#software QA fundamentals
12 related articles
·3 min
Spring AI Explained: Unified API, RAG, and Enterprise AI Development Guide
Deep dive into Spring AI framework's core features including provider-agnostic unified API abstraction, RAG retrieval-augmented generation, and structured output to help Java developers build enterprise AI apps.
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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.
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·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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·4 min
Open Source AI Closes the Gap: How Chinese Models Are Challenging America's AI Dominance
From DeepSeek to Kimi K3 and Qwen 3, Chinese open source AI models are closing in on OpenAI and Anthropic at stunning speed. A deep dive into narrowing gaps, IPO valuation risks, the "open source decelerationism" debate, and why Google may be the biggest winner.
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·6 min
Multimodal and Embodied Intelligence: A Comprehensive Look at Progress, Pitfalls, and Opportunities
At the Microsoft Research India summit, top experts explore the real progress of multimodal AI and embodied intelligence: fusing classical robotics with large models, healthcare AI deployment challenges, perceptual bottlenecks in reasoning, and possibilities beyond scaling.
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·2 min
Harvard's Open-Source Textbook cs249r: A Complete Guide to Machine Learning Systems Engineering
Harvard's open-source textbook cs249r (Machine Learning Systems) has 25,600+ GitHub stars. It covers ML systems engineering, TinyML, and MLOps — free for everyone.
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·6 min
LangChain 1.0 Comprehensive Guide: Three Core Frameworks and Hands-On AI Application Development
Deep dive into LangChain 1.0's architecture: LangChain framework, LangGraph multi-Agent orchestration, and LangSmith observability platform, with hands-on RAG and intelligent customer service projects.
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·4 min
AI Agent Learning Roadmap for Beginners: A Three-Stage Journey from Fundamentals to Hands-On Practice
A systematic AI Agent learning roadmap for beginners covering core theory, the ReAct paradigm, and multi-agent collaboration, with hands-on project suggestions.
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·4 min
Learning Path for AI Agent Development: A Complete Guide from Core Architecture to Production Deployment
A complete learning path for AI Agent development covering core architecture, ReAct paradigm, multi-agent collaboration, RAG integration, and lightweight deployment to guide developers from basics to production.
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·3 min
AI-Driven Zero-Code Automated Testing: A Complete Path from Python Basics to Hands-On Practice
A detailed guide to AI-driven automated testing: from Python basics to PyTest, covering API automation, Playwright UI testing, and AI-assisted coding for beginners.
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Product Reviews·3 min
Superpowers vs GStack In-Depth Comparison: How to Choose Your Claude Code Plugin
In-depth comparison of Claude Code's top open-source plugins Superpowers and GStack — their skills, workflows, and use cases to help developers choose the best AI coding assistant setup.
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Expert Opinions·2 min
Zero-Code Browser Extension in One Day: How AI Programming Is Disrupting Traditional Learning
A developer built a browser extension in one day with zero code using AI assistance. Learn the AI programming workflow, how developer roles are changing, and new methods for learning tech efficiently.
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