#distributed LLM inference
509 related articles
·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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·2 min
Jeff Dean's Commencement Speech at UW Allen School: A Message to the Next Generation of Engineers in the AI Era
Jeff Dean delivers commencement speech at UW Allen School of Computer Science & Engineering, sharing insights with the next generation of CS graduates in the AI era.
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·2 min
A Complete Guide to LLM Infrastructure: Core Challenges from GPU Clusters to Inference Optimization
A deep dive into core challenges and key technologies for LLM infrastructure, covering GPU cluster management, inference optimization, distributed training, cost control, and observability.
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·3 min
Headroom: The Open-Source Compression Tool That Cuts AI Agent Token Costs by 10x
Headroom is an open-source token compression tool by a Netflix engineer that achieves 60%-95% token savings for AI coding tools through intelligent category-based compression.
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·3 min
AI Hospital Appointment Booking Mini-Program: Full-Stack Development with Spring Boot + Vue 3 + DeepSeek
Full-stack guide to building a hospital appointment booking mini-program with Spring Boot, Vue 3, and DeepSeek AI — featuring smart consultations, AI report interpretation, and three-terminal architecture.
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·3 min
LangChain Beginner's Guide: From Environment Setup to RAG — A Complete Hands-On Walkthrough
A systematic guide to LangChain covering environment setup, model invocation, Prompt Templates, Output Parsers, LCEL chain expressions, and hands-on RAG implementation for beginners.
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·3 min
4 Iron Rules of Industrial Agent Development: Why AI Deployments in Factories Are Doomed to Fail
Deep dive into why industrial AI Agents fail: 4 critical strategies covering edge deployment, closed-loop control, rule verification, and safety mechanisms for reliable factory AI systems.
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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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·3 min
Xiaomi Open-Sources MiMo Code: Can an Infinite Memory Mechanism Solve AI Coding's Biggest Pain Point?
Xiaomi open-sources MiMo Code, an AI coding tool with infinite memory, multi-Agent collaboration, and Claude Code compatibility — solving context forgetting in large projects.
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·3 min
DeepSeek + Claude Code: A Deep Dive into How AI Automatically Builds Simulink Models
A deep dive into how DeepSeek and Claude Code automatically build Simulink models, explaining the roles of five core tools and their collaboration logic.
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·4 min
OpenAI Engineer's Practice: Agent-Driven Development Methodology in the Era of Free Code
OpenAI engineer Ryan Lopopolo shares 9 months of pure AI agent coding practice, revealing core methodologies including prompt engineering, automated code review, and skill design in the new paradigm where code is free.
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·3 min
Deep Dive into Claude Cloud Agent Memory System Architecture: CMA Design Philosophy and Technical Choices
Deep analysis of Anthropic's Cloud Managed Agents memory architecture, covering file-first strategy, memory store reuse, Dreaming async consolidation, and key differences from Claude Code's memory system.
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·1 min
Why Now Is the Best Time to Be a Software Engineer
Software engineers are entering a golden era in the age of AI. Learn how AI tools amplify engineer value, why demand is exploding, and how to stay competitive.
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·3 min
CrewAI Multi-Agent Collaboration in Practice: A Complete Guide from Setup to API Service
A detailed guide to CrewAI multi-Agent collaboration covering core concepts, FastAPI service setup, and real-world comparisons of GPT-4o Mini, Qwen Max, and Llama 3.1.
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·3 min
Cursor AI Editor Beginner's Guide: From Installation to Building a Personal Homepage
A detailed guide to Cursor AI code editor's core features, pricing plans, Design Mode, and a hands-on demo building a personal homepage with structured prompts.
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·4 min
MCP vs Function Calling: Core Differences and Selection Guide
Deep dive into 3 core differences between MCP and Function Calling: coupling, interaction breadth, and security. Learn why AI needs a unified protocol and how to choose the right approach.
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·3 min
Multi-Model Unified Orchestration Framework: Runtime Dynamic Switching Across Eight AI Services in Practice
Deep dive into a runtime AI chatbot integrator architecture covering unified orchestration of OpenAI, Claude, DeepSeek text models and 11Labs, Azure TTS services with latency testing and streaming synthesis.
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·3 min
Vibe Coding in Practice: A Junior Student Uses Cursor to Build a Multi-Agent System with 51 AI Officials Based on the Three Departments and Six Ministries Framework
A junior student uses Cursor and Vibe Coding to build a multi-agent system with 51 AI officials modeled on China's Three Departments and Six Ministries, featuring task distribution, approval workflows, and Token cost visualization.
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