AceDataCloud

If you already live in the terminal, the fastest way to use an AI coding agent is not another dashboard. It is a command you can run from inside the project you are debugging, reviewing, or refactoring. This guide walks through a practical setup for using claude , the Claude Code terminal CLI, with Ace Data Cloud as the API base URL. The goal is simple: configure the CLI once, keep your project context local to the terminal, and use natural language for day-to-day engineering tasks such as reviewing diffs, writing tests, and creating commits. What you can do The Claude Code CLI is designed for agentic coding workflows from a shell. Once installed and configured, you can: Start an interactive coding session with claude . Run one-off tasks such as claude "fix build error" . Ask a question and exit with claude -p "explain this function" . Continue the most recent conversation in the current directory with claude -c . Restore a previous conversation ...

When a coding agent lives in your terminal, the hard part is not learning a new UI — it is making sure the CLI talks to the model endpoint you actually want to use, with a configuration you can inspect and reproduce. This guide walks through configuring OpenAI Codex CLI to use Ace Data Cloud as an OpenAI Responses-compatible provider. You still run the normal codex command in your project directory; the important change is that Codex reads a custom provider from ~/.codex/config.toml and sends Responses API traffic to https://api.acedata.cloud/v1/responses . What you can do Codex CLI is a local programming agent that runs in a terminal. Once configured, it can help with everyday development tasks such as reading code, modifying files, running commands, explaining errors, and working inside an existing repository. The useful part of the Ace Data Cloud setup is that it does not require a separate local proxy process or a Codex plugin. Codex already supports custom model provid...

Most image features start the same way: a user gives you a prompt, your app needs a reliable image result, and sooner or later someone asks, “Can we edit an existing image too?” The Nano Banana Images API is useful because it keeps both paths—text-to-image generation and image-guided editing—behind one endpoint with one request shape. What you can do The API supports two actions through POST /nano-banana/images on the base URL https://api.acedata.cloud : generate : create images from a text prompt . edit : provide one or more source images in image_urls , then describe the desired change with a prompt . That makes it a practical fit for product mockups, thumbnail experiments, creative tooling, content pipelines, and internal design helpers where you want a single integration rather than separate generation and editing services. How it works Every request goes to: POST https://api.acedata.cloud/nano-banana/images The request uses JSON and requires an API token in the HTTP heade...

Image editing APIs are most useful when they fit into an existing product pipeline: take a user-uploaded image, apply a precise instruction, preserve the parts that matter, and return a usable asset without forcing your backend to juggle temporary files. This guide walks through the OpenAI Images Edits API on Ace Data Cloud using gpt-image-2 . The goal is not to make a toy demo, but to show how you can wire a practical server-side flow around documented fields such as model , image , prompt , size , and, when needed, callback_url . What you can do The edits endpoint accepts an existing image plus instructions, then returns a modified image. In the documented flow, the GPT Image series can accept up to 16 reference images at the same time, which makes it useful for workflows like product composition, poster cleanup, visual style changes, and controlled redesigns. Change a color system while keeping layout and composition intact. Replace a scene element while preserving o...

Image editing APIs become much more useful when they fit into an application pipeline instead of requiring every source image to be downloaded, re-uploaded, and handled manually. What you can do The OpenAI Images Edits API on Ace Data Cloud lets you send one or more reference images together with an instruction prompt, then receive modified image results. The same editing endpoint supports gpt-image-1 , gpt-image-2 , and the nano-banana family, including nano-banana-2-lite , nano-banana-2 , and nano-banana-pro . The most practical part for backend developers is that gpt-image-2 supports image URLs in JSON. That means a service can pass an existing CDN or object-storage URL directly through the image field, instead of first downloading the file locally and converting it into a multipart upload. The same image field can also accept base64 input, either as data:image/png;base64,... or raw base64. Edit a product, poster, infographic, or UI screenshot while preserving its s...

When you add image generation to a product, the hard part is rarely the first prompt—it is turning prompts, image inputs, formats, async jobs, and failures into a workflow your application can trust. What you can do The SeeDream Images API on Ace Data Cloud is built around a single image endpoint, POST https://api.acedata.cloud/seedream/images . With it, you can generate images from text prompts, edit existing images by passing an image URL or Base64 input, choose a model string explicitly, control output size and format, and decide whether to wait for the result synchronously or handle it as an async task. The useful mental model is simple: send a JSON request that describes the image operation, then read a response containing success , task_id , trace_id , and a data array. Each item in data can include the resolved prompt , generated size , and image_url . How it works The basic request needs an authorization header and a JSON body. The docs show two required headers for no...

Голосовое клонирование в музыкальной генерации — не экзотика, а рабочий инструмент: вокал конкретного человека можно «пересадить» в новый трек, сгенерированный моделью. Разберём, как это сделать через Suno Voice Clone API на платформе Ace Data Cloud, единый endpoint которой даёт доступ сразу к нескольким моделям — GPT, Claude, Gemini, Midjourney, Suno и другим — без переключения между провайдерами. Зачем клонирование голоса в музыкальной генерации Стандартный Persona API в Suno работает с уже существующими голосовыми пресетами (по audio_id ). Voice Clone API устроен иначе: вы передаёте публично доступную ссылку на собственную аудиозапись ( audio_url ), и система создаёт персональный голосовой профиль, который затем можно использовать при генерации новых песен. Это полезно для: создания музыкального контента с узнаваемым «фирменным» вокалом; кавер-версий существующих треков в конкретном голосе; прототипирования вокальных партий до записи в студии. Требования к исходному аудио ...

