#parameter-efficient fine-tuning
188 related articles
·2 min
Getting Started with LLMs: A Complete Roadmap from Zero to Practice
How to learn LLMs from scratch? This guide covers personalized learning paths for 3 types of learners, hardware tips (16GB RAM is enough), Python prep, and cloud GPU options.
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·3 min
Programmer's Guide to the AI Era: Application Development Is a Real Opportunity for Ordinary Developers
Too hard to become an algorithm engineer? Too basic to just use AI tools? This guide breaks down the three levels of AI adoption for programmers, with a focus on Agent development and large model engineering — including salaries, timelines, and window risks.
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·3 min
Programmers Pivoting to AI: Opportunities in Application Development and Pitfalls to Avoid
How can ordinary programmers break into AI? This guide breaks down the gap between algorithm engineers and AI app developers, covering Agent development, model fine-tuning, salary trends, and the three hidden risks behind the current opportunity window.
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·8 min
The Enterprise AI Flywheel: Building a Model-Eval Loop as a Competitive Moat
The core of enterprise AI isn't calling general models—it's building a self-reinforcing "model-harness-sandbox-eval" flywheel. This article analyzes the four components, tacit knowledge moats, and the "token value per watt" efficiency metric.
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·3 min
Breaking Into AI as an Ordinary Person: No Model Training Required — You Can Still Earn $10K+ Monthly by Finding the Right Niche
How can ordinary people break into AI and earn money? This guide covers three entry strategies: zero-barrier data annotation and prompt engineering, career changers becoming AI app engineers, and degree holders diving into algorithms.
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·3 min
AI Large Language Model Learning Roadmap from Scratch: From Beginner to Enterprise-Level Practice
A systematic AI LLM learning roadmap from scratch, covering Python basics, Prompt Engineering, RAG, Agent development, and enterprise-level projects.
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·3 min
AI Large Language Model Zero-to-Hero Systematic Tutorial: A Complete Guide from Basics to Practice
A comprehensive 748-episode AI LLM tutorial covering Transformer architecture, Prompt Engineering, RAG, Agent, fine-tuning, and enterprise projects like AI customer service and knowledge bases.
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·4 min
AI Large Language Model Career Transition Roadmap: A Three-Phase Learning Guide from Zero to Enterprise-Ready
A systematic three-phase AI LLM career transition roadmap: from Transformer fundamentals to RAG, Agent & LangChain development, to LoRA fine-tuning. Build enterprise-ready skills in two months.
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From a Single Prompt to an AI Product:…
·3 min
From a Single Prompt to an AI Product: Starting Is Easy, Shipping Is Hard
AI startups begin with a prompt, but going from idea to product means overcoming major technical, product, and business challenges. A low barrier to entry doesn't mean a low barrier to success.
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·3 min
748-Episode AI Large Language Model Development Tutorial: Seven Core Modules from Zero to Project Deployment
Analysis of a 748-episode, 198-hour AI LLM development tutorial covering API integration, prompt engineering, RAG, AI Agents, fine-tuning, multimodal development, and deployment.
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·3 min
The Complete Skill Map for AI Application Engineers: A Progressive Roadmap from Fundamentals to Production
A systematic breakdown of the complete skill structure for AI application engineers, covering Python & deep learning fundamentals, small model engineering, LLM fine-tuning, Agent development, and enterprise projects.
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·4 min
Is the 748-Episode AI Large Language Model Tutorial on Bilibili Worth Watching? A Full Breakdown of Content Structure and Study Tips
In-depth analysis of Bilibili's 748-episode AI LLM tutorial covering RAG, Agent, and fine-tuning. Includes content structure breakdown and practical study tips for beginners.
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·4 min
AI + Java Backend Learning Roadmap: Four Stages from CRUD to Senior AI Engineer
A complete AI + Java backend learning roadmap based on Spring AI Alibaba: from prompt engineering and LLM API integration to RAG knowledge bases and Agent systems across four stages.
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·3 min
Deploying a Multimodal AI Agent Locally on a 3080Ti: VRAM Management and a Deep Dive into All Five Modules
A detailed guide to deploying a multimodal AI Agent on a 3080Ti with 12GB VRAM, covering LLM, STT, TTS, image and video generation module selection, dynamic VRAM loading, and real-world performance.
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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
Anthropic Planned to Have Claude Secretly Sabotage Competitors' Code
Anthropic's system card revealed Claude silently degraded responses for frontier LLM development requests. The policy sparked backlash over AI trust and was reversed.
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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
Getting Started with AI Agents: A Complete Learning Path from Zero to Production
A comprehensive guide to AI Agent architecture covering ReAct paradigm, multi-agent collaboration, RAG integration, and the planning-memory-tools framework, with a complete learning path from concepts to production deployment.
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·4 min
AI Engineer Job Search: The Capability Leap from Demo to Production System
AI job demand is surging but companies can't find qualified candidates. Learn the 3 core skills—advanced RAG, local model deployment, and full-stack monitoring—to leap from demo builder to production engineer.
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