AI app for it and development · no coding needed
Full-stack app generation and deployment workspace
Reduce tool switching and handover work while keeping the generated codebase owned and deployable.
Made for: Product teams and agencies building and deploying full-stack applications from natural language descriptions
What it does for you
The problem
App generation, deployment, monitoring, security review and code ownership are spread across several rented tools, so teams lose time moving code and context between them.
What it gives you
A reviewed, deployable full-stack application with owned source code
What you give it
Plain English descriptionsframework choicesdeployment targets
Build your own version of GitHub Spark, Lovable and more
One app with what these 8 AI tools do, yours to keep and change: GitHub Spark, Lovable, CodeAI Studio Pro, Emergent 2.0, Leap, marpy.io, co.dev MCP, Defang.
Everything these tools do, in one app
- Natural language app generation Turns plain English descriptions into working full-stack applications.Found in GitHub Spark, Lovable, Emergent 2.0 and 1 more
- Full-stack scaffolding Generates both frontend UI and backend functionality such as data storage and authentication.Found in GitHub Spark, Lovable, CodeAI Studio Pro and 4 more
- One-click deployment Publishes the application with a single action.Found in GitHub Spark, Lovable, CodeAI Studio Pro and 1 more
- GitHub integration Connects to GitHub for repository creation, syncing, and version control.Found in GitHub Spark, Lovable, CodeAI Studio Pro and 1 more
- AI coding assistance Helps write, debug, and optimize code using AI.Found in GitHub Spark, CodeAI Studio Pro, marpy.io
- Visual editing Allows building and modifying apps through a visual interface.Found in GitHub Spark
- Precision component edits Lets users select specific components to update without affecting others.Found in Lovable
- Large codebase support Manages projects with over 100,000 lines of code.Found in Lovable
- Multi-framework support Supports popular frontend and backend frameworks like React, Next.js, Vue, and Node.js.Found in CodeAI Studio Pro
- Performance monitoring Provides built-in tools to monitor application performance.Found in CodeAI Studio Pro
- Interactive agent Asks clarifying questions and refines code based on user feedback.Found in Emergent 2.0
- Live preview Shows changes in real time with easy editing through VS Code integration.Found in Emergent 2.0
- One-click rollback Reverts updates with a single action to maintain control.Found in Emergent 2.0
- Security and scalability checks Performs built-in security reviews, scalability assessments, and design evaluations.Found in Emergent 2.0
- Cloud infrastructure deployment Deploys applications on AWS or GCP with real infrastructure.Found in Leap
- Isolated preview environments Provides separate environments for safe testing before production.Found in Leap
- Python-native autocomplete Offers AI inline completions that reference cross-file project context for Python.Found in marpy.io
- Framework-aware analysis Analyzes Django, FastAPI, and SQLAlchemy models to track relationships and dependencies.Found in marpy.io
- Migration safety checks Blocks destructive database operations against production.Found in marpy.io
- Third-party service integrations Connects to services like Prisma, PayPal, and over 25 others.Found in co.dev MCP
- Code ownership and export Allows transferring and owning generated code repositories on GitHub.Found in co.dev MCP
- Automated code fixes Enforces standards and automates fixes using tools like Semgrep.Found in co.dev MCP
- Issue tracking integration Manages issues through integrations with tools like Linear.Found in co.dev MCP
- Malicious URL detection Automatically detects malicious or suspicious URLs.Found in Defang
- Link neutralization Defangs harmful link components to prevent accidental clicks.Found in Defang
- Real-time analysis Analyzes links quickly with fast response times.Found in Defang
How it works, step by step
- Generate a full-stack app from a plain English description
- Scaffold frontend UI, data storage and authentication
- Ask clarifying questions and refine code from feedback
- Support React, Next.js, Vue and Node.js projects
- Offer visual editing and precision component edits
- Provide AI coding assistance for writing, debugging and optimization
- Handle codebases above 100,000 lines
- Show a live preview with VS Code integration
- Connect to GitHub for repository creation, syncing and version control
- Deploy with one action to AWS or GCP
- Create isolated preview environments before production
- Run security, scalability and design checks
- Block destructive database migrations against production
- Monitor application performance after deploy
- Roll back an update with one action
- Connect third-party services such as Prisma and PayPal
- Enforce standards and apply automated code fixes
- Sync issues with tools like Linear
- Detect and neutralize malicious or suspicious URLs
- Export and transfer the generated repository to the buyer's GitHub
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 Full-stack app generation and deployment 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 Full-stack app generation and deployment 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 links5 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare26 KB
- prompt-vps.mdThe same build on your own server (Docker)26 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria13 KB
- demo/index.htmlThe working demo on sample data200 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
Reduce tool switching and handover work while keeping the generated codebase owned and deployable. For product teams and agencies building and deploying full-stack applications from natural language descriptions, convert plain English descriptions, framework choices and deployment targets into a reviewed, deployable full-stack application with owned source code. The benefit is a testable hypothesis, measured through accepted deployments per delivery hour and post-deploy correction rate; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect plain English descriptions, framework choices and deployment targets, then follow this sequence: 1. Generate a full-stack app from a plain English description. 2. Scaffold frontend UI, data storage and authentication. 3. Ask clarifying questions and refine code from feedback. Resolve uncertain cases with qualified reviewers, approve a reviewed, deployable full-stack application with owned source code, and measure accepted deployments per delivery hour and post-deploy correction rate 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. One approved framework set and one deployment target; final security sign-off and production release remain human. 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. Buyers approve substantive changes and production release scope. One approved framework set and one deployment target; final security sign-off and production release remain human. 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 approved framework set and one deployment target; final security sign-off and production release remain human. Implement one approved input format, a bounded representative case set and the first two task modules: generate a full-stack app from a plain English description; scaffold frontend UI, data storage and authentication. Support the third module with operator review: ask clarifying questions and refine code from feedback. 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
Buyer-owned GitHub repositories, cloud accounts and issue trackers. Cloud deployment targets, design-file import/export and monitoring destinations. 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: Project brief and framework choice, Editable generation workspace, Deployment and monitoring. Use a project list with repository and environment status, a large central code and preview canvas, and a right-hand panel for agent questions, component selection, security findings and deployment targets. Let users compare generated versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant component. Make the task-specific outcome a reviewed, deployable full-stack application with owned source code visible beside its evidence, review state and value baseline.
