#AI Engineering
6733 related articles
Migrating a Production AI Agent to GPT…
·2 min
Migrating a Production AI Agent to GPT-5.6: 2.2x Faster, 27% Cheaper — Engineering Lessons Learned
A production AI Agent migrated to GPT-5.6 achieved 2.2x faster speed and 27% lower cost. Deep dive into prompt compatibility, eval frameworks, and migration best practices.
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·3 min
LangChain V1.3 Hands-On Tutorial: A Complete Breakdown of RAG, Multi-Agents, and Engineering Mindset
An in-depth look at LangChain V1.3's core philosophy: from RAG to multi-agent workflows. Master LangGraph, Chain, and DeepAgent, learn token control and Human-in-the-loop, and become a true master of AI app development.
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·2 min
AI Over-Engineering: A Deep Reflection on Why One Line of Code Becomes a Thousand
A Reddit meme struck a collective nerve among developers: AI coding assistants routinely over-engineer simple tasks—turning one line into a thousand. This article dissects the technical roots of this phenomenon.
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Reverse-Engineering Web Apps: A New Ap…
·3 min
Reverse-Engineering Web Apps: A New Approach to Breaking Through the API Barrier for AI Agents
Explore a new approach to AI Agent tool integration: reverse-engineering web apps to turn API-less pages into callable Agent tools, with analysis of MCP synergy and challenges.
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·2 min
The Real Bottleneck for AI Adoption: Context Engineering, Not Model Capability
How you use AI determines whether it's just a gimmick. This article breaks down three real business scenarios showing how context engineering turns Claude from a hallucinating toy into an operations partner saving 5-10 hours a week.
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·6 min
A Deep Dive into AI Agent Harnesses: The Core Engineering Behind Agent Frameworks
What is an AI Agent's harness? This article systematically dissects the core components of agent frameworks: context management, tool use, control loops, and caching strategies—revealing why the same model performs so differently across harnesses.
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·4 min
Why Do AI Agents Crash the Moment They Go Live? A Practical Guide to Harness Engineering for Runtime
A deep dive into the Claude Code source code, systematically analyzing the five-layer Harness Engineering architecture: environment, tool, control, memory, and evaluation. Build a stable runtime system for production AI Agents.
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·2 min
AI Agents for SGLang Kernel Optimization: Real-World Performance Gains and Engineering Methodology
SGLang's team converted expert knowledge into agent skills, achieving 71.4% throughput gains, TTFT reduced from 456ms to 168ms. A deep dive into agent-assisted kernel optimization methodology.
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·2 min
xAI Teams Up with SpaceX to Train Grok 4.5: Entering a New Era of Hardcore Engineering Intelligence
xAI announces a partnership with SpaceX to train Grok 4.5, positioned as its first general-purpose model going beyond software engineering. A deep dive into the collaboration logic, SpaceX's exclusive data value, and its significance in AI competition.
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·2 min
Agent Skills: Bringing Production-Grade Engineering Capabilities to AI Coding Agents
Google engineer Addy Osmani's open-source project agent-skills (76k+ GitHub stars) injects production-grade engineering skills into AI coding agents, covering performance, security, and code quality.
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·5 min
From Prompts to Harness Engineering: The Evolution of AI Agent Development Paradigms
Why has AI engineering methodology evolved from prompts to context engineering and now Harness engineering? This article examines three paradigms, key bottlenecks, and the Agent = Model + Harness formula.
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·4 min
AI-Powered Automatic Environment Patching: JS Reverse Engineering with Sandbox + Prompt
Learn how to automate JavaScript reverse engineering environment patching with AI + sandbox. Three-step flow diagnoses missing BOM/DOM objects, generates patches, and self-validates. Get encrypted data in seconds.
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·6 min
Rust AI Agent System Prompt Engineering in Practice: A Seven-Dimensional Design Guide
An in-depth analysis of the four core roles of AI Agent system prompts and the four-step tool-calling method, covering behavioral boundaries, autonomous execution principles, and trigger pattern design—helping Rust developers build high-quality agents.
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·7 min
Previewing the AI Engineer Summit: Industry Signals of AI Engineering Reaching Maturity
An exclusive look at the AI Engineer Summit dress rehearsals, decoding the paradigm shift from research to production. A deep dive into AI Engineer challenges, RAG, agent systems, and AI engineering as a distinct discipline.
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·4 min
Prompt Engineering 101: Core Concepts, Value, and Limitations Explained
What is prompt engineering? This guide covers prompts, their four key functions, the 6-step engineering process, business value, and technical limitations for a complete foundation.
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·4 min
Harness Engineering: The Third Phase of AI Coding and a Practical Guide to agents.md
A deep dive into Harness Engineering — the third phase of AI coding. Based on research across 2,853 GitHub repos, explore agents.md, Skills, MCP, and eight configuration mechanisms to control your AI coding assistant.
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·6 min
Playwright E2E Builder: Engineering AI-Driven Web Automation Testing
Playwright E2E Builder is an AI Skill installed in Cursor that transforms UI automation from throwaway scripts into sustainable engineering assets through a four-step workflow, with built-in locator health checks.
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·7 min
Harness Engineering: The Six-Layer Engineering Framework for Enterprise AI Agent Deployment
From Prompt Engineering to Harness Engineering, a deep dive into the core challenge of truly deploying AI Agents in enterprises. This article breaks down the six-layer architecture and shares real-world Hermes Agent practice.
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·6 min
Understanding the Limits of Vibe Coding: A Practical Guide to AI Engineering-Grade Programming
An in-depth look at the difference between Vibe Coding and AI engineering-grade programming. Can AI replace programmers? From Claude Code and Codex to Cursor, revealing the real limits, enterprise pain points, and engineering solutions of AI coding tools.
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Skill Engineering: Why AI Design Can't…
·2 min
Skill Engineering: Why AI Design Can't Rely on 'One-Shot' Generation
Paul Bakaus debunks the one-shot AI myth. Learn how Skill Engineering and loopmaxxing reshape human-AI collaboration for better design outcomes.
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