AceDataCloud

Code review queues get noisy fast: a small PR needs a safety pass, an issue needs a first implementation, and a teammate asks the same “can someone take a look?” question again. If your project already lives on GitHub, one practical way to reduce that friction is to put Claude Code directly into the GitHub Actions workflow and trigger it from issues or pull requests. This guide walks through a minimal, review-friendly setup using the Claude Code GitHub Action with Ace Data Cloud as the API endpoint. The goal is not to replace human review. It is to make routine analysis, first-pass fixes, and structured PR feedback easier to start. What you can do With the workflow configured, you can ask Claude from an issue or PR comment by mentioning @claude . The source documentation describes several concrete uses: Create a pull request from a described issue. Implement code based on an issue description. Review PR safety and code quality. Use repository conventions from a root-le...

When an image task is part of a coding workflow, switching from terminal to browser to design tool breaks momentum. The Claude Code + NanoBanana MCP setup gives you a practical way to ask for image generation or image edits from the same terminal session where you are already building. What you can do NanoBanana MCP is useful when image work is close to development work: documentation illustrations, landing-page placeholders, product screenshots, UI assets, or quick visual experiments. The documented integration exposes two tools inside Claude Code: nanobanana_generate_image for text-to-image generation. nanobanana_edit_image for image editing, including workflows that use multiple image URLs as input. The interesting part is not only generation. The guide describes NanoBanana as an image model with strong image comprehension, so you can ask it to understand relationships between images. For example, you can provide two images and ask it to place an object from one i...

Image editing pipelines get messy when every input format, model choice, and output size requires a different branch in your code. The OpenAI Images Edits API on Ace Data Cloud gives you one practical interface for editing an image from a URL, a base64 string, or a normal file upload, while keeping the workflow close to the OpenAI-style API shape. What you can do The core endpoint for this workflow is POST https://api.acedata.cloud/openai/images/edits . With gpt-image-2 , you can send an existing image plus a text instruction and receive an edited image. The documentation highlights a few useful production behaviors: URL input through JSON, base64 input for local files, multiple reference images through an array, and high-resolution redraws by setting size . That makes the endpoint useful for tasks such as: converting an infographic from light mode to dark mode while preserving layout; replacing the background or surface style of a product image; combining several product referenc...

When an AI assistant helps with API work, the hard part is often not writing code. It is keeping the assistant grounded in the current account state, the exact API surface, the available services, and the right documentation page. The AceData Cloud MCP gives your assistant a controlled way to inspect that platform context instead of guessing from memory. What you can do AceData Cloud MCP , published as the PyPI package mcp-acedatacloud , is a unified MCP server for platform management and documentation lookup. It is not a generation endpoint. Unlike service-specific MCPs for tools such as image, music, video, or search generation, this server connects to the management and console side at platform.acedata.cloud . That distinction matters. In practice, it means your assistant can help with questions such as: How many Credits are left across subscriptions? Which applications and API keys exist, and which keys have spending limits? What services, APIs, OpenAPI specs, model ...

When you are building a product, a docs site, or a small internal tool, image work often becomes a context-switching tax: open a separate web app, upload references, write prompts, download results, and then return to the terminal. The NanoBanana MCP integration for Claude Code is useful because it moves that loop into the same place where you already plan, code, and review changes. What you can do The NanoBanana MCP server lets Claude Code call Gemini-powered image tools from a terminal session. The documented tool surface is intentionally small: nanobanana_generate_image for text-to-image generation. nanobanana_edit_image for image editing, including multi-image input. The practical difference from a basic image generator is image understanding. You can describe a relationship between multiple inputs, such as taking an object from one image and placing it into another scene while matching angle and lighting. That makes it useful for product mockups, page illustrations,...

When you are building an assistant, an internal code helper, or a small automation that needs Claude-specific behavior, the first design choice is the API shape. You can use an OpenAI-compatible chat endpoint, but if you want Claude’s native message structure, streaming events, tool use, visual inputs, and extended thinking, the Messages API is the more direct fit. This guide walks through the practical pieces you need to build against the Ace Data Cloud Claude Messages endpoint without hiding the wire format behind a framework. The goal is simple: send a request, understand the response, add streaming when the UI needs it, and know how to handle tool calls when Claude asks your application to run external logic. What you can do The Claude Messages API uses the Anthropic-native request and response format at /v1/messages . In practice, that means you can build: single-turn and multi-turn assistants using a messages array with user and assistant roles; role-controlled...