493 related articles

Coze is ByteDance's homegrown agent-building platform. This article explains getting started with Coze, comparison with Dify, its skill system, workflow orchestration, and multi-agent collaboration.

A detailed guide on building a full-process HR recruitment Workflow Agent with Spring AI Alibaba Graph, covering resume parsing, multi-dimensional screening, tiered questions, human-in-the-loop, and state rollback.

An in-depth look at the three-layer funnel architecture for agent intent recognition: rules for fast interception, context for routine intents, and LLM as fallback. Exploring the engineering trade-offs of accuracy, latency, and cost.

Anthropic's Applied AI team breaks down a methodology for choosing AI models: building custom evals, avoiding three common pitfalls, measuring value by cost per success, and cutting costs with prompt caching and context engineering.

Agent Device is an open-source tool from CallStack that lets AI truly "understand" interfaces via structured accessibility snapshots, closing the loop of open, observe, operate, and verify across iOS, Android, and React Native.

A recursive technical proposition: Can we build a "meta-Skill" that auto-transforms any Skill into a Dify workflow? This article dissects the boundary between deterministic orchestration and autonomous Agent decisions.

How Pinterest engineers built Medic for Apache Spark — a multi-agent auto-diagnosis tool — covering the evolution from a single ReAct agent, observability, log denoising, and end-to-end testing.

NanoClaw founder David Boyd breaks down the core engineering of enterprise autonomous Agents: a triple security isolation model, LLM Wiki memory design, and the real-world path from personal Agents to team-scale deployment.

In-depth analysis of the five core dimensions of AI Agent testing: command safety, tool-calling accuracy, task planning, output consistency, and error self-repair. Master automated testing and the transition path for test engineers.

An in-depth analysis of the five core dimensions of AI Agent testing: command safety, tool-calling accuracy, task planning, output consistency, and error self-repair. Master automated testing methods and the transition path for test engineers.
Alibaba Open-Sources Code Review Tool …
Alibaba open-sources code review tool open-code-review, using a hybrid architecture of deterministic rule pipelines and LLM Agents. Supports line-level comments, OpenAI/Anthropic APIs, battle-tested at Alibaba scale, written in Go, fully free and open-source.

Can beginners really earn over 10,000 yuan in their first month with AI coding gigs? This article breaks down the four-week AI coding learning path week by week and objectively assesses the real monetization barriers.

Claude Code isn't just a chat AI—it can directly read projects, modify code, and run commands. This article compares Claude Code with regular AI across five dimensions to help you decide if it's worth trying.

Claude Code isn't just a chat AI—it can directly read projects, modify code, and run commands. This article compares Claude Code with ordinary AI across five dimensions: interaction, context, execution, memory, and tool calling.
Pumpkin: A High-Performance Minecraft …
Pumpkin is an open-source Minecraft server rewritten from scratch in Rust. Learn how it eliminates Java GC pauses, reduces resource usage, and its current limitations.

A focused guide to the core interview topics for LLM application engineers, covering agent architecture, Multi-Agent, Langfuse evaluation & tracing, security, and RAG optimization.

A focused guide to core LLM application engineer interview topics, covering agent architecture, Multi-Agent, Langfuse evaluation, security, and RAG optimization.

From the autocomplete nature of LLMs, tokens, and context windows to RAG vector databases, the MCP protocol, and AI agent loop design — this article uses vivid analogies to unpack the reality of AI engineering.

As AI coding assistants like Codex become standard, the risks of overreliance grow too. Learn when developers should "show a red card," reclaim control, and safeguard code quality and responsibility.

A deep dive into engineering AI applications: from a simple chat page to a multi-layer Agent platform, covering RAG knowledge bases, Workflow scheduling, multi-model management, and run tracing.