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.
ChatTS is a time‑series‑native multimodal large language model trained on purely synthetic data, offering superior understanding and reasoning over both real and synthetic time‑series datasets, and outperforming existing LLM baselines across alignment and inference tasks.
Volcengine’s vRDMA brings high‑performance, low‑latency RDMA acceleration to cloud VPCs, combining self‑developed congestion control, elastic ENI integration, and compatibility with HPC, AI, and big‑data workloads to deliver up to 320 Gbps bandwidth and microsecond‑level latency.
This article introduces TickIt, a ByteBrain system that leverages large language models to automatically identify and escalate critical Oncall tickets, detailing its multi‑class escalation, deduplication, and category‑guided fine‑tuning modules, experimental results, and the operational impact on cloud services.
DeerFlow, an open‑source deep‑research platform built on LangStack, showcases a novel multi‑agent architecture, Meta‑Prompt engineering, MCP integration, and AI‑generated podcasts, providing developers with a powerful, extensible framework for advanced LLM‑driven workflows.
This step‑by‑step tutorial explains how to add a self‑deployed large language model to Volcengine's Edge Large Model Gateway, configure a secure calling channel, bind it to a gateway access key, and integrate the provided sample code for seamless API access.
ByteBrain-LogParser is a cloud‑native log‑parsing framework that transforms unstructured logs into dynamic templates with real‑time precision control, delivering parsing speeds up to two orders of magnitude faster than state‑of‑the‑art methods while maintaining near‑SOTA accuracy and low storage overhead.
This article explains how the Model Context Protocol (MCP) decouples tool providers from AI agents, introduces a USB‑C‑like standard for context and tool exchange, and demonstrates its impact on development paradigms, tool ecosystems, and future AI agent architectures.
ByteDance’s ByteBrain team showcases how large‑model AI, operations research, and system‑level innovations have produced award‑winning papers and billions of yuan in cost savings while improving on‑call efficiency, database estimation, and cloud infrastructure reliability.
Model Context Protocol (MCP) is an open standard that standardizes how large language models interact with external tools and data, enabling seamless function calls, simplifying prompt engineering, and allowing developers to build modular AI applications without handling low‑level integration details.
ByteDance’s Doubao model team has open‑sourced Multi‑SWE‑bench, a multilingual benchmark covering seven major programming languages with 1,632 real‑world bug‑fix tasks, complete Docker environments, difficulty grading, and strict human validation, aiming to evaluate and advance large‑language‑model code‑repair capabilities beyond Python.
