#prompt engineering
3866 related articles
Tutorials·3 min
Phone + Claude Code: Building an iNaturalist Observation Display Tool While Camping
Simon Willison built a complete iNaturalist observation display tool using only his phone and Claude Code while camping. A breakdown of the three-layer architecture: Python CLI, Git Scraping automation, and pure frontend display.
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Product Reviews·3 min
Stock-MCP: An Open-Source MCP Server That Gives AI Agents Real-Time Access to Chinese and US Stock Data
Stock-MCP is an open-source MCP-based financial data server supporting China A-shares, US stocks, and crypto. Learn its core features, architecture, and use cases for building AI investment research assistants.
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Tech Frontiers·3 min
CL4R1T4S Project: Massive Leak of Major AI System Prompts Sparks Transparency Debate with 25,000+ Stars
GitHub project CL4R1T4S leaked system prompts from ChatGPT, Claude, Gemini and other major AI products, earning 25,000+ Stars and igniting debate over AI transparency vs. security.
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Tutorials·2 min
Building an iNaturalist Observation Display Tool Entirely on a Phone with Claude Code
Simon Willison built an iNaturalist observation display tool on his phone while camping using Claude Code, Git Scraping, and GitHub Actions with zero servers.
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Tech Frontiers·3 min
Kimi-Writer: An Open-Source AI Novel Writing Agent Powered by Deep Reasoning
Deep dive into the Kimi-Writer open-source project — an AI novel writing Agent built on Kimi-K2-Thinking with deep reasoning, autonomous planning, and long-form narrative capabilities.
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Tutorials·3 min
Phone + Claude Code: Building an iNaturalist Observation Tool with Zero Servers — Full Workflow
Simon Willison built a complete iNaturalist observation tool during camping using his phone and Claude Code. A deep dive into the three-layer architecture: Python CLI, Git Scraping, and AI-generated frontend — all on GitHub with zero servers.
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Product Reviews·3 min
Simon Willison's Complete Process of Building an iNaturalist Observation Gallery with Phone + Claude Code
Simon Willison built an iNaturalist observation gallery using only his phone and Claude Code while camping. A breakdown of the three-layer architecture: Python CLI clustering, Git Scraping automation, and AI-generated frontend.
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Deep Dives·4 min
Agent, MCP, and Function Calling: A Complete Guide to Their Differences and Relationships
A deep dive into the relationships between AI Agent, MCP protocol, Function Calling, and Prompt. From basics to full architecture, build a clear cognitive framework for AI app development.
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Tutorials·3 min
Building an AI Digital Human Agent from Scratch: Architecture Design & Full Technical Breakdown
Complete guide to building an AI digital human Agent, covering Agent, RAG, WebRTC, and Docker deployment with architecture design and engineering best practices.
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Tech Frontiers·2 min
Codex Adds OpenAI Developers Plugin — Building AI Apps with AI Just Got Easier
OpenAI Codex adds the Developers plugin, letting developers directly access OpenAI API docs and best practices to rapidly build AI Agents and smart apps.
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Tech Frontiers·3 min
OpenAI Codex Adds Computer Use: AI Agents Autonomously Control Mac in the Background
OpenAI adds Computer Use to Codex, enabling AI agents to autonomously click, type, and operate across Mac apps in the background without taking user control.
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Deep Dives·3 min
Claude's Advisor Strategy: Small Models Execute, Large Models Supervise — A New Cost-Saving Paradigm for AI Agents
Anthropic's Advisor Strategy lets Sonnet execute tasks while Opus serves as advisor, cutting costs 12% while boosting SWE-Bench by 2.7 points. A new multi-model AI Agent paradigm explained.
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Tech Frontiers·3 min
OpenAI Codex 2.0 Deep Dive: From Coding Assistant to All-Purpose AI Agent
OpenAI Codex major update: new Computer Use, built-in browser, long-term memory features. 3M weekly developers. How Codex evolved from coding assistant to full SDLC AI Agent.
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Industry Insights·3 min
AI-Native Engineering Transformation: Real Emotions and Coping Strategies from 218 Technical Leaders
Based on research with 218 engineering leaders, this deep dive explores the contradictory emotions in AI-native transformation — excitement and anxiety coexisting — and how leaders navigate successful change.
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Tutorials·3 min
AI Project Evaluation Framework: A Practical Guide to 5 Levels and 30 Key Metrics
A battle-tested AI project evaluation framework covering 5 levels and 30 core metrics—model quality, UX, system efficiency, business value, and data loops—to scientifically assess LLM Agent performance.
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Tutorials·3 min
Andrew Ng's New Course: A Practical Guide to Data Governance for Enterprise AI Agents
Andrew Ng and Databricks launch an AI Agent data governance course covering least privilege principles, Unity Catalog permissions, MLflow tracing, and a complete governance lifecycle from build to deployment. Free to learn.
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Expert Opinions·2 min
AI Coding Tools ≠ Development Transformation: How Engineering Teams Can Truly Restructure the Software Development Lifecycle
Adopting AI coding tools isn't the same as transforming how you build. Learn how engineering teams can restructure their SDLC around AI for true competitive advantage.
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Industry Insights·3 min
AI Tool Adoption ≠ Transformation: 219 Engineering Leaders Reveal the Real Gap
A survey of 219 engineering leaders reveals most teams adopted AI dev tools without truly changing how they build software. Discover the gap and three key traits of successful transformation.
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Product Reviews·3 min
AGENTS.md Coding Rules Put to the Test: Code Quality Unchanged, Development Efficiency Significantly Improved
A developer tested AGENTS.md coding rules across 40 PRs on three AI coding Agents. Results: code quality unchanged, but fewer tool calls, faster completion, and lower costs.
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Research·2 min
Prompt Tuning Delivers 3-10% Efficiency Gains: The Hidden Dividend of Scaling Coding Agents
Prompt engineering optimizations for coding Agents reduce tool calls, lower output tokens, and improve completion speed by 3-10%—delivering significant cost savings and latency reduction at scale.
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