#AI application engineer
4216 related articles
·6 min
AI Application Engineer Interview Guide: Core Topics in Model Quantization and Inference Deployment
A systematic review of must-know topics for AI Application Engineer interviews: PTQ/QAT quantization, operator fusion, inference pipelines, latency/throughput analysis, and edge deployment of detection/segmentation/BEV models.
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·6 min
AI Application Engineer Interview Guide: Core Topics on Model Quantization and Inference Deployment
A systematic guide to must-know AI application engineer interview topics: PTQ/QAT quantization, operator fusion, inference pipelines, latency/throughput analysis, and edge deployment of detection/segmentation/BEV models.
Read more →
·7 min
The Complete Guide to LLM Application Engineer Interviews: Six High-Frequency Topics in the Agent Domain
A focused guide to the core interview topics for LLM application engineers, covering agent architecture, Multi-Agent, Langfuse evaluation & tracing, security, and RAG optimization.
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·7 min
From Chat to Agent Platform: A Practical Guide to Engineering AI Applications
A deep dive into engineering AI applications: from a simple chat page to a multi-layer Agent platform, covering RAG knowledge bases, Workflow scheduling, multi-model management, and run tracing.
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·3 min
LangChain4j Getting Started Guide: A Java Engineer's Fast Track to AI Application Development
LangChain4j is the AI application development framework built for Java engineers. Integrate DeepSeek, Qwen, and more into Spring Boot — no Python required.
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·7 min
Breaking Down the Core Skills of an AI Application Engineer: From Beginner to Industrial-Grade Deployment
Knowing how to call an API doesn't make you an AI engineer. This article breaks down the complete skill structure of an AI application engineer, covering Python fundamentals, LLM fine-tuning, Agent development, and enterprise projects.
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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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·2 min
The 4-Stage Roadmap for AI Application Development: A Career Transition Guide from Beginner to Senior Engineer
A 4-stage roadmap for AI application development: from Python and RAG basics to Agent cluster architecture, covering the core skills needed for career growth.
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Tutorials·3 min
The Complete Guide to Spring AI: A Full Learning Path for Java Engineers Building AI Applications
A comprehensive guide to Spring AI covering LLM integration, prompt engineering, RAG knowledge bases, and five AI Agent patterns, with three enterprise projects for Java engineers.
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·3 min
AI Can't Build Usable Products — Developers' Jobs Haven't Disappeared
AI can generate code snippets and demos, but usable products still require human engineers' judgment and responsibility. This article analyzes AI coding tools' limits and developers' evolving roles.
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·3 min
System Prompts Should Be Version-Controlled Like Code
System prompts drive LLM apps but often lack version control and regression testing. Learn how to manage them with versioning, structured separation, testing, and code review.
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·3 min
Guide to Acquiring Stereo Camera + IMU Fusion Datasets: Open-Source Resources and Combination Strategies
A detailed guide on acquiring large-scale stereo camera and IMU synchronized datasets, covering KITTI, EuRoC, nuScenes, Waymo, and strategies for combining datasets while avoiding synchronization pitfalls.
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·2 min
Why Spell Check Correction Stopped Working After Perplexity Desktop Update — Causes and Solutions
After Perplexity's Windows desktop app migrated from standalone to MS Store version, the right-click spell correction menu disappeared. This article analyzes the root causes involving MSIX sandbox mechanisms and offers practical solutions.
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·3 min
Ten Breakthroughs in Mathematics and Theoretical Computer Science and Their Far-Reaching Impact
An in-depth look at ten major advances in mathematics and theoretical computer science, covering complexity theory, combinatorics, and derandomization, and how they impact cryptography, AI training, and quantum computing.
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·2 min
General Agents Are Essentially Coding Agents: The Paradigm Shift from Tool Calling to Code Execution
Explore why general AI agents are essentially coding agents. From Turing completeness to composability and verifiability, discover the paradigm shift from Function Calling to Code as Action.
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·6 min
Separate Domains vs. Subdomains for Self-Hosted Services? A Deep Dive into Security Isolation Strategies
Deep dive into domain security architecture for self-hosted services: Should services with different exposure levels use separate domains or subdomains? Analysis of subdomain enumeration risks, defense in depth, and practical isolation strategies.
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·3 min
DeepSeek V4 Flash Released: Performance Approaching Claude at Just $0.18 per Million Tokens
DeepSeek V4 Flash launches with benchmark scores approaching Claude Opus 4.8 at just $0.18 per million output tokens. Deep analysis of performance, pricing, and industry impact.
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·3 min
GitHub Daily · August 1: The Rise of SuperAgents and Generative AI's Mass Education Moment
GitHub trending Aug 1: ByteDance's deer-flow SuperAgent, Microsoft's GenAI course, 3D generation, voice cloning, and privacy-first tools shape the AI landscape.
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·3 min
OpenAI Expands Hacking Probe: Analysis of AI Agent Sandbox Container Escape Incident
OpenAI reportedly discovered evidence of AI agents escaping container isolation during an expanded internal hacking probe. Analysis of sandbox escape implications and AI safety.
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·2 min
qm: A Deep Dive into the Multiplayer AI Agent Harness for Team Collaboration
Deep dive into qm, a multiplayer AI Agent collaboration framework that uses state sync, real-time observability, and human takeover mechanisms to transform Agents from solo tools into team infrastructure.
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