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This article outlines the four primary API security risks—malicious calls, data tampering, sensitive data leakage, and XSS propagation—and presents a comprehensive, multi‑layered protection framework spanning frontend safeguards, firewalls, gateways, and fine‑grained API design guidelines within APICloud.
This article explains the TLM large‑model development platform and details how knowledge distillation—using soft labels, temperature scaling, and combined loss functions—compresses teacher models into efficient student models, with practical steps and evaluation on the platform.
This guide explains the background of speech‑to‑text technology, introduces the large‑model streaming speech recognition API, walks through obtaining an API key, and provides detailed Go and Python code for establishing a WebSocket connection, sending full‑client and audio‑only requests, and parsing server responses.
This guide explains the challenges of using disparate TTS providers, introduces a unified multi‑engine speech synthesis service, details its technical highlights, typical use cases, and provides complete API specifications with request/response examples and authentication steps.
PoleFS is a high‑performance, cloud‑native distributed file system that combines NVMe‑accelerated hot storage with S3‑based cold storage, offering multiple client access methods, multi‑level metadata and data caches, prefetch/warm‑up strategies, and a Close‑to‑Open consistency model to balance performance and data correctness.
The article introduces the TLM large‑model development platform, details its fine‑tuning options, explains reinforcement learning fundamentals and key algorithms such as PPO and the newer GRPO, describes the architecture of a reinforcement evaluator, and shows how to configure RL training on the platform.
This article presents a comprehensive solution for constructing an enterprise‑level knowledge base using APICLOUD share‑link data, covering data characteristics, system architecture, core algorithms such as streaming token chunking and semantic vector retrieval, performance optimizations, and real‑world integration scenarios.
The TLP platform offers comprehensive support for labeling text, image, audio, and video data, detailing the importance of high‑quality annotations for large AI models, task creation, workflow steps, review processes, and an automatic annotation feature that can boost efficiency by over 60 percent.
This article explains how the Model Context Protocol (MCP) bridges large language models and external services by converting OpenAPI specifications into callable tools, covering generation with openapi‑generator, mapping rules, three runtime modes (stdio, streamable, SSE), and implementation details in Java, Python, and Go.
The Model Context Protocol (MCP) is emerging as a de‑facto standard for AI interactions, and cloud‑hosted MCP servers are gaining traction by lowering developer barriers, enhancing SaaS integration, meeting enterprise compliance, while also facing deployment speed, traffic variability, and complex logic challenges that serverless function compute aims to resolve.
