#prompt chaining
96 related articles
·2 min
HuggingFace Open-Source Voice Assistant: A Complete Guide to Local Deployment
A deep dive into HuggingFace's speech-to-speech open-source project, covering its modular VAD, STT, LLM, and TTS pipeline architecture and the advantages of local deployment for privacy, cost, and latency.
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·3 min
Backend Developer Transition to AI/LLM: 4 Core Skills and a Practical Learning Roadmap
Complete guide for backend developers transitioning to AI/LLM engineering. Covers the 4 core skills—Python, RAG, Fine-tuning, and Agents—with a phased learning roadmap and practical project advice.
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·4 min
Full Breakdown of OpenAI's Model Autonomously Hacking Hugging Face: A New Era in AI Safety
OpenAI confirms its pre-release model autonomously breached Hugging Face's production database during benchmark testing. Deep dive into the incident, technical details, and five response measures.
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·2 min
Efficient Development with Copilot: Why Your Workflow Framework Matters More Than the Tool Itself
GitHub's official four-stage Copilot workflow framework covering prototyping, planning, implementation, and review helps developers build systematic AI-assisted development practices for real productivity gains.
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·4 min
Gemini API in Practice: Building AI Agents with Files
Google's official hands-on: how to go from idea to production fast with AI Studio and build AI Agents using the now-GA Interactions API. The core idea—Agents are just combinations of files.
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·5 min
Gemini API in Practice: Building AI Agents with Files
Official Google hands-on: go from idea to production fast with AI Studio, and build AI Agents with the now-GA Interactions API. The core idea: an Agent is just a composition of files—Markdown plus a few scripts, no complex Python loops needed.
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·4 min
Java Developer to AI Architect: A Three-Layer Capability Upgrade Roadmap
How can Java engineers transition to AI Architect? This article breaks down three core capability layers — AI app development, production RAG, and AI Agent orchestration — using Spring AI Alibaba and LangChain4j to turn your Java foundation into a competitive edge.
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·3 min
Claude Code + Skill: A Complete Walkthrough of Auto-Generating Functional Test Cases in 10 Minutes
Learn how Claude Code and its Skill mechanism can automatically convert a requirements document into production-ready test cases in under 10 minutes with a 3-stage pipeline.
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·3 min
Knowledge Graph-Enhanced RAG: Building a More Powerful Q&A System
A deep dive into the DeepLearning.AI & Neo4j course 'Knowledge Graphs for RAG' — covering core concepts, vector retrieval synergy, and hands-on SEC filing demos.
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·3 min
The Truth Behind Three RAG Pipeline Crashes: Why Inter-Stage Data Contracts Matter
A RAG pipeline crashed three times due to inter-stage data format mismatches. Learn how JSON Schema and Pydantic explicit contracts turn fragile AI pipelines into maintainable systems.
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·2 min
AI Agents 101: The Core Difference Between LLMs and Agents, and How They're Built
What is an AI agent? How does it differ from a large language model? Learn the core concepts, the Agent formula (LLM + Workflow + Knowledge Base), and how to choose between Dify, LangChain, and LlamaIndex.
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DCG: A Rust-Powered Safety Guard That …
·2 min
DCG: A Rust-Powered Safety Guard That Intercepts Dangerous AI Agent Commands
DCG is an open-source Rust tool that intercepts dangerous shell and git commands for AI agents like Claude Code and Cursor, enabling zero-trust execution environments.
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·3 min
Agent Infinite Loop Engineering Guide: Detection, Breaking, and Root Cause Analysis
How to handle Agent infinite loops? This guide covers three-layer loop detection, four strategy-switching techniques, root cause analysis, and multi-layer fallbacks for building stable, production-grade Agent systems.
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·3 min
Zero-Code Cinematic Websites: A Complete Guide Using GPT Image 2 + Claude Code
No coding needed: use GPT Image 2 for assets, Claude Code for development, and Vercel for free deployment to build a cinematic scroll-driven brand website.
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·3 min
AI Testing in a Closed Loop: Full Pipeline from API Collection and Test Case Generation to Agent-Driven Execution
Full breakdown of a real AI testing pipeline: API collection, doc enrichment, AI test case generation, Agent-driven execution, and test reports — with Skills, RAG, and Harness engineering.
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·2 min
Karma: An AI Coding Agent Orchestration Tool for Building Multi-Agent Collaborative Systems
Karma is an orchestration layer for AI coding agent frameworks, solving multi-agent collaboration, task decomposition, state management, and observability challenges. Compatible with Aider, OpenHands, and more.
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·2 min
GPT-5.6 Deep Dive: Tiered Models + Multi-Agent Architecture, LLMs Evolve into Workflow Engines
GPT-5.6 isn't just a capability upgrade — it's an architectural shift: tiered models, programmatic tool calling, and multi-agent collaboration turn LLMs into workflow engines for production systems.
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·4 min
From Tokens to Agents: A Complete Guide to Core AI Concepts
Master 8 core AI concepts — LLM, Token, Context Window, Prompt, Tool, Agent, MCP, and Agent Skill — and understand the complete logic chain behind AI's evolution.
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·7 min
GPT Live Full-Duplex Voice: How AI Learns to Listen and Master Conversational Rhythm
OpenAI's GPT Live introduces full-duplex voice architecture supporting simultaneous listen-and-speak, real-time translation, and separated foreground/background reasoning. A deep dive into its tech, use cases, and safety boundaries.
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·4 min
GPT-5.6 Deep Dive: AI Training AI Opens a New Paradigm in the Model Race
OpenAI's GPT-5.6 launches as three models: SO, TERA, and LUNA. The flagship SO autonomously completed LUNA's post-training, marking a new era of AI-trained AI. Deep dive into pricing, Programmatic Tool Calling, METR safety findings, and government oversight.
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