorials·2 min
Learning AI Large Language Models from Scratch: A Guide to Learning Paths, Hardware, and Programming Languages
A beginner's guide to learning AI large language models — covering learning paths, hardware requirements, Python essentials, and cloud services for learners at every level.
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Tutorials·3 min
Getting Started with Spring AI: How Java Developers Can Integrate Large Language Models
Spring AI is the LangChain for Java, helping Java developers integrate LLMs using Spring Boot conventions. This guide covers its 6 core features, setup requirements, and enterprise positioning including RAG, Tool Calling, and Chat Memory.
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Tutorials·3 min
Getting Started with AI Full-Stack Development: A Knowledge Framework from Machine Learning to Large Language Models
A systematic guide to the relationships between AI, machine learning, deep learning, and large language models, helping developers build a clear knowledge framework and find an efficient learning path.
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