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186 related articles
·6 min
Databricks Agent Framework in Practice: A Guide to ChatAgent Wrapping and Production Deployment
A deep dive into Databricks Agent Framework (Mosaic AI): unify LangGraph/OpenAI agents via ChatAgent, log & evaluate with MLflow, version with Unity Catalog, and deploy Model Serving Endpoints for production AI agents.
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·3 min
IMGNet: A New Face Verification Approach Where Sign Pattern Matching Challenges Cosine Similarity
IMGNet is a 10MB face verification model replacing cosine similarity with sliding window sign pattern matching. Sign metrics outperform cosine on LFW and other benchmarks, suggesting sign consistency may be intrinsic to quality face embeddings.
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·6 min
Deploying Enterprise AI Agents to Production: The Five Pillars from Demo to Launch
A Databricks expert breaks down the complete methodology for taking AI Agents from demo to production, covering the five pillars of evaluation, observability, data foundation, multi-Agent orchestration, and AI governance, with a real eight-week banking chatbot POC case.
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·4 min
How AI Is Rewriting the Economics of Software Rewrites: Costs Plummet, But Risks Remain
Once called "the single worst strategic mistake," code rewrites are back on the table thanks to AI. Explore how AI slashes comprehension, translation, and validation costs — and what risks remain.
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·6 min
Using Skills to Let AI Automatically Manage LLM API Gateway Stations
An in-depth look at the Skills paradigm in AI programming: through intent routing and script encapsulation, let AI agents auto-manage multi-channel LLM APIs on a One API gateway for one-click distribution, health checks, and auto-degradation.
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·4 min
Grok vs GPT vs Claude: Which AI is the Best Coding Assistant?
Grok 4.5, GPT-5.5, and Claude go head-to-head on the same coding tasks. A deep comparison of code quality, UI design, and engineering standards to help you choose the right AI coding assistant.
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·6 min
Dify Local Deployment in Practice: A Complete Guide to Building an AI Application Platform with Docker
A complete guide to Dify local deployment: from Docker environment setup, source code pulling, and container startup to first access. Build a private AI app development platform across Linux, Windows, and Mac for fast enterprise AI deployment.
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·7 min
Getting Started with LangChain 1.3: Core Concepts of LLMs and Agents
An in-depth breakdown of LangChain 1.3's core concepts, covering the three major limitations of LLMs, Agent architecture, memory management, and a complete learning path. Master LangChain and LangGraph to quickly build AI development skills.
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·7 min
Andrew Ng's Prompt Engineering Course Essentials: Core Methodology Every Developer Must Learn
An in-depth analysis of the essentials of Andrew Ng and OpenAI's ChatGPT Prompt Engineering course. Covers the difference between base and instruction-tuned models, two core prompting principles, and how to wield LLM APIs to build apps.
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·7 min
The Agent Engineer Transition Roadmap: A Deep Dive into Three Core Skill Systems
Want to become an Agent engineer? This article systematically covers three core skill tracks—LLM fundamentals, LangChain architecture development, and enterprise deployment—to help you avoid detours.
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·7 min
LangChain Hands-On Guide: From LLM Calls to Agent Development
Master LangChain from scratch: the three limitations of LLMs, init_chat_model unified interface config, the Message type system, and the path from LLM calls to Agent development.
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·5 min
Embedded Linux vs. AI Agent Development: A Comprehensive Comparison of Salary, Job Availability, and Career Stability
Embedded Linux or AI Agent development? This in-depth comparison covers salary, job availability, and career stability to help developers pick the right path.
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·7 min
Getting Started with OpenAI LLM Development: A Practical Guide to Model Selection, Token Pricing, and API Usage
A systematic guide to the three cores of OpenAI LLM app development: GPT-4/GPT-3.5 model selection, token billing and cost-saving tips, and practical use of the Models, Completion, and Chat Completion APIs.
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·7 min
Fable AI Coding in Practice: Optimizing Reasoning Levels and Multi-Model Collaboration to Save Money
Hands-on guide: Use Anthropic's Fable model to optimize AI coding workflows — control reasoning levels, leverage Claude-Codex multi-model collaboration, and cut costs from thousands to $150.
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·7 min
Fable AI Coding in Practice: Optimizing Reasoning Levels and Multi-Model Collaboration to Cut Costs
How developer Theo used Anthropic's Fable model to rebuild his AI coding workflow — controlling reasoning levels, multi-model routing with Codex, and sub-agent orchestration to cut costs from thousands to $150.
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·6 min
AutoScout24's AI-Native Journey: How Codex and Agents Are Reshaping Enterprise Efficiency
As one of the world's largest car marketplaces, AutoScout24 is going AI-native with OpenAI Codex and agents. It built a CapEx agent in 48 hours, saving ~$1M/year, and explores hands-off coding.
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·3 min
WASM for Data Processing: Can Browsers Really Handle Big Data?
Can WASM really handle big data? This deep dive covers DuckDB-WASM's in-browser SQL capabilities, edge data processing architectures, and where WebAssembly fits in modern data engineering.
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·6 min
Claude Code Chinese Guide: A Complete Path from Zero to an Efficient Workflow
A systematic Claude Code learning guide built for Chinese developers, covering ten core modules including Slash Commands, Memory, MCP, and Hooks, with a three-tier path to build an AI coding workflow in 11–13 hours.
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·7 min
The C Readability Crisis: Engineering Reflections Behind Clever Tricks
Why do C programmers keep creating readability disasters? An in-depth look at macro abuse, fancy pointer tricks, and over-nesting—and how coding standards, static analysis, and code review protect maintainability.
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·3 min
Three Fatal Flaws of AI Script Writing — and How MCP Services Fix Them
AI script development plagued by amnesia, blindness, and repeat errors? This article examines how MCP service tools — history nodes, node preprocessing, and an error library — systematically fix AI programming's structural flaws.
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