SimpleOne launched SimpleGen — an AI tool for development and deployment on the platform

SimpleOne has launched SimpleGen — an AI tool that helps generate solutions for the SimpleOne platform directly from the project repository. Along with the release, the company demonstrated a basic launch scenario: from CLI installation to the first task and deployment to the environment.

What SimpleOne Demonstrated

SimpleGen is a new layer of AI automation around the SimpleOne platform. The tool is installed on a developer's machine via npm, connects to a platform instance by URL, and uses existing credentials or an API key. Nothing needs to be installed on the environment itself: the logic works through repository access and integration with the environment. Essentially, the company is offering not a separate chatbot for code generation, but a managed workflow tied to project structure, Git, and development scenarios within SimpleOne.

In the published guide, SimpleOne emphasizes not abstract AI capabilities, but a reproducible process. First, the developer initializes the project, pulls application data from the environment, and fixes the initial state in Git. After that, SimpleGen can be used as a working tool for task specification, change generation, and subsequent build. This approach is important for enterprise development: you can see exactly what the AI created, what changed after each iteration, and to what state you can quickly rollback if the generation didn't produce the desired result.

What's Needed to Get Started

The entry threshold looks quite straightforward: you need access to a SimpleOne instance, a local repository, and a basic set of development tools. The company specifically notes that almost any environment will work — dev, test, stage, or a personal setup, and deployment can be either cloud-based or on an internal network. The main requirement is that the instance must be reachable over the network and the developer must have working credentials.

• URL of the SimpleOne environment
• API key or administrator login and password
• Node.js, npm, and Git on the local machine
• Cursor editor for running commands via chat

Configuration then proceeds through environment variables: the .env file specifies the instance address and authorization method. If the project is empty, SimpleGen initializes it with the init command and exports application records, as well as required platform dependencies. After the first export, SimpleOne recommends making an initial commit right away. This is not a formality: basic snapshots simplify change comparison, enable safe rollbacks, and make team collaboration more predictable if other developers or reviewers will work on the same product later.

What the Workflow Looks Like

The main workflow takes place in Cursor, where SimpleGen commands are run as slash commands within the chat. The user first creates a task specification, then asks the system to break it down into a plan, and then enables change generation based on the prepared structure. In practice, this looks like transitioning from a human feature description to files that can already be checked in a diff, refined, and accepted. SimpleOne specifically shows that AI here doesn't eliminate engineering discipline: after each step, there's still review, commit, and conscious acceptance of changes.

"Commands are run in the

Cursor chat via the / menu, not as plain text."

As an example, the company walks through a simple task: making the urgency field required when saving a task. To do this, the developer formulates the title, description, and acceptance criteria, initiates specification creation, gets a plan in task.md, then triggers code and configuration generation. After reviewing the changes, the result is committed and sent for build with subsequent deployment to the SimpleOne environment.

An important detail: the guide immediately describes typical failures — from missing Git in PATH to fetch errors and Cursor connection issues — meaning the product is presented not as magic, but as a tool with clear operating conditions.

What This Means

SimpleOne is moving toward more structured AI development for its platform: not just "generate code for me," but a complete cycle from specification to deployment. For teams that already live in Git and work with environments, this can significantly reduce time spent on typical changes and lower the barrier to entry for the platform. If the company brings integrations like Figma and expands scenarios for complex tasks, SimpleGen could transform from a starter helper into a permanent tool for product development.