AI app for it and development · no coding needed
Local app generation and delivery workspace
Generate working apps from plain-language descriptions while keeping projects and data on the user's own machine.
Made for: Developers and technical teams building internal or client apps from plain-language descriptions
What it does for you
The problem
App generation tools run in hosted clouds, scatter project data across subscriptions, and leave no owned, portable codebase.
What it gives you
A locally run, versioned, publishable app
What you give it
Plain-language descriptionsexisting repositorieschosen stacksconnected service accounts
Build your own version of Capacity Desktop, Moldable and more
One app with what these 5 AI tools do, yours to keep and change: Capacity Desktop, Moldable, Wandesk, Dualite Alpha, Plow Mac App.
Everything these tools do, in one app
- Natural language app generation Turns a plain-language description into a working app with UI, logic, and data.Found in Capacity Desktop, Moldable, Wandesk and 1 more
- Local app execution Runs the generated apps on the user's own machine rather than in a hosted cloud workspace.Found in Capacity Desktop, Moldable, Wandesk and 1 more
- Local data storage Keeps project files, prompts, and app data on the user's device for privacy and control.Found in Capacity Desktop, Moldable, Wandesk and 1 more
- Bring your own AI keys Lets users connect their own model provider API keys and pay the provider directly.Found in Capacity Desktop, Wandesk
- AI usage spend dashboard Shows what each AI session costs so users can track spending.Found in Capacity Desktop
- Version restore timeline Creates a restorable version for every change so users can roll back a bad AI edit with one click.Found in Capacity Desktop
- Standard project folders Stores projects as normal folders and git repositories that can be opened in any editor or handed to another developer.Found in Capacity Desktop
- Starter template library Provides ready-made templates or starter apps to speed up common use cases.Found in Capacity Desktop, Moldable
- Import existing repository Lets users bring in an existing codebase or repository to continue development.Found in Capacity Desktop, Dualite Alpha
- One-click publishing Connects deployment services and deploys the app directly from the tool.Found in Capacity Desktop, Dualite Alpha
- Automatic dev tool installation Detects and installs required development tools such as Git and Node without terminal access.Found in Capacity Desktop
- Conversational iterative edits Refines the app through plain-language commands like add this or change that.Found in Moldable
- Filesystem permission approvals Requires explicit user approval before the app or agent accesses files.Found in Moldable, Plow Mac App
- Standard web app code output Produces standard Next.js React projects that can be published or adapted outside the tool.Found in Moldable
- Shared memory across apps Keeps preferences and context persistent between different apps in the same workspace.Found in Wandesk
- Editable app structure and change logs Exposes editable files organized as UI, logic, and data with visible logs of AI-run commands.Found in Wandesk
- Framework and stack selection Lets users choose different frameworks and technology stacks to start a project.Found in Dualite Alpha
- Messaging service connectors Connects to services like iMessage, Gmail, and Slack so the agent can read and send messages.Found in Plow Mac App
How it works, step by step
- Turn a plain-language description into a working app with UI, logic and data
- Run generated apps on the user's own machine
- Keep project files, prompts and app data on the user's device
- Connect the user's own model provider API keys
- Show AI session spend in a usage dashboard
- Create a restorable version for every change
- Store projects as normal folders and git repositories
- Provide starter templates for common use cases
- Import an existing codebase or repository
- Deploy the app through connected deployment services
- Detect and install required development tools without terminal access
- Refine the app through plain-language iterative edits
- Require explicit approval before file or agent access
- Output standard Next.js React projects
- Keep preferences and context persistent across apps in the workspace
- Expose editable UI, logic and data files with visible command logs
- Let users choose frameworks and technology stacks
- Connect messaging services such as iMessage, Gmail and Slack
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Local app generation and delivery workspace with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Local app generation and delivery workspace with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links4 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare24 KB
- prompt-vps.mdThe same build on your own server (Docker)24 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria11 KB
- demo/index.htmlThe working demo on sample data199 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Generate working apps from plain-language descriptions while keeping projects and data on the user's own machine. For developers and technical teams building internal or client apps, convert plain-language descriptions, existing repositories, chosen stacks and connected service accounts into a locally run, versioned, publishable app the team owns. The benefit is a testable hypothesis, measured through accepted app changes per developer hour and rollbacks after AI edits; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect plain-language descriptions, existing repositories, chosen stacks and connected service accounts, then follow this sequence: 1. Turn a plain-language description into a working app with UI, logic and data. 2. Run generated apps on the user's own machine. 3. Keep project files, prompts and app data on the user's device. Resolve uncertain cases with qualified reviewers, approve a locally run, versioned, publishable app, and measure accepted app changes per developer hour and rollbacks after AI edits against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Local execution with user-supplied model keys; final code review, security checks and deployment approval remain with the development team. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve code ownership, source attribution, license accuracy and usage permissions. Developers approve substantive changes and deployment scope. One chosen stack and one local operating system; final code review, security checks and deployment approval remain with the development team. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One chosen stack and one local operating system; final code review, security checks and deployment approval remain with the development team. Implement one approved input format, a bounded representative case set and the first two task modules: turn a plain-language description into a working app with UI, logic and data; run generated apps on the user's own machine. Support the third module with operator review: keep project files, prompts and app data on the user's device. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
User-owned repositories, local development tools and permitted model provider APIs. Cloud deployment services, messaging services such as iMessage, Gmail and Slack, and code hosting. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Description and stack setup, Local project workspace, Build and publish. Use a project list with local folder paths, a central editor showing UI, logic and data files, and a right-hand panel for AI chat, permissions and change logs. Let users compare versions and restore from the timeline. Display draft, changes requested and approved states. Provide a spend view per session and a publish panel with deployment targets. Make the task-specific outcome a locally run, versioned, publishable app visible beside its evidence, review state and value baseline.
