#AI large models
8129 related articles
·3 min
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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·3 min
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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·3 min
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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·3 min
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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Tutorials·2 min
Getting Started with Qwen3-0.6B Fine-Tuning: Core Concepts of Large Language Models and Fine-Tuning Methodology Explained
Starting from the three core characteristics of LLMs, this article systematically covers foundational knowledge needed for Qwen3-0.6B fine-tuning, including model comparisons, fine-tuning value analysis, and the complete learning path.
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Deep Dives·2 min
The Rise of AI Operating Systems: How Large Models Are Evolving from Tools to OS
AI is evolving from a single tool into a complete operating system. This article analyzes AI OS core architecture, MCP/A2A protocol standardization, and how Agents are becoming the next OS paradigm.
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Product Reviews·2 min
GPT-5.5 in Healthcare AI: How Abridge Uses Large Language Models to Revolutionize Clinical Documentation
Abridge leverages OpenAI's GPT-5.5 to revolutionize clinical documentation with significantly improved fact extraction and cross-context coherence, reducing physician documentation burden.
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Tutorials·2 min
Beginner's Guide to AI Large Language Models: GPU Requirements & Core Tech Stack Explained
2025 complete guide to AI LLMs: local deployment GPU/VRAM requirements (RTX 4090/24GB) and core tech stack including Prompt Engineering, Agents, MCP, LangGraph, and WorkFlow orchestration.
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Deep Dives·3 min
What Are Large Language Models? A Deep Dive into Parameters, Models, and the Essence of AI LLMs
What exactly is a large model? This article explains the essence of LLMs from the core concepts of "models" and "parameters," covering GPT parameter scales, vector dimensions, and open-source model selection.
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Deep Dives·3 min
Claude's Advisor Strategy: Small Models Execute, Large Models Supervise — A New Cost-Saving Paradigm for AI Agents
Anthropic's Advisor Strategy lets Sonnet execute tasks while Opus serves as advisor, cutting costs 12% while boosting SWE-Bench by 2.7 points. A new multi-model AI Agent paradigm explained.
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Product Reviews·1 min
CowAgent: An Analysis of the Super AI Assistant Open-Source Project Powered by Large Language Models
CowAgent is an open-source super AI assistant powered by LLMs with 44K GitHub Stars, supporting WeChat, Feishu, DingTalk with agent capabilities.
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·2 min
Google SDK Accidentally Leaks Gemini 4 Flash: Real Roadmap or Just a Placeholder?
Google's public SDK was found containing Gemini 4 Flash references, sparking developer speculation about next-gen models. We analyze the leak's credibility and what it means.
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·1 min
Brandfetch MCP: Giving AI Accurate Access to Brand Logos, Colors, and Assets
Brandfetch MCP provides AI Agents with 50M+ brands' logos, colors, and fonts via MCP protocol, solving the problem of AI fabricating brand assets in design work.
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·1 min
Implant: Letting AI Coding Assistants Directly Call VS Code APIs
Implant is an open-source VS Code extension that exposes editor LSP APIs to AI coding agents, enabling precise symbol navigation, safe renames, and diagnostics reading.
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