The Volcano Engine Developer Community, Volcano Engine's TOD community, connects the platform with developers, offering cutting-edge tech content and diverse events, nurturing a vibrant developer culture, and co-building an open-source ecosystem.
This article explains how veDB-Search provides a one‑stop hybrid search service that combines vector, full‑text, and scalar queries using only SQL, reducing development complexity and operational overhead for AI agents and large‑scale AI applications.
The article examines the emerging security challenges of AI agents, outlines four fundamental vulnerabilities, and introduces the AgentArmor framework—featuring a graph constructor, property registry, and type system—to compile agent behavior into verifiable programs and dramatically reduce attack success rates.
This guide breaks down the complex terminology of large language models—explaining tokens, transformers, self‑attention, RAG, scaling laws, dense vs. sparse architectures, and training stages—using clear analogies and step‑by‑step explanations so readers can confidently understand and work with modern AI systems.
This article examines the security challenges of the Model Context Protocol (MCP) in AI applications, analyzes attack surface expansion across creation, runtime, and update phases, and presents a comprehensive AI‑enhanced scanning architecture with mitigation strategies to protect the entire AI ecosystem.
This article examines the growing problem of hallucinations in large language models, outlining their causes across the model lifecycle, classifying four main hallucination types, and presenting both retrieval‑augmented generation and detection techniques—white‑box and black‑box—to reduce factual errors in critical applications.
After Anthropic halted Claude services for Chinese‑controlled firms, Volcano Engine introduced a migration plan with Claude Code integration, generous token incentives, and a TRAE‑based alternative, providing developers with step‑by‑step setup, configuration, and usage instructions.
This article explains how to re‑implement ByteDance's DeerFlow deep‑research framework in Go (Deer‑Go), covering the multi‑agent architecture, control‑hand‑off, interrupt & checkpoint mechanisms, integration with the Hertz SSE server, and step‑by‑step deployment instructions.
This article examines the OAuth security challenges in Volcano Engine's Model Context Protocol (MCP) ecosystem and outlines a comprehensive, three‑stage defense strategy—including pre‑authorization double confirmation, token identity isolation, and API‑level permission controls—to protect user assets and data.
This article examines the evolution of LangChain's Open Deep Research project from a monolithic LLM pipeline to a multi‑agent system, highlighting the role of context engineering, architectural trade‑offs, practical code examples, and best‑practice guidelines for building scalable, token‑efficient AI solutions.
Since last year, the debate over “Prompt Engineering” has split between practitioners who favor “Context Engineering” for building scalable agent systems and scholars who treat Prompt Engineering as a broad umbrella term, highlighting the need to dynamically construct and manage context for reliable, extensible AI applications.
