The article analyses how EdgeOne Makers provides a comprehensive engineering foundation—including managed runtime, sandbox tools, conversation storage, observability, model integration, authentication, and Git‑based deployment—to turn AI Agent demos into production‑ready services, and compares it with alternative approaches.
OpenAI Codex CLI can be paired with any open‑source model that implements the Responses API; this guide explains the prerequisite change, shows how to locate and edit the configuration files, add a StepFun API key, configure the model parameters, verify the connection, and use Codex to review code changes.
OpenCode is an open‑source AI coding agent that natively supports over 75 models, offers dual Plan/Build modes, provides a mouse‑friendly CLI, and can generate a full Microsoft To Do‑style web app, while also highlighting its current limitations compared with Claude Code.
This guide shows how to bypass subscription limits and high costs of Claude Code and Codex by configuring them to use domestic large models like DeepSeek via the free, open‑source CC Switch tool, with step‑by‑step installation, API key setup, and model switching.
This comprehensive guide walks you through installing Hermes Agent, configuring over 20 model providers, setting up the Skill system, enabling MCP client/server modes, and connecting the agent to 17 messaging platforms, while also covering execution back‑ends, security settings, and troubleshooting tips.
Researchers from Shanghai Jiao Tong University introduce the Meta‑Encoder, a unified integration framework that dynamically combines multiple pathological foundation models, achieving superior cancer detection performance across diverse tasks and datasets while maintaining low computational cost.
This guide shows developers how to replace costly OpenAI APIs by configuring Codex to use Chinese large‑language models such as DeepSeek, GLM‑4.7, and Qwen, detailing three setup methods, benchmark results, cost savings of up to 90 %, and best‑practice tips for optimal performance.
Claude Code offers a great AI coding experience but quickly becomes costly, so this guide shows how to route its requests through Google AI Studio’s free Gemini 2.5 Flash model via OpenRouter or an open‑source proxy, compares performance and pricing, and provides step‑by‑step configuration, advanced switching tips, and common pitfalls.
This guide shows how to enable Claude Desktop's hidden Configure Third‑Party Inference feature, connect it to OpenRouter, and switch among over 400 large language models without changing the client, covering cost savings, model selection strategies, four-step setup, and advanced MCP extensions.
After a nine‑day silence, OpenClaw released version 2026.3.22‑beta.1, delivering a complete rewrite of its plugin system with a new SDK and ClawHub distribution, extensive Windows security hardening, model upgrades to GPT‑5.4 and MiniMax M2.7, UI refinements across Android, Telegram and Feishu, and agent engine improvements such as longer timeouts and a /btw side‑question command.
The OpenClaw v2026.3.7 release introduces webhook compatibility fixes, typing‑feedback UI, prompt‑caching that cuts token usage by 33%, smarter model routing, native support for domestic LLMs, and persistent binding storage for container deployments, making the platform far more reliable and cost‑effective for enterprise automation.
AutoClaw repackages OpenClaw into a one‑click desktop app that eliminates the multi‑step setup, automatically integrates with Feishu, bundles free GLM models and the new Pony‑Alpha‑2 LLM, and offers a plug‑and‑play skill system for AI‑driven tasks.
The article analyzes Claude Skills' shortcomings—including limited platform support, lack of synchronization, sparse skill libraries, high installation barriers, dependence on precise descriptions, skill conflicts, model capability variance, context‑window consumption, nested reference limits, strict formatting rules, script robustness, unsuitability for simple tasks, and security risks—offering concrete examples and practical guidance.
The article examines why Anthropic’s Claude Code runs as a terminal‑based CLI rather than a full IDE, outlining the strategic reasons, practical advantages such as parallel development, cross‑device collaboration, lightweight operation, native model integration, flexible vendor switching, and powerful built‑in features, all illustrated with concrete user experiences and tool examples.
This article answers the most frequent questions about AgentScope Java, comparing its design and capabilities with Spring AI Alibaba, outlining future integration plans, core features, model support, and providing practical resources for developers.
This article explains how to replace cumbersome, city‑specific prompt strings with modular AI Skills, demonstrates the food‑diorama‑skill that generates 3D gourmet dioramas, and provides a step‑by‑step guide for connecting the DeepSeek V3.2 model to Claude Code using environment variables or the CC Switch GUI.
Mozilla.ai's any-llm v1.0 is an open‑source Python library that unifies access to multiple large language model providers, enabling developers to move between cloud‑based and on‑premise LLMs without rewriting code, while offering async‑first APIs, reusable connections, and extensive compatibility features.
This guide explains how to overcome access restrictions to Claude’s large‑model APIs in China by configuring Claude Code to work with Zhipu AI’s GLM‑4.6, covering background, model features, required environment setup, installation steps, environment variables, usage commands, and best‑practice scenarios for AI‑assisted development.
The article compiles senior asset‑management executives' perspectives on applying large‑model AI—covering vertical versus generic models, integration strategies, talent and cost considerations, innovative C2C development, AI‑native platforms, and the practical challenges of using LLMs in investment research.
This guide explains how to install Claude Code and the ClaudeCodeRouter, configure them to use DeepSeek‑V3.1 via the Anthropic API format, run the tool, and troubleshoot common connection and Windows environment issues.
This article explores how an AI workflow platform built on the open‑source Dify project automates data collection, processing, and report generation, reducing manual effort while improving analysis accuracy and providing actionable insights for project progress.
This article compares OpenRouter and Higress as emerging AI Gateways, examining their origins, target users, feature sets, deployment models, and pricing structures to show how the traditional gateway concept is being reshaped for AI‑driven cloud‑native applications.
This tutorial explains LangChain's core modules and three‑layer architecture, shows how to set up a Python environment, and provides concrete code examples for connecting SiliconFlow Qwen3‑8B and DeepSeek models via the init_chat_model API, including result inspection and references to official documentation.
This article introduces major international and domestic AI large models, recommends desktop aggregation tools and APIs such as POE, Monica, and OpenRouter, and provides complete Python code examples for synchronous and streaming text and multimodal conversations, along with additional API and compute‑rental resources.
