#large model
9235 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 Model Learning Roadmap: A Systematic Path from Zero to Project Implementation
A detailed AI LLM learning roadmap covering Transformer architecture, Prompt Engineering, RAG, Agent development, model fine-tuning & deployment, with enterprise project guides.
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·3 min
AI Large Model Learning Roadmap Breakdown: Three Stages from Application Development to Model Fine-Tuning
Deep breakdown of a popular AI large model learning roadmap covering LangChain, RAG, Agent, and LoRA fine-tuning across three stages, with analysis of its strengths and limitations for career changers.
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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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·4 min
Complete AI Large Language Model Learning Roadmap: From Beginner to Production-Ready
A systematic guide to learning AI large language models, covering Transformer architecture, prompt engineering, RAG, AI Agents, fine-tuning, and enterprise projects from beginner to production-ready.
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·3 min
AI Large Language Model Learning Roadmap for Beginners: A Complete Plan from Basics to Hands-On Practice
A systematic AI LLM learning roadmap for beginners covering prompt engineering, RAG, LangChain, Agents, and more — with timelines and project suggestions.
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·2 min
LLM Observability & Eval in Practice: A Complete Guide to Monitoring and Evaluating Large Models in Production
A deep dive into LLM observability, evaluation systems, and experimentation loops for production AI. Covers OpenTelemetry, trace monitoring, five eval signal types, four scope levels, and automated improvement flywheels.
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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
AI Large Language Model Learning Path: A Three-Step Practical Guide from Zero to Freelancing
A three-step guide to LLM app development: from Prompt Engineering and API calls, to RAG knowledge bases, to Agent development and multi-agent collaboration.
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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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·4 min
AI Large Language Model Learning Roadmap: A Complete Guide from Zero to Project Implementation
A systematic AI LLM learning roadmap covering prompt engineering, RAG, AI Agent development, and fine-tuning — with beginner-friendly paths and practical tips.
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·3 min
AI Large Language Model Learning Roadmap for Beginners: A Three-Month Hands-On Guide
A detailed zero-to-hero AI large model learning roadmap covering four phases—fundamentals, RAG, Agents, and engineering deployment—with a practical three-month study plan and career advice.
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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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Deep Dives·2 min
The "Worse is Better" Philosophy of Large Model Design: Why Simple and Brutal Beats Refined and Complex
Analyzing the "worse is better" philosophy in large model architecture: why DeepSeek V4 dropped N-gram, why Transformer dominates AI, and three iron laws of simple, efficient model design.
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Industry Insights·4 min
In-Depth Analysis of the AI Large Model Job Market: Two Core Directions and Future Trends
In-depth analysis of the AI large model job market, breaking down the two core directions—algorithm research and engineering deployment—covering requirements, barriers, and career prospects.
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Tutorials·1 min
AI Large Language Model Learning Roadmap: Six Stages from Zero to Engineer
A systematic LLM engineer learning roadmap covering Transformer basics, prompt engineering, RAG, Agent development, API integration, fine-tuning, deployment, and project practice across six stages.
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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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