If you are building a product that needs image generation or image edits, the hard part is usually not the prompt itself; it is turning a user request into a predictable API workflow with authentication, result tracking, retries, and useful error handling. This guide walks through the Nano Banana Images API on Ace Data Cloud as a practical builder workflow: send a prompt, choose whether you are generating or editing, receive image URLs, and keep task_id and trace_id around so you can debug the pipeline later. What you can do The Nano Banana Images API exposes one image endpoint: POST https://api.acedata.cloud/nano-banana/images The endpoint supports two actions through the action field: generate : create images from a text prompt . edit : edit one or more existing images using image_urls plus a text prompt . That makes it useful for common product flows: creating hero images from user input, producing concept art, changing a product photo, combining a person image with a...

If your day-to-day work already happens in a terminal, jumping into a separate image tool just to generate a mockup, clean up a screenshot, or combine reference images breaks the flow. The NanoBanana MCP server lets Claude Code call image generation and image editing tools from the same command-line session where you are writing code. This guide walks through the practical setup: adding the nanobanana MCP server to Claude Code, choosing the right configuration scope, verifying the connection, and using the two documented tools in real builder workflows. What you can do The documented Claude Code integration exposes two NanoBanana tools: nanobanana_generate_image for text-to-image generation. nanobanana_edit_image for image editing, including multi-image input. The useful part is not just “make an image.” The docs describe NanoBanana as an image model that understands image content well enough to combine objects and scenes. For example, you can provide two images a...

If you already use an AI client as your day-to-day coding workspace, the next useful step is letting that client create and edit images without leaving the conversation. The Nano Banana MCP Server from Ace Data Cloud is designed for exactly that workflow: connect an MCP-compatible client such as Claude Desktop, VS Code, or Cursor, provide an Ace Data Cloud API token, and then ask the client to generate or edit images through standard MCP tools. What you can do The public Nano Banana MCP guide describes a compact but practical image workflow surface. Once configured, the server exposes tools for: Image generation : create images from text prompts. Image editing : modify an existing image or combine multiple images. Virtual try-on : apply clothing to a photo of a person. Product placement : place a product into a real-world scene. Multi-model usage : use nano-banana , nano-banana-2 , or nano-banana-pro . Task querying : monitor generation progress and retrieve res...

Adding text-to-speech sounds simple until you need reliable audio URLs, controllable output formats, cloned voices, and a way to handle longer synthesis jobs without keeping one HTTP connection open forever. This guide walks through the Fish TTS API exposed through Ace Data Cloud. It focuses on the practical integration path: sending text to POST https://api.acedata.cloud/fish/tts , choosing the right audio format, controlling speech output, and using callback_url when a request should run asynchronously. What you can do The endpoint synthesizes text into audio and returns an audio_url hosted on the platform CDN. The same API can also use a cloned voice through reference_id or inline references , adjust prosody, and switch between Fish TTS models using an HTTP header. Generate speech from a non-empty text string. Return mp3 , wav , or pcm output. The default format is mp3 . Use reference_id or references for cloned voice scenarios. Use common sample rates such as 16000 , 22...

Code review and small implementation tasks often get stuck between “someone should look at this” and “someone has time to look at this.” A practical way to reduce that gap is to wire Claude Code into GitHub Actions, so an issue or pull request comment can trigger an agentic coding workflow directly inside your repository. What you can do The Claude Code GitHub Actions setup described in the Ace Data Cloud documentation lets you use GitHub comments and workflow triggers as the control surface for coding automation. Once configured, you can mention @claude in an issue or pull request comment and ask it to inspect code, implement a feature, fix a bug, or answer a repository-specific question. The workflow can also run automatically. For example, you can configure a pull request review job that passes prompt: "/review" , or a scheduled job that asks for a daily summary of commits and unresolved issues. The important part is that everything runs through GitHub Actions, so ex...

If you already use Claude Code as your working terminal companion, the next friction point is usually media: you can reason about a UI, a landing page, or a content workflow in chat, but you still have to switch tools when you need an image generated or edited. The NanoBanana MCP integration documented by Ace Data Cloud gives Claude Code two image-oriented tools through an MCP server: nanobanana_generate_image for text-to-image work, and nanobanana_edit_image for editing images, including multi-image input. This guide walks through a practical setup and a few builder-style workflows you can run without leaving the terminal. What you can do Once configured, Claude Code can call NanoBanana from a normal session using natural language. The documented capabilities are deliberately simple: Generate an image from a text prompt with nanobanana_generate_image . Edit an existing image with nanobanana_edit_image . Provide multiple image URLs to nanobanana_edit_image so the ...

Голосовое клонирование в музыкальной генерации — не экзотика, а рабочий инструмент: вокал конкретного человека можно «пересадить» в новый трек, сгенерированный моделью. Разберём, как это сделать через Suno Voice Clone API на платформе Ace Data Cloud, единый endpoint которой даёт доступ сразу к нескольким моделям — GPT, Claude, Gemini, Midjourney, Suno и другим — без переключения между провайдерами. Зачем клонирование голоса в музыкальной генерации Стандартный Persona API в Suno работает с уже существующими голосовыми пресетами (по audio_id ). Voice Clone API устроен иначе: вы передаёте публично доступную ссылку на собственную аудиозапись ( audio_url ), и система создаёт персональный голосовой профиль, который затем можно использовать при генерации новых песен. Это полезно для: создания музыкального контента с узнаваемым «фирменным» вокалом; кавер-версий существующих треков в конкретном голосе; прототипирования вокальных партий до записи в студии. Требования к исходному аудио ...