#machine learning vs deep learning
557 related articles
·3 min
OpenAI Codex Beginner's Guide: From Environment Setup to Enterprise-Level Practice
A systematic guide to OpenAI Codex and AI LLM learning, covering Transformer basics, dev environment setup, prompt engineering, RAG deployment, LoRA fine-tuning, and AI Agent enterprise projects.
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·3 min
GitHub Uses LLM Reasoning to Reduce Secret Scanning False Positives: AI-Driven DevSecOps in Practice
GitHub integrates context-aware LLM reasoning into Secret Scanning to dramatically reduce false positives, combat alert fatigue, and boost security alert credibility for developers.
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·3 min
What Is an Agent Skill? Core Structure and Hands-On Beginner's Guide
A detailed guide to AI Agent Skills: core concepts, four key components (skill.md, references, scripts, assets), Skill vs. prompt comparison, and a practical path to building Agent capability packages from scratch.
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·3 min
AI-Assisted Full-Scale Refactoring in Practice: A Methodology for Clearing All Technical Debt in 1 Day
A complete case study of AI-assisted full-scale project refactoring: from technical debt analysis and AI collaboration strategies to parallel building and final results — how to rebuild a project from scratch in just 1 day.
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·3 min
Codex vs Claude Code: The Truth Behind the 10x Bill Difference & a Practical Selection Guide
Same coding task: Codex costs $15, Claude Code costs $155. The 10x gap isn't in unit price — it's in token usage. A practical guide to choosing the right AI coding tool by scenario.
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·2 min
Claude Code Deep Dive: A Comprehensive Review of Code Accuracy, Context Understanding, and Automated Debugging
Deep dive into why Claude Code is called the strongest AI coding assistant — analyzing code accuracy, full project context understanding, and automated debugging vs. Cursor, Trae, and Copilot.
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Loop Engineering: The Paradigm Shift f…
·3 min
Loop Engineering: The Paradigm Shift from Prompt Engineering to Automated Programming Agents
Deep dive into Loop Engineering's five core components including worktree isolation, skill files, and sub-agent separation. Explore why loop design is harder than prompt engineering.
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·4 min
Getting Started with Vibe Coding: Setting Up Your AI Development Workspace from Platform to Local
A complete guide to setting up a local Vibe Coding workspace: export from platforms, run code with AI IDEs, make your first local edit, with prompting tips and error troubleshooting.
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·3 min
OpenAI Codex Deep Dive: Core Capabilities and Engineering Best Practices Guide
Deep dive into OpenAI Codex's core capabilities including multi-task parallelism, Agent Loop mechanism, and Specification-Driven Development (SDD), with learning resources and advanced paths for mastering AI programming workflows.
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·3 min
Complete Guide to Pi Coding Agent: Building Customized AI Programming with Minimalist Four Tools + Extension System
In-depth guide to Pi Coding Agent: its four core tools, extension system development, Skills management, and detailed comparison with Claude Code for building customized AI programming workflows.
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·4 min
Complete Guide to Coze Platform: Building AI Agents Without Code
In-depth guide to ByteDance's Coze AI platform: core advantages, domestic vs. international versions, agent and app development modes explained for building AI agents without code.
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·2 min
Top Cambridge Lecture: Why Quantum Mechanics' Century-Long Impact May Far Exceed AI's
In a Rothschild Lecture at Cambridge's Newton Institute, Bristol professor Marklof explains how quantum mechanics reshaped civilization over a century and why its impact may surpass AI.
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·2 min
AI Coding Far Outpaces Biology: Infrastructure Determines How Fast AI Gets Deployed
Why is AI racing ahead in coding but struggling in biology? This article analyzes the structural infrastructure gap and explores how to build Agent-friendly scientific data platforms.
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·3 min
Pangu Skill: An Open-Source Project That Distills 18 Business Leaders into Callable AI Protocols
Pangu.skill is an open-source project that distills 18 top business leaders' cognitive patterns into callable AI protocols, enabling 24/7 decision analysis.
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·2 min
Building a Weather Data Visualization & Prediction System with Cursor in 40 Seconds
Learn how Cursor AI built a weather data visualization & prediction system in 44 seconds, covering frontend-backend architecture, prompt engineering, and tips for graduation projects.
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·3 min
Erste Group's AI Transformation in Practice: How a European Bank Balances Compliance and Innovation
Erste Group's CPO shares real-world AI transformation lessons: connecting customer data from day one, dual-track strategy, serving the neglected 80%, and rapid iteration under European banking regulations.
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·3 min
Google CEO Admits Gemini Lags in Coding: Lacking an Entry Point Is Just the Surface — What's the Real Problem?
Google CEO Pichai admits Gemini lags behind Cursor and Claude Code in AI coding, blaming lack of product entry points. But the real issues are failed product experience, lost developer trust, and ecosystem advantages that haven't converted to competitiveness.
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·2 min
OpenAI Codex Parallel Tasks in Practice: Multi-Task Concurrent Execution Tips and Best Practices
A detailed guide to OpenAI Codex Cloud parallel task execution, covering isolated container principles, concurrent UI component generation demos, merge conflict handling, and maintaining human review in AI programming.
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·3 min
Prompt Template Design for Document Summarization: A Practical Guide to Three-Layer Progressive Prompt Engineering
A practical guide to three-layer progressive Prompt template design for document summarization, covering requirements analysis, architecture design, validation, and optimization — boosting information extraction completeness from 78% to 91%.
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·3 min
Andrew Ng's AI Prompting Guide: Four Key Gaps Between Beginners and Experts
Andrew Ng's AI prompting methodology reveals four core gaps between beginners and experts: deep thinking, sufficient context, neutral questioning, and iterative writing. Applicable to ChatGPT, Claude, Gemini, and all major AI tools.
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