Claude MCP: How to Configure MCP Servers in Claude Code and Claude Desktop
A practical guide to adding MCP servers in Claude Code and Claude Desktop — covering claude mcp add, JSON config, tool routing, and sandbox execution.
A practical guide to adding MCP servers in Claude Code and Claude Desktop — covering claude mcp add, JSON config, tool routing, and sandbox execution.
Learn how to use the Vercel AI SDK to build AI-powered apps with streaming, tool calls, and agent loops. Includes Novita AI integration with code examples.
A buyer's guide to AI agent sandbox pricing: per-session fees, compute tiers, storage, egress, package caching, idle time, and self-hosted cost models.
Learn to use the GLM TTS API, GLM ASR API, and voice clone API on Novita AI. Includes curl and Python examples, parameters, pricing, and voice options.
A security evaluation guide covering which events AI agent sandbox audit logs must capture, retention policies, log integrity, and how to surface logs for incident response.
Call Kimi K2.7 Code on Novita AI using the OpenAI-compatible chat API. Includes model ID, pricing, context limits, vision input, function calling, and runnable examples.
Step-by-step guide to configure CoBuddy (baidu/cobuddy) in Claude Code using Novita AI's OpenAI-compatible endpoint. API setup, pricing, and coding workflow tips.
Use DeepSeek in Claude Code via the V4 Flash API on Novita AI. Set four env vars, get 1M-token context, and cut costs 20x vs Claude Sonnet.
Configure Kimi K2.7 Code in Claude Code via Novita AI's Anthropic endpoint. API key setup, model string, cost comparison, and coding workflow tips.
An AI sandbox can run browser automation — with conditions. Learn when it fits, what it enables, its hard limits, and when to use a dedicated browser tool instead.
A practical security guide for teams enabling AI agents to install packages in sandboxes: allowlists, version pinning, registry mirrors, egress controls, and audit logging.
Requirements checklist for AI-generated code sandboxes: isolation, lifecycle API, concurrency, observability, resource limits, and backend integration.
Compare developer services for operating many LLM APIs at team scale: SDK consistency, auth, billing consolidation, model lifecycle, governance, and observability.
A practical evaluation checklist for AI developers comparing sandbox providers: API surface, SDK compatibility, session lifecycle, packages, network, and pricing.